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Core recovery and quality: Important factors in mineral resource estimation

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Abstract

The estimation of mineral resources is critical to all mining operations irrespective of size or commodity.(1,12) The risks associated with mining are varied and complex, where the dominant source of risk is the orebody itself Reverse circulation (RC) and diamond core drilling methods are used extensively for the collection of samples from depth. These data generally form the critical base for both geological and grade modelling, leading to the mineral resource estimate and ultimately the ore reserve estimate. It is well known that diamond drilling generally provides a higher quality sample, better suited to resource estimation than RC drilling, especially for gold deposits.(1,6,7,11) RC methods are sometimes applicable to the resource evaluation of alluvial/unconsolidated deposits, though can be highly problematic when applied to gold deposits.(7) Current methods of resource classification relate to the geological, economic and technical confidence in the resource. (14-16) Geological confidence is largely related to the level of drilling and sampling in the orebody, to the geologist's perceived level of confidence in his or her work, and to the continuity of the mineralisation. This contribution reviews the geological and technical factors that affect core recovery, how core recovery is measured, the impact of poor recovery on the resource estimate, and how to deal with lost core during estimation.
Dr A E Annels (aannels@srk.co.uk) is Principal Mining
Geologist, SRK Consulting (UK) Ltd, Windsor Court, 1–3
Windsor Place, Cardiff CF10 3BX, Wales, UK. Dr S C
Dominy is MCA Lecturer in Mining Geology and Resource
Engineering, Economic Geology Research Unit (EGRU),
School of Earth Sciences, James Cook University, Townsville,
Queensland 4811, Australia. Correspondence to Dr S. C.
Dominy (simon.dominy@jcu.edu.au)
© 2003 IoM Communications Ltd. Published by Maney for the
Institute of Materials, Minerals and Mining.
Keywords: Mineral resource estimation, Core recovery, Core
quality
Introduction
The estimation of mineral resources is critical to all
mining operations irrespective of size or commodity.1,12
The risks associated with mining are varied and
complex, where the dominant source of risk is the
orebody itself. Reverse circulation (RC) and diamond
core drilling methods are used extensively for the
collection of samples from depth. These data generally
form the critical base for both geological and grade
modelling, leading to the mineral resource estimate and
ultimately the ore reserve estimate. It is well known that
diamond drilling generally provides a higher quality
sample, better suited to resource estimation than RC
drilling, especially for gold deposits.1,6,7,11 RC methods
are sometimes applicable to the resource evaluation of
alluvial/unconsolidated deposits, though can be highly
problematic when applied to gold deposits.7Current
methods of resource classification relate to the geo-
logical, economic and technical confidence in the
resource.14–16 Geological confidence is largely related to
the level of drilling and sampling in the orebody, to the
geologist’s perceived level of confidence in his or her
work, and to the continuity of the mineralisation. This
contribution reviews the geological and technical factors
that affect core recovery, how core recovery is measured,
the impact of poor recovery on the resource estimate,
and how to deal with lost core during estimation.
Potential sources of error
Many potential sources of error exist which will affect
the accuracy of resource estimates at all stages.1–3,12
These will combine to enhance the random component
of the data variation, and thus contribute to nugget
variance. Errors can be introduced at a number of stages
including, drilling and core logging, geological map-
ping, sampling, assaying, geological modelling and
grade/tonnage estimation.
The effects of poor sampling regime at any stage of a
mining operation can introduce unpredictable random
errors, and negative or positive bias into the estimate.
Some of the sources of error that may be related to
drilling include: (i) inappropriate drill hole inclination
relative to orebody dip; (ii) poor core recovery and
quality; (iii) blocking errors; (iv) selection criteria for
sample length; (v) poor quality sampling practice and/or
sampling bias; (vi) poor sample preparation protocols;
and (viii) core handling and checking (including tam-
pering with, and removal of, core).
Core recovery and quality
The precision of estimation is very much dependent on
the quality of the sample database. The application of
sophisticated computer techniques will not offset poor
data quality and often renders the results meaningless. It
is thus essential that the quality and quantity of samples
recovered be maintained at a high level.
Core loss is a relatively common occurrence during
diamond drilling, though there is very little information
in the literature on how to deal with it. Intersections to be
used in a resource estimate should have a total core
recovery (TCR) value of at least 85%, and preferably
greater than 90%.3,10 The attitude that recovery
measurement is unimportant or even unnecessary must
be dispelled. If recovery cannot be maintained at high
levels due to technical or geological problems, then it is
important that this fact is not concealed. Allowances
should be made for loss of sample in the resource
estimate and/or in its subsequent classification. This
requires that the geologist make the effort to measure
core recovery carefully and incorporate this information
into the computer database. It is unacceptable to
estimate recovery or to assume that it is 100% – a rare
occurrence, but sometimes observed. Where excessive
core recovery (> 100%) is recognised, it is important that
attempts be made to determine the reason. This may be
purely due to displacement of marker blocks during
handling and transport, or to stick-ups at the bottom of
the hole after one drill run which are picked up during
the next run. In this latter case, the preceding run will
show an apparent core loss. This problem should be
rectified before sampling of the core.
Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112 B305
DOI 10.1179/037174503225011306
Technical note
Core recovery and quality: important factors in mineral resource
estimation
A. E. Annels and S. C. Dominy
Technical factors affecting core recovery
The following list presents a summary of some of the
factors that could contribute to either low recovery or
to badly broken core, even in good ground conditions:
(i) bent inner tube so that: (a) the core will not travel
up the tube and will be subject to grinding; (b) it
rotates with the outer tube again disturbing and
grinding the core; and (c) it fails to seat properly
in the outer barrel resulting in total core loss
(ii) failure of back-end bearing resulting in: (a) loss
of core due to grinding; and (b) grinding of core
leaving flat faces
(iii) bent outer tube of core barrel resulting in: (a)
failure of the inner tube to latch in and thus loss
of core; and (b) less than full diameter cores
(iv) core spring missing, displaced, damaged, worn
or not lubricated
(v) badly worn or damaged crowns
(vi) diamonds inside kerf damaged, worn or displaced
causing core to jam in inner tube
(vii) worn stabilisers
(viii) vibration induced by poor equipment, insecure
rig mountings and hole deviation
(ix) blocked waterways
(x) inadequate flow/pressure of flushing medium
and unsuitable flushing medium
(xi) loss of water return
(xii) excessive/inappropriate head pressure and
rotation rate
(xiii) inexperienced driller or driller chasing production
bonus.
