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Abstract and Figures

Enhanced digital outcrop models attributed with hyperspectral reflectance data, or hyperclouds, provide a flexible, three-dimensional medium for data-driven mapping of geological exposures, mine faces or cliffs. This approach allows the collection of spatially contiguous information on exposed mineralogy and so provides key information for understanding mineralising processes, interpreting 1-D drillhole data, and optimising mineral extraction. In this contribution we present an open-source python workflow, hylite, for creating hyperclouds by seamlessly fusing geometric information with data from a variety of hyperspectral imaging sensors and applying necessary atmospheric and illumination corrections. These rich datasets can be analysed using a variety of techniques, including minimum wavelength mapping and spectral indices to accurately map geological objects from a distance. Reference spectra from spectral libraries, ground or laboratory measurements can also be included to derive supervised classifications using machine learning techniques. We demonstrate the potential of the hypercloud approach by integrating hyperspectral data from laboratory, tripod and unmanned aerial vehicle acquisitions to automatically map relevant lithologies and alterations associated with volcanic hosted massive sulphide (VHMS) mineralisation in the Corta Atalaya open-pit, Spain. These analyses allow quantitative and objective mineral mapping at the outcrop and open-pit scale, facilitating quantitative research and smart-mining approaches. Our results highlight the seamless sensor integration made possible with hylite and the power of data-driven mapping approaches applied to hyperclouds. Significantly, we also show that random forests (RF) trained only on laboratory data from labelled hand-samples can be used to map outcrop scale data.
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5Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, 09599 Freiberg, Germany
6TheiaX, HZDR Innovation, Bautzner Landstraße 400, 01328 Dresden, Germany
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1. Introduc tion
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S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
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2. Geological setting
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;9B9F5@@M 9L<=6=H 5 7<@CF=H=7 5@H9F5H=CB H<5H =B7F95G9G =B =BH9BG=HM HC
K5F8G H<9 A=B9F5@=N5H=CB =9N&CBH9G 9H 5@  K<9F95G H<9 :9@G=7
JC@75B=7 FC7?G G<CK 5 NCB98 5@H9F5H =CB K=H< D9FJ5G=J9 G9F=7=H=N5H=CB
7FCGG7IH 6M 5 @5H9F 7<@CF=H9F=7< 5@H9F5H=CB DFCL=A5@ HC A=B9F5@=N5H=CB
&5FH=B"N5F8 9H 5@  "B B95F GIF:579 5F 95G 5B8 58>5 79BH HC :5 I@HG
5 @C75@@M G=;B=:=75BH G=@=75 HC 7<@CF=H9F=7< 5@H9F5H=CB =G 5@GC DF9G9BH
-<9 9BH=F9 7CAD@9L =G 7FCGG7IH 6M 5 G9F=9G C: 0 GHF=?=B; /5F=G75B
H<FIGHG 5B8 @C75@@M C::G9H 6M 5 7CB>I;5H9 G9H C: @5H9 GHF=?9G@=D :5I@HG
K=H< '0, HF9B8=B; 89LHF5@ 5B8 ',0 HF9B8=B; G=B=GHF5@ ?=B9A5H=7G
88=H=CB5@@M 5B 9LH9BG=CB5@ 89H57<A9BH <5G 699B A5DD98 5H H<9 =BH9F
:579 69HK99B H<9 JC@75B=7 FC7?G 5B8 H<9 I@A C:H9B =B GD5H=5@ DFCL=A
=HM C: A5GG=J9 GI@]89 CF9 6C8=9G *I9G585 
3. Photogrammetric survey
 D<CHC;F5AA9HF=7 GIFJ9M C: H<9  CD9BD=H K5 G 7CB8I7H98 HC
;9B9F5H9 5 <=;<F9GC@IH=CB 8=;=H5@ CIH7FCD AC89@  '=?CB  , %+
75A9F5 5B8 '=??CF  AA : @9BG K5 G IG98 HC 57 EI=F9  + 
D<CHC;F5D<G :FCA AI@H=D@9 J=9KDC=BHG 5FCIB8 5B8 K=H<=B H<9 D=H ;
=GC:H &9H5G<5D9 )FC:9GG=CB5@ J K5 G H<9B IG98 HC ;9B9F5H9 5 D<C
HC;F5AA9HF=7 F97CBGHFI7H=CB G99 DD9B8=L  :C F 89H5=@G 5B8 H<9 F9
GI@H=B; DC=BH 7@CI8 ;9CF9:9F9B798 6M A95BG C: 7CF9;=GHF5H=CB HC  A
F9GC@IH=CB 5=F6CFB9 %=+ 85H5 )'(%=+ :FCA H<9 ,D5B=G< ;CJ
9FBA9BH IG=B; H<9 =H9F5H=J9 7@CG9GH DC=BH A9H<C8 =AD@9A9BH98 =B
@CI8CAD5F9  =F5F895I&CBH5IH 
4. Hyperspectral data pr oces sing with hylite
!MD9FGD97HF5@ =A5;9FM K5G 75DHIF98 IG=B; 5 HF=DC8 ACIBH98
=G5'"1 <MD9FGD97HF5@ G9BGCF ,D97HF5@ "A5;=B; %H8 (I@I =B@5B8
5B8 ./6CFB9 +=?C@5 G9BGCF +=?C@5 %H8 (I@I =B@5B8 88=H=CB
5@@M H<9 GD97HF5@ 7<5F57 H9F=GH=7G C: <5B8 G5AD@9G 7C@@97H98 8IF=B; ]9@8
75AD5=;BG K9F9 A95GIF98 IB89F @56CF5HCFM 7CB8=H=CBG IG=B; H<9 G=
5'"1 ACIBH98 CB 5 8F=@@ 7CF9 G75BB9F "B H<9 :C@@CK=B; G97H=CB K9
CIH@=B9 H<9 DF9DFC79GG=B; KCF?^CKG =AD@9A9BH98 =B hylite =; 
5B8 H<9=F 5DD@=75H=CB HC H<9  85H5G9H CAD@9H9 89G7F=DH=CBG C: 957<
A9H<C8 5B8 H<9=F =AD@9A9BH5H=CB 75B 69 :CIB8 =B H<9 5DD9B8=79G 57
7CAD5BM=B; H<=G DI6@=75H=CB 5B8 H<9 hylite 8C7IA9BH5H=CB <HHDG
H=BMIF@7CA<M@=H98C7G -<9 hylite GCIF79 7C89 5B8 89H5 =@98 #IDMH9F
BCH96CC?G 7CJ9F=B; 957< DFC79GG=B; KCF?^CK 75 B 5@GC 69 8CKB@C5898
:FCA =H!I6 <HHDGH=BMIF@7CA<M@=H9
4.1. Laboratory acquisition
"A5;=B; <MD9FGD97HF5@ G75BB9FG :CF <5B8 GD97=A9BG 5B8 8F=@@ 7CF9G
5F9 =B7F95G=B;@M 69=B; IG98 HC 5B5@MG9 @=H<C@C;M 5B8 A=B9F5@C;M 5H H<9
AA7A G75@9 7CGH5 9H 5@  -I`5 9H 5@  !MD9FGD97HF5@ 57
EI=G=H=CB IB89F @56CF5HCFM 7CB8=H=CBG 9; 7CBHFC@@98 @=;<H=B; GA5@@
GCIF79H5F;9HG9BGCF 8=GH5B79G 5@@CKG :CF 89H5=@98 5B8 577IF5H9 GD97
HF5@ 7<5F57H9F=G5H=CB C: <5B8 G5AD@9G 5B8 8F=@@ 7CF9G K=H< F9@5H=J9@M @=A
=H98 DFC79GG=B; =; 5
=:HM G9J9B <5B8 G5AD@9G F9DF9G9BH=B; H<9 @=H<C@C;=9G 9L DCG98 5H 
K9F9 G75BB98 IG=B; 5 ,=,I+($ 8F=@@7CF9 G75BB9F 9EI=DD98 K=H< 5B
=G5'"1 <MD9FGD97HF5@ G9BGCF ,D97HF5@ "A5;=B; %H8 (I@I =B@5B8
hylite K5 G H<9B IG98 HC 7CBJ9FH 957< F5K 9B=L 85H5G9H HC F58=5B79 6M
GI6HF57H=B; 5 85F?F9:9F9B79 5B8 5DD@M=B; G9BGCF 75@=6F5H=CBG 5B8 HC
F9^97H5B79 IG=B; 5 K<=H9 75@=6F5H=CB D5B9@ DD9B8=L  9CA9HF=7
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Fig. 1. (J9FJ=9K A5 D C: H<9 "69F =5B )MF=H9 69@H 5 K= H< @C75H=CBG C: H<9 A5 =B A5 GG=J9 GI @]89 89 DCG=HG <CGH98 =B H<9 %5H9 9JCB=5B HC 5F@M 5F6CB=:9FCIG /C@
75B=7 ,98=A9BH5FM CAD@9L ;F99B  &5 D AC8=]98 :FCA &5 FH=B"N5F8 9H 5@  -<9 ;9C@C;M C: H<9 CFH5 H5@5M5 5B8 9FFC C@CF58C CD9B D=HG 5G
A5DD 98 6M S9N &CBH9G 5B8 5F7S5F9GDC    =G 5@GC G<CKB 6
@9BG8=GHCFH=CB K5 G 5@GC 7CFF97H98 :CF F9GI@H=B; =B 5 G9H C: D@5B=A9HF=7
=A5;9G K=H< 5 D=L9@ G=N9 C: O AA
-<9 G75BB98 <5B8 G5AD@9G K9F9 G9;A9BH98 IG=B; 5 G9A=5IHCA5H=7
;F567IH 5@;C F=H<A DD9B8=L  +CH<9F 9H 5@  ,D97HF5 :FCA
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@=H<CHMD9G 5B8 HC 7F95H9 5 @569@@98 HF5=B=B; 85H5G9H :CF GI6G9EI9BH GI
D9FJ=G98 7@5GG=]75H=CB ,97H=CB  -C A=H=;5H9 7<5@@9B;9G 7CAD5F=B;
@56CF5HCFM GD97HF5 K=H< CIH7FCD GD97HF5 57EI=F98 IB89F @9GG 7CBHFC@@98
7CB8=H=CBG 5 FC6IGH 5B8 <5B87F5:H98 :95HIF9 G9H 65G98 CB GD97HF5@ =B
8=79G 5B8 A=B=AIA K5J9@9B;H< A5DG ,97H=CB  K5 G IG98 :CF H<=G GI
D9FJ=G98 7@5GG=]75H=CB ,97H=CB 
4.2. Tripod acqu isition
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5B8 CF=9BH98 GI6D5 F5@@9@ HC H<9 A=B9 :579 -<9G9 K9F9 H<9B 5IHCA5H=
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S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Fig. 2. (J9F J=9K C: H<9 hyli te KCF?^CK :CF 7CFF97H=B; 5B8 @C75@=G=B; @56CF5HCFM 5 HF=DC8 6  5B8 5=F6C FB9 9; ./ 65G98 <MD9FGD97 HF5@ 85H5 7 -<9G9 8=:
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EI=F=B; =B:CFA5H=CB CB H<9 G79B9 ;9CA9HFM =G B99898 69:CF9 D=L9@ F9
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9GG9BH=5@ HC 89F=J9 A95B=B;:I@ CIH7FCD F9^97H5B79 GD97HF5 H<CI;< F9
A5=B 5 G=;B=:=75BH 7<5@@9B;9 :CF B95FGIF:579 F9ACH9 G9BG=B; 5D
DFC57<9G 89D@CM=B; ./G 5B8 HF=DC8ACIBH98 G9BGCFG #5?C6 9H 5@
 %CF9BN 9H 5@ 
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K5 G 5DD@=98 "@@IA=B5H=CB 57FCGG H<9 G79B9 K5 G AC89@@98 5G 5 A=LHIF9 C:
8CKBK9@@=B; GIB@=;<H 5B8 G?M@=;<H M 7CAD5F=B; H<9 8=::9F9B79 =B F5
8=5B79 A95GIF98 69HK99B G<58CK 98 5B8 BCBG<58CK98 F9;=CBG 5B8
5GGIA=B; H<9G9 F9;=CBG <58 H<9 G5A9 A98=5B F9^97H5B79 =H K5 G DCGG=6@9
HC 9GH=A5H9 H<9 5A6=9BH G?M@=;<H GD97HFIA 5B8 GC 897CADCG9 H<9 GD97
HF5 A95GIF98 :CF H<9 + ,D97HF5@CB 75 @=6F5H=CB D5B9@G =BHC 5 GIB@=;<H
5B8 5A6=9BH G?M@=;<H GD97HF5 ,79B9 ;9CA9HFM 5B8 GIB@=;<H =B7=89B79
5B;@9 K5 G H<9B IG98 HC 9GH=A5H9 H<9 =BH9BG=HM C: H<9G9 HKC @=;<H GCIF79G
5H 9J9FM D=L9@ 5B8 H<IG F97CBGHFI7H D9F D=L9@ =@@IA=B5H=CB 57 FCGG H<9
G79B9 G G9BGCF HC H5 F;9H 8=GH5B79G K9F9 ;9B9F5@@M GA5@@   ?A D5H<
F58=5B79 9::97HG K9F9 7CBG=89F98 B9;@=;=6@9 CIHG=89 C: H<9 5H ACGD<9F=7
F97H98 D=L9@ F9^97H5B79 9GH=A5H9G K9F9 89F=J98 6M 8=J=8=B; 957< D=L9@
6M =HG F97CBGHFI7H98 =@@IA=B5H=CB GD97HF5  :I@@ 89G7F=DH=CB C: H<=G BCJ9@
7CFF97H=CB A9H<C8 =G 69MC B8 H<9 G7CD9 C: H<=G GH I8M 6IH =G H<9 GI6>97H C:
5B ID7CA=B; DI6@=75H=CB (H<9F GH5H 9 C: H<9 5FH 5H ACGD<9F=7 5B8 HCDC
;F5D<=7 7CFF97H=CB A9H<C8G =AD@9A9BH98 =B hylite 5F9 89G7F=698 =B 89H5=@
6M #5?C6 9H 5@  5B8 %CF9BN 9H 5@ 
=B5@@M F9^97H5B79 9GH=A5H9G :FCA 957< <MD9FGD97HF5@ G79B9 75B 69
657? DFC>97H98 CBHC H<9 DC=BH 7@CI8 5B8 5J9F5;98 CF 6@9B898 65G98 CB
GCA9 CH<9F A9HF=7 GI7< 5G ;FCIB8 G5AD@=B; 8=GH5B79 CF CF=9BH5H=CB F9
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
GI@H=B; =B 5 <CAC;9B9CIG  <MD9FGD97HF5@ DC=BH 7@CI8 CF H<9 
85H5 G9H 5 HCH5@ C:  ;FCIB8 65 G98 G79B9G :FCA  8=::9F9BH J=9K=B; @C
75H=CBG K9F9 65 7?DFC>97H98 5B8 7CFF97H98 IG=B; H<=G KCF?^CK F9GI@H
=B; =B 5 <MD9F7@CI8 C: H<9 CD9BD=H K=H<  DC=BHG 5B8 
4.3. UAV acquisition
./<MD9FGD97HF5@ =A5;=B; =G 5 F5D=8@M 89J9@CD=B; ]9@8 5G DF9J=
CIG@M @5F;9 5B8 <95JM G9BGCF H97<BC@C;M =G BCK A=B=5HIF=G98 !MD9F
GD97HF5@ :F5A9G9BGCFG 7CJ9F=B; H<9 J=G=6@9 5B8 B95F=B:F5F98 F5B;9 5F9
5J5= @56@9 G99 5G9B 9H 5@  :CF 5 F9J=9K K<=@9 G9J9F5@ 7CAD5B=9G
<5J9 5@GC F979BH@M 89J9@CD98 ./ 75D56@9 DIG<6FCCA G9BGCFG H<5H
7CJ9F H<9 G<CFHK5J9 =B:F5F98 5G9B 9H 5@  3<CB; 9H 5@ 
;9CA9HF=7 7CFF97H=CB <CK9J9F ACGH :F5A965G98 G9BGCFG @57? GI7< 9G
H56@=G<98 KCF?^CKG -<9 <=;<:F9EI9B7M ACJ9A9BHG HMD=75@ C: ./
D@5H:C FAG DF9G9BH G=;B=:=75BH 7<5@@9B;9G :CF H<9 577IF5H9 ;9CF9:9F9B7=B;
5B8 65B85@=;BA9BH C: :F5A965G98 G9BGCFG 5B8 K<=@9 GCA9 5IH<CFG
<5J9 89J9@CD98 GD97=]7 7CFF97H=CBG !CB?5J 55 F5 9H 5@  
#5?C6 9H 5@  BC K=89@M IG56@9 GC@IH=CB =G 5J5=@56@9 M9H 88=
H=CB5@ 7CFF97H=CBG GI7< 5G F9^97H5B79 7CBJ9FG=CB A5G?=B; 5B8 HCDC
;F5D<=7=@@IA=B5H=CB 7CFF97H=CBG 5F9 7CAD5F56@9 7<5@@9B;9G HC H<CG9
89G7F=698 =B H<9 DF9J=CIG G97H=CB 56CIH HF=DC8 57EI=G=H=CB
hylite 7CBH5 =BG 5 7CAD@9H9 KC F?^CK :CF H<9 5@=;BA9BH 657?
