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472
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Depositional Rec. 2022;8:472–501.
wileyonlinelibrary.com/journal/dep2
Received: 27 April 2021
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Revised: 12 October 2021
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Accepted: 22 October 2021
DOI: 10.1002/dep2.169
ORIGINAL ARTICLE
Quantifying the lateral heterogeneity of distal submarine
lobe deposits, Point Loma Formation, California:
Implications for subsurface lateral facies prediction
Kaci B.Kus1
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Zane R.Jobe1
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FabienLaugier2
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WylieWalker1
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MorganSullivan2
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2021 The Authors. The Depositional Record published by John Wiley & Sons Ltd on behalf of International Association of Sedimentologists.
1Department of Geology and Geological
Engineering, Colorado School of Mines,
Golden, Colorado, USA
2Chevron Energy Technology Company,
Houston, Texas, USA
Correspondence
Kaci B. Kus, Department of Geology and
Geological Engineering, Colorado School
of Mines, Golden, CO 80401, USA.
Email: kacikus@gmail.com
Present address
Wylie Walker, Ovintiv Inc., Denver,
Colorado 80202, USA
Funding information
Chevron Corporation; AAPG Grants-
In- Aid; Bartsche Endowment Fund;
Colorado School of Mines Graduate
Student Government
Abstract
Submarine fan deposits are volumetrically the largest sediment accumulations
on Earth and host significant hydrocarbon reserves. Extensive research has
documented the bed- scale architecture of high sand- to- mud ratio, proximal and
axial environments, which can have bed thicknesses of several metres; however,
less well- understood are thin- bedded turbidites, which are typically lower N:G
and deposited in more distal environments. Conceptual models assume that
lobe- fringe- to- basin plain environments consist of tabular, sheet- like beds that
extend out continuously and predictably over long distances— up to several
kilometres. Extensive lateral continuity, however, is not necessarily reflected in
ancient outcrop analogues. This study seeks to apply a quantitative approach
to the characterisation of thin- bedded turbidites to assess the impact of multi-
scale heterogeneity on reservoir predictability. The sea- cliff outcrop exposures
of the Upper Cretaceous Point Loma Formation in San Diego, California, ex-
hibit a wide range of bed thicknesses and stratigraphic architecture, which have
been used to interpret an off- axis- to- fringe depositional environment. The study
area spans 700 m of laterally continuous outcrop, across which 10 correlated
stratigraphic sections are used to quantify changes in metrics such as bed thick-
ness, N:G, lithofacies proportions, etc. Results of this study demonstrate that thin
sand beds experience both lateral facies changes and rapid thickness changes
more frequently than conceptual models would predict. A single measure of lat-
eral heterogeneity does not reflect the true architecture of sandstone beds, and
significant information is lost when beds are correlated over ten to hundreds
of metres. Sands are commonly deposited in irregular, ‘finger- like’, planform
geometries, which compounds at the lobe element scale and influences lateral
lithofacies predictability. This study of the Point Loma Formation offers high-
resolution bed- to- element scale data, which may be used as inputs for reservoir
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KUS et al.
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INTRODUCTION
Submarine lobe deposits host important archives of
palaeo- environmental change (Hessler & Fildani, 2019)
and are major targets for the exploration and production
of hydrocarbons (Weimer & Pettingill, 2007). Lobes can
be generalised as radial bodies having a length- to- width
ratio of ca 2 (Pettinga et al., 2018; Prélat et al., 2010) and
down- system decreases in bed thickness, amalgamation
and sand- to- mud ratio (i.e. net- to- gross, N:G) (Figure
1; Deptuck et al., 2008; Mutti, 1977; Natland & Kuenen,
1951; Normark et al., 1979; Prélat et al., 2009). These sim-
plistic lateral relationships that are used to generalise the
internal characteristics of lobe bodies stem from research
commonly focussed on more proximal and axial environ-
ments, as they tend to be more conventional targets in hy-
drocarbon exploration.
The distal, frontal and lateral portions of submarine lobe
deposits, however, are less well- documented, and yet con-
ceptual models continue to assume simple architectures
and lateral persistence of tabular bedding (Groenenberg
et al., 2010; Lucchi & Valmori, 1980; Mutti, 1977; Tokes
models and horizontal facies predictions in both conventional and unconven-
tional hydrocarbon reservoirs.
KEYWORDS
bed thickness, lateral heterogeneity, lobe fringe deposits, submarine lobe, thin- bedded
turbidites
FIGURE Schematic of an idealised lobe element in plan- view (left). Based on characteristics documented in near- sea floor,
seismic and outcrop studies (inspired by Fleming, 2010). Cross- sections through two lobe elements (right) indicating different levels of
compensational stacking (inspired by Prélat et al., 2009)
Down
current
Total sand
Low High
Decrease in bed thickness, amalgamation, net-to-gross
(No scale implied)
Axis
O-axis
Fringe
Distal-fringe
Basin plain
A
B
C
C’
B’’
A’
A
B
C
A’
B’
C’
Increase in compensational stacking
axis-on-axis
fringe-on-axis
fringe-on-fringe
474
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KUS et al.
& Patacci, 2018) despite many studies having challenged
this notion, including those from sea floor (Picot et al.,
2016; Twitchell et al., 1992), shallow- subsurface (Fildani
et al., 2018; Schwab et al., 1996; Talling et al., 2010) and
outcrop data (Fryer et al., 2021; Kane et al., 2017; Spychala
et al., 2017). Notably, the idealised ‘lobate’ shape of lobe
deposits has been challenged to include ‘finger- like’ mor-
phologies (Groenenberg et al., 2010; Kane et al., 2017),
believed to result from unequal spreading of high- density
flows over the outer portions of the lobe (Figure 1). These
high- density flows occur more commonly in distal lobe
regions in response to flow expansion and eventual de-
celeration, which can result in a catastrophic loss of tur-
bulence and suspension capacity (Kane et al., 2017). Low
N:G environments are more difficult to study than their
proximal counterparts due to the increase in abundance of
thin beds (3– 10cm thick) which, in turn, commonly cre-
ate poorly exposed outcrops where primary sedimentary
structures are difficult to discern.
Nevertheless, event bed- scale reservoir heterogene-
ity in distal environments is well below the resolution
of most standard logging tools (Basu & Bouma, 2000)
and typical seismic data, making detailed outcrop stud-
ies the only way to quantify thin- bedded, submarine lobe
deposits. Borehole data (e.g. core, logs) are the highest
resolution data available from an industry perspective,
but they only provide a one- dimensional view of a three-
dimensional problem. Despite the increased importance
of thin- bedded submarine deposits as hydrocarbon reser-
voirs (Basu & Bouma, 2000; Stow et al., 1990), very few
studies have both performed outcrop- based correlations
at the event bed scale (Fonnesu et al., 2015, 2018; Marini
et al., 2015) and compiled quantitative information on the
lateral thickness variability of turbidite deposits (Clark,
1998; Fryer & Jobe, 2019; Tökés & Patacci, 2018).
Exposures of the Point Loma Formation in San Diego,
California, provide an opportunity to assess the lateral
event bed heterogeneity in thin- bedded submarine lobe
deposits. This study utilises high- resolution field observa-
tions to quantify and characterise the lateral bed- to- lobe
element scale heterogeneity in thin- bedded deposits from
sea- cliff outcrops of the Point Loma Formation. The focus
is on eight metrics (lithofacies proportion, N:G, amalga-
mation ratio, lenticularity, stratigraphic completeness, bed
thickness, bed thinning rate and element thinning rate)
to interpret lobe element boundaries, stacking patterns,
and the way bed- scale changes in thickness compound to
form significant complexity at the reservoir scale. While
short- distance changes in bed character have commonly
been overlooked due to the averaging of sparse data over
spatially large areas, these relatively small- scale variations
seen in the outcrop are necessary to calibrate more real-
istic depositional models required to calibrate models of
reservoir connectivity and flow performance. Thus, this
quantification of bed architecture in submarine lobe de-
posits provides the data to (1) better understand the dep-
ositional mechanics of sediment gravity flows in distal
lobe settings, and (2) accurately model reservoir- scale
heterogeneity.
2
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GEOLOGICAL BACKGROUND
The Campanian– Maastrichtian- age (84– 66 Ma; Sliter,
1968) Point Loma Formation forms the middle unit of
the Rosario Group (Kennedy & Moore, 1971; Figure 2A)
and is exposed along the western sea- cliffs of southern
California between La Jolla Bay and the tip of the Point
Loma Peninsula (Figure 2B). The Point Loma Formation
was deposited in the Peninsular Ranges forearc basin
that formed along the western coast of California in re-
sponse to subduction of the Farallon Plate beneath the
North American Plate (Bottjer & Link, 1984; Nilsen &
Abbott, 1981; Yeo, 1984). During the Cenozoic, southern
California was converted to a right- lateral strike- slip sys-
tem, resulting in extensive deformation, including up to
550km of translation and 40degrees of clockwise rotation
of the Point Loma Formation (Atwater, 1998; Marshall &
McNaboe, 1984).
The basal unit of the Rosario Group is the Lusardi
Formation, which consists of cobble and boulder con-
glomerate with lenses of medium- grained sandstone
(Kennedy & Moore, 1971; Nordstrom, 1970). At its type
locality, it is unconformably overlain by Eocene rocks,
but in other locations it is conformably overlain by the
Point Loma Formation (Kennedy & Moore, 1971). The
Lusardi Formation is believed to have formed in an al-
luvial fan environment derived from the Peninsular
Range Batholith (Kennedy & Moore, 1971; Nordstrom,
1970). The Point Loma Formation is comprised of inter-
bedded marine sandstone and mudstone (Kennedy &
Moore, 1971; Nilsen & Abbott, 1981). Its type locality is
located along the sea- cliffs at the tip of the Point Loma
Peninsula (Yeo, 1982), with only 83m of an estimated
400+ m of gross thickness exposed (Kennedy & Moore,
1971). Petrographic and provenance studies on sand-
stones from the Point Loma Formation show that the
source of detritus was largely from plutonic and meta-
morphic rocks of the Peninsular Range Batholith to the
east (Girty, 1987; Jiang & Lee, 2017). Based on the east-
erly position of the Peninsular Range Batholith source
area and abundant palaeocurrent measurements, sedi-
ment transport has been interpreted to have flowed in
a modern- day west- northwest direction (Fleming, 2010;
Fryer et al., 2021; Girty, 1987; Nilsen & Abbott, 1981;
Stammer, 2014).
