Deepwater Hydrocarbon Seep Detection: Tools and
Techniques using Multibeam Echosounders
Garrett Mitchell1, Jim Gharib2, David Millar3
1
Project Geoscientist, Fugro Marine GeoServices, Inc. 6100 Hillcroft, Houston, Texas 77274
Email:[email protected] Phone: + 1 (713) 778-6880
2
Global Product Line Manager for Seep Studies, Fugro Marine GeoServices, Inc. Houston, Texas
3
President, Fugro Pelagos, Inc. San Diego, California
Abstract
Despite recent declines in oil and gas market expenditures, demand for marine hydrocarbon seep
surveys continues to grow. Geochemical analysis of seafloor seep sediments is an effective
hydrocarbon exploration tool. Identifying and sampling sites where thermogenic hydrocarbon
fluids have migrated to the seafloor provides information on reservoir characteristics and
commercial viability. Hydrocarbon seep features are ephemeral, small, discrete, and often difficult
to precisely sample on the deep seafloor. Low to mid-frequency multibeam echosounders are an
efficient exploration tool to remotely locate and map seafloor features associated with seepage.
Geophysical signatures from hydrocarbon seeps are evident in bathymetric datasets (fluid
expulsion features), seafloor backscatter datasets (carbonate outcrops, gassy sediments, methane
hydrate deposits), and midwater backscatter datasets (gas bubble and oil droplet plumes).
Interpretation of these geophysical seep signatures in backscatter datasets is a fundamental
component of seep hunting. Degradation of backscatter datasets resulting from environmental,
geometric, and system noise can interfere with the detection and delineation of seeps. We present
a relative backscatter intensity normalization method and a 2X acquisition technique that can
enhance the geologic resolution within seafloor backscatter datasets and ultimately assist in the
interpretation and characterization of seafloor hydrocarbon seeps. As frontier exploration surveys
migrate into deeper waters in search of oil and gas reserves, it is necessary to evaluate and develop
tools and techniques that improve both data quality and the interpretation of multibeam datasets.
Fugro has conducted over fifty seep hunting campaigns globally since 2001 and include single
exploration blocks to multi-client “mega surveys” in Indonesia, Brazil, and most recently an
industry-funded multi-client seep survey – the Otos multibeam survey (353,700 km2) in the
northern Gulf of Mexico and the Gigante multibeam seep survey (625,000 km2) in the Southern
Gulf of Mexico and Caribbean Sea. In total, over two million square kilometers of seafloor have
been mapped with modern multibeam systems optimized to detect hydrocarbon seeps. This paper
will provide an overview of seep detection methodologies applied during our marine seep hunting
surveys.
Author Biography
Garrett Mitchell is a Project Geoscientist with Fugro Marine GeoServices, Inc. He has been
involved with marine seep hunting surveys in the Exploration Department within Fugro’s Global
Center of Excellence for Seep Studies in Houston, Texas since 2013.
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Introduction
Deep seafloor exploration and the present understanding of the geomorphological and biophysical
processes that shape it and closely linked to advances in multibeam echosounder (MBES)
technology (Mayer, 2006). Low to mid-frequency (12 – 30 kHz) acoustic waves generated by
MBES sonars can penetrate kilometers of water column and remotely measure the deep seafloor
and shallow subsurface. Bathymetric and reflectivity measurements are both extracted from MBES
datasets. An acoustic wave’s reflected energy provides information on seafloor geometry (local
angle of incidence), physical characteristics (rugosity and density), and intrinsic properties such as
composition, surficial and volumetric scattering (Lurton, 2010). Analyzing the geophysical
signature of reflected acoustic beams has demonstrated to be an effective quantitative and
qualitative tool to remotely characterize the lithologic composition and geologic nature of the
seafloor (Fonesca and Mayer, 2007). Analyzing seafloor backscatter, and more recently midwater
backscatter has wide-ranging applications (Colbo et al., 2014) including fisheries research
(Trenkel at al., 2014; Innangi et al., 2016), marine biomass (Korneliussen et al., 2009), benthic
habitat mapping (Brown and Blondel 2009), geological classification (Lamarche et al., 2011),
subsea engineering and geohazard mitigation (Chiocci et al., 2011), and hydrocarbon seep studies
(Orange et al., 2002; Skarke et al., 2014; Weber et al., 2010).