DFC>97H=CB 7CFF97H=CB 5B8 :IG=CB C: :F5A965G98 <MD9FGD97HF5@ ./
85H5 =; 7 09 <5J9 5@GC =AD@9A9BH98 H<=G =B 5 K5 M H<5H 5@@CKG
7CFF97H=CB KCF?^CKG :CF B9K G9BGCFG HC 69 95G=@M 58898
-<9 hylite 7CFF97H=CB KCF?^CK :CF ./ =A5;9FM 75B 69 :CIB8 =B
DD9B8=L  G 5B =B=H=5@ GH9D =BH9FB5@ F58=CA9HF=7 5B8 ;9CA9HF=7 7CF
F97H=CBG GI7< 5G 85F? 7IFF9BH GI6HF57H=CB H<9 5DD@=75H=CB C: ;5=B 5B8
C::G9H J5@I9G 5B8 H<9 ;9CA9HF=7 7CFF97H=CB C: =BH9FB5@ CDH=75@ 8=GHCF
H=CBG 5F9 5DD@=98 "B8=J=8I5@ <MD9FGD97HF5@ 65B8G 5F9 H<9B 5@=;B98 HC 5
DF989]B98 F9:9F9B79 65B8 6M 75@7I@5H=B; 5B 5:]B9 HF5BG:CFA IG=B;
,"- :95HIF9G %CK9  5B8 %'' DC=BH A5H7<=B; &I>5 5B8
%CK9  -<=G FCI;< 5@=;BA9BH =G 58>IGH98 HC 577CIBH :C F HCDC
;F5D<=7 8=GHCFH=CBG 6M 75@7I@5H=B; H<9 CDH=75 @ ^CK 69HK99B 58>579BH
65B8G IG=B; H<9 (D9B/ =AD@9A9BH5H=CB C: 899D^CK 09=BN59D:9@ 9H
5@  5B8 F9ACJ=B; H<9 =89BH=]98 D=L9@ C::G9HG "B8=J=8I5@ 75A9F5
DCG=H=CBG 5B8 CF=9BH5H=CBG 5F9 H<9B =89BH=]98 IG=B; 9=H<9F H<9 )B) GC@I
H=CB 89G7F=698 =B ,97H=CB  CF :CF @5F;9 ./ GIFJ9MG IG=B; 9LH9FB5@
CF H<9  75G9 GHI8M ;=GC:H &9H5G<5D9 )FC:9GG=CB5@ J K5 G
IG98 HC 5@=;B  +=?C@5 85H57I69G 75 DHIF98 8IF=B; HK C GI6G9EI9BH
=A5;9 89F=J5H=J9G +     BA K9F9 IG98 HC
D9F:CFA H<=G 5@=;BA9BH hylite K5 G IG98 HC D9F:CFA 5 @9BG 7CFF97H=CB CB
H<9 85H5 DF=CF HC D<CHC;F5AA9HF=7 F97CBGHFI7H=CB 5B8 H<9 @9BG 8=GHCF
H=CB 7C9:]7=9BHG =B &9H5G<5D9 ?9DH ]L98 5H N9FC 8IF=B; H<9 6IB8@9 58
>IGHA9BH -<9G9 75 A9F5 DCG9 9GH=A5H9G K9 F9 H<9B IG98 6M hylite HC 65 7?
DFC>97H 5@@ C: H<9 <MD9FGD97HF5@ 65 B8G CBHC H<9 <=;< F9GC@IH=CB DC=BH
7@CI8 -<9 F9GI@H=B; <MD9F7@CI8 K5G 7CFF97H98 :CF 5H ACGD<9F=7 5B8 =@@I
A=B5H=CB 9::97HG IG=B; H<9 G5 A9 KCF?^CK 89G7F=698 =B ,97H=CB  5B8
GI6G9EI9BH@M :IG98 K=H< H<9 9B=L 85H5 5G 89G7F=698 =B ,97H=CB  !M
D9F7@CI8 DFC8I7HG K9F9 5@GC 7CBJ9FH98 HC ;9CF9:9F9B798 CFH<CACG5=7G
:CF IG9 K=H< 9LH9FB5@ ", GC:HK5F9
4.4. Data fu sion
 6=; 58 J5BH5;9 C: H<9 <MD9F7@CI8 5DDFC57< =G H<5H 5B 5F6=HF5FM
BIA69F C: 85H5G9HG 75 B 69 657?DFC>97H98 CBHC 5 G=B;@9 DC=BH 7@CI8 5B8
7CAD5F98 CF :IG98 65G98 CB H<9 GD97HF5@ CF ;9CA9HF=7 5HHF=6IH9G C: 957<
DC=BH CF H<9  75G9 GHI8M H<9 +=?C@5 <MD9FGD97HF5@ 85H5 =BH9BG=H=9G
K9F9 58>IGH98 IG=B; ACF9 577IF5H9 6IH @CK9F GD5H=5@ F9GC@IH=CB 9B=L
85H5 IG=B; <=GHC;F5A 9EI5@=G5H=CB DD9B8=L  -<9G9 <=;< GD5H=5@ F9G
C@IH=CB GD97HF5 K9F9 H<9B :IG98 K=H< /'"+ 65B8G C: H<9 9B=L <MD9F
7@CI8 HC =B7F95G9 F9GC@IH=CB =B H<9 5F95 7CJ9F98 6M +=?C@5 =A5; 9FM -C
A=B=A=G9 8=GHCFH=CBG 8I9 HC H<9 B58=F CF=9BH5H=CB C: H<9 +=?C@5 75A9F5
5B8 C6@=EI9 J=9K =B; 5B ;@9GC: H<9 9B=L =A5; 9FM H<9 HK C 85H5 G9HG K9F9
A9F;98 IG=B; DC=BH 5J9F5;9G K=H< 5 K9=;<H=B; :57HC F 9EI5@ HC H<9 N
D@5B9G 5F9 8CA=B5BH @M 89F=J98 :FCA H< 9 <=;< F9GC@IH=CB +=?C@5 =A5; 9FM
5B8 GD97HF5 CB J9FH=75@ D@5B9G :FCA H<9 9B=L =A5;9FM
5. Results: Data analysis and integration
 ?9M 89G=;B :95HIF9 C: hylite =G =HG IG9 C: DC@MACFD<=7 GHFI7HIF9G HC
F9DF9G9BH J5F=CIG <MD9FGD97HF5@ 85H5 9; GD97HF5@ @=6F5F=9G =A5;9G
<MD9F7@CI8G =B 5 IB=:CFA K5 M !9B79 GH5B85F8 5B5@MG=G H97<B=EI9G
GI7< 5G A=B=AIA K5J9@9B;H< A5DD=B; CF <I@@ 7CFF97H=CB 75B 69 >IGH 5G
95G=@M 5D D@=98 HC 5 GD97HF5@ @=6F5FM 5G 5 <M D9F7@CI8 -<=G =G :IB85A9BH5@
HC :5 7=@=H5H=B; 85H5 =BH9;F5H=CB 69HK 99B :CF 9L5AD@9 GD97HF5 57EI=F98
IG=B; 5 ]9@8 GD97HFCA9H9F 5 ;FCIB865G98 <MD9F7@CI8 5B8 59F=5@ =A
"B H<9 :C @@CK=B; G97H=CB K9 89ACBGHF5H9 H<=G 5DDFC57< 5B8 H<9 J5 
F=9HM C: 5B5@MG=G HCC@G =AD@9A9BH98 =B hylite 6M =BH9;F5H=B; ,=GI+($
=A5;9FM C: <5B8G5AD@9G K=H< 5 <MD9F7@CI8 89F=J98 :FCA H9FF9GHF=5@
5B8 ./ =A5;9FM HC A5D H<9 ;9C@C;M C: H<9  CD9BD=H
5.1. Qualitative analysis and visualisation
G 5 DF9@=A=B5FM 85H5 9LD@CF5H=CB GH9D hylite K5G IG98 HC 7F95H9 5
G9F=9G C: HFI9 =; 5 5B8 :5 @G97C@CIF J=GI5@=G5H=CBG C: H<9 G75BB98
<5B8 G5AD@9G 5B8  <MD9F7@CI8 5@G97C@CIF F9DF9G9BH5H=CBG K9F9
7F95H98 IG=B; K5 J9@9B;H<G =B H<9 ,0"+   5B8
 BA H<5H 5F9 G9BG=H=J9 HC 7@5M 7<@CF=H9 5B8 A=75 A=B9F5@C;M
=; 6 /9889F 5B8 &7CB5@8  %C<  )CBHI5@ 9H 5@ 
%5I?5AD 9H 5@  5B8 IG=B; H<9 K=89@M 5DD@=98 A=B=AIA BC=G9
:F57H=CB &' F99B 9H 5@  8=A9BG=CB5@=HM F98I7H=CB H97<B=EI9
=; 7 :C@@CK=B; H<9 =AD@9A9BH5H=CB =B SPy C;; G  "BH9F57
H=J9  J=GI5@=G5H=CBG C: H<9 <MD9F7@CI8 5B8 =HG J5 F=CIG 89F=J5H=J9G 75 B
69 J=9K98 5H <HHDGH=BMIF@7CA75<MD9F7@CI8
0=H< H<9G9 J=GI5@=G5H=CBG 5G 5 F9:9F9B79 957< <5B8 G5AD@9 K5G 7@5G
G=]98 =BHC B=B9 GD97HF5@ @=H<C@C;=9G =;  65G98 CB =BH9FDF9H5H=CBG C:
=B8=J=8I5@ D=L9@ GD97HF5 -5 6@9  -<9G9 @=H<C@C;=9G K9F9 75F9:I@@M 7<C
G9B GI7< H<5H H<9M 5F9  GD97HF5@@M 8=GH=B;I=G<56@9 5B8  ;9C@C;=
75@@M A95B=B;:I@ !9B79 H<9M 5F9 @5F;9@M 89]B98 6M H<9 56IB85B79 C:
GD97HF5@@M 57H=J9 D<5G9G GI7< 5G 7@5M A=75 7<@CF=H9 5B8 =FCB CL=89
9B9F5@ F9^97H5B79 7<5F57H9F=GH=7G GI7< 5G 6F=;<HB9GG 5B8 ^5HB9GG K9F9
5@GC 7CBG=89F98 K<9B 8=GH=B;I=G<=B; @=H<C@C;=9G K=H<CIH 8=5; BCGH=7 56
GCFDH=CB :95HIF9G 9; G<5@9 5B8 A5GG=J9 GI@D<=89 1F5M 8=::F57H=CB
A=B9F5@C;M K5 G 5@GC D9F:CFA98 CB G9@97H98 G5AD@9G HC J5 @=85H9 CIF
GD97HF5@ =BH9FDF9H5H=CBG DD9B8=L  5B8 7@5F=:M H<9 A=B9F5@ 5GGC7=5
H=CBG H<5H 75IG9 H<9 C6G9FJ98 GD97HF5@ :95HIF9G
5.2. Quantitative measurem ent and feature extraction
=F97H 7CAD5 F=GCB C: GD97HF5 57 EI=F98 6M 8=: :9F9BH G9BGCFG 5B8CF =B
8=GD5F5H9 9BJ=FCBA9BHG 9; @5 6CF5HCFM JG CIH8CCFG F9A5=BG 5 G=;B=:=
75BH 7<5@@9B;9 !CK9J9F GCA9 GD97HF5@ 89F=J5H=J9G GI7< 5G GD97HF5@ =B
8=79G 9; 65B8 F5H=CG 5B8 A=B=AIA K5J9@9B;H< A5DG 5F9 F9A5F?