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475
KUS et al.
FIGURE (A) Stratigraphic column of upper Cretaceous strata (modified from Nilsen & Abbott, 1971). Point Loma Formation is
highlighted in yellow with a green rectangle around Units 2 and 3showing the approximate position of Fleming's (2010) lobe complexes.
(B) Map of study area on the Point Loma Peninsula in San Diego, California (modified from Fleming, 2010). Map modified after Kennedy
and Moore (1971); USGS Topographic Map 1:24,000; and the geologic map of San Diego 30′×60′ quadrangle, California (Kennedy & Tan,
2005). (C) Palaeogeographic reconstruction of the Point Loma Formation lobe complex evolution across the Point Loma Peninsula, modified
from Fleming (2010)
A
C
B
476
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KUS et al.
Yeo (1982) divides the Point Loma Formation into three
units: (Unit 1) a lower sandstone unit interpreted to rep-
resent an inner- shelf- to- shoreface environment, (Unit 2)
a middle mudstone unit with thin- bedded sandstone in-
terpreted to represent distal submarine lobe deposits, and
(Unit 3) an upper interbedded mudstone and thick- bedded
sandstone unit that is interpreted to represent a channelised,
medial submarine lobe environment. The transgression
from the Lusardi Formation to the Point Loma Formation is
commonly attributed to Late Cretaceous global eustatic rise
in sea level (Yeo, 1982) during a time of active subduction
(Nilsen & Abbot, 1981). The lobe deposits of the Point Loma
Formation include mass- transport deposits, debrites, tur-
bidites and hybrid event beds (HEB) ranging from <10cm
up to 5 m in thickness (Fleming, 2010; McGlown, 2015;
Stammer, 2014). Sliter (1984) used foraminiferal faunal as-
semblages to interpret Unit 2 as having been deposited in
bathyal depths of at least 850m and Unit 3 as having been
deposited in depths of 600– 700m or greater. The change
in facies from lower net- to- gross in Unit 2 to higher net- to-
gross in Unit 3 is interpreted to reflect progradation of a sub-
marine fan (Nilsen & Abbott, 1981; Yeo, 1982).
The Point Loma Formation is conformably overlain
by the Cabrillo Formation, which is a sandstone and con-
glomerate unit. The Cabrillo Formation is well- exposed
at its type locality in National Monument (Kennedy &
Moore, 1971), where it has been interpreted as consisting
of coarse- grained inner- to- middle submarine lobe depos-
its (Nilsen & Abbot, 1981) but is most probably conglom-
eratic submarine channel deposits (cf. Jobe et al., 2010).
The coarse- grained nature of the Cabrillo Formation sup-
ports the interpretation that the depositional system was
prograding, resulting in deposits which transition from
distal to medial (Point Loma Formation) to proximal en-
vironments (Cabrillo Formation; Nilsen & Abbot, 1981).
The Point Loma and Cabrillo formations form a broad,
east- plunging syncline centred at Mission Bay and ex-
tending from La Jolla Cove to the tip of the Point Loma
Peninsula (Figure 2B; Nilsen & Abbott, 1981). There are
numerous near- vertical faults throughout the study area
with dip- slip motion that sub- vertically crosscuts bedding
planes. Displacement is often minimal (<1m) and strata
are typically easily correlated across small faults. Faults
are interpreted as post- depositional based on both brittle
deformation of the strata associated with the faults and
absence of stratal thickening across faults (Fleming, 2010).
2.1
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Previous work in the study area
The overall submarine lobe architecture of Units 2 and 3
(Yeo, 1982) of the Point Loma Formation is described by
Fleming (2010) across a >6km long, 20m high outcrop
belt (Figure 2B). Fleming (2010) identified four lobe com-
plexes, 27lobe elements, and four mass- transport deposits
that generally conform to existing models of lobe facies
changes, including decreasing thickness, N:G and grain-
size patterns from proximal- to- distal locations across the
outcrop (Figure 1). Fleming’s (2010) findings also sup-
port Yeo’s (1982) interpretation that the depositional sys-
tem was generally progradational from Unit 2 to Unit 3
(Figure 2C).
Several other studies have tried answering more spe-
cific questions related to sediment gravity flow processes
in the Point Loma Formation. Stammer (2014) focussed
on the architectural description of a single lobe element
(‘Goldie’) at the base of Fleming’s (2010) Complex 2 and
documented mineral fractionation based on grain size,
density and shape within this lobe element. McGlown
(2015) studied the continuity and grain- size character-
istics of HEB (sensu Haughton et al., 2009) in the Point
Loma Formation. Fryer (2018) reinterpreted the depo-
sitional environment of lobe deposits to the south of the
current study area (Figure 2B) focussing on metrics such
as bed thickness and thinning rate. The current study area
is contained within Unit 2 of Nilsen and Abbot (1981),
Lobe Complex 1 of Fleming (2010). The focus here is on
the thin- bedded interval just below the sand- rich lobe el-
ement studied by Stammer (2014), and the strata overlap
the lower central lobe complex of McGlown (2015).
3
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DATA AND METHODS
3.1
|
Lobe hierarchy
Lobe hierarchy schemes describe the depositional build-
ing blocks of submarine lobes and can be used to com-
pare architectural differences at multiple scales. Several
classification schemes exist for lobe deposits (Deptuck
et al., 2008; Gardner & Borer, 2000; Mutti & Normark,
1987; Prélat et al., 2009; Pyles, 2007), with the hierarchy
generally consisting of four units: the event bed, the lobe
element, the composite lobe and the lobe complex. The
smallest unit is the event bed, comprised of the stacked
lithological units derived from a single sediment grav-
ity flow ‘event’ (e.g. a turbidity current). Over time, two
or more event beds stack to form lobe elements; a sub-
sequent lobe element is formed when avulsion of an
up- dip feeder redirects sediment deposition to a new loca-
tion. Composite lobes form where one or more lobe ele-
ments stack, which in turn stack to form lobe complexes
(Deptuck et al., 2008; Prélat et al., 2009).
Compensational stacking results from deposits in-
fluencing the deposition of subsequent sediment gravity
flows, and can create predictable stratal and architectural
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477
KUS et al.
patterns (Figure 1; Deptuck et al., 2008; Groenenberg
et al., 2010; Mutti & Normark, 1987; Prélat et al., 2010).
For example, in an ideal scenario where a lobe element
infills the topography created by a previous lobe element,
the resulting strata will be such that the sandiest portions
of both lobe elements are offset from one another, and the
thinnest, muddiest portion of the second lobe element
will be positioned above the thickest, sandiest portion of
the first lobe element (see Figure 1 transect B– B′ for ex-
ample). With these idealised stacking patterns in mind the
compensational relationships in outcrop can be identified,
allowing predictions of lobe deposit characteristics to be
extrapolated beyond the study area.
While compensation can be difficult to identify at the
bed scale, especially when beds are <10cm thick (Fryer
et al., 2021), these patterns can occur at all lobe hierar-
chy levels. Moreover, it can be challenging to identify
the boundaries of higher order hierarchical units (lobe
elements and larger) in outcrop due to limited vertical
and lateral exposure compared to lobe dimensions. For
example, typical lobe elements have lengths and widths
in the 1,000s to 10,000s of metres, and thicknesses of
1– 10m (Pettinga et al., 2018; Prélat et al., 2010). Due to
the size of the study area (ca 700 m wide and ca 12 m
thick) and the orientation of the outcrop oblique to pa-
laeoflow direction, it is unlikely that the full length/
width of any single element is visible. Lobe deposits can
also have highly variable thicknesses depending on their
relative position and system- level boundary conditions
(Deptuck et al., 2008; Pettinga et al., 2018; Prelat et al.,
2010); thus, boundaries between hierarchical units can-
not be determined based on rigid thickness ranges, nor
can an expected number of constituent smaller units
simply be counted to predict the boundaries of a larger
unit. For instance, more proximal units may be com-
posed of generally thicker deposits but may also expe-
rience more erosion or sediment bypass and therefore
preserve fewer depositional events in a single element
(Carlson & Grotzinger, 2001; Stevenson et al., 2014).
Conversely, lobe elements in distal lobe- fringe environ-
ments may have boundaries that are difficult to distin-
guish (Mattern, 2005; Prélat & Hodgson, 2013) due to the
inter- element similarity in bed thickness and grain size
(Figure 1; Fryer et al., 2021).
3.2
|
Study area and data collection
The current study area is located approximately 4km
to the north of that of Fryer et al. (2021), and the strata
of interest are directly below the thick, sandy lobe ele-
ment of Complex 2 described by Stammer (2014). Ten
measured sections— M1 through M10— are numbered
in ascending order from north to south, spanning a total
distance of 700m and a stratigraphic thickness of ap-
proximately 12m (Figure 3). Seven of the 10measured
sections (M3– M9) are located across a 300 m stretch
of well- exposed outcrop with no significant faulting,
allowing for nearly continuous, two- dimensional bed-
scale observations and tracing. Measured sections M3–
M9 are also constrained by a three- dimensional outcrop
model.
Measured sections were logged at the centimetre-
scale to record sedimentary structures, grain size, event
bed boundaries, amalgamation surfaces and palaeoflow
indicators (Figure 3B; Kus, 2021). Palaeoflow indica-
tors include ripples, parting lineations and sole marks.
Amalgamation surfaces were recognised based on abrupt
grain- size changes and scour features but locally narrow
grain- size ranges within a single bed and the weathered
outcrop surface may increase uncertainty in identifying
these boundaries. Measured section data are used to:
(1) define lithofacies; (2) define and evaluate architec-
tural units; and (3) create a cross- section through the
study area. Sections were tied together through physical
correlation (i.e. walking out beds in the field) and trac-
ing using the three- dimensional outcrop model. Using
MATLAB, bed- scale correlations were sampled at 5 and
20 m spacing to compare lateral variations across the
study area.
The thicknesses of seven correlated sandstone beds
were measured at 5m increments along lateral distances
ranging from 55 to 125m (ca 540m total measured dis-
tance). These beds were specifically chosen to assess the
variation in bed thicknesses over short (5m) distances.