A major cause of poor core quality and loss, is the
failure of the wireline inner tube to seat or latch
properly. This usually results from bent inner/outer
tubes, wrong inner tube length, latch failure (broken
spring), or a hole angle that is too shallow to allow the
inner tube to travel.
Geological factors affecting core recovery
A summary of some of the geological reasons why
core recovery may be poor is presented below:
(i) soft friable ground due to alteration, weathering
or leaching
(ii) unconsolidated materials
(iii) broken ground with clay infill
(iv) soluble components removed by unsuitable
flushing medium
(v) low intersection angles with rock discontinuities
(cleavage, open bedding, joints, schistosity, foli-
ation, etc.), particularly joints following the core
axis, and cleavage disking leading to a ‘rasher of
bacon’ effect in the inner tube
(vi) high frequency of discontinuities per metre
(vii) unexpected fault zones
(viii) secondary porosity or vug development due to
karstic solution or dolomitisation or hydration
of anhydrite
(ix) cavities induced by karstic weathering along
joints and faults and also mining (stopes and
caved zones)
(x) alternating rocks of variable hardness and
abrasiveness
(xi) over stressing (disking on stress release)
(xii) sheared or brecciated host rocks associated with
mineralised zones
(xiii) high clay content leading to blocking of
waterways/airways
(xiv) water saturated ground.
Many of the above problems can be ameliorated by
the use of larger diameter barrels, a more suitable
flushing medium or the use of triple-tube barrels. In
the case of broken ground, which quickly results in the
blocking of the inner tube, the use of shorter drill runs
is recommended, thus not attempting to fill the inner
tube to capacity.
Measurement of core recovery
The measurement of recovery is a key part of the core
logging process, which includes recording geological
information and taking samples.13,17 The overall logging
exercise is one of great importance and should not be
left in the inexperienced geologist/geological technician.13
During core sampling, errors can be induced by the
selection of unsuitable sample intervals in relation to
changes in mineralogy, host lithology, metallurgy, etc.
Similarly, errors in the estimation of true sample length
due to measurement of intersection angles and depths,
and problems related to core recovery are possible. The
latter is particularly serious, as no satisfactory way has
been proposed to allow for the fact that we know
nothing about the grade of the portion of the core that
has been lost.
Heavy core losses throughout an ore body inter-
section can seriously undermine the confidence in a
resource estimate. In most cases this is totally ignored
and the assumption made that the grade of the missing
sample is the same as that recovered. It is important to
determine whether a relationship exists between grade
and recovery (either positive or negative) to assess the
potential for grade bias.
Assuming that depth measurement and blocking
has been done correctly and checked prior to logging,
core recoveries can be determined using the total core
recovery (TCR) parameter, which is defined as:
Total length of core recovered
TCR = × 100
Drilled length Eq. 1
However, this hides the fact that the quality of the
core may be poor and the measurement of solid core
recovery (SCR) is more relevant:
Total length of core in pieces > core diameter
SCR = × 100
Drilled length Eq. 2
For example, with NQ diameter core (47·6 mm),
only core pieces greater than 47·6 mm are counted in
the determination of SCR. Core sections of this length
are only included if a full core diameter exists. If a core
piece has a length of 60 mm, but does not possess a
full core diameter (i.e. is split longitudinally), it is not
counted.
B306 Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
Barton et al.5suggests that TCR, and by default SCR,
should be measured and reported to the nearest 2%.
Where geotechnical logging accompanies the
geological logging (and it should), the rock quality
designation (RQD) may be determined.5,9 RQD is a
modified core recovery percentage and can be taken as
a measure of core quality. The RQD was developed to
provide a quantitative estimate of rock mass quality. It
is the percentage of intact core pieces longer than 100
mm in the total length of the core:
Length of core in pieces > 100 mm
RQD = ×100
Drilled length Eq. 3
The core should be at least NQ (47·6 mm) and drilled
with a double- or triple-tube core barrel. Care must be
taken to ensure that fractures, which have been
produced by handling or drilling, are identified and
ignored when determining the RQD value. Material that
is obviously weaker than the surrounding rock (such as
over-consolidated gouge) is discounted, even if it
appears as intact pieces that are 100 mm or more in
length.5The length of individual core pieces should be
assessed along the centre line of the core, so that
discontinuities that happen to parallel the drill hole will
not unduly penalise the RQD values of an otherwise
massive rock mass.5It is recommended (for geo-
mechanical purposes) that RQD be determined for
variable rather than fixed lengths of core run. Values of
individual beds, structural domains, fracture zones, etc.
should be logged separately, so as to give a more
accurate picture of the distribution and width of zones
with low RQD values.5
RQD and, indeed, TCR and SCR are directionally
dependent parameters and their values may change
significantly, depending upon borehole orientation.
This feature must be carefully considered in the
interpretation of such data.
Table 1 shows a comparison between TCR, SCR
and RQD for three intersections within the same
quartz vein. An NQ core barrel was used. Core A has
a TCR value of 83% that, whilst not good, is fair.
However, the corresponding SCR and RQD are 51%
and 30%, respectively, and reveal the true poor quality
nature of the core.
Core B shows a TCR of 99% indicating an excellent
recovery; however, the SCR and RQD values of 57%
and 0%, respectively, reveal the true very poor quality
of the core due to severe fragmentation. In such a
situation, the measurement of TCR is very difficult, as
it can only be determined after an attempt to
reconstitute it to its prefragmentation equivalent. An
intersection could return a TCR > 100%, which could
be due to measurement problems or displacement of
depth blocks. However, excessive recovery could also
be due to retrieval of core left behind in the hole after
the previous drill-run. Core C is the highest quality
and receives a 96% score for each measure. There is
very little fragmentation of the core; it is composed of
11 > 100 mm lengths.