The rapidly-developing science of multibeam backscatter among users in various fields is
prone to imperfect acquisition and processing methodologies that affect quality and ultimately
interpretability of the data. Numerous detrimental issues may exist including user error from a lack
of commonly-accepted acquisition and processing procedures, errors in sonar installation and
calibration, acquisition hardware and processing software settings, specular reflection, grazing
angles, and beam pattern residuals can degrade these datasets and interfere with geologic
interpretation. Furthermore, differences in processing algorithms within the available software
packages can create slightly varying backscatter imagery.
To address these issues regarding consistency of multibeam seafloor backscatter data
quality, members of the Marine Geological and Biological Mapping Group, GeoHab
(http://geohab.org/), an international association of seafloor mapping scientists, formed the
Backscatter Working Group in 2013. GeoHab’s BSWG published a report identifying existing
gaps in knowledge and presenting best practices and standardized guidelines regarding the use of
seafloor backscatter (Lurton and Lamarche, 2015). In this study, we describe our efforts into
incorporating these guidelines and recommendations into Fugro’s commercial hydrocarbon seep
hunting practices. Specifically, we present the results of our multibeam backscatter intensity
normalization and discuss an acquisition technique used to improve backscatter data quality for
detecting and delineating seeps on the deep seafloor.
Hydrocarbon Seeps
Most of Earth’s major hydrocarbon deposits have been located in areas where petroleum fluids
have migrated, accumulated, and pooled at the surface (Berge, 2013). Marine petroleum seepage
involves the flow of buoyant hydrocarbon-rich liquids that are generated by the deep burial and
heating of kerogen-containing source rock that percolates to the seafloor (Judd and Hovland,
2007). Most significant hydrocarbon reservoirs experience varying degrees of fluid leakage where
failures in the top seal of a reservoir allows buoyant fluids to migrate to the surface through
networks of faults, fractures, and fissures (Aminzadeh et al., 2013). Upwelling hydrocarbon fluids
reaching the seafloor can influence the chemical composition of the hydrosphere (Leifer et al.,
2000; MacDonald et al., 2002; Milkov et al., 2003), atmosphere (MacDonald et al., 2002; Leifer
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et al., 2006; Solomon et al., 2009), seafloor morphology (León et al., 2007) and mineralogy (Canet
et al., 2006), and sustain diverse chemosynthetic communities (Fisher et al 2007; Cordes et al.,
2009). At seafloor seeps, the anaerobic oxidation of methane (AOM) increases the alkalinity in the
sediment promoting carbonate precipitation (Roberts et al., 2010):
Ca2+ + 2HCO3- → CaCO3 + CO2 + H2O
Petroleum fluid interactions where chemically-reduced hydrocarbons originating from
deep anoxic environments react with shallow sulfate-rich pore fluids and create carbonate nodules,
chimneys, slabs, and crusts in the subsurface and seafloor. These features are indicative of both
active and fossil seeps. Deep thermogenic hydrocarbon fluids reach the seafloor as seepage along
fault interfaces from commercially-important oil and gas deposits. These fluids are targeted for
geochemical sampling by the oil and gas industry because they provide insight into the petroleum
system and contain geochemical fingerprints that can source maturity, source rock, and thermal
history (Abrams, 2005).