Some authors use the terms ‘bed’ and ‘bed thickness’ to
mean only the sandstone portion of a turbidite (Carlson
& Grotzinger, 2001; Marini et al., 2016), whereas oth-
ers use bed thickness to refer to the entire sedimenta-
tion unit (e.g. total thickness of a sandstone– mudstone
couplet; Sylvester, 2007). For the purposes of this study,
the terms ‘bed’ and ‘bed thickness’ are equivalent to the
sandstone portion of a sedimentation unit, unless spec-
ified otherwise. Since it was largely impossible to differ-
entiate the boundaries between turbiditic mudstone caps
and hemipelagic muds directly from field observations,
mudstones are referred to here as an ‘interval’ rather
than a bed so as to not imply a single mode of deposi-
tion (sensu Fryer & Jobe, 2019). When considering the
sedimentation unit as a whole, the term ‘event bed’ is
used to indicate a sandstone– mudstone couplet that was
probably deposited by the same flow (cf. Fryer & Jobe,
2019). When possible, clearly amalgamated sandstone
beds, demarcated by a sand- on- sand contact with abrupt
grain- size break, were separated into two distinct bed-
thickness measurements.
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KUS et al.
3.3
|
Three- dimensional outcrop model
Photographs were taken at ground level and at vari-
ous azimuths to capture the study area between meas-
ured sections M3 and M9 and were processed in Agisoft
Metashape to create a three- dimensional digital outcrop
model (Stright et al., 2014), which was then exported
as a two- dimensional orthomosaic (Figure 4A). While
the model is not georeferenced, the horizontal scale of
the model is constrained by known distances between
measured sections (measured using a tape measure and
GPS), and the vertical scale is constrained by field- based
measured sections and photographs with scales. A sec-
ond, smaller three- dimensional model and orthomosaic
were created between M4 and M5 to examine bed- scale
changes at a higher resolution for individual bed tracing
(Figure 4B). The resulting orthomosaics were imported
into ArcMap, where lithofacies and the deposits were
traced by hand and exported as shapefiles to analyse
using MATLAB.
4
|
RESULTS
4.1
|
Sedimentary lithofacies and facies
associations
In this study, two basic lithologies were measured: mud-
stone (<63µm size grains) and sandstone (>63µm size
grains). The definition of sandstone used here encom-
passes both ‘clean’ sandstone, which includes the Ta– Td
divisions deposited by fully turbulent turbidity currents
(Bouma, 1962; Lowe, 1982), and ‘muddy’ sandstone,
which includes the H1– H4 divisions deposited by hybrid
sediment gravity flows (sensu Haughton et al., 2009; Figure
5). Muddy sandstones are often associated with HEBs,
which result from a progressive increase in dispersed clay
and dampened turbulence as flows evolve through time
(Haughton et al., 2009). Event beds are stacked couplets of
sandstone and mudstone deposited by a single flow (e.g. a
Bouma sequence or HEB). Although orthomosaics are a
powerful tool to view the outcrop- scale lateral continuity
FIGURE (A) Satellite image of central Point Loma Peninsula and tracing of the coastline to emphasise bends in the outcrop. Black
dots and red lines represent measured section locations on satellite image and tracing, respectively. Palaeaoflow rose diagrams summarise
measurements taken from each measured section. Below is a photograph of strata immediately to the south of M3 (red box). Highlighted in
the photograph is a sandstone bed, which exhibits repeating pinching and swelling to the north and south. Person on the beach for scale. (B)
Portion of 3D outcrop model (Figure 4) with measured section 7 and lobe element boundaries demarcated.
0100
Metres N
M1 M2
M4 M5 M6 M7 M8 M9
M10
M3 M4 M5
M6
M7
M8
M9 M10
M1
M2
Photopanel
N
0100
Metres
n = 3
mean = 295.3
n = 10
mean = 262.1 n = 7
mean = 266.4
n = 8
mean = 232.6 n = 11
mean = 254.8
n = 24
mean = 231
n = 73
mean = 241.7
Palaeoflow measurements
n = 10
mean = 206.5
1
1
M
M
e
e
t
t
r
r
e
e
M
M
7
7
N
Photograph at 2x VE
Element 1 top
Element 2 top
Element 3 top
Element 4 top
Element 5 top
M3
Directly south of M3
AB
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KUS et al.
of beds, the variable image resolution, salt precipitates and
scree obscuring the outcrop face (Figure 5) at this locality
present challenges to tracing of each and every event bed
consistently on the orthomosaic.
Stacked event beds of similar character (e.g. thick-
ness, structures, N:G) are defined as one of four litho-
facies (Figure 5). Table 1summarises the documented
lithofacies, which are amalgamated sandstone (ss),
sandstone with minor mudstone (m- ss), thin- bedded
sandstone and mudstone (s- ms) and mudstone with
minor sandstone (ms). Because defining lithofacies is
often a qualitative process and can vary significantly
depending on the researcher and their objectives, an
automated process was employed using only N:G cal-
culated from one- dimensional grain- size profiles from
the 10measured sections. The net sandstone proportion
used to calculate N:G includes HEB sandstone deposits.
One of four lithofacies labels were assigned to measured
FIGURE Digital outcrop model between M3 and M9. Elements are highlighted with corresponding colours. (B) Smaller model
positioned between M4 and M5. Individual beds in elements 2 through 5 are traced, and upper element boundaries are coloured
corresponding to (A). Cumulative sand thickness for each element is plotted using the bed tracings from the 3D model. Sand thickness is
shown to emphasise the effects of changing bed thicknesses and pinch outs
ca 305 m between M3 and M9
M3 M4 M5 M6 M7 M8 M9
3x Vertical Exaggeration
Fault indicating
slip direction
10 m
10 m
Element 1
Element 2Element 4
Element 3Element 5
North
0
0.5
1
1.5
Sand thickness (m)
0510
Distance (m)
15 20 25
1 m
1 m
2x Vertical Exaggeration
ca 35 m between M4 and M5
Extent of
smaller model
A
B
480
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KUS et al.
sections at sub- centimetre scale as determined by k-
means clustering. K- means is an unsupervised learning
method that groups data into a set number of clusters,
k, by minimising the within- cluster variances (squared
Euclidean distances) of each data point from k cen-
troids (means). Final cluster groupings were selected
to best match the qualitative geological interpretation
of four lithofacies. Using running averages of the N:G
calculated at 30, 60 and 90cm windows produced re-
sults most in agreement with the qualitative geological
interpretation.
Since clustering is an unsupervised machine learning
method, the quality of clustering results largely depends
on human expertise to judge. Multiple running average
windows were selected to help avoid over and under- fitting
of the model. The 30, 60 and 90cm windows are believed
to best capture the variable scales of vertical changes rele-
vant to lobe element dimensions based on previous stud-
ies, which interpreted lobe elements to range from 1 to
4m thick in the Point Loma Formation (Fleming, 2010;
Fryer et al., 2021). Given that sandstone beds are on aver-
age 5cm thick in the study area, using running averages
also helps to reduce noise (i.e. over- fitting). These three
running averages were the only features used to perform
the clustering, which produced four clusters correspond-
ing to the four lithofacies. The mean N:G values of each
lithofacies are: ss = 0.81, m- ss = 0.62, s- ms = 0.40, and
ms=0.23.
Facies associations (Figure 5) correspond with the
sub- environments of a lobe element such as the axis,
off- axis, fringe and distal fringe (Prélat et al., 2009;
Spychala et al., 2017; Figure 1). Each facies associa-
tion is defined qualitatively by the lithofacies present
and their lateral relationships. Facies association 1
(FA1) contains primarily ss and m- ss and deposits are
dominated by thick- bedded, structureless and amal-
gamated sandstone. FA1 is interpreted to represent the
most axial sub- environment, which is not present in
the measured sections of this study, but documented
in the Point Loma Formation by Fleming (2010). FA1
consists of primarily medium and thick- bedded amal-
gamated sandstones. Facies association 2 (FA2) consists
of primarily m- ss with smaller proportions of ss and s-
ms. Sandstones tend to be thick- bedded with frequent
planar- laminated and banded structures. Banding ap-
pears as alternating lighter and darker sands, with the
darker sands being muddier and commonly intermixed
with dispersed organic matter. FA2 is interpreted as an
off- axis environment. Facies association 3 (FA3) has lit-
tle to no ss, and can consist of a mix of m- ss, s- ms and
ms. The qualitative difference between FA2 and FA3
is the significantly higher ms component and increase
in abundance of HEB in FA3. FA3 deposits tend to be
medium- to- thin- bedded with pronounced pinch- and-
swell geometries, interpreted to have formed in a fringe
environment. Facies association 4 (FA4) is composed of
primarily ms and s- ms (Figure 5), which are rarely pres-
ent within the digital outcrop model area (Figure 4A)
and more abundant at M1, M2 and M10 at the edges
of the study area (Figure 6). FA4 is dominated by thin-
bedded sandstone interbedded with medium- to- thick
bedded mudstone intervals. Irregular, yet persistent,
sandstone stringers (discontinuous sandstone beds
<1cm thick) are very common within mudstone inter-
vals. This facies association is interpreted to represent a
distal- fringe environment.
FIGURE Overview of the facies scheme used in this study. This facies scheme is open- ended upwards, meaning the larger orders of
observation may exist beyond the scope of this study. Here, the largest order of observation considered is represented by a facies association.
Histograms shown to represent the relative lithofacies proportions that may be found in each facies association are approximated and do not
represent hard cut- offs
s-ms
Event bed
1st-order 2nd-order 3rd-order
ms
m-ss
ss
4th-order
Lithofacies Facies association
(no scale implied)
Lithology
(sedimentation unit)
Sandstone
Mudstone
Haughton et al., 2009
Bouma, 1962
Turbidite
Hybrid event bed
A’
A
FA1FA2
FA3
FA4
AA
’
FA1
FA2
FA3
FA4
axis
o-axis
fringe
distal fringe
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KUS et al.