The key conclusion from this information is that
TCR alone is not the best indicator of core quality
(Table 1 & Figs. 1–3). It is strongly recommended that
all three parameters are determined during logging.
The extra work involved in doing this is relatively
minimal in the big picture, and worth the extra
information. The measurement of RQD will aid mine-
planning engineers at a later date.
Impact of sample loss (poor recovery) on the
resource estimate
If core is lost in a mineralised interval or badly broken
and disturbed, it presents three major problems: (i)
depth and thickness estimation is difficult for specific
lithological or grade zones in the overall mineralised
zone; (ii) accurate estimation of the grade is impossible;
and (iii) accurate determination of tonnage factor is
impossible.
In the first case, this affects the thickness used to
weight the associated grade having a knock-on affect
on both tonnage and local or global grade; in the
second case, this not only affects the final grade
estimate but also affects the delimitation of ore fringes
(vertical and lateral) based on a cut-off grade which in
turn will affect the tonnage estimate. If the material lost
is of lower grade than the recovered section then
overestimation of grade results and a sample, which
should, perhaps, have been allocated to waste, is
incorporated into the potential ore zone. Conversely, if
the lost material is of higher grade, the resulting
underestimation results in the loss of ore zone thickness
if the sample is at the margin or underestimation of the
grade of the ore zone. Badly broken core may present
problems in recognition of grade changes during
sampling and also biased sampling due to the difficulty
of making an accurate longitudinal split of the core.
This further exacerbates the grade estimation problem.
In the case of the third problem referred to above,
tonnage factors can be calculated from assay grades;
however, if these are suspect due to uncertainties as to
the grade of the lost core, then the bulk density will be
in error. Alternatively, if bulk density is directly
measured on core then the assumption is made that
there is no change in its value between the recovered
and lost sections. The loss may reflect poor ground
which, in turn, may be reflected in lower densities.
Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112 B307
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
Table 1 Comparison between core properties of three 3 m
intersections within the same orebody
Feature/property Core A Core B Core C
TCR 83% 99% 96%
Variation from 3 m of core –51 cm –3 cm –10 cm
SCR 51% 57% 96%
Number of >48 mm core lengths 13 12 11
RQD 30% 0% 96%
Number of > 100 mm core lengths 5 0 11
*Rock quality Poor Very Very
poor good
*Based on relationship between the numerical value of RQD
and the engineering quality of the rock proposed by Deere.9
Also, if the core is fully recovered but is in bad condition,
then it may be impossible to obtain a representative
measurement.
Dealing with lost core
Since core loss is a relatively common occurrence, the
question is how to assess losses so that undesirable bias,
to either lower or higher values, are avoided during
estimation. The practicalities of dealing with
mineralised intersections that show poor recovery (e.g.
<85% TCR and SCR) are not simple. Geological
observation and experience are very important if core
loss does occur. For example, does the mineralisation
mainly occur on fractures, or is the core recovery lower
in strongly fractured or broken zones? Is the mineralised
zone softer than the surrounding rocks? Such
observations can help in controlling potential bias.
If there are differences in the core recovery within a
deposit, then the homogeneous zones should be
selected according to the same degree of core recovery.
These zones can then be subdivided according to their
geology. It is very important not to combine a zone of
say 100% recovery with a zone of 45% recovery into
B308 Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
2Mineralised core length of 4·35 m from an epithermal gold system in Australia, showing excellent recovery (TCR
= 95%), but poor quality (e.g. fragmented). The SCR and RQD values of 58% and 41%, respectively, support this
observation. Without the SCR and RQD values, the resource estimator would have no idea of the quality of this
intersection. It is highly likely that; (i) fine material is missing from the intersection; and (ii) that the
sampling/core cutting process was poor due to the broken core. Any intersection grade(s) produced from this core
is likely to be suspect
1Mineralised core length of 4·20 m from an epithermal gold system in Australia, with at least 25% of the zone
poorly recovered. The TCR value for this run is 73% (moderate recovery), whereas the SCR and RQD values are
55% and 49%, respectively (poor quality)
one sample. These zones of differing recovery should be
separated otherwise errors are compounded through
different sample supports.
The project stage and database size also has some
bearing on the magnitude of the problem. Clearly, if
only three or four intersections out of, for example, a
few hundred are below 85%, then the issue is potentially
not significant. However, if many intersections show a
poor recovery (say 50% with < 85% recovery) at the
prefeasibility/feasibility stage, this raises the questions
of: (i) is the database valid for resource estimation; and
(ii) should more drilling be undertaken to see if better
recoveries can be achieved? In some instances of very
poor ground conditions, then RC drilling may have to
be used. In whatever situation, how should these poor
quality samples be treated and what grade should be put
into the database?
Core loss and sample support
Sample support is an important geostatistical con-
sideration that refers to the volume and size of a sample.
Taking NQ core as an example, a 1 m length of core has
a different support to that of 0·25 m length of core.
Similarly, 1 m of NQ core differs from 1 m of BQ core.
In the core loss framework, we have a support issue
when; for example, 0·5 m of recovered core is being used
to represent a 1 m composite. David8noted an example
for a low nugget effect, highly continuous orebody,
where the support discrepancy is not problematic as the
variance between the two supports (6 inches and 10 feet,
respectively) is not great. However, in less continuous
ore with a higher nugget effect the situation is very
different, where the small sample (poor recovery)
variance is much higher than the larger sample variance.
In such as case, there would be a very real danger of not
properly resolving the semi-variogram model,
especially any small-scale structures, with the mixed
sample population. Of course this situation will only
be a problem if a substantial number of samples have
a poor recovery.