56@M FC6IGH HC J5F=5H=CBG =B G75@9 57EI=G=H=CB A9H<C8 5B8 =@@IA=B5H=CB
09 H5?9 58J5 BH5;9 C: H<9G9 DFC8I7HG 9; I85<M 9H 5@ 
,CBBH5; 9H 5@  !59GH 9H 5@  J5 B +I=H9B699? 9H 5@ 
!97?9F 9H 5@  J5B 89F &99F 9H 5@  5B8 7CBGHFI7H 5 :95HIF9
G9H H<5H 75B 69 IG98 HC 8=F97H@M 7CAD5F9 GD97HF5@ 7<5F57H9F=GH=7G 69
HK99B <5 B8G5AD@9 5B8 CD9BD=H G75@9G K=H< H<9 DF=B7=D5@ 5=A C: HF5=B
=B; 5 GID9FJ=G98 7@5GG=]9F CB @569@@98 <5B8G5AD@9G 5B8 5DD@M=B; =H HC
A5D @=H<C@C;M =B H<9  <MD9F7@CI8
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Fig. 3. C@CIF 7CADCG=H9G C: H<9  <MD9F7@CI8 7F95H98 IG=B; <M@=H9 5B8 G<CK=B ; 5 HFI97C@CIF +    BA    BA     BA
J=GI5@=G5H=CB 5 :5@G97C@CIF J=GI5@=G5H=CB +     BA    BA     BA <= ;<@=;< H=B; J5F=5H=CBG =B 7@ 5M 7< @CF=H9 5B8 A=75
7CBH9BH 6  5B8 H<9 ]FGH H<F99 &' 65B8 G 7  J=GI 5@=G5H=CBG C: H<9G9 85H5 75B 69 :CIB 8 5H <HHDGH=BMIF @7CA75 <MD9 F7@CI8
 K=89 J5F=9HM C: GD97HF5@ =B8=79G <5J9 699B 89J9@CD98 :CF 9LHF57H
=B; ;9C@C;=75@ =B:CFA5H=CB :FCA AI@H= 5B8 <MD9FGD97HF5@ 85H5  G
H<9G9 5F9 HMD=75@@M 5DD@=98 CB F9ACH9@M G9BG98 G5H9@@=H9 85H5 H<9M 5J C=8
GD97HF5@ 65B8G G9BG=H=J9 HC 5HACGD<9F=7 9::97HG 5B8 GC 75B 69 5D D@=98 CB
6CH< CIH8CCF 5B8 @56CF5HCFM 85 H5 F6=HF5F=@M 7CAD@9L GD97HF5@ =B8=79G
75B 69 75@7I@5H98 IG=B; hylite IG=B; =B6I=@H HCC@G :CF GD97HF5@ F9G5AD@=B;
5B8 7CA6=B5H=CB CF H<FCI;< 8=F97H A5B=DI@5H=CB C: H<9 IB89F@M=B;
85H5 5FF5M IG=B; numpy !5FF=G 9H 5@  09 9LHF57H  ;9C@C;=
75@@M G9BG=H=J9 GD97HF5@ =B8=79G -56@9  :FCA H<9 7CF9G75BB9F =A5;9FM
5B8 <MD9F7@CI8 85H5  -<9G9 =B8=79G K9F9 89J9@CD98 6M I85<M 9H 5@
 HC A5D H<9 ;9C@C;M C: H<9 &H "G5 F9;=CB IG=B; ,-+ 5B8 5=F
6CFB9 <MD9FGD97HF5@ 85H5 5B8 5F9 G9BG=H=J9 HC A=75 7@5M 75F6CB5H 9
5B8 =FCB CL=89 A=B9F5@G -<9 F9GI@HG =;  DD9B8=L  5F9 8=F97H@M
7CAD5F56@9 69HK99B H<9 @56CF5HCFM 5B8 CIH8CCF 85H5G9HG 5B8 <=;<
@=;<H G=A=@5F=H=9G 5B8 8=::9F9B79G 69HK99B H<9 J5 F=CIG GD97HF5@
@=H<C@C;=9G 89]B98 =B ,97H=CB 
&=B=AIAK5 J9@9B;H< &0% A5DG 5F9 5@GC 7CAACB@M 5DD@=98 HC
A5D H<9 8=GHF=6IH=CB C: GD97=]7 A=B9F5@G IG=B; <MD9FGD97HF5@ 85H5
!97?9F 9H 5@  CF 9L5AD@9 H<9 DCG=H=CB C: GD97=]7 56GCFDH=CB
:95HIF9G =B H<9 G<CFHK5J9 =B:F5F98 F9;=CB 5F9 J9FM G9BG=H=J9 HC H<9
@9B;H< C: @(! 9(! 5B8 &;(! 6CB8G =B G=@=75H9 A=B9F5@G 5@@CK=B;
577IF5H9 8=G7F=A=B5H=CB 69HK 99B 5 J5F=9HM C: 7@5M A=75 7<@CF=H9 75F
6CB5H9 5B8 5AD<=6C@9 A=B9F5@G !97?9F 9H 5@  J5 B 89F &99F 9H
5@  J5B +I=H9B699? 9H 5@  &=B=AIA K5J9@9B;H< A5DG
K9F9 75@7I@5 H98 :CF H<9  85 H5G9HG =;  DD9B8=L  6M ]HH=B; H<F99
;5IGG=5B :IB7H=CBG HC 5 <I@@ 7CFF97H98 GD97HF5 :FCA  HC  BA
G G<CKB 6M =;G  5B8  5B8 DD9B8=L  H<9 GD97HF5@ =B8=79G 5B8
&0% A5DG :CF H<9 @56CF5HC FM 5B8 CIH8CCF 85H5G9HG 5F9 EI5BH=H5 H=J9@M
7CAD5F56@9 !9B79 K9 DFCDCG9 H<5H H<9M F9DF9G9BH 5 G9H C: ;9C@C;=
75@@M F9@9J5BH :95HIF9G H<5H 5F9 5H @95GH =B H<=G 75 G9 GHI8M =BJ5F=5BH HC
G75@9 5B8 57 EI=G=H=CB A9H<C8
5.3. Automa ted ge ological mapping
-<9 =B8=79G 5B8 A=B=AIA K5J9@9B;H< A5DG 89G7F=698 =B ,97H=CB 
 DFCJ=89 5 A9H<C8 :C F 8=F97H@M 7CAD5 F=B; GD97HF5 57EI=F98 IB89F ]9@8
5B8 @56CF5HCFM 7CB8=H=CBG -<=G CD9BG H<9 DCGG=6=@=HM C: HF5=B=B; 5 GI
D9FJ=G98 7@5GG=]9F IG=B; H<9 @=6F5FM C: 7@5GG=]98 <5B8 G5AD@9G =; 
5B8 H<9B 5D D@M=B; =H HC 5IHCA5H=75@@M A5D H<9 ;9C@C;M C: H<9  CD9B
D=H "B H<9 :C @@CK=B; G97H=CB K9 9LD@CF9 H<=G CDH=CB 89J9@CD=B; 5 <5B8
H5=@CF98 897=G=CB HF99 H<5H 75B 8=GH=B;I=G< 69HK99B H<9 A5=B 5@H9F5H=CB
A=B9F5@G 5H  5B8 HF5=B=B; 5 F5B8CA :CF9GH 5@;CF=H<A H<5H 75B D9F
:CFA @=H<C@C;=75@ A5DD=B;
5.3.1. Alteration ma pping using a decision tree
,=A=@5F HC DF9J=CIG GHI8=9G =BJ9GH=;5H=B; A=B9F5@ 5@H9F5H =CB 9;
I85<M 9H 5@  @5F? 9H 5@  K9 A5DD98 H<9 8=GHF=6IH=CB C:
G9F=7=H9 5B8 7<@CF=H9 =;  K<=7< 5F9 C: H9B 5GGC7=5H 98 K=H< <M
8FCH<9FA5@ 5@H9F5H=CB =B /!&, 89DCG=HG IG=B; 5 897=G=CB HF99 65 G98 CB
8=GH=B7H=J9 GD97HF5@ 7<5F57H9F=GH=7G C: 957< A=B9F5@ -<9 DF9G9B7956
G9B79 C: 8=5;BCGH=7 GD97HF5@ 56 GCFDH=CBG 5H   5B8  BA
7: )CBHI5@ 9H 5@  K5G A5DD98 IG=B; 5 89DH< 7IHC:: C:  5B 8
H<9 897=G=CB HF99 IG98 HC G9D5F5H9 ?5 C@=B=H=7 7@5M :95HIF9 5H  BA
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Fig. 4. ,D97HF5@ @=6F 5FM DF9G9BH=B ; <I@@ 7CFF97H98 A9 8=5BGD 97HF5 ;F9 9B 5B8 5GGC7=5H98 =BH9FEI5 FH=@9 5F95 ;F9M 75@7I @5H98 CB 5@@ D=L9@G C: H<9 <5B8 G5A
D@9G 7 : =;G    F9D F9G9 BH=B; 957< C: H<9 GD97 HF5@ @=H<C@C;=9G =B H9FDF 9H98 5H  (L=8=G98 (L A5 GG=J9 GI@D< =89 &, HKC J5F=9H=9G C: 7<@CF=H9 <@ 
5B8   HKC G9F=7= H=7 IB =HG ,9F  5B 8  G<5@9 5B 8 DI FD@ 9 G<5@9 ), -<9 5D DFCL=A5H9 DCG=H=CB C: 7<5F57 H9F=GH=7 56 GCFDH=CB :95HIF9G )CBHI5@ 9H 5@  
5F9 5@GC D@CHH98 :CF F9:9F9B79 ,99 -56@9  :CF 89H5=@98 GD97HF5@ 89 G7F=DH=CBG
Table 1
,D97 HF5@ 89 G7 F=DH=CBG C: H<9 7< 5F57 H9F=GH=7 :95HIF9G 7: )CBHI5@ 9H 5@    %5I?5A D 9H 5@   C: 957< GD97 HF5@ @= H<C@C;M IG98 8I F=B ; GID9 FJ=G9 8 7@ 5GG=]75 H=CB
,97H=CB   ,D97 HF5 :CF 957< C: H<9G9 @=H<C@C;=9G 5F9 D@CHH98 =B =; 
@(! :95H IF 9 9(!
:95HIF 9
:95HIF 9
9]B= B; 7<5F 57 H9F= GH=7 G ,D97HF 5@ @=H< C@C; M
   BA 99D =F CB 56 GCFD H=CB :95H IF9G 5B 8 GA 5@@ 8CI6 @9@(! :95H IF9 8I9 HC 5GGC7= 5H98 ?5 C@=B =H9 (L =8=G98 ( L
@5H GD 97HF5 K=H< 6FC58 K5H9F :95H IF9 5H  BA &5 GG =J9 GI @D <=89
OBA OBA C I6@9 56 GCFDH= CB 5H   5B 8  BA HMD=75@ C: 7<@CF= H9 @=A= H98 @(! 56 GCFDH=CB 5H   BA 5B 8 @CK
=BH9BG =HM 6F C5 8 K5 H9F :95H IF9 69HK99B  5B 8  BA
<@C F= H9  <@ 
OBA OBA G K=H< < @  6IH K= H< 899D9F K5 H9F :95HIF 9G 5F CIB8  BA @=?9@M 8I9 HC H< 9 DF9G 9B79 C: A= BCF G9F=7= H9
%CK9F =BH9BG=H M C: 9(! 5B8 &;(! :95H IF9G
<@C F=H9  <@ 
O BA OBA OBA = GH=B 7H @ (! :95HIF9 :F CA G9F= 7=H9 5H Z  BA 5B 8 9(! :95H IF 9 :FCA 7<@C F= H9 5H Z  BA ,9F= 7=H9 5B 8
<@C F= H9
,9F  < @
 BA O BA =GH=B 7H @(! :95H IF 9 K= H< A= B= A5 5H  BA AIG 7CJ= H=7 7CAD CG =H=C B != ;< 7F MG H5 @@ =B=HM =B8= 75H98 6M
8=_9F9B79 =B 89DH< C: @(! 5B 8 K5H9F :95H IF 9 7FMGH5@@= B= HM  
,9F= 7=H9  ,9F 
 BA O BA &IG7CJ =H 9HF 5B G=H= CB=B ; HC K5F8 G D< 9B;= H9 @( ! :95H IF9 @C 75H98 5H Z   9DH< C: K5H9F :95HIF 9 F9@5 H=J9 HC
@(! :95HIF 9 GI ;; 9GHG =B7F 95G9 8 K5 H9F 7C BH9BH 7FM GH 5@ @=B= HM  
,9F= 7=H9  , 9F 
@5H GD 97HF5 6IH 8=GH =B7H :F CA &, 8I9 HC GA 5@ @9F B5FF CK 9F K5 H9F :95HIF 9 69HK99B   5B 8  BA BA  @CK
@(! 5B8 9(! 56GCFD H=CB :95HIF9 5B8 GH99D9F G@ CD 9 69HK99B  5B 8  BA
,<5@ 9
G 56 CJ9 6IH K=H< @CK9F G@ CD9 69HK99B  5B 8  BA 5B8 G@=; <H@M 899D9F @(! :95HIF 9 )IFD@9 G<5@9 ),
K<=H9 A=75 :95HIF9 5H  BA 6IH BCH  BA 7<@CF=H9 :95HIF9 5H
 BA 6IH BCH  BA 5B8 7<@CF=H9K<=H9 A=75 A=LHIF9G :95HIF9
5H  5B8  BA 88=H=CB5@@M A=75 5B8 7<@CF=H9 7CADCG=H=CB
K5G GD@=H 65G98 CB H<9 DCG=H=CB C: H<9  BA 5B8  BA 56GCFD
H=CBG F9GD97H=J9@M 5@@CK=B; 8=G7F=A=B5H=CB C: 7CADCG=H=CB5@ 9B8A9A
69FG "AD@9A9BH5H=CB 89H5=@G C: H<=G 897=G=CB HF99 5F9 =B7@I898=B DD9B
8=L 
5.3.2. Litholog y mapping using rand om fo rests
-<9 ;9C@C; M C: H<9  D=H K5 G 5@GC =BJ9GH=;5H98 IG=B; 5 F5 B8CA :CF
9GH 7@5GG=]9F =AD@9A9BH98 IG=B; ,7=?=H@95FB )98F9;CG5 9H 5@ 
!MD9FGD97HF5@ =A5;9G C: @569@@98 <5B8 G5AD@9G ,97H=CB  K9F9 IG98
5G HF5=B=B; 85H5 5B8 H<9 F9GI@H=B; AC89@ 5DD@=98 HC A5D @=H<C@C;=9G =B
H<9 <MD9F7@CI8 C: H<9  D=H 7: DD9B8=L  G 89G7F=698 =B ,97H=CB
 H<9 IG9 C: FC6IGH 5B8 ;9C@C;=75@ A95B=B;:I@ :95HIF9G GI7< 5G GD97
HF5@ =B8=79G 5B8 A=B=AIA K5J9@9B;H< A5DG =G ?9M HC H<=G 5DDFC57< 5G
=H 5@@CK98 EI5BH=H5H=J9 7CAD5F=GCB 69HK99B <5B8G5AD@9 G75@9 GD97HF5
57EI=F98 =B 5 @56CF5HCFM 5B8 CIH7FCD G75@9 GD97HF5 57 EI=F98 CIH8CCFG
-<9 F5B8CA :CF9GH A9H<C8 =G 5B 9BG9A6@9 @95FB=B; 7@5GG=]9F 5 @5F;9
G9H C: 897=G=CB HF99 7@5GG=]9FG 5F9 HF5=B98 GI7< H<5H H<9=F =B8=J=8I5@ F9
GI@HG 7CA6=B9 HC ;=J9 5 7CBG9BGIG 7@5GG=]75H=CB 57< HF99 7CBHF=6IH9G 5
IB=H JCH9 5B8 H<9 ]B5@ 7@5GG=]75H=CB @569@ =G 5@@C75H98 65 G98 CB H<9
7@5GG K=H< H<9 ACGH JCH9G -<9G9 9BG9A6@9G C: HF99 7@5GG=]9FG 5F9 9L
D97H98 HC D9F:CFA ACF9 FC6IGH@M 5B8 577IF5H9@M H<5B 5B =B8=J=8I5@ 89
7=G=CB HF99 7@5GG=]9F F9=A5B 
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Table 2
,D97 HF5@ =B8=79G IG98 HC 7<5F57H9F=G9 5B8 7CAD5 F9 G5A D@9 5B8 CIH7FCD
A=B 9F5@C;M -<9 7C@CB =B 8=75H9G H<5H 5B 5J9F 5;9 F9^97 H5B7 9 K5G 75 @7I
@5H98 :CF 5@@ 65 B8 G K=H<=B H<9 GD 97=]98 F5B; 9 @ @ K5J9@9 B;H<G 5F 9 =B
"B89L 9]B=H=CB
@(! FCID 7C BH9BH       
@(! FCID
    
&;(! FCI D 7CBH 9BH   
   
&;(! FCI D
    
9(! F CID 7C BH9BH     
9FF CIG "F CB "B89L      
9FF =7 (L =89 7CBH9BH    
9FF =7 (L =8 9
    
$5 C@=B FCID =B 89L    
(D5EI9 =B 89L      
+9;C @=H< "B89L      
+9;C @=H< "B89L     
G K=H< A5BM A57<=B9 @95FB=B; A9H<C8G F9GI@HG C6H5=B98 IG=B;
F5B8CA :CF9GHG 75B 69 G9BG=H=J9 HC <MD9FD5F5A9H9FG H<5H 7CBHFC@ :CF 9L
5AD@9 HF99 GHFI7HIF9 5B8 89DH< G =G GH5B85F8 DF57H=79 F9=A5B
 K9 D9F:CFA98 5 ]J9:C@8 7FCGG J5@=85H=CB HC CDH=A=G9 H<9G9 <M
D9FD5F5A9H9FG  HCH5@ C:  7CA6=B5H=CBG C: <MD9FD5F5A9H9FG K9F9
H9GH98 IG=B; 5 :CF9GH C:  HF99G -<9 F5B;9 C: <MD9FD5F5A9H9F J5@I9G
H<5H K9F9 H9GH98 5B8 H<9=F CDH=AIA J5@I9 =G GIAA5F=G98 =B -56@9 
IF=B; H<9 HF5=B=B; D<5G9 H<9 7@5GG=]9F 57<=9J98 577IF57=9G C: Z