TABLE Description of facies documented in the field area, with sedimentary structures and interpreted depositional processes
Abbrev. Lithofacies
Grain- size and
mean N:G
Bed
thickness
(cm)
Sedimentary structures/
equivalent divisions from Bouma
(1962) and Haughton et al. (2009) Flow type (process) Photograph example
Facies 1 ss Amalgamated
sandstone
Very coarse sands to
very fine sands
Mud/silt
N:G µ=0.81
Sandstone
µ=6.1
Mudstone
µ=3.1
Structureless and often amalgamated
(Ta). Scours are often filled
with coarser grains with normal
grading
High- density turbidity currents
(Talling et al., 2012)
Facies 2 m- ss Sandstone
with minor
mudstone
Very coarse sands to
very fine sands
Mud/silt
N:G µ=0.62
Sandstone
µ=4.6
Mudstone
µ=3.9
Structureless and planar- laminated
(Tb) to ripple- laminated (Tc).
Erosive bases are common.
Distribution of structures
within bed are highly variable.
Alternating convolute/banded
(H2) and muddy sands (H3)
High- density to low- density
turbidity currents (Talling
et al., 2012) Transitional flows
with intermittent turbulence
(Haughton et al., 2009)
Facies 3 s- ms Thin- bedded
sandstone,
mudstone
Very coarse sands to
very fine sands
Mud/silt
N:G µ=0.40
Sandstone
µ=3.8
Mudstone
µ=5.0
Structureless and planar laminated
(Tb) to ripple- laminated (Tc).
Erosive bases are common.
Distribution of structures within
bed are highly variable. Muddy
sands (H3) with laminated
organic- rich layers (H4)
Low- density turbidity currents
(Talling et al., 2012).
Transitional flows that switch
from non- cohesive to cohesive
behaviour (Haughton et al.,
2009)
Facies 4 ms Mudstone
with minor
sandstone
Very coarse sands to
very fine sands
Mud/silt
N:G µ=0.23
Sandstone
µ=1.8
Mudstone
µ=6.1
Pseudonodular and/or massive mud
(Te). Sands are often cross-
laminated with common starved
ripples (Tc and Td)
Low- density turbidity currents
(Talling et al., 2012)
482
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KUS et al.
4.2
|
Classification of lobe elements
This study uses both qualitative and quantitative ap-
proaches to identify lobe element boundaries. Although
previous workers (Fleming, 2010; McGlown, 2015) have
defined lobe elements within the current study area, the
laterally variable, thin- bedded exposure makes it difficult
to transfer these interpretations from previous studies;
therefore, lobe element boundaries were defined to be
consistent with the data and lithofacies scheme used here.
Lobe element boundaries are identified where there are
(1) geometric relationships between packages (i.e. a pack-
age tapers in one direction as an overlying package thick-
ens) and/or (2) abrupt vertical shifts in lithofacies (e.g.
predominantly muddier lithofacies overlain by predomi-
nantly sandier lithofacies). Using these criteria, five lobe
elements are identified and named in ascending strati-
graphic order, from Elements 1 to 5 (Figure 6). Lobe ele-
ments range in maximum thickness from 0.72m (Element
3) to 1.11m (Element 1).
4.3
|
Quantification of internal
characteristics of lobe elements
4.3.1
|
Lithofacies proportion
Lithofacies proportion (Fproportion) is defined as the cumu-
lative thickness of each lithofacies relative to the thick-
ness of a lobe element at any location. Fproportion exhibits
vertical variations across element boundaries and lateral
variations within elements. At any one location, there is
typically an alternating pattern of sandier then muddier
then sandier lithofacies across vertically stacked elements
(Figure 7A). This vertical trend is apparent at M3, where
Elements 1 and 3 are comprised of 93% and 94% m- ss, re-
spectively, and Elements 2 and 4 are comprised of 89%
and 100% s- ms respectively. In some locations, however,
two vertically adjacent elements may have very similar
Fproportion (e.g. Elements 4 and 5 at M6; Figure 7A), which
makes the boundary between the elements difficult to dis-
tinguish based on Fproportion alone.
FIGURE Correlated cross- section of the study area (see Figure 3 for location)
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483
KUS et al.
Elements are typically dominated by one or two
lithofacies at any one location, whereas laterally a sin-
gle element can exhibit all four lithofacies. For exam-
ple, Element 4 is comprised of s- ms and ms at M9 and
transitions to m- ss and ss just 100 m to the south at
M7 (Figures 6 and 7A). Each element exhibits a region
of maximum sandiness (e.g. M7 location for Element
3, Figure 7A), away from which lithofacies generally
become muddier (Figure 7A). The locations of maxi-
mum sandiness are not vertically aligned between el-
ements (Figure 7A) and can be offset up to ca 100m.
Additionally, not all elements exhibit the same degree
of maximum sandiness. Elements 1, 3 and 5 contain re-
gions with high ss Fproportion, (e.g. ss Fproportion ca 85%,
N:G ca 0.77) whereas Elements 2 and 4hardly contain
any ss and instead have high m- ss Fproportion (e.g. m- ss
FIGURE (A) Stacked bar charts showing relative Fproportion; of each lithofacies at correlated measured sections. Rose diagrams
represent palaeoflow measurements logged at measured sections in each element. Palaeoflow indicators such as ripples, parting lineations,
and sole marks were used for roses. Grain size profiles from measured sections were used to assign lithofacies. (B) (Top) Bar charts of
Fproportion; for each lithofacies in Element 3. (Bottom) Bed- scale correlations from M2 to M9. The lithofacies bars drawn to the left of the
stratigraphic sections were plotted based on data from the overlying and underlying strata, and cropped to show only what falls within
Element 3
M2 M3 M4 M5 M6 M7 M8 M9
Element 1
Element 2
Element 3
Element 4
Element 5
North
ss m-ss s-ms ms
Lithofacies
sandier muddier
Stacked bar chart
01
Element 3 lithofacies proportions
ca 540 m
M2 M9
(Distance to scale)
Distance not to scale
Palaeo-
current
100%
70
30
79
11
10
83
17
74
26
95
5
80
20
94
6
79
13
8
North
1 metre
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
c
c
l
l
a
a
y
y
s
s
i
i
l
l
t
t
v
v
f
f
s
s
f
f
s
s
m
m
s
s
c
c
s
s
A
B
484
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KUS et al.
Fproportion ca 92%, N:G ca 0.62) at their thickest loca-
tions. The Fproportion of ms is typically low in the study
area (ca 5% of total) but is highest at M1, M2 and M10.
Although both very high ss Fproportion and very high ms
Fproportion exist at various locations within the study
area, the most common lithofacies are m- ss and s- ms,
which combined comprise ca 80% of all elements be-
tween M2 and M9 (Figure 7A).
While elements exhibit an overall decrease in sandi-
ness away from their respective locations of maximum
ss or m- ss Fproportion (e.g. Element 3; Figure 7A), most el-
ements demonstrate a laterally complex and non- linear
decay in Fproportion, with several lateral transitions be-
tween sandier and muddier lithofacies (Figure 7B). For
example, from M7 to M4 in Element 1, the Fproportion of
s- ms increases as the Fproportion of ss decreases. Yet, at
M3 there is an increase in the Fproportion of m- ss and ss,
followed by an increase of s- ms at M2, reestablishing an
overall trend in which Element 1 becomes less sandy
to the north. This pattern repeats in Elements 2 and 3,
where the Fproportion of s- ms increases to the north with
intermediary pulses of increased sandiness. Element 4
is the muddiest element and exhibits the most laterally
constrained sandy region of all the elements (between
M5 and M7; Figure 7A) and becomes generally muddier
both to the north and south.
4.3.2
|
Net- to- gross
Net- to- gross (N:G) is the ratio of total sand thickness (net)
to total stratigraphic thickness (gross) within a defined
interval. This is a useful metric for describing lobe ele-
ments because, like Fproportion, it allows for the comparison
of the vertical and lateral changes in sand content, which
in turn can inform our understanding of how the depo-
sitional system shifted through time (Macdonald et al.,
2011). The N:G of lobe elements, sampled at vertical sec-
tions through cross- section correlations (Figure 6) in the
study area ranges from 0.15 to 0.88, with a mean value of
0.58 (Figure 8A).
Elements 1– 4 exhibit a trend of overall decreasing N:G
from the south to the north (Figure 8A). When the N:G
of each element is compared with that of the element
directly above or below, there is a pattern of alternating
higher and lower N:G. Element 1has an average N:G of
0.69; Element 2has an average N:G of 0.45; Element 3has
an average N:G of 0.81; and Element 4has an average N:G
of 0.30. Limited data were collected for Element 5 due
to the height of the outcrop and poor exposure, but the
available data from M5 to M9show that the average N:G is
0.73, which is consistent with the expected trend of alter-
nating high/low N:G of stacked elements.
4.3.3
|
Amalgamation ratio
Amalgamation ratio (AR) is defined as the number of amal-
gamation surfaces (i.e. sandstone- on- sandstone contacts)
divided by the total number of event beds in a given inter-
val (Romans et al., 2009) and can provide insight into the
turbulent energy of the flows depositing sediment. Similar
to the patterns observed in N:G, elements exhibit alternat-
ing degrees of amalgamation relative to that of the element
directly above or below (Figure 8B). The average amalga-
mation ratio across all elements is 0.12, which suggests that
there is a 12% chance that a successive flow will be erosive
enough to cause amalgamation. Element 4 exhibits the low-
est average AR at 0.09 (consistent with its muddy charac-
ter), while Element 3has the highest average AR at 0.20.
4.3.4
|
Lenticularity
The lenticularity index (L) uses the change in sandstone-
bed thickness measured at equally spaced intervals and
divided by the average thickness of the bed to get a propor-
tional thickness as a function of lateral position. The aver-
age of all the proportional thickness measurements from
a single bed is used to determine the L of that sandstone
bed. This metric is equivalent to that defined by Romans
et al. (2009), and it is a proportional metric that can be
used to compare and contrast the geometries of sedimen-
tary bodies within a deep- water succession. As noted by
Romans et al. (2009), it is important to remember that
this metric is derived from two- dimensional exposures of
three- dimensional sedimentary bodies.
To calculate the lenticularity of sandstone beds, bed
thicknesses were vertically sampled from the cross- section
correlations (Figure 6) at 5m intervals. Sandstone beds
within the study area have an average L of 0.19 (n= 92
beds). Elements 1, 2 and 3have average L values of 0.12,
0.17 and 0.13 respectively. Elements 4 and 5have an av-
erage L equal to 0.24 and 0.22, respectively. While L can
provide more insight into the irregularity of a sandstone
bed's geometry than a single value of average thickness
or thinning rate, it does not provide any information on
whether thinning/thickening is occurring from the base
or the top of the bed.