Investigation recovery-grade bias
Where poor core recovery is notable (say at least
20–30 intersections) it is worth producing X–Y plots
of core recovery (SCR and TCR) versus grade (%, g/t,
etc.). This will allow the relationship between recovery
and grade to be investigated. A simple regression
calculation will permit the nature of any grade bias to
be determined. If the regression line is zero, then there
is no correlation between recovery and grade. If a
random scatter is produced, then there is a negative
correlation and core loss can be suspected of causing a
positive bias – thus grades of mineralisation appear
higher than they are. If the gradient of a best-fit
regression line is positive, then a negative bias is likely
to be present. This is a useful method for investigation
of recovery bias, but it should be used intelligently.
For example, it is possible that grade can correlate
with the mechanical properties of the rock, in that the
softer sections with poor core recovery will in reality
have a high grade.
Methods to deal with core loss and grade
There are a number of methods that have been used
calculate the grade of an intersection with poor core
recovery. These are summarised briefly below.
One approach is to consider only the recovered
core. Thus, if the recovery of a 1 m section has a 50%
(TCR) with a grade of 2 g/t Au, then it is assumed that
the mineralised intersection is 0·50 m thick at 2 g/t Au.
As a consequence, the tonnage in this region is
reduced and it is assumed that the assayed grade is
correct for that intersection. The latter of course may
not be true.
Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112 B309
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
3Mineralised core length of 4·60 m from an epithermal gold system in Australia, showing the ultimate aim of any
resource drilling programme – 100% recovery (TCR) and good core quality (SCR 99% and RQD 99%). With
good sampling and assaying protocols this intersection should produce high quality grades for the resource
database
Another and very common approach is to assume
that all lost material has a zero grade, but take thickness
to be that represented by the core. Whilst this is unlikely
to be true, at least the estimate will be conservative in its
underestimation of grade. Thus with our 1 m section at
50% (TCR) and 2 g/t Au grade, we take this as 1 m at 1
g/t Au calculated from:
Σn
i=1 (Recovered core grade ×Recovered core length)
G =
Σn
i=1 (Represented length) Eq. 4
The criticism of this method is that it assumes that
the non-recovered core has zero grade, which is unlikely,
but at least there is less chance of over-estimation of
grade.
Other approaches have included setting the lost
material grade to the deposit average, the average of the
two closest samples (e.g. mineralised samples either side
of lost core) or some sort of weighted grade.
Baker and Binns4used a weighting approach,
where gold grades were reduced, on the basis of core
recovery to a nominal 95% TCR. For example, a 1 m
interval, with 50% recovery and an assay value of 2 g/t
Au, would be calculated as 1.05 g/t Au according to:
(2 g/t × 0.5 m)
G = Eq. 5
(1 m × 0.95)
A further approach to dealing with core loss is to
undertake a point kriging exercise down the hole. The
aim here would be to estimate a grade for the missing
core based on interpolation from high-quality mineral-
ised samples (e.g. > 85% TCR and SCR) up and down
the hole from the poor recovery interval. The definition
of down-hole variogram parameters would be a critical
part of this exercise, following de-regularisation of the
original composite samples. Clearly, this method is only
applicable to thicker deposits where a large number of
samples are present. It would not be applicable to a 1 m
narrow vein system for example.
An alternative approach to dealing with core loss
Rather than attempting to correct for the impact of
core loss on grade estimation, an alternative approach
might be to accept the grade information for all the
samples, but to assign individual confidence ratings to
each. This is based on ranges of TCR or SCR. For
example, we could rate the sample for resource
estimation as shown in Table 2.
Where the SCR is low, or there are other reasons to
assign a low confidence to a sample, then this rating could
be further reduced by say 0·5. This methodology has been
used to down-rate sample data obtained from old drilling
campaigns, where the sampling procedure was non-
standard and where there were doubts as to the quality of
the analysis. In this particular instance also, check re-
sampling was only possible in a small percentage of the
holes originally drilled. Confidence was then further
reduced justifying the deduction applied.
A similar approach can be made in the case of RC
samples where the rating is based on a statistical analysis
of the sample weights recovered over constant hole
lengths. This approach was applied in a situation where
the theoretical weight of each sample could not be
precisely determined due to variability in the nature of the
mineralisation, its competence and in the quantity of vugs
and solution cavities intersected. The population was
clearly bimodal, with the dominant one reflecting the
natural variability of the mineralisation and the second,
lower, population the product of heavy sample loss. All
sample weights less than the mean minus three standard
deviations (X–3σ) of the dominant population were
assigned a low rating (Table 3). Similarly, any sample
whose weight exceeded mean plus two standard deviations
(X + 2σ) was also given a low score. This value closely cor-
responded with the maximum possible weight of a sample.
The rating system applied is shown in Table 3.
Where the field geologist’s log indicated a problem
due to possible contamination, sampling bias due to
water injection or the intersection of groundwater,
these ratings were down graded by 0·5. This system
allowed both RC and diamond drill data (both old
and new) to be combined into a single confidence
database. This information was then kriged into each
resource block along with grade, and used as a basis
for resource classification. Measured and indicated
mineral resource categories were defined in areas of
uniform drilling density and, where there was a
combination of low sample density and quality,
inferred mineral resources were defined. This case
history demonstrates that even though the density of
drilling might have been high enough to justify a
measured resource status, it was considered that the
quality of the samples was unsatisfactory, and the
resource blocks were down graded to an indicated
resource status as a result.
Concluding comments
The above discussion demonstrates that diamond drill
core quality can affect all the parameters used to
evaluate a mineral deposit, namely, thickness, area,
B310 Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
Table 2 Confidence rating of core recovery values (SCR)
Core recovery (SCR) Rating Description
> 85% 4 High confidence
60–84% 3 Moderately reliable
30–59% 2 Unreliable
< 30 1 Unacceptably low
Table 3 Confidence rating of RC recovery values
Statistical basis Rating Description
of confidence
Mean ± 1 SD 4 High
Data beyond above to +2 SDs 4 High
Data between 1 and 3 SDs below mean 3 Moderate
Data above mean +2 SDs 2 Unreliable
(contamination)
Data below mean –3 SDs 1 Unacceptable loss
No recovery or cavity 0 No grade assigned
grade and bulk density. It is doubtful whether any
other potential error can have such a pervasive affect.