CB 6CH< HF5=B=B; 5B8 H9GH=B; GI6G9HG C: H<9 <5B8G5AD@9 =A5;9 'CH9
H<5H H<9G9 577IF57=9G 5F9 BCH F9DF9G9BH5H=J9 C: H<9 5@;CF=H<AG 5DD@=75
H=CB HC H<9 <MD9F7@CI8 85H5 5G :CF H<=G 85H5G9H H<9 ;FCIB8 HFIH< =G BCH
-<9 F9GI@HG C: H<9 F5B8CA :CF9GH 7@5GG=]75H=CB 5F9 GIAA5F=G98 5G 
 J=GI5@=G5H=CBG C: H<9 7@5GG=]98 <MD9F7@CI8 =B =;  -<9 8=GHF=6IH=CB
C: @=H<C@C;=9G G<CK 9L79@@9BH GD5H=5@ 7CBG=GH9B7M 9J9B H<CI;< BC GD5
H=5@ ]@H9F=B; <5G 699B 5DD@=98 5B8 CIH@=B9 ;9C@C;=75@@M F95GCB56@9
;9CA9HF=9G H<5H ;9B9F5@@M A5H7< K=H< DF9J=CIG A5DD=B; =;  98
8=B; A95GIF9A9BHG 9LHF57H98 IG=B; H<9 CAD5GG D@I;=B =B @CI8CA
D5F9 -<=9@9 9H 5@  5@CB; 7CBH5 7HG C: H<9 7@95F@M :C@898 G<5@9 IB=H
CIH7FCDD=B; =B H< 9 BCFH<95GH C: H<9 D=H H<9 CFH5 H5@5M5 7CB;@CA9F
5H9 89]B9 5 :C@8 5L=G D@IB;=B; X HCK5F8G X 5B8 5L=5@ GIF:579 8=D
D=B; 5H X HCK5 F8G X -<9G9 A95GIF9A9BHG 5F9 6FC58@M 7CBG=GH9BH
K=H< H<9 ;9B9F5@ GHFI7HIF9 C: H<9 F9;=CB5@ ;9C@C;M S9N&CBH9G 5B8
5F7S5F9GDC  5B8 5@@CK98 IG HC 89J9@CD 5B ID85H98 =BH9FDF9
H5H=CB C: H<9 GHFI7HIF9 C:  =; 
6. Discussion
CAD@9L ;9CA9HF=9G 5B8 =@@IA=B5H=CB 9::97HG A5?9 F9ACH9 G9BG=B;
C: 7@=::G 5B8 CD9BD=H A=B9G K=H< <MD9FGD97HF5@ =A5;=B; 5 G=;B=:=75BH
7<5@@9B;9 #5?C6 9H 5@  %CF9BN 9H 5@  -<9 KCF?^CK DF9
G9BH98 =B H<=G 7CBHF=6IH=CB 5B8 =AD@9A9BH98 =B H<9 CD9BGCIF79 hylite
DMH<CB D5 7?5;9 <9@DG 5A9@=CF5H9 H< 9G9 =GGI9G 6M DFCJ=8=B; A9H<C8G :C F
657? DFC>97H=B; 7CFF97H=B; 5B8 :IG=B; <MD9FGD97HF5@ 5B8 ;9CA9HF=7
85H5 :FCA 5 J5 F=9HM C: G9BGCFG -<9 F9GI@H=B; F9^97H5B79 GD97HF5 5F9
GHCF98 =B 5 ^9L=6@9 <MD9F7@CI8 :CFA5H GI7< H<5H 85H5 :FCA <IB8F98G C:
=B8=J=8I5@ <MD9FGD97HF5@ =A5; 9G 75B 69 =BH9;F5H 98 =BHC 5 G=B;@9 DFC8I7H
:CF J=GI5@=G5H=CB =BH9FDF9H5 H=CB 5B8 5B5@M G=G -<9   B5 HIF9 C: H< =G 5D
DFC57< A=H=;5H9G C77@IG=CB 5B8 8=GHCFH=CB =GGI9G H<5H @=A=H 5DDFC57<9G
F9@M=B; CB CFH<C;F5D<=7 DFC>97H=CB 5B8 :57=@=H5H9G 7FI7=5@ 7CFF97H=CBG
C: =@@IA=B5H=CB 5B8 5HACGD<9F=7 9::97HG =B H<9 <MD9FGD97HF5@ 85H5
-<9  75G9 GHI8M =@@IGHF5H9G H<9 DCH9BH=5@ C: H<=G 5DDFC57< 5D D@=98
HC EI5BH=H5H=J9 ;9C@C;=75@ A5DD=B; C: 5B CD9BD=H A=B9 <@CF=H=7 5B8
G9F=7=H=7 5@H9F5H =CB 5GGC7=5H 98 K=H< A=B9F5@=G5H=CB 7CI@8 69 A5DD98 IG
=B; 5 G=AD@9 897=G=CB HF99 =;  K<=@9 GHF5H=;F5D<=75@@M =ADCFH5BH
<CF=NCBG GI7< 5G H<9 DIFD@9 G<5@9 K9F9 GI779GG:I@@M A5DD98 IG=B; F5B
8CA :CF9GHG =;  ,=;B=:=75BH@M 6CH<  A5DG 8=GH=B;I=G< GI6H@9
J5F=5H=CBG =B A=B9F5@ 7<9A=GHFM =B7@I8=B; H<9 HF5BG=H=CB HCK5 F8G AIG
7CJ=H=7 G9F=7=H9 7@CG9 HC H<9 A5GG=J9 GI@D<=89 A=B9F5@=N5H=CB 5B8 @=H<C
@C;=75@ J5 F=5H=CBG =B 7<@CF=H9 7CADCG=H=CB -<9G9 DFCJ=89 IG9:I@ 5B5
5B8 <=BH 5H H<9 DCH9BH=5@ :C F AI@H=G75@9 <MD9FGD97HF5@ =BJ9GH=;5H=CBG
H<5H =BH9;F5H9 85H5 :FCA F9;=CB5@ A=B9 5B8 8F=@@ 7CF9 G75@9G
-<9 7CAD@9L 6IH ;9C@C;=75@@M F95GCB56@9 ;9CA9HF=9G 89@=B95H98 6M
H<9 F5B8CA :C F9GHG A5DD=B; F9GI@H :FCA H<9 G9F=9G C: H97HC B=7 89:CFA5
H=CBG H<5H H<9 <CGH FC7?G <5J9 699B GI6>97H98 HC 5B8 DFCJ=89 =ADCFH5BH
=BG=;<H =BHC H<9 GHFI7HIF9 C: H<9 89DCG=H CF 9L5AD@9 CIF F9GI@HG 7@95F@M
G<CK H<5H H<9 7FMDH=7 CFH5 H5@5M5 7CB;@CA9F5H9IB=H <5G 699B
:C@898 =BHC 5B 5BH=:C FA5@ GHFI7HIF9 =;  -< =G K5G A=GG98 8IF=B; DF9
Fig. 5. 5@G9 7C@CIF 7CA DCG=H9 J=GI5@=G5H=CB ;9B 9F5H98 6M A5 DD= B; H<9 @ (! 9FFCIG 5B 8 (D5EI9 =B8=79G 89]B9 8 =B -56@9  HC F98 6@I9 5B 8 ;F99B F9GD 97
H=J9@M :CF H<9 @56CF5HCFM <5B8 G5AD@ 9 G75BG 5 5B 8 <MD9F7@CI8 6   J=GI5@=G5H=CBG C: H<9G9 85H5 75B 69 :CIB 8 5H <HHDGH=BMIF @7CA75<MD9 F7 @CI8
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Fig. 6. &=B =AI A K5 J9@9B; H< A5D C: H<9 899D 9GH 56GCFDH=CB :95HIF9 69HK99B   5B 8   BA  75@7I@5H98 6M ]HH=B; H<F9 9 ;5IGG=5B :95HIF9 G HC H<=G F5B;9
5B8 G9@97H=B; H<9 899D9G H CB9 +9GI@HG :CF <5B8 G5A D@ 9G 5  5B 8 CD 9B D=H <MD9 F7@CI8 6 G<CK 9L79@@9BH 5;F99A9 BH C@CIF F9 DF9G9BHG :95HIF9 DCG=H=CB 5B 8
6F=;<HB9GG H<9 :95HIF9 89 DH< <@CF=H=7 FC7? G K=H< 5 899D 9(! :95HIF9 5F9 <=;< @=;<H98 =B ;F99B K< =@9 A= 75 CF 7@5M F=7< @=H<C@C;=9G K=H< 5 899D @(! :95HIF9
5DD9 5F =B DI FD@9 5B8 6@I9 6 6F 9J=5H=CBG 7CFF9 GDCB8 HC H<CG9 89]B98 =B =;  5B8 -56@9    J=GI5@=G5H=CBG C: H<9G9 85H5 75B 69 :CIB8 5H <HHDGH=BMIF@
7CA 75<MD9F7 @CI8
Fig. 7. =G HF=6IH=CB C: 5@H9F5H=CB A= B9F 5@G K= H< 8=GH=B7 H=J9 5@H9F5H=CB :95HIF9 G =B H< 9 ,0"+ ;9 B9F5H98 6M D5 GG=B; H<9 AI @H=:95HIF9 A= B=AI A K5J9 @9B;H<
A5DG  =;  H<FCI; < 5 897=G=CB HF99  -<9 F9 GI@HG <= ;<@=;<H H<9 AIG7CJ=H=7 G9F=7=H9 5@H9F5H=CB DF CL=A5 @ HC H<9 A5GG=J9 GI@D< =89 A=B 9F5@=G5H=CB &;F=7< F9
;=CB5@ 7<@CF=H=7 5@H9F5H=CB 5B8 AC F9 9F=7< 7< @CF=H9G 5GGC7= 5H98 K=H< 5@H9F5H=CB 5@CB; :5I@HG 5B8 K=H<=B H<9 GHC7?KCF? NCB9 C: H<9 89DCG=H  A=75 5B8 7@ 5M
F=7< NCB9 B9 5F H<9 GIF:579 =G 5@GC 9J=89BH K<=7 < K9 =B H9FDF 9H HC F9GI@H :FCA F979BH K9 5H<9F=B; -<=G A5D 75B 69 J=9K98 =B   5H <HHDG H=BMIF @7CA
75<MD9F7 @CI8
J=CIG A5DD=B; 75AD5=;BG 9; S9N&CBH9G 5B8 5F7S5F9GDC
 DF9GIA56@M 8I9 HC H<9 8=:]7I@H=9G 8=GH=B;I=G<=B; H<=G @=H<C@C;M
5B8 5779GG=B; H<9 F9@9J5BH CIH7FCDG -<9 <MD9F7@CI8 A5DD=B; F9GI@HG
5B8 5GGC7=5H98 GHFI7HIF5@ A95GIF9A9BHG =;G  5B8  <5J9 H<IG 5@
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
Table 3
!MD9FD5 F5A9 H9F J5@I9 G H9GH98 8I F=B; <MD9 FD5F5A9 H9F CDH=A= G5H=CB 5B 8
H<9=F 7CFF9GDCB8=B ; J5@I9G CF 5 89H5=@98 89G7 F=D H=CB C: 957< <MD9FD5F5
A9H9F D@95G9 F9:9F HC H<9 G7=?=H @95FB 8C7 IA9 BH5H=CB
!M D9FD 5A 9H9F -9GH98 F5 B; 9 (DH= AIA J5 @I 9
&5 L= AIA :95H IF9G D9F HF 99  
&5 L= AIA HF 99 89DH<  
&=B= AI A G5 AD @9G GD @=H  
&=B= AI A G5 AD @9G @95:  
CCHGH F5 D -FI9 5 @G9 -FI9
@CK98 IG HC 7CA9 ID K=H< 5 F9J=G98 GHFI7HIF5@ =BH9FDF9H5H=CB C:  =;
 :IFH<9F <=;<@=;<H=B; H<9 J5@I9 C: CIF 5DDFC57< LD@CF5H=CB ;9C@C
;=GHG 5H  <5J9 G=B79 J5@=85H98 5GD97HG C: H<=G GD97HF5@ =BH9FDF9H5H=CB
H<FCI;< H5 F;9H98 ]9@8 A5DD=B; C: H<9 DIFD@9 G<5@9 IB=H 5B8 :C@898 
-<9 + F9GI@HG 5@GC <=;<@=;<H GCA9 7<5@@9B;9G H<5H 7CI@8 69 58
8F9GG98 =B :IHIF9 GHI8=9G =FGH@M 5 7CAD5F=GCB C: H<9 7@5GG=]75H=CB F9
GI@HG K=H< H<9 ;9C@C;M A5D C: S9N&CBH9G 5B8 5 F7S5F9GDC 
GI;;9GHG H<5H H<9 F5B8CA :C F9GH 7@5GG=]9F =G BCH 56@9 HC 577IF5H9@M 8=G
H=B;I=G< 69HK99B 6@57? G<5@9G 5B8 A5GG=J9 GI@D<=89G -<=G =G IBGIFDF=G
=B; 5G 6CH< IB=HG 5F9 7<5F57H9F=G98 6M ^5H 5B8 ;9B9F5@@M @CK
F9^97H5B79 GD97HF5 =B H<9 /'"+ 5B8 ,0"+ !CK9J9F =H =G DCGG=6@9 H<5H
H<9G9 IB=HG 7CI@8 69 GI779GG:I@@M 8=GH=B;I=G<98 IG=B; :95HIF9G H<5H <=;<
@=;<H GI6H@9 8=::9F9B79G =B H<9 J=G=6@9 5B8 /'"+ D5 FHG C: H<9 GD97HFIA 7:
=;  CF 6M 75DHIF=B; 5B8 =BH9;F5H =B; @CB;K5 J9 =B:F5F98 =A5;9FM
H<5H 75 B 69HH9F 8=G7F=A=B5H9 G=@=75H9 A=B9F5@G 5GGC7=5H98 K=H< H<9 A=B9F
5@=N5H=CB 9; EI5FHN
,97CB8@M K9 5@GC =BH9FDF9H H<5H H<9 ,9F=7=H9  IB=H 5GGC7=5H98 K=H<
CL=8=G98 5B8 K95H <9F98 A5H9F=5@ 5@CB; H<9 BCFH<9FB F=A C: H<9 D=H F9
GI@HG :FCA GID9F;9B9 7@5M A=B9F5@G :CFA=B; =B K95H <9F98 7<@CF=H9 JC@
75B=7G .B:CFHIB5H9@M <5B8G5AD@9G :FCA H<9G9 @C75H=CBG 7CI@8 BCH 69
Fig. 8. &5D 5 7F CGGG97H=CB 6 5B8 @CB;G97H=CB 7 J=9KG C: H<9 + 7@5GG=]75H=CB -<9 <5 B8 G5AD @9G IG98 HC HF5=B H<9 + 5F9 CJ9F@5=B =B 5 IG=B ; 5DDF CL=
A5H9 @C75H=CBG A95GIF98 K=H< 5 <5B8<9 @8 ), 5B8 7C@CIF98 6M H<9=F @569 @ 7 : -56@9  ,5A D@9G G9@97H98 :CF 1+ 5B5@MG9G  DD9B8=L  5F9 5@GC <=;<
@=;<H98  J=GI 5@=G5H=CBG C: H<=G 7@5GG=]75H=CB 75 B 69 :CIB8 5H <HHDGH=BMIF@ 7CA 75<MD9F7 @CI8
Fig. 9. ,7<9 A5H=7 GHFI7HIF5@ 7FCGG G97H=CB C: H<9 CFH5 H5@5M5 CD9B D=H 65G98 CB 5B =B H9FDF 9H5H=CB C: H<9 <MD9FGD97 HF5@ A5DD= B; F9GI @HG 5B8 GHFI 7HIF5@ A95
GIF9A9BHG 9LHF57H98 :FCA H<9  <MD9 F7@CI8 .B =HG 5F9 @569 @@98 57 7CF8=B; HC =;G  5B8  5B 8 8F 5D98 CB H<9 &'  J=GI5@=G5H=CB DF9G9BH98 =B =;  9 8
8=B; A9 5GI F9A9 BHG 9LHF57H98 65 G98 CB H<9G9 A5DD= B; F9GI @HG 5B8 H<9  HCDC;F5D< M 5F9 CJ9F@5=B CB 5 5B 8 D@ CHH98 CB 5 @CK9F <9 A=GD<9 F9 9EI5@5F95
GH9F9C;F5D <=7 DFC>97H=CB 6 IG=B; ,H9F9CB9H  @ @A9B8= B;9F    CBHCIFG =B 6 K9F9 75@7I@5H98 IG =B; H<9 $5A6 A9 H<C8 @ @A9B8= B;9F 9H 5@
 5B8 IG 98 HC 89]B9 H<9 5J9F5;9 CF=9BH5H=CB C: 957< :C@8 @=A6 5B8 <9 B7 9 9GH=A5 H9 H<9 CF=9BH5H=CB C: H<9 :C@8G 5L=5@ D@5B 9
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
F9HF=9J98 8I9 HC 5779GG @=A=H5H=CBG 5B8 GC K9F9 BCH =B7@I898 =B CIF
HF5=B=B; 85H5G9H "H =G @=?9@M H<5H =: G5AD@9G 7CI@8 69 F9HF=9J98 H<9B H<=G
GID9F;9B9 NCB9 7CI@8 69 A5DD98 5G 5 8=GH=B7H @=H<C@C;M 6M H<9 + ,=A=
@5F@M 7<@CF=H=7 G<5@9G CIH7FCDD=B; 5@CB; H<9 GCIH<9FB A5F;=B C: H<9 D=H
7CI@8 BCH 69 8=GH=B;I=G<98 :FCA 7<@CF=H=G98 JC@75B=7G =B H<9 BCFH< 8I9 HC
5 @57? C: G5AD@9G :FCA H<=G IB=H =B H<9 HF5=B=B; 85H5G9H 7: =; 
9GD=H9 H<9G9 @=A=H5H=CBG CIF F9GI@HG 5F9 5 7@95F 89ACBGHF5H=CB H<5H 
K=H< 589EI5H9 7CFF97H=CBG 5B8 75F9:I@ :95 HIF9 G9@97H=CB <MD9FGD97HF5@
=B:CFA5H=CB 75B 69 EI5BH=H5H=J9@M 7CAD5F98 69HK99B @56CF5HCFM 5B8
CIH7FCD G75@ 9G J9B H<CI;< H<9 F5 B8CA :CF9GH 7@5GG=]9F K5 G HF5=B98 9L 
7@IG=J9@M CB @56CF5HCFM57EI=F98 GD97HF5@ =A5;9FM C: =B8=J=8I5@ <5B8
G5AD@9G =H K5G 56@9 HC GI779GG:I@@M 7@5GG=:M H<9  <MD9F7@CI8 5B8 9L
HF57H A95B=B;:I@ ;9C@C; =75@ =B:CFA5H=CB  -<=G 5DDFC5 7< <5G G=;B=:=75BH