4.3.5
|
Stratigraphic completeness
It is well known that successive flows can rework or even
completely erode previous deposits, yet very few studies have
tried to quantify the magnitude of how incomplete deep-
water deposits may be (Hubbard et al., 2008; Vendettouli
et al., 2019). Understanding the number of depositional
|
485
KUS et al.
FIGURE Data collected from vertical sampling of the cross- section correlations (Figure 6) every 5m laterally. (A) Net- to- gross by lobe
element. (B) Amalgamation ratio by lobe element. (C) Element thinning rates (cm/m). A positive value indicates thinning to the north
1
1
1
1
0100 200300 400500 600
1
0.5
0.5
0.5
0.5
0.5
N:G
Distance (m)
Element 1
Element 2
Element 3
Element 4
Element 5
M1M2 M3 M4 M5 M6 M7 M8 M9
0100 200300 400500 600
Amalgamation Ratio
0.5
0.5
0.5
0.5
0.5
0.25
0.25
0.25
0.25
0.25
Distance (m)
M1M2 M3 M4 M5 M6 M7 M8 M9
Element 1
Element 2
Element 3
Element 4
Element 5
0100 200300 400500 600
M1M2 M3 M4 M5 M6 M7 M8 M9
0
-1
1
0
-1
1
0
-1
1
0
-1
1
0
-1
1
Thinning rate northward (cm/m)
Distance (m)
Element 1
Element 2
Element 3
Element 4
Element 5
Element thinning rate
Amalgamation ratio
Net-to-Gross
North
North
North
A
B
C
486
|
KUS et al.
events that are not preserved in a sequence is necessary to
model the frequency of flows and estimate sedimentation
rates in submarine lobes (Jobe et al., 2018). The frequent
number of sandstone beds that pinch out within the study
area offers a rare opportunity to calculate the minimum
number of unpreserved flows in any one location. Some sce-
narios in which a flow may not preserve an event bed are
(but are not limited to): (1) only the muddy tail of a flow is
deposited due to bypass of sand, and the muddy tail is indis-
cernible from the mudstone cap of the previous deposit on
which it was deposited; (2) a subsequent flow eroded the un-
derlying sand bed(s), thus erasing the previous depositional
event(s); and (3) an amalgamation surface is undetectable to
the naked eye due to a limited grain- size range.
Stratigraphic completeness is defined here as the pro-
portion of preserved events (i.e. basal sand beds deposited
by individual flows) relative to the minimum total number
of potential events that were deposited in a location (in-
cluding those not preserved; Figure 9A; cf. Durkin et al.,
2018; Vendettuoli et al., 2019). To calculate the minimum
total potential depositional events, it is assumed that every
preserved sandstone bed has an associated mudstone inter-
val that was deposited over the entire study area, regardless
of how uniformly the sand was deposited. For example, it
is assumed that if a sandstone bed pinches out, its associ-
ated mudstone interval will still be present across the en-
tire length of the outcrop. No distinction is made between
hemipelagic and turbiditic mud. Using these assumptions,
the boundaries for mudstone intervals associated with every
sandstone bed in the cross- section are extended across the
entire length of the correlation panel (Figure 6). The effect
of this is twofold: (1) mud- on- mud surfaces that are not
identifiable in the field (cf. Dennielou et al., 2006) are ac-
counted for to calculate the number of potential events at a
given location, and (2) the average thickness of mudstone
intervals is lessened. This method of adjusting the thick-
ness of mudstone intervals, when possible, will also aid in
producing more realistic results when calculating mud-
stone interval thinning rates. For example, Fryer and Jobe’s
(2019) study shows how the lumping of multiple mudstone
intervals due to indistinguishable mud- on- mud boundaries
may not reflect true mudstone depositional geometries.
At any location along the outcrop, the number of mea-
sured events can thus be compared to the minimum total
number of potential events in the cross- section to assess
the completeness of the stratigraphic record. The data
show a positive linear relationship exists between the num-
ber of preserved events and the number of potential events
FIGURE (A) Photograph
highlighting sandstone beds that
pinch out. White dashed lines indicate
mudstone- mudstone boundaries that are
otherwise undetectable after a pinch out.
On the left is a measured section with
three event- beds and seven “potential
events” based on knowledge of nearby
sand bed pinch outs as recorded by the
measured section on the right. Red circles
on the left- most measured section identify
where potentially unrecorded events
may exist. (B) Scatter plot of “preserved
events”, aka those that deposited and
preserved sand, versus the minimum
number of “potential events”, which
considers sandstone beds that have
pinched out along the outcrop. Each
point on the scatter plot represents values
from the cross- section sampled at 20m
increments. The 95% confidence interval
is shaded
A
B
|
487
KUS et al.
(Figure 9B). The study area has an average stratigraphic
completeness of 66%, revealing that approximately one-
third of flows did not deposit sand at any given location. In
other words, any measured section can only explain 66% of
the variance shown by surrounding strata, and when pre-
dicting lateral event bed changes, there is significant (33%)
uncertainty in these predictions. While other studies have
used satellite imagery (Durkin et al., 2018) and time- lapse
bathymetric surveys (Englert et al., 2020; Vendettuoli et al.,
2019) to calculate net sediment accumulation as a proxy
for stratigraphic completeness, the method used here uti-
lises the preservation of stratigraphic surfaces in cross-
section rather than changes in planform morphology and
is therefore more applicable to outcrop studies.
4.3.6
|
Thickness distribution
The thickness distribution of sandstone beds visibly
changes from element to element in an alternating pat-
tern (Figure 10). Two- sample Kolmogorov– Smirnov
(KS) tests performed on sandstone beds from each
element show that the thickness distribution differs
significantly when compared to those of elements di-
rectly above and below (p< 0.0001). Mudstone inter-
vals exhibit less dramatic changes in thickness between
elements, but their thicknesses are still statistically
different between adjacent elements at the 95% signifi-
cance level (p<0.05).
Bed thicknesses from the cross- section (Figure 6) can
also be compared in greater detail by grouping the data
not just by element and lithology but also by adding lat-
eral position (e.g. comparing only sandstone bed thick-
nesses between M3 and M4 to, for instance, sandstone
bed thicknesses between M4 and M5). When grouped
by lithology, sandstone beds (vertically sampled at 20cm
lateral intervals from the cross- section) have an average
thickness of 5cm and mudstone intervals have an average
thickness of 2.5 cm. The KS tests comparing sandstone
bed thicknesses between adjacent measured section pairs
show that bed thickness does not change significantly
within a single element over a relatively short distance
FIGURE Boxplots of bed thicknesses for sandstone beds and mudstone intervals grouped by element. Bed thicknesses were
sampled from cross- section correlations at 20cm lateral intervals (Figure 6)
median
75th percentile
25th percentile
maximum
minimum
outlier
Explanation
6.7 cm (n = 81)
3.6 cm (n = 90)
6.6 cm (n = 50)
1.2 cm (n = 99)
5.7 cm (n = 29)
3.8 cm (n = 66)
2.8 cm (n = 130)
1.6 cm (n = 41)
1.4 cm (n = 267)
1.3 cm (n = 44)
0510 15 0510 15
Sandstone BedsMudstone Intervals
Thickness (cm)
Element 1
Element 2
Element 3
Element 4
Element 5
488
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KUS et al.
(tens of metres); however, sandstone bed thickness dis-
tributions between measured section pairs separated by
hundreds of metres are statistically different (Figure 11).
Bed thicknesses compared from element- to- element gen-
erally exhibit an alternating thinning- to- thickening pat-
tern (Figure 11).
Some differences between the bed thickness dis-
tributions of the four lithofacies were also observed.
Sandier lithofacies have a wider, sometimes bimodal
range of sandstone bed thicknesses (Figure 12),
whereas the sandstone bed thickness distributions of
muddier lithofacies (such as s- ms and ms) are more
log- normal. Mudstone interval thicknesses are simi-
larly distributed for all four lithofacies, with the me-
dian thickness increasing as the lithofacies becomes
less sandy (Figure 12).
4.3.7
|
Bed thinning rate
Bed thickness and lateral distance can be used to calcu-
late bed- thinning rate (TRb) by dividing the difference
in bed thickness between two nodes by the distance be-
tween those nodes (Fryer & Jobe, 2019); for this metric,
bed thicknesses are upsampled from the cross- section
(Figure 6), sampled laterally at 20cm intervals. Bed thin-
ning rates are compared based on their lithology, lateral
distribution (e.g. TRb between M1 and M2 can be com-
pared to TRb calculated between M2 and M3), and vertical
distribution (grouped by lobe element). The relative TRb
was calculated such that positive values indicate thinning
to the north and negative values indicate thinning to the
south (as is shown in boxplots in Figure 13A); however,
the absolute thinning rate is used when reporting TRb
FIGURE Boxplots from cross- section correlations (Figure 6). Sandstone beds (above) are separated from mudstone intervals
(below), and boxplots are organised by element and lateral position. Data comes from sampling 20m lateral intervals on cross- section
correlations
|
489
KUS et al.
because it allows for easier comparison of descriptive
statistics (Tökés & Patacci, 2018). The 10th to 90th per-
centile range (P10– P90) of TRb of sandstone beds is 0.0027
to 0.12cm/m, and the median thinning rate of all sand
beds is 0.022cm/m (µ=0.047 cm/m). In general, sand-
stone beds exhibit a wider range of thinning rates than
mudstone intervals (Figure 13A), which have a P10– P90 of
0.0012– 0.055cm/m. The median TRb of all mudstone in-
tervals is 0.0092cm/m (µ=0.017cm/m). Both sandstone
beds and mudstone intervals generally share the same
vertical relationships from element to elements at any lo-
cation, and there is often an alternating stacking pattern
of higher- to- lower- to- higher thinning rates. This pattern
is less pronounced than the bed thickness distributions
between elements (Figure 11).