One other aspect not yet considered is the impact of
core loss on the geological modelling of a deposit. If
important structural features are not recognised due
to poor recovery in critical areas, then the model
applied may be incorrect and hence the resulting block
grade model will not reflect the situation in the
ground. Non-recognition of such features can also
affect estimates of mining recovery and of rock mass
stability underground.
In Table 1 (Checklist of Assessment and Reporting
Criteria) of the 1999 JORC Code and proposed 2003
revision (and similarly in other codes/guidelines)14,18 the
importance of proper logging and core recovery is
stressed. The checklist is not prescriptive, but encour-
ages the competent person into reporting matters that
might materially affect a reader’s understanding or
interpretation of the results or estimate being reported.
Specifically Table 1 states:
Logging: Whether core or chip samples have been
logged to a level of detail to support appropriate
mineral resource estimation, mining studies and
metallurgical studies. Whether logging is qualitative
or quantitative in nature. Core (or costeen, channel,
etc.) photography.
Drill sample recovery: Whether core or chip sample
recoveries have been properly recorded and results
assessed. In particular whether a relationship exists
between sample recovery and grade and whether
sample bias may have occurred due to preferential
loss/gain of fine/coarse material.
The resource estimator/competent person should
seriously consider: (i) use of TCR, SCR and RQD
parameters to describe better both core recovery and
quality; and (ii) more effectively use this recovery and
quality data in the resource estimate. The next step is
to consider these values as regionalised variables,
leading to block modelling alongside those usually
considered in a resource estimate.3
Acknowledgements
This contribution results from on-going work into the
reporting of errors and uncertainty in mineral resource
estimates. SRK Consulting (UK), Ltd, James Cook
University and various other industry collaborators have
provided funding. The authors are grateful to a number
of colleagues who have promoted discussion relating to
core recovery and quality. In particular, M. A. Noppé
(Snowden Mining Consultants Pty, Ltd, Australia), P.
Creenaune (Newcrest Mining, Ltd, Australia), W. J.
Shaw (Golders, Ltd, Chile), Dr M. G. Armitage (SRK
Consulting UK, Ltd), Dr A. G. Royle (Consultant, UK),
Dr S. Henley (Resource Computing International, Ltd,
UK), G. Williams (Drilling Consultant, UK) and F.
Mann (Carnon Contract Drilling, UK). Comments
from an IMM reviewer are acknowledged.
References
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Applied Earth Science (Trans. Inst. Min. Metall. B) December 2003 Vol. 112 B311
Annels and Dominy Core recovery and quality: important factors in mineral resource estimation
... Mineral resources estimation, regardless of type and size of ore deposit, is vital to a mining project (Annels and Dominy 2003). To reduce the risk in mineral resources estimation, it is necessary to take samples from the surficial and underground parts of a deposit. ...
... Borehole drilling is essential but expensive to access directly to the ore minerals (Hassani-pak 2001;Committee 1997). Data obtained from exploration drilling are the basis of geological and grade modeling of a deposit (Annels and Dominy 2003). There are multiple error sources in drilling that can affect the accuracy of mineral resource estimation, including low core recovery (Annels and Dominy 2003;Hassanipak and Sharafoddin 2005;Moon et al. 2006;Haldar 2013). ...
... Data obtained from exploration drilling are the basis of geological and grade modeling of a deposit (Annels and Dominy 2003). There are multiple error sources in drilling that can affect the accuracy of mineral resource estimation, including low core recovery (Annels and Dominy 2003;Hassanipak and Sharafoddin 2005;Moon et al. 2006;Haldar 2013). ...
Article
Full-text available
Three-dimensional modeling of a mineral deposit was conducted based on the samples collected from the surficial and deep parts of the deposit. One of the factors that affect the quality of data is the recovery of cores acquired during exploration drilling. As the core recovery reduces, the grade in core or drilling mud increases. Core loss can introduce unpredictable errors and negative or positive bias into the mineral resources estimation. Investigating the grade–recovery relationship in a bivariate space helps to detect the grade bias. In an ideal state, there is no correlation between grade and recovery. In case the grade–recovery relationship possesses a negative or positive correlation, the grade bias is likely to be due to core loss. In practice, however, because there are many data in boreholes sample database, the grade–recovery relationship is not properly determined on a simple scatter plot of grade versus core recovery. Application of conditional distributions in a bivariate space provides the necessary tool for investigating the simultaneous variations of grade and core recovery. Therefore, having converted the grade and core recovery data into the normal space, the conditional grade distribution was determined for various recoveries, and then, the conditional grades expectations were determined and plotted against the recovery to investigate the grade–recovery relationship. Application of the proposed method in three different deposits showed that in low recoveries, due to waste loss, the grade values were overestimated, whereas in high recoveries, due to ore loss, they were underestimated.
... The sample collection process is of concern, given that this stage can impart substantial errors into the sampling programme. A major issue with core is that of total core recovery and quality [85]. From a sampling perspective, perfect core drilling shows low DE and EE. ...
... From a sampling perspective, perfect core drilling shows low DE and EE. However, due to poor ground conditions and/or poor drilling control, EE may be high as seen in poor core recovery (e.g., core fragmentation and/or material loss) [85]. In such a case, metallurgical test work, either comminution or recovery tests must be interpreted with caution or preferably avoided. ...