Detecting Seafloor Seeps
Locating and sampling seafloor seeps is an important component in offshore hydrocarbon
exploration. Seepage can determine if an active petroleum system is present, identify areas with
high potential and to risk prospects, and provide insight into the character of the oil (Abrams,
2005). While hydrocarbon seeps are clustered along the edges on the continental shelf and slope,
seeps can be found far offshore in deeper basins (Cordes et al., 2007; Cordes et al., 2010). Highresolution marine geophysical techniques are used to detect seeps by exploiting their acousticallyreflective properties. Hydrocarbon seeps and associated features are discrete, hard (carbonate
production and seep fauna), and surrounded by softer hemipelagic sediments that produce
characteristic patterns in acoustic reflectivity datasets. Various geophysical, biophysical, and
morphological signatures associated with active and relic seeps are detected through both optical
and acoustic remote sensing techniques. Seeps physically modify their depositional environment
by supporting extensive chemosynthetic communities, precipitating authigenic carbonates, and
sediment displacement via fluid expulsion. Such features were recognized on seafloor amplitude
datasets originating from 2D seismic surveys and confirmed by subsequent dives to confirm the
presence of chemosynthetic communities and seep features (Roberts et al., 2010; Roberts et al.,
2010). Roberts et al., report analyzing patterns in reflectivity in BOEM seismic datasets, ranking
areas of increased hardness and reflectivity amplitude, and then confirming with DSV Alvin dives
in the Northern Gulf of Mexico (GoM) (Roberts et al., 2007; Roberts et al., 2010). Many of these
areas predicted as potential seep sites using seafloor amplitude data were confirmed1 as areas of
extensive authigenic carbonate hardgrounds supporting active chemosynthetic communities.
Several of these initial seep sites are now considered some of the “classic” seep areas in the GoM
and have been the focus for numerous studies of their geological and ecological characteristics of
Lower Continental Shelf (<1,000 m) seeps.
1
https://www.boem.gov/seismic-water-bottom-anomalies-map-gallery/
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Remote Sensing of Seep Features
Fluid expulsion processes at the seafloor are associated with mud volcanoes, pockmarks and
localized depressions, dense aggregations of chemosynthetic fauna (clam, mussel, and tubeworm
communities), subsurface faults serving as fluid conduits, gas hydrate deposits, and shallow gas
accumulations. These distinctive features along with other seep indicators such as surface slicks,
midwater gas bubbles, and oil droplets are detectable by their distinctive geophysical signatures in
synthetic aperature radar (SAR) (MacDonald et al., 1996; De Beukelaer et al., 2003), 2D and 3D
seismic (Roberts et al., 2006; Roberts et al., 2010), sub-bottom profiler (SBP) (Hovland, 2007),
side-scan sonar (Coleman and Ballard 2001, Sager et al. 2004) and MBES datasets (Orange et al.,
2002; Orange et al., 2010; Weber et al., 2012).
Exploration Seep Hunting
Geochemical analysis of seafloor seep sediments is an effective hydrocarbon exploration tool
(Abrams, 2005, Orange et al., 2002; Bernard et al., 2008; Orange et al., 2008; Orange et al., 2009;
Orange et al., 2010; McConnell and Orange, 2014). Seafloor geochemical exploration programs
are based on the principle that hydrocarbons migrating upwards from deep source rocks and
reservoirs can be sampled from seafloor and shallow subsurface sediments and analyzed to
evaluate commercial potential. Though seepage is ephemeral across various time scales (Leifer et
al., 2004), the geochemical and biological signals persist and can be directly sampled by simple
analytical tools to determine the geochemical makeup and commercial viability. Seafloor features
interpreted as hydrocarbon seeps are the primary targets for geochemical exploration programs.
The association between seafloor seeps and commercial reserves is firmly established – the direct
linkage between the subsurface reservoirs, migration pathways, and seafloor seeps has been
confirmed in calibration tests (Abrams and Dahdah, 2011). Identifying and sampling sites where
deep fluids have migrated to the seafloor provides high quality geochemical data for evaluating
deep hydrocarbon reservoirs.