DF57H=75@ 58J5BH5;9G CJ9F HF58=H=CB5@ A9H<C8G 6975IG9 H<9 B998 :CF
GI6>97H=J9 @56CIF=BH9BG=J9 5B8 DCH9BH=5@@M IBG5:9 ]9@8A5DD=B; HC
7F95H9 HF5=B=B; @569@G =G F98I798
-<=G 5DDFC57< =G A589 DCGG=6@9 6M H<9 =BH=A5H9 =BH9;F5H=CB C: ;9C
A9HF=7 5B8 GD97HF5@ 85H5 J=5 5 <MD9F7@CI8 )F9J=CIG GH5H 9C:H<95FH 5D
DFC57<9G 9; $FIDB=? 5B8 $<5B  5F9 @=A=H98 6M 7CB:C IB8=B; 5H
ACGD<9F=7 5B8 =@@IA=B5H=CB 9::97HG K<=7< F98I79 H<9 577IF57M C: 5IHC
A5H=7 A5DD=B; A9H<C8G -<9 =BH9;F5H=CB C: <=;<F9GC@IH=CB ;9CA9HFM
=BHC H<9 GD97HF5@ DFC79GG=B; KCF?^CK 5@@CKG G=;B=:=75BH@M 69HH9F 7CF
F97H=CB C: H<9G9 9::97H G 5B8 H<IG ACF9 57 7IF5H9 GD97HF5@ 89F=J5 H=J9G 5B8
7@5GG=]75H=CBG )F9J=CIG A9H<C8G :CF 7CF9;=GH9F=B;  ;9CA9HFM K=H<
GD97HF5@ 85H5 9; #5?C6 9H 5@  <5J9 699B @=A=H98 6M =B577IF5H9
5B8 @56CF=CIG A9H<C8G :CF 9GH=A5H=B; H<9 DCG=H=CB 5B8 CF=9BH5H=CB C:
H<9 <MD9FGD97HF5@ G9BGCF -<9 )B) GC@IH=CB =AD@9A9BH98 =B hylite 7=F
7IAJ9BHG H<9G9 =GGI9G 5B8 5@ @CKG A5BM 8=::9F9BH <MD9FGD97HF5@ 85 H5G9HG
HC 69 657?DFC>97H98 5B8 :IG98 =BHC 5 <MD9F7@CI8 -<9 J5 @I9 C: H<9G9
85H5 =G :I FH<9F 9B<5B798 6M H<9 5GGC7=5H98 <=;< F9GC@IH=CB ;9CA9HFM 5G
H<=G  =B:C FA5H=CB =G 7F=H=75@ :CF =BH9FDF9H=B; H<9 ;9CA9HFM C: ;9C @C;=
75@ 6C8=9G -<=G =G 9L9AD@=]98 6M CIF 56 =@=HM HC A5D :C @898 IB=HG 5H 
J=GI5@=G9 H<9 F9GI@HG =B  5B8 9LHF57H GHFI7HIF5@ A95GIF9A9BHG H<5H
7CBGHF5=B H<9 :C @8G 5L =5@ D@5B9 =; 
=B5@@M CIF F9GI@HG 5@GC GI;;9GH H<5H GCA9 <MD9FGD97HF5@ 89F=J5H=J9G
9; A=B=AIA K5J9@9B;H< A5DG 5B8 65B8 F5H=CG 5F9 F9@5H=J9@M G75@9
=BJ5F=5BH :C F DFCD9F@M 7CFF97H98 85H5G9HG -<=G F5=G9G H<9 =BH9F9GH=B;
DCGG=6=@=HM H<5H &% 5@;CF=H<AG HF5=B98 CB @=6F5F=9G C: <5B8G5AD@9G
7CI@8 5=8 A5DD=B; 57 H=J=H=9G 5H @5F;9F G75@9G 5B8 697CA9 8=;=H5@ ;9C@C
;=GHGH<5H 5GG=GH =BH9FDF9H5H=CB 9::CFHG 6M C6>97H=J9@M 7CBJ9FH=B; !,"
85H5 :FCA 8F=@@ 7CF9 CIH7FCD A=B9:5 79 5B8 5=F6CFB9 75AD5=;BG =BHC
@=H<C@C;=75@ A5 DG 5B8 @C;G -<=G KCI@8 DFCJ=89 5 IB=]98 5D DFC57< H<5H
5A9@=CF5H9G =GGI9G K=H< =B7CBG=GH9BH @C;;=B; 5B8 A5DD=B; K<=7< 7CA
ACB@M D@5;I9 A=B9F5@G 9LD@CF5H=CB 5B8 =B 8C=B; GC :5 7=@=H5H9 9::CFHG HC
GMBH<9G=G9 5B8 <CAC;9B=N9 F9;=CB5@ 85H5 G9HG "H 5@GC DFCJ=89G 5 A97<5
B=GA :CF ;9B9F5H=B; H<9 @5 F;9 5B8 FC6IGH HF5=B=B; 85H5 G9HG H<5H 5F 9 7F=H=
75@ :CF H<9 GI779GG:I@ 5DD@=75H=CB C: AC89FB A57<=B9 @95FB=B; A9H<C8G
7. Conclusion
-<9 CD9BGCIF79 hylite D57?5; 9 DFCJ=89G 5 IB=:CFA KCF?^CK :C F
:IG=B; F5K <MD9FGD97HF5@ 85 H5 :FCA AI@H=D@9 G9BGCFG 5B8 57EI=G=H=CB
;9CA9HF=9G =BHC F58=CA9HF=75@@M 5B8 ;9CA9HF=75@@M 7CFF97H98 <MD9F
7@CI8G -<9 D57?5;9 75 B H<9B 69 IG98 HC 89F=J9 ;9C@C;=75@@M F9@9J5BH
85H5 DFC8I7HG GI7< 5G A=B=AIA K5J9@9B;H< A5DG 65B8 F5H=CG 5B8
GD97HF5@ 7@5GG=]75H=CBG -C 89ACBGHF5H9 H<=G K9 IG98 hylite HC 7CA6=B9
 ;FCIB865G98 5B8  ./6CFB9 <MD9FGD97HF5@ =A5;9G =BHC 5 G=B
;@9 F9^97H5B79 <MD9F7@CI8 -<=G K5G IG98 HC A5D @=H<C@C;M 5B8 5@H9F
5H=CB A=B9F5@C;M HC 5H H<9 C FH5 H5 @5M5 A=B9 5B 8  7<5F57H9F=G9 H<9
GHFI7HIF9 5B8 ;9CA9HFM C: H<9 FC7?G <CGH=B; H<9 89DCG=H 5B8  =89B
H=:M GI6H@9 7<5B;9G =B 5@H9F5H =CB A=B9F5@C;M H<5H 7CI@8 69 IG98 5G A=B
9F5@=N5H=CB J97HC FG -<9G9 F9GI@HG <=;<@=;<H H<9 DCH9BH=5@ C: <MD9FGD97
HF5@ =A5;=B; :CF F5D=8 5B8 C6>97H=J9 A=B9:579 A5DD=B; 5B8 7CF9
@C;;=B; 6CH< C: K<=7< 5F9 9GG9BH=5@ HC AC89FB A=B=B; 9LD@CF5H=CB 5B8
CF989DCG=H F9G95F7<
Decl aration of Competing Interest
-<9 5IH<CFG 897@5F9 H<5H H<9M <5J9 BC ?BCK B 7CAD9H=B; ]B5B7=5@
=BH9F9GHG CF D9FGCB5@ F9@5H=CBG<=DG H<5H 7CI@8 <5J9 5DD95F98 HC =B^I
9B79 H<9 KCF? F9DCFH98 =B H<=G D5D9F
Acknowledgem ents
-<9 5IH< CFG KCI@8 @=?9 HC 57 ?BCK@98;9 9LH9BG=J9 GIDDCFH :FCA H5
@5M5 &=B=B; 8IF=B; ]9@8KCF? 7CB8I7H98 :CF H<=G DI6@=75H=CB 5B8 GI6
G9EI9BH J5@=85H=CB C: H<9 F9GI@HG -<=G F9G95F7< 5@GC F979=J98 :IB8=B;
:FCA H<9 IFCD95B .B=CBG !CF=NCB  F9G95F7< 5B8 =BBCJ5H=CB DFC
;F5A IB89F ;F5BH 5; F99A9BH 'C  ,- K5 G GIDDCFH98 K=H< :IB8
=B; :FCA -CH5@ +C69FH 3=AA9FA5BB =G ;F5H9:I@@M 57?BCK@98;98 :CF
]9@8 5GG=GH5B79 CF99B 69FH #I5B9@=D9 IGHCG 5B8 '5 CA= F5NNC
5F9 57?BCK@98;98 :CF <9@D=B; K=H< G5AD@9 DF9D5F5H=CB 5B8 8F5:H=B;
];IF9G 09 KC I@8 5@GC @=?9 HC H< 5B? %5 IF9BH =@@9F9G 5B8 5B 5B CBMACIG
F9J=9K9F :CF H<9=F =BG=;<H:I@ 5B8 7CBGHFI7H=J9 7CAA9BHG
Append ix A. Suppleme ntary data
,IDD@9A9BH5FM 85H5 HC H<=G 5FH=7@9 75B 69 :CIB8 CB@=B9 5H <HHDG
Referenc es
5G9 B !  !C B?5J 55 F5   %I7=99F  35F7 C-9>5 85  )#   *I 5B H=H5 H=J9
F9AC H9 G9BG =B; 5H I@HF 5<=;< F9GC @IH= CB K= H< ./ GD97HF CG7CDM  F9 J=9K C:
G9BG CF H97<BC@C;M  A95G IF9A 9BH DFC798IF9G 5B8 85 H5 7CFF 97H=CB KCF? ^CKG
+9AC H9 ,9BG    <H HDG 8C= CF ;  FG  
7CG H5 " $< C858 58 N589< & -IG5 % <5A =G = ) @C5 ;I9B + 
A5 7<=B9 @9 5FB= B; :F 5A 9KCF ? :C F 8F =@@ 7CF9 A= B9F5 @ A5 DD=B ; IG =B;
<MD9FG D97HF5 @ 5B8 <=;< F9GC @IH= CB A= B9F5 @C ;=75 @ 85 H5 :I G= CB "  # ,9@ -CD
DD@  5FH < (6G +9 ACH9 ,9BG   <HHDG 8C= CF ;  
#,-+,  
@@A 9B8= B;9F  + 0  ,H9F9CB9H 
@@A 9B8= B;9F  + 0 5 F8 CNC '  =G< 9F  &   ,HFI7HIF 5@ ;9C@ C; M
5@ ;CF= H<AG  /97HCF G 5B 8 H9BG CF G 5 A6F= 8; 9 .B=J 9FG= HM )F 9GG
9A=G  , ) &= 7?@9H<K5 =H9 ,  -IFB 9F   #5A9 G & +  ? 7=N ,  -<=9@9 ,-
5B; 5G < !   FCIB865 G98 5B 8 ./5G9 8 D<CHC; F5 AA 9HFM AI @H=
G75@ 9 <= ;<F9 GC @IH=CB A5 DD=B ; HCC@ :C F GH FI7HIF 5@ ;9C@ C;M 5B8
D5@9 CG9= GA C@C;M # ,HFI 7H 9C@   <H HDG 8C= CF ;  > >G;
  
C;; G -   ,D 97HF5@ )MH< CB ,)M
CI65B ;5 -CA6 9H ,  !I CH   /=H=BG " !9 I69F;9 F ,  /9IJ 9   =G9@9 
!9KG CB + IMCH  &5 F7CHH9  <5A 69F@5B 8 &  -<9F A5 @
=B:F 5F 98 <M D9FG D97HF5 @ =A 5; =B; :CF A= B9F5 @C;M A5 DD=B; C: 5 A= B9 :579
+9AC H9 ,9BG   
F58 G? =  $5 9<@9F   %95F B= B; (D 9B/ CAD IH9F J= G=CB K= H< H<9
(D 9B/ @= 6F 5F M (+9=@@M &98=5 "B 7
F9=A5 B  %  +5 B8CA :CF9 GHG  &5 7< %95F B  
I7?@9M  , # $I FN - !  !C K9@@ #  ,7<B9= 89F    -9FF9G HF=5 @ @= 85F 5B8
<MD9FG D97HF5 @ 85H5 :IG= CB DFC8 I7HG :CF ;9C@ C;=7 5@ CIH7FC D 5B 5@ MG =G  C AD IH
9CG7=    <HHD G 8C= CF ; > 75 ;9C   
@5F ? +' ,K 5M N9  %= JC $  $C ?5 @M +  ,IH@ 9M ,# 5@HCB #
&7CI;5@ + + 9BH    "A 5; =B; GD 97HFCG7C DM 5FH < 5B8 D@ 5B 9H5F M
F9AC H9 G9BG=B; K=H< H<9 ., , -9HF57 CF 89F 5B8 9LD9FH GM GH9A G # 9CD <MG +9G
)@5B 9HG  
I85 <M - #CB9G &  -<CA 5G  &  %5 I?5A D   5 779HH5  &  !9KG CB +
+C8; 9F   /9FF 5@ @ &  '9LH ;9B9F5 H=CB A=B9F5@ A5 DD=B; *I 99BG@5 B8
5= F6CF B9 !M &5 D 5B8 G5 H9@@=H 9 ,-+ GIFJ 9M G    )9FH< )I6@=7 @M
J5= @56@9 +9 D ) 
9F=B; & &=7? @9H<K5 =H9 , -<=9@9 ,- /C @@ ;; 9F , F I89B + 
+9J= 9K C: 8F CB9G  D< CHC; F5 AA 9HFM 5B8 9A9F;= B; G9BG CF H97<BC@C ;M :CF H<9
GHI8M C: 8M?9 G 9GH DF 57 H=G9G 5B8 :IHIF9 DCH9BH=5@ # /C @75B C@ 9CH<9FA +9 G
  <HHD G 8C= CF;  > >J C@ ;9CF 9G   
=9N&C BH9G  9@@ =8C &I@5 G  ,P B7<9N 5 F7S5 - 5F7 =5 F 9GDC # 
%=H<C;9C7<9A=G HFM C: /C@75B=7 +C 7?G =B H<9 +SC -=BHC &=B9 "6 9F=5 B )MF= H9 9@H
!I9@J 5 ,D5=B "BGH =HIHC 9C@U;=7 C M &=B9FC 89 G D5T5 &5 8F=8
=9N&C BH9G  5 F7=5 F 9GDC #   9C@C; =75@ A5D C: H<9 +=C -=BHC 5F 95 
F5G9F , 0<=H 6CIF B % 25 B; $ +5 A5 B5 =8CI  C BBCF ) )CFC D5 H 
,CC@ 9 ) &5GCB ) C K5 F8   )<=@ =DG  +   &=B9F5 @C ;= 75@ :5 79
A5 DD=B ; IG=B ; <MD9FG D97HF5 @ G75B B=B; :CF A=B9 A5DD =B; 5B 8 7CBHFC @ "B
IGH F5 @5 G=5B "BGH =HIH9 C: &= B=B; 5B8 &9H5 @@IF ;M )I6@ =75H=C B ,9 F=9G  )F 9G9BH98
5H H<9 H< "BH9FB 5H=CB5 @ &= B=B; 9C@ C;M C B:9F9B79 DD 
=F5 F8 95I &CBH 5IH   @ CI8 CA D5F9
F99B  9FA5B & ,K =HN9F ) F 5=; &  HF 5BG: CF A5 H=CB :CF
S.T. Thiele et al. Ore Geology Reviews xxx (xxxx) 104252
CF 89F= B; AI @H=G D97H F5 @ 85 H5 =B H9FA G C: =A 5; 9 EI 5@ =HM K=H< =A D@ =75H =C BG :CF
BC=G 9 F9AC J5 @ " -F 5BG 9CG7= +9ACH9 ,9BG   <H HDG 8C= CF ;
 
!5 9GH & I 85<M - %5 I? 5A D  F 9;CF M ,  *I 5B H=H5 H=J9 A=B9F5 @C;M
:FCA =B :F 5F 98 GD97HF CG7C D=7 85 H5  " /5 @=85 H=CB C: A= B9F5 @ 56IB 85B7 9 5B8
7CAD CG =H=C B G7F= DHG 5H H< 9 +C7?@9 5 7<5B B9@ =F CB 89DC G= H =B 09GH9FB IGH F5 @= 5
7CB 9C@   
!5 FF =G  + &=@@ A5 B $ # J5 B 89F 05@H , # C AA 9FG + /=FH 5B 9B )
CIFB5 D95I   0=9G9F   -5M@ CF  # 9F;  ,  ,A =H < '# $9FB + )= 7IG
&  !CM9F ,  J5 B $9F? K= >? & !  F 9HH &  !5 @85B9   89@ +SC  #
0=969 & )9H9FG CB ) RF5 F8 &5 F7 <5BH ) ,< 9DD5F8 $ +988M -
097?9GG9 F 0 665 G= ! C<@?9  (@ =D <5BH -   F F5 M
DFC; F5 AA =B ; K=H< 'IA)M '5 HIF9    <HHD G 8C= CF ;  
G   
!97?9F   J5 B +I=H9B699?  #  J5 B 89F 09F:: ! & 5?? 9F 0 !  !9KG CB
+ J5 B 89F &99F     ,D97HF 5@ 56 GC FDH=CB :95H IF9 5B5@ MG =G :CF
]B8=B; CF 9  HIHCF= 5@ CB IG=B; H<9 A9H<C8 =B ;9 C@C; =75@ F9AC H9 G9BG =B;  "
9CG7= +9 ACH9 ,9BG &5 ;   <HHD G 8C= CF ; & +, 
!CB?5J 55 F5    G?9@= B9B &   )V@V B9B "  ,5 5F = !  (> 5B 9B !  &5 BB=@ 5  +
!C@A@IB8   !5 ?5@5  - %= H?9M  ) +C GB9@@ -   +9ACH9 G9BG=B; C: 
;9CA 9HFM 5B8 GIF: 57 9 AC=G HIF9 C: 5 D95H DFC8 I7H=CB 5F 95 IG =B; <MD9FG D97H F5 @
:F5A 9 75 A9F5 G =B J= G= 6@ 9 HC G<CF HK5 J9 =B:F 5F 98 GD97HF 5@ F5 B;9G CB6C 5F 8 5
GA 5@ @ IBA5 BB 98 5=F6 CF B9 J9<=7@9 ./ " -F 5BG 9CG 7= +9AC H9 ,9BG 
!CB?5J 55 F5    $5 =JCG C> 5 # &Q ?M B9B # )9@@ =? ?5 " )9GC B9B %  ,5 5F = !