Article
Full-text available
When developing a process flowsheet, the risks in achieving positive financial outcomes are minimised by ensuring representative metallurgical samples and high quality testwork. The quality and type of samples used are as important as the testwork itself. The key characteristic required of any set of samples is that they represent a given domain and quantify its variability. There are those who think that stating a sample(s) is representative makes it representative without justification. There is a need to consider both (1) in-situ and (2) testwork sub-sample representativity. Early ore/waste characterisation and domain definition are required, so that sampling and testwork protocols can be designed to suit the style of mineralisation in question. The Theory of Sampling (TOS) provides an insight into the causes and magnitude of errors that may occur during the sampling of particulate materials (e.g., broken rock) and is wholly applicable to metallurgical sampling. Quality assurance/quality control (QAQC) is critical throughout all programmes. Metallurgical sampling and testwork should be fully integrated into geometallurgical studies. Traditional metallurgical testwork is critical for plant design and is an inherent part of geometallurgy. In a geometallurgical study, multiple spatially distributed small-scale tests are used as proxies for process parameters. These will be validated against traditional testwork results. This paper focusses on sampling and testwork for gold recovery determination. It aims to provide the reader with the background to move towards the design, implementation and reporting of representative and fit-for-purpose sampling and testwork programmes. While the paper does not intend to provide a definitive commentary, it critically assesses the hard-rock sampling methods used and their optimal collection and preparation. The need for representative sampling and quality testwork to avoid financial and intangible losses is emphasised.
... The recovery measures the fraction of total extracted core, which is obtained through Eq.7b, where 'Σl' represents the total core length and 'L' is the length of drill run. The Rock Quality Designation (RQD) measures the percentage of the unbroken core having length greater than 0.1 m (l greater than 0.1 m) in a single drill run (Annels and Dominy, 2003 The strength of rock has been discussed in terms of its Uniaxial Compressive Strength (UCS), which measures the amount of load taken up by intact rock before failure. The UCS is obtained through Eq.8a by measuring the compressive load 'f' required for inducing failure in the rock having initial cross-section area 'a'. ...
Article
Coal mining, especially the underground mines are often encountered with several types of geological constraints, where massive-hard roof and trapped gas are the most serious threats leading to catastrophic (fatal) effects in form of roof falls, mine explosion and gas contamination. Thus, for enhancing the safety of underground coal mines, identification of aforesaid constraints during detailed exploration is very essential. The minable seams of Sohagpur Coalfield are often associated with rich Coal Bed Methane content, which enhances their economical importance, but presence of stable sandstone roof parallely induces challenges during their extraction. CBM also acts as environmental hazard having twenty-one times more greenhouse effect than carbon dioxide. Therefore, the present paper attempts to investigate the depositional environment of the high-quality methane-rich sub-bituminous reserves of eastern Sohagpur Coalfield through integration of geochemical, geophysical and geomechanical analysis of drilled boreholes along with their respective gas content. The results showed that the six major Barakar coal seams possessed nearly consistent distribution in terms of reserve distribution and chemical properties. These seams consisted of good quality reserves (Grade: G5-G8) with high heat values and organic content, and had less impurity content. These coal seams are overlain by stable roof having lower cavablity in form of thick hard medium-grained sandstone beds with moderate elasticity and strength. The seam structures showed a stable profile without any abrupt discontinuity except Bahmni-Chilpa fault in southern part of block. Some of the higher rank coal seams also contained potential CBM content. Statistical analysis was also carried out for understanding the distribution of reserve properties and monitored their interdependence using suitable fitting tools. Thus, the present study helped in summarizing different aspects related to reserve quality, roof properties and gas content for understanding the geology and working environment of coal reserves of less explored but economically important Central Indian coalfields.
... In the beginning, the conventional methods such as core drilling combined with chemical analysis would apply to achieve an ore grade model. However, too much core drilling without considering the spatial dependency is expensive and time-consuming (Annels & Dominy, 2003). Geostatistical methods can be used to generate estimates of the block grades using core drilling, although it requires assumptions of homogeneity of the grades over each geological unit (i.e., stationarity decision) as well as the knowledge of the model parameters (Cressie, 1990). ...
Article
Full-text available
Investments and progress of mineral projects depend on the quantity (tonnage) and quality (grade) of mineral resources and reserves. This study examines the impact of various criteria used in the classification of mineral deposits or parameters defining these criteria. The data used in this study include the uranium assay analysis from 127 exploratory boreholes, which were then subjected to a three-directional variography after statistical studies to identify regional anisotropy. A grade block model was built using the optimal parameters of variograms and with the help of kriging estimator. Then, by using different methods of estimating the block model uncertainty including kriging estimation variance, block error estimation, kriging efficiency and slope of regression, classification of mineral reserves was carried out in accordance with the JORC standard code. Based on different cut-off grades, the tonnage and average grade were calculated and plotted. An innovative quantitative method based on the distribution function of the mentioned parameters and the fractal pattern of separation of populations was used for the classification of mineral reserves. The existence of the least difference between the use of standard and fractal patterns in the slope of regression method indicated less error and was a proof of more reliable results.
... Whilst TCR is the common core recovery metric, it can mask quality as fragmented core may still yield a 100 per cent value. The addition, SCR provides a measure of fragmentation, for example where TCR may be 99 per cent, but SCR 57 per cent indicating broken core where fines loss is likely (Annels and Dominy, 2003). During the GDP, TCR and SCR values were greater than 90 per cent, indicating good core quality (e.g. ...
Conference Paper
Full-text available
Sheeted vein gold deposits are characterised by multiple sub-parallel veins and often free milling gold. Variability in gold grade and recovery parameters are enhanced by poorly designed sampling and testwork protocols. Poor quality samples generally equate to an enhanced nugget effect. A sample can be described as being representative when it results in acceptable levels of bias and precision. Total sampling variability can be quantified by the relative sampling variance, which measures the total empirical sampling variance influenced by the heterogeneity of the lot being sampled under the current sampling/testwork protocol. Effort should be made to minimise the relative sampling variance through Theory of Sampling and QA/QC application. This contribution reports on a case study, which exemplifies how high-quality grade and recovery data can be gained from a well-designed and planned drilling, sampling and testwork programme followed by bulk sampling. Results across historical resource drilling; geometallurgical drilling; laboratory assay/testwork, geometallurgical modelling and a bulk sampling/pilot processing study are reconciled. A whole-core entire-sample testwork protocol was used to acquire fit-for-purpose gold grade and recovery data for inclusion into a pre-feasibility study reported in accordance with The JORC Code.