MBES Exploration Surveys
With depressed oil prices, the industry seeks cost-efficient exploration tools and techniques that
enabled the use of MBES systems to become an integral component of the offshore exploration
survey workflow. The higher frequencies used in deepwater MBES mapping (12-30 kHz) are able
detect most of the physical proxies of hydrocarbon seeps including small isolated areas of
hardgrounds or chemosynthetic shell deposits and midwater bubble plumes that may not be
detectable with conventional seismic mapping (Brooks et al., 2014). Seep hunting and geochemical
surveys typically follow 2D reconnaissance surveys and occur before more expensive 3D and highresolution AUV site surveys. Fugro geoscientists use an integrated science-based approach for
locating, delineating, and sampling deep-water hydrocarbon seeps during MBES exploratory
surveys2. Bathymetric datasets (~ 15 m gridded cells) allow for detailed identification of fine-scale
seep-related features – mud volcanoes, seafloor faults, salt diapirs, mounds, and depressions – in
water depths exceeding 4,000 m. Co-located multibeam backscatter imagery (5 m gridded cells)
is used to find authigenic carbonate deposits, chemosynthetic shell clusters on and embedded in
the sediment, and gassy sediments. Midwater backscatter imagery detects midwater plumes of gas
2
https://www.fugro.com/our-services/marine-site-characterisation/marine-geotechnical/seep-hunting-andgeochemical-campaigns#tabbed1
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bubbles and oil droplets (Figure 1). Integrating these MBES-derived datasets allows for
characterization and ranking of seafloor seeps for coring targets during geochemical surveys (6 m
piston or gravity cores) to sample sediments for geochemical analysis.
Figure 1. Seep hunting methodology using multibeam echosounders. Three datasets are derived from multibeam sonar data that
aid the remote detection of hydrocarbon seeps – bathymetry, seafloor and midwater backscatter data.
MBES Backscatter for Seep Detection
Hydrocarbon seeps and their geophysical and biophysical proxies are acoustically-reflective
features (Roberts, 2006). Multibeam backscatter is our primary tool to locate these features on the
seafloor during exploratory seep surveying. Obtaining high-quality backscatter data is a critical
component of a seep hunting survey – the importance of knowing the accurate delineation and
extents is required for successful geochemistry surveys. Accurate seep delineation is necessary for
cost-effective ultra-short baseline (USBL) navigated coring. The geochemical signal found in the
sediment near seeps has an exponentially-steep lateral chemical gradient (Abrams, 1996; Abrams,
2005). Missing a coring target on the order of tens of meters may result in a negative geochemical
result leading to flawed conclusions about the potential of the reservoir (McConnell and Orange,
2014).
This steep chemical gradient requires knowing seafloor reflectivity on a pixel-level
resolution. To aid our interpretations and mitigate the slight differences in imagery between
existing commercial processing software, we use three separate backscatter processing packages.
Each of the software packages used for seep surveys convert the backscatter intensity signal
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slightly differently, resulting in slightly different seafloor images depending on the algorithm used,
especially in areas of complex surface relief and abundant specular reflection. Meter-scale seafloor
backscatter data is used to pinpoint our USBL coring target location. Seafloor seeps are often
associated with a distinctive, anomalous backscatter ‘fingerprint’ on MBES data (Johnson et al.,
2003) and we take a relative and qualitative approach to interpreting multibeam backscatter data.
Coring has shown that seafloor seeps often appear as anomalous bright red “bloodspots” (using a
rainbow palette in ArcGIS where high intensity backscatter = red, low intensity backscatter = blue)
surrounded by relatively lower backscatter (Figure 2). This classic signature is related to the harder
and discrete authigenic carbonate deposits and chemosynthetic fauna (active and relic)
encompassed by softer hemipelagic muds and clay. This fingerprint needs to be analyzed in each
software package in both 2D and 3D in light of beam geometry, seafloor morphology and various
shaded relief surfaces with varying artificial sun azimuths and low artificial sun elevations to
exacerbate noise-related rugosity that may affect interpretation (Orange et al., 2010). We find that
this signature on a pixel-level scale can change dramatically and we aim to minimize that change
through a comprehensive understanding of the various survey hardware and software parameters
in light of beam geometry related to local seafloor slope.