,5 @C !  !5 ?5@5  - &5 F? @9@=B %  +C GB 9@@ -   !M D9FG D97HF5@
F9^ 97H5B79 G= ;B5HIF 9G 5B 8 DC =BH 7@CI8G :CF DF 97=G =CB 5;F= 7I@HIF9 6M @= ;<H
K9=;<H ./ =A5; =B ; GMGH9A ",)+ , BB )<CH C;F5 AA +9ACH9 ,9BG ,D5H "B:
,7=  
"BJ9FB C  S 9N&CBH9G  +C G5  5F7 S5 F 9GDC # &5 HC G # 5F7 S5 %C 6UB
#%  5 FJ 5@ <C #  9@@=8 C   5 GH9@@CF5B 7C # & M5@ 5   5H=GH5 & #
+I6= C  F 5B58 C " -CFB CG  (@ =J 9=F5 #- +9M  F 5W>C / ,P B7<9N
5F7 S5 - )9F9 =F5 3 +9DF 9G5G ) ,C@P  + ,C IG5 )  "BHFC8 I7H=CB
5B8 9C@C; =75@ ,9HH=B; C: H<9 "69F =5 B )MF= H9 9@H =B 09=<98 ) 8  
5B8 )F98=7 H=J9 &C 89@@ =B; C: &5 >C F &= B9F5 @ 9@HG =B IFC D9 &= B9F5 @ +9GC IF79
+9J= 9KG ,D F= B;9F "B H9FB5H =CB5@ )I6@ =G <=B;  <5A  DD  <H HDG 8C=
CF;  4
#5?C 6 , 3=AA 9FA5 BB + @C5 ;I9B +   -<9 B998 :CF 577IF5 H9 ;9CA 9HF= 7
5B8 F5 8=CA 9HF= 7 7CFF 97H=CB G C: 8F CB96C FB9 <M D9FG D97HF5 @ 85 H5 :CF A= B9F5 @
9LD@ CF5H=C B &)! M, -C -CC@ 6CL :CF DF9DFC7 9GG= B; 8F CB96CFB 9
<MD9FG D97HF5 @ 85H5 +9 AC H9 ,9BG  <HHDG 8C= CF ;  FG  
$=FG7< & %CF9 BN ,  3=AA 9FA5 BB +  B8F 95B=  %  -IG5  %  )CGD =97< ,
#57?=G7< + $<C858 58N589< & <5A =G = ) .B;9F  !V8@ ) @ C5;I 9B
+ &=88 @9HCB & ,I H=B9B + (>5@5   &5 HH=@ 5 # 'CF86Q7?  ' )5 @A I #
) -=@>5B 89F & +IG? 99B=9A = -   !M D9FG D97HF5 @ CI H7FC D AC89@G :C F
D5@5 9CG9=G A= 7 GHI8=9G  )<CHC; F5 AA  +9 7   <HHDG 8C =CF ; 
D<CF 
$=FG7< & %CF9 BN ,  3=AA 9FA5 BB +  -IG5  %  &V 7?9@ +  !V 8@  ) CCM G9B
+ $< C858 58N589< & @ C5 ;I9B +  "BH9;F 5H=C B C: H9FF 9GHF =5 @ 5B 8
8FCB 96CFB 9 <MD9 FGD9 7HF5 @ 5B 8 D<CH C;F5 AA 9HF= 7 G9 BG=B ; A9 H<C8G :CF
9LD@ CF5H=C B A5DD=B; 5B8 A= B=B; AC B= HCF= B; +9AC H9 ,9BG   <HHD G 
8C= CF ;  FG  
$@IMJ9F - +5 ;5 B$9 @@9M  )RF9N  F5B ;9F  IGG CBB= 9F & F 989F=7
# $9@@ 9M $ !5 AF =7 ? # FCIH # CF@ 5M ,  #IDM H9F 'CH96C C?G5
DI6@ =G<= B; :C FA 5H :C F F9 DFC8 I7=6@9 7CAD IH5H=C B5 @ KCF? ^ CKG =B  % ). DD
$F IDB=?  $< 5B  ,   @ CG 9F5 B; 9 ;F CIB8 65G9 8 <MD9FG D97HF5 @ =A 5; =B ; :CF
A= B=B; 5D D@=75H=CBG 5H J5 F=CIG G7 5@ 9G +9J= 9K 5B8 75 G9 GHI8=9 G 5 FH< ,7= +9J
 
$F IG9    989@@ + %  -5F5 B=? # /  )9DD=B 0   095H<9F6 99 (  5 @J =B 0
&   &5 DD =B; 5@ H9F5 H=CB A=B9F5 @G 5H DFCG D97H CI H7FC D 5B8 8F =@@ 7CF9
G75@ 9G IG =B; =A 5; =B; GD97HF CA9HFM "B H # +9A ,9BG   
$IFN  -!  I7? @9M , #  !C K9@@ #  ,7<B9= 89F    "BH9;F 5H=C B C:
D5BC F5 A= 7 <MD9FG D97H F5 @ =A 5; =B; K=H< H9FF 9G HF=5 @ @=85 F 85 H5  )<CH C;F5 AA
+97    <HHD G 8C= CF ; >      L
%5I?5A D  +C8; 9F  %9 F 5G & %5 AD =B9B ! %5 I "  )9>7 =7 
,HFC A6 9F;  # F 5B 7=G ' +5 A5 B5 =8CI    &= B9F5 @ D<MG =7C7 <9A=GHFM
IB89F@ M= B; :95H IF965G9 8 9LHF 57H= CB C: A= B9F5 @ 56IB85B79 5B8 7CAD CG =H=CB
:FCA G<CF HK 5J9 A= 8 5B 8 H< 9FA5 @ =B:F 5F 98 F9^97H5 B79 GD97HF 5 &= B9F5 @G  
%9=G H9@ #& &5F7 CIL  -<=R6@9A CBH   *I 9G58 5   ,PB7<9N   @AC8U J5 F
+ )5G7 I5@  ,P9N +   -<9 JC@7 5B=7 <CGH 98 A5 GG =J 9 GI@D<=89
89DCG= HG C: H<9 "69F=5 B )MF=H9 9@H +9J= 9K 5B 8 DF 9:57 9 HC H<9 -<9A 5H=7 "G GI9
&=B9F 9DCG= H5   
%C<    (DH=75@ J=6F 5H=CBG =B G< 99H G=@= 75H9G  # )<MG   ,C @=8 ,H5H9 )<MG  
 
%CF9 BN , ,5 @9<= , $=FG7< & 3=AA 9F A5 BB + .B;9 F  /9GH ,[F9 BG9B 
@C5 ;I9B +  +5 8=CA 9HF= 7 7CFF 97H=CB 5B 8  =B H9;F 5H=C B C: @CB; F5 B; 9
;F CIB8 65 G98 <M D9FG D97HF5 @ =A 5; 9FM :C F A= B9F5 @ 9L D@CF 5H =CB C: J9FH =75@
CIH7FC DG  +9AC H9 ,9BG    <H HDG 8C= CF ;  FG  
%CK9    (6>9 7H F97C;B=H =CB :F CA @C 75@ G75@ 9=B J5 F= 5BH :95HIF 9G =B
)FC7 998=B;G C: H<9 ,9J9BH< "  "BH9FB 5H=C B5@ C B:9F 9B79 CB C ADIH9F /= G=CB
)F9G 9BH98 5H H<9 )FC7 998=B;G C: H<9 ,9J9BH< " "B H9FB5H =CB5@ CB:9F9B79 CB
CADIH9F /=G= CB DD    JC@  <HHDG 8C = CF;  " /  
&5 FH=B "N5F 8  F =5 G  F=5G & IA= 9@ ) ,5 B89FGC B # 5 GH 5T CB 
%5J5B89=F5    ,5 B7<9N #   B9K  ;9C@C;=75@ AC 89@ 5B8
=BH9FD F9H5 H=CB C: GH FI 7HIF5@ 9JC@ IH=C B C: H<9 KCF@ 87@5G G += C -=BHC /& , 89DC G=H
"69F=5 B )MF= H9 9@H ,D5=B  (F 9 9C@ +9 J   <H HDG 8C= CF ;
> CF 9;9CF9J  
&I>5 & %C K9     5GH 5D DFCL=A 5H 9 B95F 9GH B9=;<6CFG K=H< 5IHCA5 H=7
5@;CF= H<A 7CB: =;IF5H =CB /", ))   
&IFD <M +#  &CBH9=FC  , -  &5 DD=B ; H<9 8=GHF= 6I H=CB C: :9FF =7 =F CB
A= B9F5 @G CB 5 J9FH =75@ A= B9 :5 79 IG =B; 89F= J5 H=J9 5B 5@ MG =G C: <M D9FG D97HF5 @
=A 5; 9FM  BA ",)+, # )<CHC;F5 AA  +9AC H9 ,9BG  
&IFD <M +#  -5M@ CF  3 ,7<B9=89F  ,  '=9HC #   &5DD=B; 7@ 5M A= B9F5 @G =B
5B CD9B D=H A= B9 IG=B; <M D9FG D97HF5 @ 5B 8 %=+ 85 H5  IF # +9ACH9 ,9BG  
'9G6=H )+  IF? =B  )+  !I ;9B<C@ HN  !  !I 665F 8  ,& $I7<5F 7NM?  &
  GHF5 H=;F 5D <=7 A5 DD=B ; IG =B; 5 8= ;=H5@ CI H7FC D AC89@ 89F= J9 8 :FCA
./ =A 5; 9G 5B8 GH FI 7HIF9:FCA AC H= CB D< CHC; F5 AA 9HFM 9CGD<9F9 
 <HHDG 8C=CF ;  , 
)98F9; CG5  /5 FCEI5IL  F5A :CFH  &= 7<9@ / -<=F =C B  F =G 9@ (
@CB89@  &  )F9HH9B<C:9F )  09=G G +  I6C IF; /   ,7=? =H@95F B
&5 7<=B9 @95F B=B; =B )MH<CB  # &57< %95F B +9G   
)CBHI5 @ , &9 FF M ' 5AG CB )  &9L ,D97HF 5@ "BH9FD F9H5 H=CB =9@8
&5 BI5@  IG, D97 =B H9FB5H =C B5@ ,M 8B9M 
*I9G 585    F95D DF 5=G5@ C: H<9 GHFI 7HIF 9 C: H< 9 ,D5B =G < G9;A 9BH C: H<9
"69F=5 B )MF= H9 9@H &= B9F 9DC G= H5   <HHDG 8C= CF ;  
G  
+5A5 B5=8 CI  +  09@@G &   !M D9FG D97HF5 @ =A 5; =B; C: =FCB CF 9G =B
)FC7 998=B;G C: H< 9  H< "B H9FB5H =C B5@ C B;F9 GG :C F DD@ =98 &=B9F5 @C ;M " &
,DF= B; 9F DD 
+CH<9F   $C @A C;CFCJ  /  @5? 9   F5 6IH "B H9F5 7H=J 9 CF9 ;F CIB8
LHF 57H= CB .G =B; "H9F5H98 F 5D< IHG =B & ," +)!  )5D9FG CB
," + )!   & )F9GG %C G B;9 @9G 5 @= :CFB =5  D <HHDG 8C= CF ;
  
,5 @9<= ,  %C F9BN  , /9GH ,[F9BG 9B  3= AA 9FA5 BB  +  9BG<C @H +  !9 BB=B;
!9=B7?9  $= FG 7< & @ C5 ;I9B +  "BH9;F 5H=C B C: J9GG 9@65 G98
<MD9FG D97HF5 @ G75B B=B; 5B 8 D< CHC; F5 AA 9HFM :CF AC 6=@9 A5 DD=B; C: GH99D
7C5G H5 @ 7@=::G =B H<9 F7H=7 +9AC H9 ,9BG   <HHD G 8C= CF;  
FG  
,CBB H5; " %5I?5A D  !5;9A5 BB ,   %C K DC H5GG =I A <M8F CH<9FA 5@
5@ H9F5 H=CB =B @C K GI @:=8 5H=C B 9D=H<9FA 5@ GM GH 9AG 5G 89H97H98 6M "+, 5B8 1+ 
B 9L5A D@9 :F CA H<9 C ( A=B9 5GH9FB &=B85B5C )<=@ =DD= B9G (F 9 9C@
+9J   
,CF= 5B C   &5 FH = #   5 7= 9G 5B5@ MG =G C: JC @75BCG98=A9BH5F M GI 779GG= CB G
<CGH =B; A5 GG =J9 GI@: =89 89DC G=HG =B H<9 "69F=5B DMF= H9 69@H ,D 5= B 7CB 9C@
  <HHDG 8C =CF ;  ;G 97CB;9 C  
-<=9@9 , - FCG9 % ,5AGI  &= 7?@9H<K5 =H 9 , /C@@;; 9F ,  F I89B  +
 +5 D=8 G9A= 5I HCA5 H= 7 :F57 HIF9 5B 8 7C BH57H A5 DD =B; :CF DC=B H 7@CI8G 
=A 5;9G 5B8 ;9CD<M G= 75@ 85 H5  ,C @=8 5FH <  
-I`5 % $9FB & $<C8 5858N5 89< & @5B B=B + @C5 ;I9B + IHNA9F #
 J5@ I5 H=B; H<9 D9F: CF A5 B79 C: <M D9FG D97HF5 @ G< CF HK5 J9 =B:F 5F 98
G9BG CFG :C F H<9 DF9GC FH=B; C: 7CAD @9L CF 9G IG =B; A5 7<=B 9 @95F B=B; A9H<C8G
&=B9F B;   <HHDG 8C =CF ;  > A= B9B;   
J5 B 89F &99F   $C D5\? CJP /  $CI7?P  % J5B 89F 09F:: ! &   J5 B
+I=H 9B699?  #  5?? 9F 0!    05J9 @9B;H< :95HIF9 A5DD=B; 5G 5
DFCL M HC A= B9F5 @ 7<9A =G HFM :C F =BJ9GH=; 5H =B; ;9C@C; =7 GM GH9A G B 9L5A D@9
:FCA H<9 +C85@E I=@5 F 9D=H<9FA 5@ GM GH 9A "B H # DD@  5FH < (6G
9C=B:CFA5 H=CB   <HHDG 8C = CF;  > >5 ;    
J5 B 89F &99F   J5 B 89F 09F: : ! &   J5 B +I=H9B699? #  !97?9F   
5?? 9F 0 !  'CCA 9B &   J5 B 89F &9=>89 & 5FF 5BN5  #&  89 ,A9H<
# 0C@85=  -   &I @H= 5B8 <MD9FGD97HF5 @ ;9C@ C;=7 F9 ACH9 G9BG=B; 
F9J= 9K "B H # DD@ 5 FH< (6 G 9C= B:CF A5 H=CB   <HHDG 8C= CF ;
 > >5 ;    
J5 B +I=H9B699? #  5?? 9F 0 !  J5 B 89F 09F:: !& 39;9FG  -  (C GH<C 9?