Within a single element, sandstone and mudstone
TRb fluctuates laterally. Element 1 experiences a slight
increase in sandstone TRb from M2- M3 to M4- M5,
whereas mudstone intervals experience an increase
from M6- M7 to M7- M8 (Figure 13A). Sandstone beds in
Element 2show significant lateral changes in TRb as the
median TRb decreases from M8- M9 to M6- M7 and peaks
again from M4- M5. Peaks in median TRb for sandstone
beds in Element 3 are seen at M8- M9 and M6- M7, yet in
the mudstone intervals the median TRb peaks slightly at
M7- M8. Element 4 experiences some modest increases
in sandstone TRb at M5- M6 and M8- M9; however, the
mudstone intervals in Element 4 do not fluctuate much.
Element 5 contains the fewest data points due to diffi-
culty in tracing its beds northward on the cliff- face. There
is a peak in sandstone TRb at M4- M5. The cumulative
distribution function (CDF) plots of TRb from each el-
ement (Figure 13B) show that sandstone and mudstone
TRb have mostly normal distributions, with exceptions
seen in the sandstone beds of Elements 3 and 5. The CDF
of sandstone beds from Element 3show a tendency to-
wards positive values, indicating that the beds within the
element tend to be thinning to the north (Figure 13B).
The CDF of sandstone beds from Element 5 displays
an opposite trend, where beds tend to thin to the south
(Figure 13B).
4.3.8
|
Element thinning rate
Element thinning rate (TRe) is the rate at which an ele-
ment thins to the north. It is calculated in the same way
as bed thinning rate, and a positive value indicates north-
ward thinning. Thinning rates were calculated every 5m
from the cross- section (Figure 6). Lobe elements thin at
significantly higher rates than individual beds, with the
average TRe across all elements being 0.18 cm/m. The
TRe ranges from −0.67 to 0.60cm/m. Elements 1, 3 and
4 thin to the north with average TRe values of 0.15, 0.15
and 0.20cm/m, respectively. Elements 2 and 5 thin to the
south with an average TRe of −0.17 and −0.33cm/m re-
spectively (Figure 8C). Elements 1, 2 and 4 both thin and
thicken in both directions at various points along the out-
crop, which seems to correspond well with the CDF plots
of TRb from those elements having normal distributions
centred around zero. The trends of predominantly posi-
tive TRe values from Element 3 and predominantly nega-
tive TRe values from Element 5 are likewise reflected in
the TRb values from those elements, respectively (Figure
13B). This intuitively makes sense that if an element is
thinning in a certain direction, the constituent beds within
that element must be thinning as well.
4.4
|
High- resolution bed measurements
Bed- scale thinning and thickening is quite common
across the study area (Figure 14A), and the lateral scale
of change occurs at a much higher frequency than can
be captured via two- point correlations between meas-
ured section locations. To illustrate the degree of lat-
eral bed thickness variability, seven sandstone beds
FIGURE Bed thickness distributions for the four
lithofacies. Sandstone (yellow) and mudstone (green) bed thickness
distributions displayed as kernel density estimates (KDEs). Median
bed thickness values are plotted as vertical lines
VV
PVV
VPV
%HGWKLFNQHVVFP
PV
Q
Q
Q
Q
Q
Q
Q
Q
490
|
KUS et al.
FIGURE (A) Box plots of sandstone bed (above) and mudstone interval (below) thinning rates from cross- section data (Figure 6).
The cross- section was upsampled at 20m intervals and grouped by lithology, element and lateral position (between measured section pairs).
(B) Empirical cumulative distribution function (CDF) plots of sandstone beds (left) and mudstone intervals (right) from each element
A
B
|
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KUS et al.
were selected that exemplified rapid changes in bed
thickness. The thickness, composition and structures of
these seven sandstone beds were measured at 5m inter-
vals, with each bed being traced 50– 125 m (depending
on exposure). There are order- of- magnitude differences
in bed thinning rates calculated over shorter (5m) and
longer (50– 100m, i.e. between measured sections) dis-
tances; for example, Figure 14B shows that between
M6 and M8, a sandstone bed has a thinning rate of
ca 0.03 cm/m, but the high- fidelity measurements re-
veal significantly more complexity, with bed- thickness
change rates up to ca 1.13 cm/m. The thinning rates
calculated from the detailed beds range from 0 to
2.2cm/m, and the average thinning rate of the detailed
sandstone beds is 0.45cm/m (eight times the average
sand bed thinning rate calculated using correlations
from cross- section).
5
|
DISCUSSION
5.1
|
Bed- scale processes and
interpretations
5.1.1
|
Depositional processes
The bed is the most fundamental building block, upon
which the blueprints for reservoir heterogeneity are de-
fined in a submarine lobe system. The hierarchical rela-
tionships between different orders of architectural units
suggest that any heterogeneity at the bed scale is probably
to be compounded at larger scales. Therefore, it is crucial
to quantify lateral heterogeneity at the bed- scale to better
understand the system as a whole. Rapid bed- thickness
changes, commonly leading to the complete loss (pinch-
out) of a bed, are interpreted to indicate that sand beds
are not tabular, but are instead deposited irregularly, per-
haps with a ‘finger- like’ fringe as suggested by Talling
et al. (2010). Although rapid changes in bed thickness,
especially in a relatively thin- bedded environment, may
appear similar from bed to bed, detailed field observa-
tions can provide insight into their formative depositional
processes.
Five mechanisms for rapid bed- thickness changes are
proposed here based on field observations of bed con-
tacts, primary structures and bed continuity away from
the thickness change (i.e. some beds thicken and thin in a
predictable manner and/or come back after pinching out;
Figure 15): (1) downstream migration of large current rip-
ples (Baker & Baas, 2020); (2) local scours subsequently
filled by coarser- grained sand; (3) erosional bypass; (4)
soft- sediment deformation, and (5) transitional flow evo-
lution, resulting in HEBs. Hybrid Event Beds are known
to exhibit rapid lateral facies changes, in which the lower
sandstone portion of a bed pinches out and is replaced
by muddier facies before again becoming sandstone-
dominated (Fonnesu et al., 2015; Sumner et al., 2009).
It is quite common for a sandstone bed to almost pinch
out completely and persist as a <1cm thick sand stringer
for tens of metres before thickening again to its previous
thickness. Sandstone beds that exhibit scours filled with
coarse to very coarse grains tend to continue to deposit
those upper grain- size ranges even if they thin to discon-
tinuous sand stringers, which is why very coarse sand
grains are reported in all lithofacies— even the lowest N:G
lithofacies, ms, which contains the most sand stringers.
These rapid thickness changes and pinch- outs are es-
pecially subtle in thin- bedded environments because the
overall thickness of the strata around the sand bed re-
mains relatively constant (Figure 4B). Detailed tracings
of beds from orthomosaics reveal that while the overall
element thickness may remain constant, the cumulative
sandstone thickness within an element is quite variable,
even over relatively short distances (Figure 4B). The cu-
mulative effect of many rapid bed- thickness changes and
pinch- outs at various lateral and stratigraphic positions—
which individually may seem insignificant at the bed
scale— compounds to create lobe element scale variability
in overall sandiness. This subtle spatial variability makes
it difficult to generalise which processes (of those shown
in Figure 15) are responsible for the observed lateral het-
erogeneity. For example, not all rapid thickness changes
preserve clear evidence as to the underlying depositional
process that caused the thickness change; moreover, a sin-
gle bed may be affected by multiple processes as a flow
evolves, even over <10 m distances (c.f. Baker & Baas,
2020). Even with this uncertainty, the data demonstrate
two key points: (1) the significant range in processes and
energy occurring in more distal reaches of submarine fans
in contrast to conventional assumptions; and (2) the re-
sulting fine- scale lateral heterogeneity which may have
significant implications for horizontal well planning and
geosteering operations, and reservoir model parameterisa-
tion from sparse data (e.g. core).
5.2
|
Implications for pinch- and-
swell geometries
This study demonstrates that while some beds gener-
ally remain consistent over the extent of the outcrop and
display very low thinning rates when calculated over
hundreds of metres, the majority of sandstone beds also
experience pronounced thickness changes at some point
laterally. Studies that rely upon thinning rates in order
to compare sub- environments (Fryer & Jobe, 2019; Tőkés
492
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KUS et al.
FIGURE (A) Photograph showing thin beds thinning and thickening over short distances in the study area. (B) Single correlated
sand bed illustrating the potential for dramatic thickness changes over very short (5m) lateral distances compared to thinning rate
measurements across tens of metres. See Kus (2021) for other examples of detailed beds. (C) Sandstone bed thickness and thinning rate
contours of 90% KDE. Median for each lobe element and the seven detailed sandstone beds (red) is plotted as a point. Lobe element bed-
scale data was upsampled from cross- section at 5m intervals. Shaded regions show the KDE plots for only sandstone beds from the basin
plain and lobe depositional environments (adapted from Fryer & Jobe, 2019)
Thinning rate (cm/m)
0.1
1
10
100
0.0001 0.001 0.01 0.1 110
Lobe
Basin Plain
Thickness (cm)
Basin Plain
n = 2,363
Lobe
n = 6,484
Submarine depositional
environments
(Fryer and Jobe, 2019)
Element 5
n = 55
Element 4
n = 190
Element 3
n = 107
Element 2
n = 202
Element 1
n = 192
90% KDE with medians
Detailed beds
n = 107
5405550656075752035340
015120
5
0
10 cm
North
(palaeocurrent direction)
Thinning rate = 0.03 cm/m
Thinning rate = 1.13 cm/m Thinning rate = 0.33 cm/m
75
0Metres
Metres
M6 M8M7
B
C
1 m
AOutcrop Photograph
|
493
KUS et al.
& Patacci, 2018) therefore may unintentionally miss the
potential variability in the system due to constraints of
scale and the fidelity of measurement. The distance over
which thinning rates are calculated (Figure 14C) can
produce differences in results on the scale of several or-
ders of magnitude. For instance, when the thickness and
thinning rate data from seven beds measured at 5m in-
crements (Figure 14C red) are plotted against data up-
sampled from the cross- section at 5m increments (Figure
14C), a significant shift is observed in the median abso-
lute thinning rate between the two groups (median of all
upsampled sandstone beds TRb=0.02 cm/m, median of
seven detailed beds TRb =0.2 cm/m). Conversely, there
is no significant difference in bed thickness distribution.
This is because the seven detailed beds were not outliers
in any way and were simply chosen as examples of the
pinch- and- swell geometries that are common throughout
the outcrop.