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Figure 2. Seafloor backscatter signatures of hydrocarbon seeps using Caris Geocoder, Fledermaus Geocoder, and Kongsberg
Poseidon processing software. Note the variations of the anomalously high backscatter areas as a function of software used. This
example shows a sector intensity imbalance that is muted with angle-varying gain in the Geocoder imagery. The imbalance is
more severe and can mask features of interest in Poseidon. Panel L shows a sector intensity and a hydrocarbon seep intensity
that have similar magnitudes. Use of a cleaned reference surface can help alleviate the anomalously low areas of backscatter due
to local slope angle shown in the Fledermaus Geocoder and Poseidon imagery.
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The acoustic response of hydrocarbon seeps is dependent on MBES frequency and can
challenge interpretive efforts and coring operations. While directly coring areas of anomalously
high backscatter using USBL-navigated cores may provide confirmation of hydrocarbon fluid
presence on the seafloor, authigenic carbonate or “hardground” outcrops can easily bend core
barrels leading to significantly increased exploration costs (Digby et al. 2016). ROV investigations
of low frequency (12-30 kHz) seafloor backscatter datasets show that these areas of high
backscatter can be due to surficial scattering due to exposed carbonate pavement on the seafloor
or volumetric scattering due to areas of scattered shells or mineralogical fragments embedded in
sediment saturated with near-surface hydrocarbon-rich fluid favorable for coring. The science of
seep hunting relies in obtaining a core close enough to the seep that the chemical fingerprints can
be measured without bending the core barrel on exposed authigenic carbonate (Figure 3). In coring
operations in deepwater, a single core can take several hours and therefore both the backscatter
dataset and interpretation of the dataset need to be high quality. Understanding the frequency
sensitivity of seep features with properly acquired and processed seafloor backscatter can assist
interpretation on whether surficial or volumetric processes (penetration) predominate. As frontier
exploration moves into deeper waters in search of oil and gas reserves, studies that examine the
acoustic frequency responses of seep features in deep water multibeam systems as well as a
comparison of the processing software and acquisition parameters are critical to understanding the
limitations of these datasets. To aid interpretation, Fugro employs two survey procedures to finetune both the quality of our multibeam data and the coring locations – an intensity balancing
normalization procedure prior to a survey and a “2X” or 200% oversampling acquisition technique
to better resolve geologic features in areas of high interest.
Figure 3. USBL coring operations using multibeam backscatter datasets for targeting thermogenic hydrocarbon seeps. The
difference between a bent core (upper left) and one filled with hydrocarbon fluids (right) can be meters. Seafloor backscatter
data needs to be optimal for interpretation and cost-effective exploration.
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Backscatter Intensity Normalization
Collecting high-quality multibeam data starts with proper calibrations of the system. Prior to the
start of a seep survey, MBES settings and processing parameters are optimized to locate seafloor
seeps. During calibration of the MBES, we perform two bathymetry patch tests in shallow and
deepwater in addition to a relative backscatter intensity normalization procedure. Uncalibrated
backscatter often appears as along-track bands or striping artifacts in the seafloor imagery (Figure
4). These artifacts are often due to offsets (gain differences) in the acoustic backscatter intensity
levels between transmit sectors. It is important to normalize these acoustic offsets between sectors
because they can increase the likelihood of missing seafloor and water column evidence of seeps
(de Moustier, 2015). This intensity misalignment may result from improper installation or
incorrect values in the BScorr.txt (backscatter correction) file within Kongsberg’s Seafloor
Information System (SIS). The factory-installed text file stores beam pattern coefficients for each
transmit sector to compensate for the gain differences and incorrect or missing values in this file
can create large intensity offsets between sectors that appear as banding artifacts between sectors.