#!  (A 9F  3  &5 FG < , !  J5 B 89F &99F     &5 DD=B; H<9
K5J9@9B;H< DCG= H=CB C: 899D9GH 56GC FDH=CB :95H IF9G HC 9LD@CF 9 A=B9F5 @ 8=J9FG =HM
=B <MD9FG D97HF5 @ =A5; 9G )@ 5B9H ,D57 9 ,7=   
/9889F 0 &7 CB5 @8 + ,    /=6F 5H =CBG C: H<9 (! =C BG =B AI G7CJ =H9 #
<9A  )<MG    
09=BN5 9D:9@ )  +9J5 I8 #  !5 F7<5 CI=  3 ,7 <A=8     99D@CK %5 F; 9
8=GD @5 79A9BH CDH= 75@ ^ CK K=H< 899D A5H7<=B; "B )FC7998= B;G C: H<9 "
"BH9FB 5H=C B5@ CB: 9F9B79 CB CAD IH9F /= G= CB DD 
25 B; $  0<=H 6CIF B %  &5 GC B )  !IBH=B;HCB #   &5 DD=B; H<9 7<9A=75@
7CAD CG =H=CB C: B= 7?9@ @5 H9F=H9G K= H< F9^97H5 B79 GD 97HF CG7C DM 5H $CB= 5A 6C '9K
5 @98CB=5 7CB 9C@    
3<CB;  2  05B;  1  1I 2  05B;  ,  #=5 - !I  1  3<5C  # 09= %  3<5B; %
 &= B= ./6C FB 9 <MD9FG D97HF5 @ F9AC H9 G9 BG=B ; F CA C6G9 FJ 5H=C B 5B 8
DFC7 9GG= B; HC 5D D@=7 5H=C BG " 9CG7= +9AC H9 ,9 BG &5 ;   <HHD G 
8C= CF ; & +,   
... The acquired hyperspectral data was preprocessed using hylite (Thiele et al., 2021) to (1) convert from radiance to reflectance, ...
... Key minerals that can be spectrally identified in the Kupferschiefer are outlined in Tables 2 and 3. We used band ratios and minimum wavelength mapping, implemented in hylite (Thiele et al., 2021) to identify minerals characteristic of a lithological 170 unit or the products of hydrothermal alteration. For simplicity reasons, we refer to these diagnostic minerals as proxies. ...
... quantify the depth of two broad but characteristic absorption features in the VNIR and SWIR range, present respectively in the 500-750 nm range and the 750-1400 nm range (GMEX 2008, Laukamp et al., 2021. The band ratios we applied to characterise the Kupferschiefer cores are listed in Table 2. Minimum wavelength (MWL) mapping fits spectral absorption features using one or more simple mathematical functions, in our case asymmetric Gaussian functions (Thiele et al., 2021), to resolve a measured (and hull-corrected) spectrum into specific absorption positions and depths (Van der Meer et al., 2004;Van Ruitenbeek et al., 2014). For example, three Gaussian functions can be fitted to hull-corrected reflectance spectra in the range 2100-2400 nm. ...
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The analysis of hydrothermal alteration in exploration drill cores allows to trace fluid-rock interaction processes, identify fluid flow paths and thus determine vectors in mineral systems. Hyperspectral imaging techniques are increasingly being employed to fill the scale gap between lab-based petrographic or geochemical analyses and the typical size of exploration targets. Hyperspectral imaging permits the rapid, cost-efficient and continuous characterisation of alteration mineralogy and texture along entire drill cores with a spatial sampling of a few millimetres. In this contribution, we present the results of an exploratory study on three mineralised drill cores from the Spremberg-Graustein Kupferschiefer-type Cu-Ag deposit in the Lusatia region of Germany. We demonstrate that hyperspectral imaging is a tool well-suited to recognize and track the effects of hydrothermal alteration associated with stratabound hydrothermal mineralization. Micro X-ray fluorescence spectrometry was used to validate the alteration mineral assemblages identified in hyperspectral data acquired in the visible, near, shortwave, mid-wave and long-wave infrared. Spectral features associated with the occurrence of iron oxide, kaolinite, sulphate, and carbonates were identified and mapped. We identify intensive hydrothermal alteration of the sandstones immediately below the Kupferschiefer horizon sensu stricto, spatially associated with or stratigraphically adjacent to Cu-Ag mineralisation. Importantly, we can clearly distinguish two mineralogically distinct styles of alteration (hematite and ferroan carbonate) that bracket high-grade Cu-Ag mineralisation. The occurrence of well crystalline kaolinite in the sandstone units is spatially and genetically related to Cu-Ag mineralisation. Proximal Fe-carbonate and kaolinite alteration have not previously been documented for the high-grade Cu-Ag deposits of the Central European Kupferschiefer. Hematite alteration is well known in Kupferschiefer-type ore deposits. It marks the flow path of oxidising, metal-bearing hydrothermal fluids towards the site of hydrothermal sulphide mineralization. Ferroan carbonate alteration in carbonate rocks located above the main mineralized zone, in contrast, is interpreted to mark hydrothermal fluid discharge from the mineralizing system. Although this study is limited to a small number of drill cores, our results suggest that hyperspectral imaging techniques may be used to identify vectors towards high-grade Cu-Ag mineralisation in Kupferschiefer-type mineral systems.
... The best practices for data acquisition We recently developed and published open-source software tools for the accurate processing of oblique hyperspectral scenery, including geometric, atmospheric and topographic corrections [1][2][3]. Several of those procedures are compiled in the open-source python package hylite [4], which, for the first time, provided a comprehensive and open-source workflow for the creation of fully corrected reflectance hyperclouds and their basic analysis. ...
... As described in detail in Section 3, the radiance data were corrected to derive reflectance estimates and fused to create a 3D hypercloud [1,3,4] containing~1 m spaced spectral measurements covering the visible-near to short-wave infrared range (380-2500 nm). These points are stored as vertices in a Stanford Triangle Format *.ply file, with scalar attributes for each hyperspectral band's reflectance. ...
... These points are stored as vertices in a Stanford Triangle Format *.ply file, with scalar attributes for each hyperspectral band's reflectance. These files can be loaded using opensource software such as CloudCompare or hylite [4]. ...
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Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting relevant processing workflows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to benefit the scientific community at large.
... 2) Dynamic update of geological complex structures and heterogeneous properties in digital twin applications. The integrated modeling of geometries and inner properties for the geological environment using multisource data (Pan et al., 2020;Thiele et al., 2021) has raised significant interest in the community. Nevertheless, to jointly represent the structures and properties, the topologies of the existing models are usually very complex He et al., 2022;Wang et al., 2020), which hinders the efficient updating of the models. ...
... Geological model structures include surface, solid, volumetric, and hybrid models (Graciano et al., 2018). Bounding representation (B-rep) is the most widely used approach (Pan et al., 2020;Thiele et al., 2021;Wang et al., 2021;Xiong et al., 2018), but B-rep can only represent geometric shapes and cannot reflect internal non-uniform properties. So, many volumetric hybrid models are constructed to achieve integrated representation, like corner point grid , octree indexed tetrahedral network , triangle irregular network, generalized tri-prism, and tetrahedral network hybrid TIN-GTP-TEN data model (He et al., 2022). ...
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Intelligent tunnel engineering requires accurate and comprehensive digital twin models to represent complex geological environments. The digital twin model of tunnel geological environment has multi-level and diversified in-depth applications such as problem diagnosis, risk assessment, trend prediction, emergency response, etc. Since tunnel construction is a long-term continuous dynamic spatial-temporal progress, the tunnel face constantly advances during excavations. Thus, the geological conditions of the construction surface and surrounding areas are continuously revealed and updated, which causes the digital twin model to have a very high update frequency of the geological information in tunnel engineering. The existing methods fail to represent and update the geological environment comprehensively and in real-time, especially for those long deep tunnels with the complicated topology of geological structures and the heterogeneous distribution of internal properties. Due to the digital twin application having a high-level requirement of completeness, accuracy, and timeliness, 3D modeling of tunnel geological environments has remained challenging within digital twin application contexts. This study aims to deliver a solution that efficiently represents and updates the geological structures and internal non-uniform properties of tunnel engineering. Since dense voxels can represent inner intricate heterogeneous property information, sparse voxels can efficiently represent complex structural information. Meanwhile, voxels using Volumetric Dynamic B + trees (VDB) data structure are easy to integrate and update. However, according to the characteristics of long strip distribution of tunnel geological environments, directly using the VDB will still cause an efficiency problem. Therefore, this study proposes an efficient linear segmentation-based multi-level voxel representation method for the prolonged deep tunnel geological environment using the VDB data structure to support dynamic updates. The typical Mountain Tunnel of China is employed for experimental analysis to validate the proposed method, and four representative data with different modalities are integrated into the digital twin model. The results demonstrate that spatial efficiency augments 28.49% after segmentation, and data access with O(1) time complexity supports efficiently dynamic updates.
... Over the last 30 years, the rapid advancement of spectroscopic techniques has enabled remote detection of surface mineralogy, opening new opportunities in geological mapping (Clark et al., 1991;Ben-Dor and Kruse, 1995;Kruse et al., 2012;Kereszturi et al., 2018;van der Meer et al., 2018;Guha et al., 2021;Lorenz et al., 2021;Rodriguez-Gomez et al., 2021;Thiele et al., 2021;Chakraborty et al., 2022). Spectroscopy measures electromagnetic radiation from an object, which can be both reflected or emitted depending on the temperature of the object (Jensen, 2007). ...
Diagnostic absorption features in hyperspectral data can be used to identify a specific mineral or mineral associations. However, it is unknown how accurate hyperspectral mapping can be for identifying alteration mineral compositions at the resolution required to describe structures such as fossil intrusions, or whether it can accurately quantify the alteration present. This study compared petrographic observation with visible, near-infrared (VNIR), and shortwave infrared (SWIR) hyperspectral remote sensing at laboratory- (centimetre-scale) and aerial- (metre-scale) scales to characterise the abundance of surface hydrothermal rock alteration in and around a shallow fossil intrusion on Pinnacle Ridge, Mt. Ruapehu, New Zealand. We classified a high-resolution aerial hyperspectral image to develop a new surface alteration map using Spectral Angle Mapper (SAM) algorithm. The petrographic thin-section and the laboratory and aerial hyperspectral imaging revealed a spectrum of hydrous alteration phases as indicated by the presence of an absorption feature at 2207 nm. Moderate correlation exists between the depth of the absorption feature at 2207 nm and the point counting-derived alteration percent values, indicating reliability of laboratory-based hyperspectral analytical methods. In contrast, aerial hyperspectral data failed to provide any clear correlations to field-mapped alteration using a band-depth approach, and we interpret this due to ‘oversampling’ of surface (supergene) alteration, spectral mixing, and sensor limitations (e.g., bandwidth, signal-to-noise ratio). The hyperspectral image-derived alteration map, created using supervised image classification, can loosely be translated to a geotechnical map where porosity and permeability play a major role in localizing hydrothermal fluid flow and the formation of alteration mineral associations.
... In particular, ground-based hyperspectral imaging is useful for the identification and geological interpretation of minerals or rocks in vertical outcrops that are difficult to study through direct investigation for safety reasons or to observe from an aerial view. Ground-based hyperspectral imaging has been effectively utilized to identify the distribution of rock types and estimate mineral abundance on mine faces Thiele et al., 2021). Beckert et al. (2018) applied ground-based hyperspectral imaging to identify different carbonate phases in natural land cliffs. ...
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Coastal cliffs undergo erosion and weathering more rapidly under the influence of strong waves and sea winds, leading to stability and environmental conservation issues. Ground-based hyperspectral imaging is useful for the identification and geological interpretation of minerals or rocks in vertical outcrops that are difficult to confirm from an aerial view or through in situ investigation for safety reasons. High spatial and spectral resolutions of visible–near infrared (VNIR) sensors can be advantageous for detecting weathering in cliffs made of volcanic rocks; however, their potential is not well known. In this study, two classification techniques, mixture-tuned matched filtering (MTMF) and support vector machine (SVM), were applied to VNIR hyperspectral data of the cliff face of a volcanic island in Dokdo, South Korea, and the classification results were compared. Results show that SVM is superior to MTMF for the classification of volcanic rocks and weathering minerals. The distinction between volcanic rocks with similar compositions and textures deteriorated using both methods. The shading of the surface owing due to unevenness and stratification also affected the accuracy of classification. This study shows that ground-based VNIR hyperspectral image analysis is a powerful and an effective approach to predict possible geomorphological changes and safety on volcanic islands, as it can explore the weathering of sea cliffs and highlight potentially vulnerable locations.
... These hyperspectral data and associated correction routines are described in detail in the accompanying data publication [33]. This workflow followed the method for illumination correction and 3-D fusion described by Thiele et al. [34,35]. Path radiance effects caused by the large (1-3 km) viewing distances were corrected using the method of Lorenz et al. [36]. ...
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The Black Angel Zn-Pb ore deposit is hosted in folded Paleoproterozoic marbles of the Mârmorilik Formation. It is exposed in the southern part of the steep and inaccessible alpine terrain of the Rinkian Orogen, in central West Greenland. Drill-core data integrated with 3D-photogeology and hyperspectral imagery of the rock face allow us to identify stratigraphic units and extract structural information that contains the geological setting of this important deposit. The integrated stratigraphy distinguishes chemical/mineralogical contrast within lithologies dominated by minerals that are difficult to distinguish with the naked eye, with a similar color of dolomitic and scapolite-rich marbles and calcitic, graphite-rich marbles. These results strengthen our understanding of the deformation style in the marbles and allow a subdivision between evaporite-carbonate platform facies and carbonate slope facies. Ore formation appears to have been mainly controlled by stratigraphy, with mineralizing fluids accumulating within permeable carbonate platform facies underneath carbonate slope facies and shales as cap rock. Later, folding and shearing were responsible for the remobilization and improvement of ore grades along the axial planes of shear folds. The contact between dolomitic scapolite-rich and calcitic graphite-rich marbles probably represents a direct stratigraphic marker, recognizable in the drill-cores, to be addressed for further 3D-modeling and exploration in this area.