... Whole core sampling followed by full sample assay via SFA, LW or PAL effectively yields FSE and GSE values of zero. High quality core drilling will yield minimal DE and EE (Annels and Dominy, 2003). With good laboratory practice, the PE and AE can be minimised. ...
Conference Paper
Full-text available
Various styles of gold mineralisation pose problems during sampling because of their complex gold grade distribution and presence of coarse gold. Effective sampling and sample preparation forms the basis of resource estimates. The design of a sampling and assaying protocol must be based on a thorough understanding of the mineralisation in question, which should include consideration of gold particle size and mass distribution. The proportion of coarse gold within samples has a profound effect on the requirements for sample preparation and assaying. Choice of preparation procedure and assay type is of critical importance if sampling errors are to be minimised. Traditional fire assays, using small charge sizes, often understate assays gained from large charge sizes and techniques such as screen fire assay and LeachWELL. Proper application of the Theory of Sampling reduces errors during collection, preparation and assaying. A quality control/quality assurance programme must be implemented and critically assessed. A case study from the Ballarat mine is presented, which is characterised by substantial quantities of coarse-gold hosted in quartz veins. Diamond core drilling is the key input into resource delineation. A number of different sampling and assay options have been trialled over the recent project history. The current sampling protocol utilises whole drill core, using LeachWELL assays after logging and photography.
... From a sampling perspective, perfect core drilling shows very low delimitation and extraction errors (eg theory of sampling; Gy, 1979;Dominy, Platten and Minnitt, 2010). However, due to poor ground conditions and/or poor drilling control extraction error may be high, as seen in poor core recovery (eg core fragmentation and/or material loss) (Annels and Dominy, 2003). Where this occurs, metallurgical test work, either comminution or recovery tests, must be interpreted with great caution. ...
Article
Submarine karstic environments are complex and challenging to study. Seismic investigation usually has difficulty to get geological information because of a lack of penetration due to the high reflectivity of the calcareous substratum. To circumvent this problem, we studied how to combine Marine Electrical Resistivity Tomography (MERT) with geotechnical data to investigate the porosity structure from the geotechnical to geophysical scale. We applied the technique to the submarine karstic plateau of Banc de Guérande (Saint‐Nazaire, France), mainly composed of hard calcarenite and sandy pockets. We obtained sections of 2D resistivity models from the MERT data inversion. We used existing geotechnical data on extracted cores at several boreholes close to the MERT profiles using a Multi‐Sensors Core Logging (MSCL) bench. We used porosity proxies derived from Archie's law and porosity data from the MSCL inferred from gamma density measurements on the core to combine the data of very different scales (meter for MERT and centimetre for MSCL). The comparison between measurements showed a good similarity between in situ MERT and borehole MSCL data at depths below ∼10 m below seafloor. A larger difference was observed close to the seabed, where the MERT porosity was higher than the MSCL porosity. The extraction of water‐saturated cores and the numerous core fractures could explain this difference in the near surface. The results were analysed with respect to the scale difference between geophysical and geotechnical data. The conclusions suggested that the difference between MERT and MSCL porosities could testify from the local heterogeneity of the soil and indicated whether the surrounding substratum was more porous (and thus fractured or dissolved) than the core or vice versa. The study highlighted the necessity of an excellent collocation of the data to retrieve reliable information from the comparison between geophysical and geotechnical data. This article is protected by copyright. All rights reserved
Article
Rock quality designation (RQD) has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling. Anisotropy index of jointing degree (AIjd) was formulated by Zheng et al. (2018) by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space. In accordance with spacing terminology by ISRM (1981), defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD. To overcome this limitation, theoretical RQD (TRQDt) introduced by Priest and Hudson (1976) can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution. Anisotropy index of the jointing degree was improved using TRQDt which was adjusted to wider joint spacing by considering Priest (1993)'s recommendation on the use of variable threshold value (t) in TRQDt formulation. After applications of the improved anisotropy index of a jointing degree (AIjd′) to hypothetical jointed rock mass cases, the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AIjd′ formulation by considering the ratings of persistency of joints as proposed by Bieniawski (1989)'s rock mass rating (RMR) classification. Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree (W_AIjd′). A structural anisotropy classification was developed using the RQD classification proposed by Deere (1963). The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.
Article
Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract The diamond drill is the most productive tool available for the earth scientist to explore and map the subsurface. However, the quality of the information obtained for analysis and modeling depends on how well the processes involved are understood so as to eliminate systematic and human error and effectively minimize the variables causing random error. This overview of the quality assurance and quality control (QA/QC) procedures required to manage these errors starts with the planning phase of a drilling program and goes through drill rig setup, borehole depth measurement, core recovery measurement, core depth registration, core orientation, borehole survey, and borehole path reconstruction. An outline follows of the methods used in the logging process to accurately depth reference the data recorded from both core and bore, as well as to ensure that the angles measured for structures are verified and correctly rotated to derive their in situ dip and dip direction or plunge and trend. To conclude, the provisions required for effective audits of the drilling and logging QA/QC processes are discussed: testing for inconsistencies, certifying that standards have been achieved, reporting on weaknesses, and making recommendations for improved performance.
Article
Full-text available
The majority of rock masses, in particular those within a few hundred meters from the surface, behave as discontinua, with the discontinuities (joints or filled discontinuities or faults) largely determining the mechanical behaviour of the rock mass in the specific location of interest. It is therefore essential that both the structure of the rock mass and the nature of its discontinuities are carefully described, in addition to the lithological description of the rock type. The specific parameters, as selected and described in these ‘ISRM suggested methods’ should be quantified wherever possible. Please note: ISRM working group of 44 participants, 14 countries.