The majority of outcrop studies, even relatively high-
resolution ones, calculate thinning rates by correlating
measured sections across tens to hundreds of metres
(Fryer & Jobe, 2019; Marini et al., 2015; Tőkés & Patacci,
2018), and the sandstone bed thinning rates calculated
from the cross- section at 5m increments align well with
findings from such studies (Figure 14C). This seems
reasonable considering that the calculations used were
derived from correlations between measured sections
separated by lateral distances ranging from 20 to >200m.
However, the significant shift in thinning rates calculated
from the seven high- resolution beds do shed light upon
the potential for larger variability in thinning rates than
commonly reported from outcrop studies relying on long-
distance correlations. Similar bed- scale variability has
been described by Fonnesu et al. (2015), albeit not quan-
tified. Since outcrop analogues are essentially the only
way to infer subsurface variability at the bed scale, the
results show how crucial high- resolution outcrop studies
are in order to accurately interpret and model subsurface
environments. The seven detailed beds (Figure 14B; Kus,
2021) demonstrate the magnitude of information lost
when short- length- scale thickness variability is averaged
in correlations made over distances of tens to hundreds
of metres.
5.3
|
Interpretation of lobe elements
A key finding from Fryer et al. (2021) was that there is no
appreciable difference in bed thicknesses or thinning rates
when lobe elements from the Point Loma Formation were
compared (Figure 2B). The results from the current study,
however, show that statistically significant differences
FIGURE Five modes for processes resulting in rapid sandstone bed thinning and pinch- outs. When beds pinch out, they may
continue as <1cm- thick sand stringers for anywhere from a few centimetres to tens of metres, and sometimes thicken again into a bed that
looks quite similar
flow direction
Erosional bypass
Scour and fill
Bedform
Loading
no scale implied
Irregular deposition
10 cm
aerial view
494
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KUS et al.
between lobe elements are indeed observable when com-
pared at a higher spatial resolution. Bed thicknesses and
thinning rates significantly change from element- to-
element when compared over short lateral distances, and
these changes generally seem to follow a predictable, al-
ternating stacking pattern. The data also seem to indicate
that there is the potential, when observed at the right spa-
tial scales, to differentiate lobe element boundaries quan-
titatively from one- dimensional stratigraphic slices using
only grain- size data, as evidenced by the abrupt changes
in lithofacies proportions between elements (Figure 7B).
Further statistical analysis and a wider variety of high-
resolution outcrop data is needed to test this hypothesis,
the results of which can be used to help constrain inter-
pretations of lobe element stacking patterns away from
well data.
The intra- element characteristics (e.g. bed thickness,
lithofacies, etc.) change laterally as well, and so the stack-
ing pattern of two or more elements at one location may
look completely different just a few hundred metres away.
Therefore, stratigraphers may need to exercise caution
when correlating stratal packages based on seemingly sim-
ilar characteristics (i.e. thickness, N:G, lithofacies, etc.) in
thin- bedded environments, especially when working with
one- dimensional data (e.g. well- logs or cores).
5.3.1
|
Compensational stacking
Stacking patterns between lobe elements can be difficult
to identify in distal environments due to the abundance
of very thin- bedded deposits, which can obscure stacking
patterns and key geometries in outcrop. The lenticularity
index (L) can be used in combination with N:G and AR to
highlight the types of sand bed geometries present in the
outcrop, which may not be as readily apparent in the field.
Sandstone bed L values seem to most closely align with the
semi- amalgamated and non- amalgamated wedge shapes
described by Romans et al. (2009), which they interpreted
to reflect compensationally stacked lobate bodies. While
L values generally increase from Elements 1 to 5, plotting
the moving average of L reveals a potentially cyclic signal,
loosely related to element boundaries (Figure 16). Intra-
element trends in L values may reflect individual events
responding to sea floor topography (e.g. progradation,
retrogradation, ‘finger- like’ sandstone beds filling in local
topographic lows). Pronounced vertical changes in: (1)
N:G; (2) AR; (3) bed thickness; (4) bed and element thin-
ning rate; and (5) lithofacies proportion between elements
corroborate this intra- element cyclicity. These changes
generally follow an alternating pattern of relatively high to
low and back to high (etc.) values, which are what would
be predicted by conceptual models of compensationally
stacked (or off- stacked) lobe elements (Figure 1). Vertical
changes in bed thickness and lithofacies proportion are
especially strong indicators of lobe element boundaries in
this study. Figures 11 and 7A show that there are distinct
stratigraphic changes in both sandstone bed thickness and
lithofacies proportion from element- to- element at nearly
every measured section location. Furthermore, the lat-
eral offset (ca 100m) of the areas of maximum sandiness
(Figure 7A) from element to element are interpreted to
reflect a spatial shift in sediment supply, probably due to
up- dip avulsion.
There are, however, instances where vertically adjacent
lobe elements are dominated by similar lithofacies. For ex-
ample, this occurs at both M2 (between Elements 2 and 3)
and at M6 (between Elements 4 and 5). One interpreta-
tion may be that this represents an aggradational stacking
pattern as opposed to a compensational stacking pattern,
but because this relationship does not persist laterally, it
is more probably that the stacking of similar lithofacies
simply reflects an area where similar lobe element sub-
environments happen to be stacked (see Figure 1 fringe-
on- fringe example). It is important to consider also that
the increased areal extent and natural grain- size filtering
that occurs as flows travel farther away from their feeder
channel also makes even strong compensation less no-
ticeable in more distal locations (Figure 1). Depending on
the magnitude and orientation in which a lobe element
depocentre shifts, it is reasonable to expect that instances
of the repetition of the same lithofacies proportions will
occur, as seen here. It is probably that the deposits in the
study area are located such that the cross- section (Figure
6) transects axis- on- fringe and fringe- on- fringe stacking
(Figure 1). Considering the similarity of the depositional
environments being stacked, it is actually more surprising
that such pronounced lithofacies changes are seen. A pos-
sible explanation is that the topography of the elements
is such that local, smaller- scale compensation is strong
enough to produce the patterns seen.
5.3.2
|
Lateral facies transitions
within elements
Conventionally the thickest and sandiest portion of a
lobe element is considered the ‘axis’ (Deptuck et al., 2008;
Fleming, 2010); however, there is not enough evidence to
make this assumption about the units within the study
area. Both the interpretation that the study area lies within
an off- axis- to- fringe environment and the relatively small
dimensions of the outcrop compared to known lobe ele-
ment dimensions (Pettinga et al., 2018) make it unlikely
that the study area is coincidentally located where several
lobe element axes are visible. Still, lithofacies proportions
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KUS et al.
change laterally within the elements in a generally predict-
able manner away from areas of maximum sandiness (i.e.
sandier lithofacies transition laterally to muddier lithofa-
cies; Figure 6). However, this transition is not simply linear;
rather, the lateral changes in lithofacies proportion occur in
a ‘stepped’ pattern where the overall trajectory is towards
decreasing sand content away from the location of maxi-
mum sandiness, yet there exist fluctuations where sand
content will increase for a short lateral distance before con-
tinuing to decrease again (Figure 7B). Elements within the
study area typically exhibit one or two such fluctuations,
suggesting that this stepped pattern probably persists be-
yond the lobe fringe seen here to the most distal lobe envi-
ronments. One explanation for this stepped pattern is that
the bed- scale irregularity of sandstone deposits may take on
a ‘finger- like’ planform shape in distal lobe environments,
similar to the sidescan sonar data from the Mississippi Fan
(Nelson et al., 1992; Schwab et al., 1996; Talling et al., 2010;
Twichell et al., 1992). When several irregularly deposited
sands with variable thicknesses are stacked, the effect is
that local sandier areas will exist within an overall environ-
ment trending towards muddier lithofacies.
5.4
|
Refining depositional environment
interpretations
The strata of the Point Loma Formation have been in-
terpreted as a prograding submarine fan environment
(McGlown, 2015; Yeo, 1982) consisting of at least four lobe
complexes (Fleming, 2010). Because this study focusses
on the bed and lobe element scales, interpretations of
depositional sub- environments are relative to the position
within a given lobe element and not the entire system, un-
less specified otherwise. The deposits within the current
study area are interpreted to represent a relatively distal
portion from Lobe Complex 1 of Fleming (2010; Figure
2). More specifically, 90% of sandstone beds fall within
either a ‘lobe’ or ‘basin plain’ setting (Figure 14C) based
on bed thicknesses and thinning rate data compiled from
various sub- environments (Fryer & Jobe, 2019). The over-
lap of results with the broad categories of lobe and basin
plain environments (Figure 14C) indicate that Elements 1
through 5 occupy an off- axis/distal/fringe lobe position.
Additionally, the N:G values of the lobe elements conform
with ranges for off- axis (N:G 0.5– 0.85) and fringe (N:G
FIGURE Moving average of lenticularity index calculated over a 5 point sliding window. Element tops are approximated due to beds
not having completely flat bases. To the right are idealised examples of bed geometries and their resulting L values. Thin beds that change
thickness frequently will probably have higher L values than relatively thicker, wedge- shaped beds
Element 1 top
Element 2 top
Element 3 top
Element 4 top
Element 5 top
0 0.2 0.4 0.6
Lenticularity Index
0
0.5
1
1.5
2
2.5
3
3.5
4
Stratigraphic Position of Sandstone Beds (m)
3-point moving avg.
Lenticularity index = 0.47
Lenticularity index = 0.5
Lenticularity index = 0.8
Simplified example of bed geometries
and theoretical L values
Thickness sampled at
equally spaced nodes
496
|
KUS et al.
0.2– 0.5) sub- environments (Figure 1) as described by
Spychala et al. (2017).
Although bed thicknesses, thinning rates and N:G values
align with those commonly reported in distal submarine
lobe environments, certain bed- scale characteristics diverge
from the classical models, which describe narrow grain- size
ranges and thin, sheet- like layers in relatively distal regions
(Bouma, 1962; Kneller & McCaffrey, 2003; Mutti, 1977;
Walker, 1967). Recent studies have focussed on the presence
of HEBs and increased architectural variability in distal set-
tings (Dennielou et al., 2017; Deptuck et al., 2008; Etienne
et al., 2012; Fonnesu et al., 2015; Groenenberg et al., 2010;
Haughton et al., 2003, 2009; Hodgson et al., 2006; Patacci
et al., 2014; Southern et al., 2016; Talling et al., 2012). The
HEBs can exhibit rapid internal changes due to increased
clay enrichment and dampened turbulence (Baker & Baas,
2020; Fonnesu et al., 2018; Haughton et al., 2009), which
can produce muddy sandstones with irregular thickness.