If these offsets are large enough, the artifacts can conceal features of interest on the seafloor and
water column. Adjacent along-track pixels having sonar-related gain differences and variations in
transmit sector intensity levels will degrade the overall quality of data by masking the underlying
acoustic backscatter characteristics of the terrain leading to missed targets on the seafloor and
water column. A normalization procedure involving analysis of reflectivity curves of two adjacent
lines over a flat, featureless area and an iterative, manual modification of the BSCorr.txt file will
balance the backscatter intensity across sectors so any observable differences in backscatter
imagery are environmentally-related (i.e. seafloor features or composition) and not system-related.
(Figure 5). The foundation for this relative backscatter normalization is based on the sea trials of
the Kongsberg EM302 installed on the R/V Falkor (Beaudoin et al. 2012) and discussed in Orange
and Kennedy (2015). A thorough background of multibeam backscatter calibration techniques can
be found in Rice et al. (2015).
Figure 4. Banding artifacts in seafloor and midwater backscatter datasets caused by artificial differences in intensity between
sectors. Hydrocarbon seeps have reflective signatures whose anomalous intensity may be masked by the artifact. Both seafloor
and midwater datasets are affected by intensity imbalances.
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Figure 5. Relative normalization correction. A BSCorr.txt file with incorrect coefficients will create an angular response curve
that has sector-step offsets across the swath (blue). A relative intensity normalization will normalize these offsets (red) creating a
consistent seafloor image across the swath on a flat a featureless seafloor.
Increasing Geologic Resolution with 2X
Following an exploration survey that maps 100% of the seafloor with typically 10-20% overlap
between adjacent lines, a “2X” or 200% survey is acquired in areas of interest or of suspected
seepage. By mapping the same area of seafloor with a different beam geometry, noise-related
artifacts can be suppressed while increasing the signal to noise ratio (SNR) of anomalous
backscatter areas analogous to seismic stacking techniques. Decreasing the survey line spacing
with offset lines (typically half the original line spacing) allows for oversampling creating highsounding densities. The overlap between adjacent lines is significant enough that when
overlapping pixel values are averaged during the mosaic generation, the increased SNR enhances
the geologic resolvability of reflective seafloor features (Orange et al., 2015, Digby et al., 2016).
Averaging of pixel dB values is fundamental to 2X. Oversampling helps dampen the effect of
irregular seafloor geometry, beam position, and signal attenuation (Figure 6). This type of
acquisition takes advantage of the “sweet spot” or “MBES paradise” between 15-60° grazing in
the beam fan swath (Lucieer et al., 2015, Rice et al., 2015). The grazing angle sector of 30-60° is
a low slope plateau in the graph of the backscatter strength versus grazing angle. Regions within
0-10° tend to be dominated by specular reflection and higher intensities shown by the characteristic
nadir stripe in backscatter mosaics. Far outer beams (> 60°) are lower intensity with noise due to
attenuation and loss of resolution from increased beam footprint size. By decreasing line spacing,
more of the seafloor can be imaged in the 15-60° zone and will help suppress undesired background
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noise that can result from angular response, seafloor grazing angles, and beam pattern residuals
(Kluesner et al. 2013). In areas of complex seafloor topography, slope geometry can mask seafloor
features where more acoustic energy is reflected off surfaces facing the transducer resulting in an
artificially high return and slopes facing away can result in a lower return. By covering the same
area of seafloor with 2X acquisition using different angles of insonification and headings these
image artifacts can be averaged out creating a vastly cleaner, more interpretable image.
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Figure 6. 2X backscatter acquisition technique. By decreasing line spacing and heading, undesired effects from seafloor
geometry and grazing angle can be suppressed while enhancing naturally high impedance areas on the seafloor.
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Gigante and Otos Seep Surveys
Fugro is currently involved in two seep surveys covering close to 1,000,000 km2 of seafloor from
the U.S. Gulf of Mexico to Belize – Gigante3 (2015-2016) and Otos4 (2017) – industry-funded
multibeam and geochemical sampling surveys in partnership with TGS and ONE LLC (Figure 7).