... Current remote sensing techniques, including passive photogrammetry and hyperspectral imaging, allow for high-resolution mapping of difficult-to-access geological outcrops Thiele et al., 2021). Recent studies have also demonstrated the potential of the corrected TLS intensity data for geological mapping and distinguishing rock types within the aboveground rock outcrops (Penasa et al., 2014;Matasci et al., 2015;Carrea et al., 2016;Ž ivec et al., 2019), without the need of eliminating the atmospheric attenuation effect. ...
Active remote sensing by laser scanning (LiDAR) has markedly improved the mapping of a cave environment with an unprecedented level of accuracy and spatial detail. However, the use of laser intensity simultaneously recorded during the scanning of caves remains unexplored despite it having promising potential for lithological mapping as it has been demonstrated by many applications in open-sky conditions. The appropriate use of laser intensity requires calibration and corrections for influencing factors, which are different in caves as opposed to the above-ground environments. Our study presents an efficient and complex workflow to correct the recorded intensity, which takes into consideration the acquisition geometry, micromorphology of the cave surface, and the specific atmospheric influence previously neglected in terrestrial laser scanning. The applicability of the approach is demonstrated on terrestrial LiDAR data acquired in the Gouffre Georges, a cave located in the northern Pyrenees in France. The cave is unique for its geology and lithology allowing for observation, with a spectacular continuity without any vegetal cover, of the contact between marble and lherzolite rocks and tectonic structures that characterize such contact. The overall accuracy of rock surface classification based on the corrected laser intensity was over 84%. The presence of water or a wet surface introduced bias of the intensity values towards lower values complicating the material discrimination. Such conditions have to be considered in applications of the recorded laser intensity in mapping underground spaces. The presented method allows for putting geological observations in an absolute spatial reference frame, which is often very difficult in a cave environment. Thus, laser scanning of the cave geometry assigned with the corrected laser intensity is an invaluable tool to unravel the complexity of such a lithological environment.
... In recent years, with the development of high-resolution sensors (spatial and spectral) on the one hand, and high processing capabilities on the other hand, high accuracy levels can be aspired to and achieved quickly and efficiently. Laboratory, field, and imaging measurements have been proven to be useful tools for geochemical assessment (Clark, 1999;Clark et al., 2006;Mishra et al., 2021;Rowan & Mars, 2003;Thiele et al., 2021;Van der Meer, 2018). Many studies investigated the capabilities of hyperspectral data for monitoring the geochemical, mineralogical and textural properties of natural resources such as in soils and rocks (Awad et al., 2018;Cudahy et al., 2001;Dkhala et al., 2020;Feng et al., 2018;Gomez et al., 2008;King et al., 2004;J. ...
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A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach, European ABSTRACT The increasing demand for precise and dependable models has led to the development of both sensors and statistical algorithms. However, numerous studies have demonstrated that model performance is highly dependent on a range of environmental factors, such as spatio-temporal fluctuations of moisture, sensor type, sample variability, preprocessing methods, and model selection. These factors can impact prediction results, leading to erroneous comparisons across lab, field, or imaging models. Samples for this study were collected from a tailing settling basin of a porphyry copper deposit near Erdenet, Mongolia. The database contains lab and field spectra and hyperspectral imagery from a HySpex imaging sensor. In this study we propose a workflow that includes a simulation that yields an appropriate regression threshold while addressing data-driven uncertainty. The workflow consists of two regression models and five classification models at different scales for quantitative geochemical, mineralogical, and tex-tural prediction of tailing samples. Each model is compared to the acquisition space's performance potential. Acceptable R 2 values for regression models are 0.58 for laboratory, 0.40 for field, and 0.31 for hyperspectral airborne data. Results of this study are not limited to tailing samples but can be applied on other fields of research such as geology, pedology or agriculture. ARTICLE HISTORY
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In surface mining operations, geological pit wall mapping is important since it provides significant information on the surficial geological features throughout the pit wall faces, thereby improving geological certainty and operational planning. Conventional pit wall geological mapping techniques generally rely on close visual observations and laboratory testing results, which can be both time- and labour-intensive and can expose the technical staff to different safety hazards on the ground. In this work, a case study was conducted by investigating the use of drone-acquired RGB images for pit wall mapping. High spatial resolution RGB image data were collected using a commercially available unmanned aerial vehicle (UAV) at two gold mines in Nevada, USA. Cluster maps were produced using unsupervised learning algorithms, including the implementation of convolutional autoencoders, to explore the use of unlabelled image data for pit wall geological mapping purposes. While the results are promising for simple geological settings, they deviate from human-labelled ground truth maps in more complex geological conditions. This indicates the need to further optimize and explore the algorithms to increase robustness for more complex geological cases.
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Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.
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Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by biases and inaccuracies. To overcome these limitations, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data to support field‐based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post‐glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co‐registered to a structure‐from‐motion point cloud. HSI‐enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi‐automatic delineation of contacts and deformational structures in a 3D virtual environment. L'étude traditionnelle des tranchées paléosismiques, impliquant l'enregistrement des coupes et l'interprétation stratigraphique et structurelle, peut prendre beaucoup de temps et être entachée de biais et d'inexactitudes. Pour surmonter ces limites, une nouvelle méthodologie est présentée, intégrant des données photogrammétriques et hyperspectrales infrarouges en appui aux observations paléosismiques de terrain. Comme étude de cas, cette méthode est appliquée à deux tranchées paléosismiques creusées à travers un escarpement de faille post‐glaciaire dans le nord de la Laponie finlandaise. L'imagerie hyperspectrale (HSI) est corrigée géométriquement et radiométriquement, traitée à l'aide d'algorithmes classiques de traitement d'images et d'apprentissage machine, et recalée sur un nuage de points photogrammétrique. Les modèles virtuels d'affleurements améliorés par HSI constituent un complément utile aux études paléosismiques de terrain, car ils fournissent non seulement une visualisation intuitive de l'affleurement et une archive de données facile d'emploi, mais permettent également une évaluation non biaisée de la composition minéralogique d'unités lithologiques ainsi qu'une délimitation semi‐automatique des contacts et des structures de déformation dans un environnement virtuel 3D. Die traditionelle Protokollierung und stratigraphische/strukturelle Interpretation paläoseismischer Gräben kann zeitaufwendig sein und durch Verzerrungen und Ungenauigkeiten beeinflusst werden. Um diese Einschränkungen zu überwinden, wird ein neuer Arbeitsablauf vorgestellt, der hyperspektrale und photogrammetrische Daten integriert, um feldbasierte paläoseismische Beobachtungen zu unterstützen. Als Fallstudie wird diese Methode auf zwei paläoseismischen Gräben angewendet, die über eine postglaziale Verwerfung im nördlichen finnischen Lappland angelegt wurden. Das hyperspektrale Bild wird geometrisch und radiometrisch korrigiert, mit etablierten Bildverarbeitungsalgorithmen und maschinellen Lernverfahren verarbeitet und mit einer fotogrammetrischen Punktwolke verknüpft. Hyperspektrale Aufschlussmodelle sind eine sinnvolle Ergänzung zu paläoseismischen Feldstudien, da sie nicht nur eine intuitive Visualisierung des Aufschlusses ermöglichen und ein vielseitiges Datenarchiv darstellen, sondern auch erlauben, die mineralogische Zusammensetzung lithologischer Einheiten zu ermitteln sowie Kontakte und Deformationsstrukturen in einer virtuellen 3D‐Umgebung zu analysieren. El estudio tradicional de las trincheras paleosísmicas, que implica la anotación, la interpretación estratigráfica y estructural, puede llevar mucho tiempo y verse afectado por sesgos e inexactitudes. Para superar éste hándicap, se presenta un nuevo flujo de trabajo que integra datos hiperespectrales infrarrojos y fotogramétricos para apoyar las observaciones paleoseísmicas de campo. Como caso de estudio, este método se aplica en dos trincheras paleosísmicas excavadas a través de una cornisa post‐glacial en el norte de la Laponia finlandesa. Las imágenes hiperespectrales (HSI) se corrigen geométrica y radiométricamente, se procesan utilizando algoritmos de procesamiento de imágenes establecidos y aproximaciones de aprendizaje automático, y se hacen corresponder sobre una nube de puntos derivada por fotogrametría. Los modelos de afloramiento virtual mejorados con HSI son un complemento útil para los estudios de campo paleosísmicos, ya que no solo proporcionan una visualización intuitiva del afloramiento y un archivo de datos versátil, sino que también permiten una evaluación imparcial de la composición mineralógica de las unidades litológicas y una delineación semiautomática de contactos y deformación en un entorno virtual 3D. 传统的古地震沟研究, 包括测井、地层和构造解释, 通常耗时并易受偏差和不准确性的影响。为了克服这些限制, 本研究提出新的工作流程, 整合红外高光谱和摄影测量数据, 以辅助古地震的现场观测。本研究以芬兰拉普兰北部的冰川期后断层陡坡上开挖的两块古地震沟, 作为研究案例。高光谱图像(HSI)经过几何和辐射校正, 使用既有的图像处理算法和机器学习方法分析, 并与由运动恢复结构所得点云进行配准。 HSI增强的虚拟露头模型是古地震现场研究的有效补充, 因为其不仅提供露头的直观可视化和多类型的数据存档, 而且能够对岩性单元的矿物组成, 在三维虚拟环境中进行无偏评估与半自动区分。 To overcome the limitations of the traditional study of palaeoseismic trenches, a new workflow is presented that integrates infrared hyperspectral and photogrammetric data. This method was applied on two palaeoseismic trenches excavated across a post‐glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) was corrected and co‐registered to a structure‐from‐motion point cloud. The resulting HSI‐enhanced virtual outcrop models provide an intuitive visualisation and archive of the outcrop, and enable an unbiased assessment of its mineralogical composition.
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Fluvial deposits are highly heterogeneous and inherently challenging to map in outcrop due to a combination of lateral and vertical variability along with a lack of continuous exposure. Heavily incised landscapes, such as badlands, reveal continuous three-dimensional (3-D) outcrops that are ideal for constraining the geometry of fluvial deposits and enabling reconstruction of channel morphology through time and space. However, these complex 3-D landscapes also create challenges for conventional field mapping techniques, which offer limited spatial resolution, coverage, and/or lateral contiguity of measurements. To address these limitations, we examined an emerging technique using images acquired from a small unmanned aerial vehicle (UAV) and structure-from-motion (SfM) photogrammetric processing to generate a 3-D digital outcrop model (DOM). We applied the UAV-SfM technique to develop a DOM of an Upper Cretaceous channel-belt sequence exposed within a 0.52 km2 area of Dinosaur Provincial Park (southeastern Alberta, Canada). Using the 3-D DOM, we delineated the lower contact of the channel-belt sequence, created digital sedimentary logs, and estimated facies with similar conviction to field-based estimations (±4.9%). Lateral accretion surfaces were also recognized and digitally traced within the DOM, enabling measurements of accretion direction (dip azimuth), which are nearly impossible to obtain accurately in the field. Overall, we found that measurements and observations derived from the UAV-SfM DOM were commensurate with conventional ground-based mapping techniques, but they had the added advantage of lateral continuity, which aided interpretation of stratigraphic surfaces and facies. This study suggests that UAV-SfM DOMs can complement traditional field-based methods by providing detailed 3-D views of topographically complex outcrop exposures spanning intermediate to large spatial extents.
Sensor-based sorting is increasingly used for the concentration of ores. To assess the sorting performance for a specific ore type, the raw materials industry currently conducts trial-and-error batch tests. In this study, a new methodology to assess the potential of hyperspectral visible to near-infrared (VNIR) and short-wave infrared (SWIR) sensors, combined with machine-learning routines to improve the sorting potential evaluation, is presented. The methodology is tested on two complex ores. The first is a tin ore in which cassiterite—the target mineral—is variable in grain size, heterogeneously distributed and has no diagnostic response in the VNIR-SWIR range of the electromagnetic spectrum. However, cassiterite is intimately associated with SWIR active minerals, such as chlorite and fluorite, which can be used as proxies for its presence. The second case study consists of a copper-gold porphyry, where copper occurs mainly in chalcopyrite, bornite, covellite and chalcocite, while gold is present as inclusions in the copper minerals and in pyrite. Machine-learning techniques such as Random Forest and Support Vector Machine applied to the hyperspectral data predict excellent sorting results in terms of grade and recovery. The approach can be adjusted to optimize sorting for a variety of ore types and thus could increase the attractivity of VNIR-SWIR sensor sorting in the minerals industry.
Detailed mapping of mineral phases at centimeter scale can be useful for geological investigation, including resource exploration. This work reviews case histories of ground-based close-range hyperspectral imaging for mining applications. Studies of various economic deposits are discussed, as well as techniques used for data correction, integration with other datasets, and validation of spectral mapping results using geochemical techniques. Machine learning algorithms suggested for automation of the mining workflow are discussed, as well as systems for environmental monitoring such as gas leak detection. Three new case studies that use a ground-based hyperspectral scanning system with sensors collecting data in the Visible Near-Infrared and Short-Wave Infrared portions of the electromagnetic spectrum in active and abandoned mines are presented. Vertical exposures in a Carlin Style sediment-hosted gold deposit, an active Cu-Au-Mo mine, and an active asphalt quarry are studied to produce images that delineate the extent of alteration minerals at centimeter scale, to demonstrate an efficient method of outcrop characterization, which increases understanding of petrogenesis for mining applications. In the Carlin-style gold deposit, clay, iron oxide, carbonate, and jarosite minerals were mapped. In the copper porphyry deposit, different phases of alteration are delineated, some of which correspond to greater occurrence of ore deposits. A limestone quarry was also imaged, which contains bitumen deposits used for road paving aggregate. Review of current literature suggests use of this technology for automation of mining activities, thus reducing physical risk for workers in evaluating vertical mine faces.
Mining companies heavily rely on drill-core samples during exploration campaigns as they provide valuable geological information to target important ore accumulations. Traditional core logging techniques are time-consuming and subjective. Hyperspectral (HS) imaging, an emerging technique in the mining industry, is used to complement the analysis by rapidly characterizing large amounts of drill-cores in a nondestructive and noninvasive manner. As the accurate analysis of drill-core HS data is becoming more and more important, we explore the use of machine learning techniques to improve speed and accuracy, and help to discover underlying relations within large datasets. The use of supervised techniques for drill-core HS data represents a challenge since quantitative reference data is frequently not available. Hence, we propose an innovative procedure to fuse high-resolution mineralogical analysis and HS data. We use an automatic high-resolution mineralogical imaging system (i.e., scanning electron microscopy-mineral liberation analysis) for generating training labels. We then resample the MLA image to the resolution of the HS data and adopt a soft labeling strategy for mineral mapping. We define the labels for the classes as mixtures of geological interest and use the classifiers (random forest and support vector machines) to map the entire drill-core. We validate our framework qualitatively and quantitatively. Thus, we demonstrate the ability of the proposed technique to fuse and up-scale high-resolution mineralogical analysis with drill-core HS data.
Geologists have been instrumental in shaping Earth observation satellite missions; likewise, geology has been the subject of many remote sensing studies [1]. Applications of optical remote sensing in geology date back to some early studies using the Earth Resources Technology Satellite-1 , the predecessor of the Landsat satellite program [2]. In the 1980s, the seventh channel in the short-wave infrared (SWIR) of the Landsat thematic mapper program was added, as a result of spectroscopic mineral studies by geologists [58]. A subsequent satellite-borne instrument, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), launched in 1999, had specific bands in the SWIR and thermal infrared dedicated to mapping mineral groups [3].
In recent years, with the rapid development of unmanned aerial vehicles (UAVs) and lightweight hyperspectral imaging (HSI) sensors, mini-UAV-borne hyperspectral remote sensing (HRS) systems have been developed and demonstrate great value and application potential. Compared to spaceborne and airborne HSI systems, mini-UAV-borne HSI systems come with relatively low manufacturing and running costs and have thus become a new research focus in the field of HRS.