Although the number of HEBs were not quantified com-
pared to turbidite event beds, McGlown (2015) reports that
the Point Loma consists of 70%– 80% HEBs in some areas.
The qualitative observations reported here suggest a slightly
lower proportion of HEBs in this specific area.
The very coarse grains found in all four lithofacies
could be evidence that these beds were deposited in a
more proximal, bypass dominated environment rather
than a distal environment, but the absence of large scours
makes this interpretation unlikely. While HEBs have also
been shown to occur in proximal, avulsion splay settings
(Power et al., 2013; Terlaky & Arnott, 2014), the outcrop
lacks any evidence of channelisation. Instead, it is pro-
posed that the large grain- size range is caused by the prox-
imity of the basin to the sediment source, the Peninsular
Ranges Batholith. Moreover, Talling et al. (2010) high-
lighted how even boulder sized clasts can be carried to the
distal fringes of a fan system (e.g. Mississippi Fan), as well
as how the presence of coarser sand grains can contribute
to ‘finger- like’ bed geometries (i.e. pinch and swell geome-
tries when viewed in cross- section). Based on the available
data, the interpretation that the study area transects rela-
tively distal portions of Elements 1 through 5 is believed to
be justified. Lobe Complexes 2, 3 and 4, however, contain
thicker, sandier beds (Fleming, 2010). This suggests that
the strata of this study were deposited in a relatively more
proximal location compared to subsequent deposits in this
system, which continued to prograde through time.
5.5
|
System evolution
This study shows that lobe elements do not follow a sim-
plistic model of decreasing bed thickness, thinning rate,
sand content, etc. in all directions away from their axis.
Figure 17shows the interpreted palaeogeography of the
five lobe elements identified in the study area, which was
constructed by honouring the stratigraphic relationships
in cross- section and palaeocurrent direction measure-
ments. This reconstruction shows that lobe element com-
pensation decreases upwards as the elements are overall
backstepping from Elements 1 to 5.
The outcrop transects Element 1 in the most proxi-
mal position of all five elements, and it is interpreted as
representing a frontal off- axis sub- environment (FA2).
It is overlain by Element 2, which generally consists of
thinner sandstone beds, lower thinning rates and lower
amalgamation ratios. The change in palaeocurrent orien-
tation from Element 1 (µ= 234°; Figure 6) to Element 2
(µ = 229°; Figure 6) is interpreted to indicate a shift in
depocentre to the east. Therefore, Element 2 is interpreted
to occupy a more frontal- to- lateral fringe position (FA3)
with lower sand content (Element 1N:G=0.69, Element
2 N:G = 0.45; Figure 8A). Element 3 is interpreted to
shift westward (µ=261°) such that the outcrop transects
a frontal off- axis sub- environment (FA2), similar to the
orientation of Element 1. The increased sand content in
Element 3 (N:G = 0.81; Figure 8A) also suggests slight
progradation of the system. Element 4 very subtly shifts
eastward again (µ=257°) but notably has very low sand
content (N:G=0.30) and the thinnest sandstone beds out
of all five elements. Element 4 is interpreted to represent
a backstepping of the system, and the outcrop transects a
frontal fringe sub- environment (FA3). Lastly, Element 4
is weakly compensationally overlain by Element 5, which
has higher sand content (N:G=0.73) and is interpreted
as a frontal off- axis- to- fringe sub- environment (FA2- FA3).
From the 90% kernel density estimate contour plots of
bed thickness and thinning rate from each of the lobe el-
ements, it also appears that there is slight ‘tightening up’
or narrowing of thickness and thinning rate ranges from
Elements 1 to 5. This may reflect the evolution of the sys-
tem through time, or in other words, of a healing topogra-
phy through time.
5.6
|
Implications
Recent studies have documented greater complexity of
fine- scale facies variations present in more proximal-
medial lobe deposits which may compromise reser-
voir quality and limit reservoir scale fluid flow (Kane &
Ponten, 2012; Kane et al., 2017; Zarra, 2007). The data
reported here, including pronounced bed thickness vari-
ations and internal bed facies changes, challenges the as-
sumption that distal lobe environments are dominated
by low- energy and homogeneous deposition. Notably, re-
ducing uncertainty in bed geometries in more distal lobe
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KUS et al.
environments provides a mechanism to constrain the hy-
drodynamics of sediment gravity flows responsible for fan
construction (Hessler & Fildani, 2019; Prélat & Hodgson,
2013). Critically, these observations and the variation in
stratigraphic completeness drive significant uncertainty
in the ability to constrain geosteering operations and pre-
dictions of the lateral facies variability that may impact
horizontal well performance. Moreover, the presence of
‘finger- like’ planform geometries interpreted from bed
thicknesses and the demonstrated compensational stack-
ing of beds and lobe elements creates a three- dimensional
non- stationarity in trends of rock properties (i.e., N:G,
facies proportions). Therefore, it is suggested that con-
ventional geostatistical approaches for modelling spatial
distribution in rock quality (i.e. variograms) will be lim-
ited in their ability to effectively recreate the heterogeneity
associated with distal lobe environments for assessments
of both conventional and unconventional reservoirs.
6
|
CONCLUSIONS
Quantifying high- resolution changes in thin- bedded lobe
deposits provides insight into the significant lateral het-
erogeneity that exists in distal environments. This work
challenges over- generalisations about how thinning rates
and lithofacies behave over short distances, as an abun-
dant variety of thinning and pinch- out geometries are
shown to occur over relatively short (<5 m) distances.
Based on sandstone bed geometries and internal charac-
teristics, many of the beds in the study area are believed to
have been deposited in a potentially ‘finger- like’ planform
shape, which ultimately is reflected at the lobe element
scale as well. Metrics such as lithofacies proportion, bed
thickness, thinning rate, etc. transition away from loca-
tions of maximum sandiness in a predictable ‘stepped’
pattern rather than a smooth linear gradation as com-
monly depicted in idealised models. Appreciable differ-
ences in the vertical distribution of metrics were observed
between lobe elements at any given location, which sug-
gests the potential for one- dimensional datasets (e.g. core)
to be useful in identifying lobe element stacking patterns.
Further statistical analysis of these data as well as more
high- resolution bed- scale measurements from other
outcrops will be needed to confirm the widespread ap-
plicability of these metrics as indicators of architectural
boundaries in the lobe- system.
This study brings attention to the lateral heterogene-
ity that may be overlooked due to coarse- resolution mea-
surements. For example, measuring beds at as little as 5m
FIGURE Lobe evolution schematic showing the relative lithofacies proportions expected in different depositional environments.
Cross- sectional slices through lobe elements highlight lithofacies extent based on outcrop measurements as well as an interpretive
reconstruction of the element beyond the study area. Bar charts to the right of the simplified cross- section show both the % ss; facies (as a
proxy for % axial in the transect) and the element's relative thicknesses measured at the M6 (marked with star)
Conceptual model of lobe element
Axis
O-axis
Fringe
Distal fringe
Mudstone dominated
ss m-ss
Lithofacies
sandier
s-ms ms
muddier
No scale implied
1
2
3
4
5
Relative position of study
area at time of element (#) deposition
#1
2
3
4
5
550 m
Facies distribution
cross-section
Artistic interpretation
beyond scope of study area
% ss
lithofacies
Element
thickness
Lobe element deposition
498
|
KUS et al.
intervals produces thinning rates up to two orders of mag-
nitude greater than those calculated from measured sec-
tion correlations alone (tens to hundreds of metres apart),
which is standard practice in outcrop studies. These results
demonstrate the need for more high- resolution studies fo-
cussing on vertical and lateral bed- scale relationships to
update current conceptual models of lobe fringe environ-
ments. Moreover, detailed correlation of every bed in the
study area reveals an average stratigraphic completeness
of 66%. The inference from this is that a minimum of one-
third of flows do not preserve sand deposits in this type
of off- axis- to- fringe environment, which implies there
is still a fair amount of energy reworking the sediment.
Because the methodology used to calculate stratigraphic
completeness is easy to replicate, it is recommended as a
potentially useful metric for interpreting relative energy
levels, and that more attention should be paid to correlat-
ing thin beds to more fully understand the range of flow
processes present in different submarine environments.
Automated clustering as a means to generate objective
lithofacies labels for stratigraphic sections can be a useful
tool in identifying lobe element boundaries in the Point
Loma Formation. While this methodology is not ‘perfect’
in the sense that it still requires a subjective human inter-
pretation to tune the clustering inputs, the reasoning and
ideas presented in this study (i.e. using some combina-
tion of grain- size profile running averages of anticipated
lobe element thicknesses based on geological knowledge
of the area) offer a starting point for future researchers
to optimise their own automated lithofacies labelling.
Ultimately, understanding the processes and spatial scales
at which changes occur in beds and lobe elements is cru-
cial for predicting the quality and mechanical properties
of a reservoir that may impact flow performance or hori-
zontal drilling expectations.
ACKNOWLEDGEMENTS
We thank the Chevron Center for Research Excellence
(core.mines.edu) for providing funding. Additional fund-
ing was provided by AAPG Grants- In- Aid, the Bartsche
Endowment Fund, and Colorado School of Mines
Graduate Student Government. We would like to thank
Luke Pettinga, Clark Gilbert, Thomas Martin, Wylie
Walker, Chance Seckinger, and Hanaga Simabrata for
assistance during field work. Lesli Wood, Piret Plink-
Björklund, Mary Carr, Jeff May, Pengfei Hou, Nataly
Chacón Buitrago, and Leonela Aguada also provided use-
ful feedback that improved the paper.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are avail-
able from the corresponding author upon reasonable
request.
ORCID
Kaci B. Kus https://orcid.org/0000-0002-1160-6484
Zane R. Jobe https://orcid.org/0000-0002-7654-4528
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How to cite this article: Kus KB, Jobe ZR, Laugier
F, Walker W, Sullivan M. Quantifying the lateral
heterogeneity of distal submarine lobe deposits, Point
Loma Formation, California: Implications for
subsurface lateral facies prediction. Depositional Rec.
2022;8:472– 501. https://doi.org/10.1002/dep2.169
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