Three dedicated survey vessels, Fugro Brasilis (30 kHz Kongsberg EM302 MBES, 1° x 1°), Fugro
Americas (30 kHz Kongsberg EM302 MBES, 0.5° x 1°), and Fugro Gauss (12 kHz EM122 MBES,
1° x 2°) collect high-resolution MBES bathymetry, seafloor backscatter, and midwater backscatter
identifying potential hydrocarbon seeps. The survey coincides with the denationalization of
Mexican waters over one of the most prolific hydrocarbon-bearing basins on Earth and the
geophysical data acquired will be used to identify and characterize seafloor seeps for geochemical
coring operations. Because of the importance in obtaining high quality backscatter data, significant
time and effort were invested into developing protocols and standardizing data acquisition and
processing settings prior to the start of the survey.
Figure 7. Gigante and Otos Seep Surveys. Prior to the start of the Gigante survey, a seep calibration study was carried out over
Green Canyon Block 600 to optimize the EM302 for deepwater seep detection.
3
https://www.fugro.com/media-centre/fugro-world/article/worlds-largest-offshore-seep-hunting-survey
4
http://www.tgs.com/News/2017/TGS_announces_new_Multibeam_project_in_U_S__Gulf_of_Mexico/
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During sea trials of the newly-installed EM302 MBES on Fugro Americas, significant
along-track bands visible as artificial striping artifacts were observed in the seafloor backscatter
imagery. This striping artifact was caused by large (3-5 dB) offsets in intensity level between the
transmit sectors within Medium, Deep, and Very Deep Modes in the Kongsberg Seafloor
Information (SIS) real-time acquisition software (de Moustier, 2015). The sea trial analysis
concluded that these 3-5 dB differences across sectors led to the banding artifacts because the
BSCorr.txt file was not properly applied during sonar installation and required adjustment to
compensate for the intensity offsets. A small diagnostic survey acquired data for Kongsberg and
was processed with Angle-Varying Gain (AVG), a post-processing function that corrects for the
change in backscatter strength as a function of angle-of-insonification. The results of the small
survey showed that the striping was indeed lessened by AVG but it also tended to smear
backscatter anomalies both along-track and across-track in Fledermaus Geocoder (FMGT) and in
some cases completely erase them using Caris Geocoder. A full backscatter intensity normalization
was planned to correct the sector imbalance before the Fugro Americas involvement in the Gigante
seep survey. The probability of missing seep-related features on the scale of a few tens of meters
would increase without normalizing these gain differences across the sectors in each of the depth
modes.
Study – GC600 Seep Calibration Site
During the backscatter normalization procedure, we conducted a pair of small seep detection
surveys in September 2015 in approximately 1,250 m of water over Green Canyon (GC) Block
600 in the Gulf of Mexico (Figure 7 inset map). Seep exploration surveys differ from traditional
hydrographic surveys and nuances in survey parameters can dramatically impact the detectability
of hydrocarbon seeps and related seafloor features and we wanted to fine-tune the system for seep
detection before mapping in a largely unexplored offshore frontier basin. GC600 is an ideal site
for the detection diagnostic survey because it is well-studied, close to the survey area, and actively
emitting hydrocarbon fluids into the overlying water column (Roberts et al., 2007; Roberts et al.,
2010; Brooks et al., 2014; Wang et al., 2014; Johansen et al., 2017). The availability of multiple
sources of multibeam data (12 – 200 kHz) and seafloor imagery allowed us to analyze the
frequency response of seeps over a range of frequencies and to evaluate penetration characteristics.
Using an autonomous underwater vehicle (AUV) MBES dataset (Eagle Ray – EM2000, 200 kHz
at 50 m altitude) acquired from Ecosystem Impact of Oil and Gas Inputs to the Gulf (ECOGIG)
consortium, and supplemented with near-seafloor imagery from the Mola Mola AUV (3 m
altitude) as control (Conti et al., 2016), we assess the results of the intensity normalization, test
various acquisition settings and software packages to optimize the EM302 MBES specifically for
seep detection, and evaluate off-nadir plume detection limits.