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Full-text available
Narrow veins are an important world-wide source of silver, tin, uranium and particularly gold. To potential financiers, this style of mineralization is viewed as high risk because of the often relatively small resource involved and high cost of estimation. In many cases diamond core drilling will not enable resource estimation beyond the inferred and indicated categories. Exploratory underground development is required to define measured resources. The definition of geological and grade continuity are major factors in narrow vein assessment. A measured resource must be based on strong geological and grade continuities. Narrow veins, generally less than 3 m wide, are complex geological phenomena, which commonly display unpredictable geometry and grade distribution. Variations in structural continuity, dip, strike, width, mineralogy and specific gravity are common. Veins may be composite,...
Article
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In many vein-gold systems grade complexity is created by the erratic, localized occurrence of coarse gold. High-grade regions are generally erratic and have little spatial continuity, but they may make a resource or stope block economic. Economic grades are generally contained within discrete ore shoots that are surrounded by barren to low-grade material. The presence of coarse gold makes sampling and assaying of this style of mineralization especially challenging. Comparisons between surface and underground drilling, linear/panel sampling and bulk sampling indicate that drilling and linear/panel samples generally understate bulk-sample grades. Traditional fire assays that use small charges consistently understate the assays that are gained from large charge sizes and from such techniques as screen fire assay and bulk-leachable extractable gold assay. These findings, though not unexpected, demonstrate the importance of bulk sampling in the grade evaluation process for coarse gold-bearing veins. Diamond drilling provides an effective measure of geological continuity, but grade distribution can be assessed reliably only from underground development. In general, it is unlikely that anything above an Inferred Mineral Resource can be estimated from surface drilling alone. Geologically controlled, closely spaced underground development and bulk sampling are likely to be the best way to determine Ore Reserves.
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This book is written as a practical field manual to effective. Each geolOgist has to develop his/her be used by geologists engaged in mineral explo­ own techniques and will ultimately be judged on ration. It is also hoped that it will serve as a text results, not the process by which these results and reference for students in Applied Geology were reached. In mineral exploration, the only courses of universities and colleges. The book 'right' way of doing anything is the way that aims to outline some of the practical skills that locates ore in the quickest and most cost-effective turn the graduate geologist into an explo­ manner. It is preferable, however, for an individ­ rationist:. It is intended as a practical 'how to' ual to develop his/her own method of operation book, rather than as a text on geological or ore after having tried, and become aware of, those deposit theory. procedures which experience has shown to work An explorationist is a professional who search­ well and which are generally accepted in indus­ try as good exploration practice. es for ore bodies in a scientific and structured way. Although an awkward and artificial term, The chapters of the book approximately fol­ this is the only available word to describe the low the steps which a typical exploration pro­ totality of the skills which are needed to locate gramme would go through. In Chapter 1, the and define economic mineralization.
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In recent years, there has been a dramatic increase in the development of gold deposits located in semi-consolidated and unconsolidated materials such as oxidised rock, gravels and soils, which may contain particles of free gold. It is often difficult to recover representative samples due to the high density and malleability of the native gold particles. In many cases alternative drilling techniques such as reverse circulation, normal circulation and auger drills have been used to obtain representative samples for the exploration and evaluation of these deposits. In the absence of accurate impartial comparative information, drillers and their equipment have often been selected for their penetration rate or cost-per-foot rather than for sampling accuracy. The resulting sample assays can be significantly different to those obtained by diamond drilling or by other bulk sampling methods. Some of these differences can be attributed to the potentially erratic distribution of free gold in both lode and alluvial deposits. However, some errors may be because of the incorrect selection, design and/or operation of the drilling equipment. In the summers of 1992 and 1994, the author designed and carried out a statistically valid research program using radioactivated gold particles as tracers (radiotracers). Two types of fully cased normal circulation (N/C) drills, two types of reverse circulation (R/C) drills and three solid auger drills were evaluated under a variety of field conditions. A frozen cylindrical core of compacted gravels containing four sizes (1.2, 0.60, 0.30 and 0.15 mm), (+14, +28, +48 and +100 mesh) of radiotracers was placed in 44 drill holes and the holes were redrilled. Scintillometers were used to track free gold losses due to spillage and blow-by around the collar (top) of the hole. Some gold particles were located in temporary traps in the drilling equipment and these particles would have contaminated subsequent samples (as carry-over). Several myths commonly attributed to particular drilling methods were dispelled. There was no significant difference between the recovery of the tour sizes of gold particles with any of the drills tested. Observations and down-hole scintillometer records indicated that the free gold particles did not follow the bit down the hole and were either carried out of the hole or forced onto the sides of the hole at or above the depth at which the radioactive gold was positioned. A brief summary of the results of these tests is included in Table 1.
Article
Precious- and base-metal mineralization at Abu Marawat, Egypt, is found in a series of auriferous quartz veins and silicified shear zones. The two largest of these are the C vein and the Fin vein. The mineralized structures are hosted in a sequence of Precambrian andesitic to felsic volcanics and volcaniclastics. Six diamond drill-holes and nine percussion holes were selected for comparative study. The analytical results from diamond drilling were composited to make them equivalent to those for the percussion samples and to allow comparison between the two drilling techniques. Intersections were defined with reference to an arbitrary cutoff value of 1 g/t gold. Graphs are presented showing the compatibility of gold, silver and zinc values for each of the matched holes. In general terms, gold and silver values were positively biased in percussion holes and copper and zinc were negatively biased. Graphs of down-hole metal distributions showed evidence of a "tail' of contamination in percussion holes when metal distributions in diamond and percussion holes were compared. -from Authors
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Ore Reserves are key mining company assets and their reliable estimation is crucial for both feasibility studies and the day to day operation of a mine. The reserve is based on the Mineral Resource after the application of certain technical and economic parameters. Engineering aspects of reserve estimation can be accurately determined to ±10 per cent, however the majority of project risk will revolve around the resource. A final Ore Reserve generally contains a set of figures quoted without reference to its potential errors. Rarely are overall confidence limits quoted and, if they are, they often do not take into account many of the factors that cause uncertainty in the grade and tonnage estimates. There is thus an unquantifiable risk, which the operator should be aware of. This paper presents a review of the possible sources of error that might occur during the various phases of an exploration and estimation programme which are carried through into the Ore Reserve and hence mine design.