Geologic Setting of GC600
The study area is located within Green Canyon Block 600 (27.370° N, 90.569° W), a 3 x 3 mile
BOEM-designated lease area found along the lower continental slope (> 1,000 m) of the Northern
Gulf of Mexico (Figure 8). GC600 is situated in a region of intensive natural hydrocarbon seepage
among an area of complex salt-controlled topographical features. Salt tectonics in the area has
created a network of subsurface faulting and fissures that promote hydrocarbon fluid and gas
expulsion from the deep subsurface to the seafloor (Garcia-Pineda et al., 2010). Buoyant and
mobile salt generates irregular geomorphic features such as domes, ridges, and knolls on the
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seafloor from extensive subsurface faulting. Subsurface faulting promotes the vertical migration
of hydrocarbons to the seafloor and is consumed by microbial consumption of methane that
develop carbonate deposits through the anaerobic oxidation of methane (AOM). Seafloor seepage
sustains chemosynthetic communities composed primarily of tubeworms, mussels, and microbial
mats that flourish in response to the upwelling methane at this site (Roberts et al., 2010; Fisher et
al., 2007).
The geologically and biologically-complex seafloor was one of the first confirmed
chemosynthetic sites in the Gulf of Mexico found at these depths. The site was initially tagged for
exploration based on seismic amplitude and reflectivity analysis (Roberts et al., 2007). Subsequent
dives on the DSV Alvin in 2006 (Dives 4174 and 4184) and by the ROV Jason in 2007. The main
seepage site is located along an elongate low-relief ridge trending NW-SE in 1,180-1,250 m depth
that separates two intraslope basins. The site features slabs, blocks, rubble, and hardground
pavement of authigenic carbonate created by subsurface AOM. Sparse aggregations of
chemosynthetic fauna such as mussels and tubeworms are located in the cracks of these porous
carbonate outcrops (Roberts et al., 2010). Geochemical analysis of these carbonate slabs show
traces of embedded biodegradable crude oil (Brooks et al., 2014). White and orange bacterial mats
are observed with interspersed dead clam and mussel shells. Numerous active seafloor vents are
found focused along cracks in the ridge line giving rise to such prolific plume emission sites as
“Birthday Candles” and “Mega Plume” emitting gas and oil-coated bubbles (Wang et al., 2016;
Johansen et al., 2017). Evidence of the persistent seepage of oily bubbles reaching the surface over
GC600 are suggested to be relatively constant flux emissions over a decadal time scale based on
satellite imagery of sea slicks above the study area (Brooks et al., 2014).
Figure 8. Perspective view of GC600. Inset view shows a perspective multi-scale visualization of GC600 with mapped water
column plumes acquired from the EM302 overlain on AUV acquired EM2000 MBES backscatter imagery.
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Methods
The Fugro Americas is a multi-purpose geophysical and seafloor mapping vessel outfitted with a
hull-mounted, gondola-lowered 30 kHz Kongsberg EM302 MBES with 432 beams (0.5° fore-aft
transmit beam width x 1° receive beam width) capable of dual-pinging in medium and deep modes
for 864 soundings/ping. Results from the sea trials revealed a significant intensity imbalance across
sectors in Medium, Deep and Very Deep Modes requiring normalization. To assess the results of
the normalization and evaluate how an imbalanced MBES would ultimately affect seep detection
and target identification, an initial survey was carried out over GC600 to provide baseline
backscatter imagery. After the backscatter intensity normalization, the second survey at GC600
assessed the results of the sector balancing in deep and very deep mode using a high-resolution
AUV MBES dataset acquired from ECOGIG as control on the backscatter signature/pattern. Line
spacing geometry was designed to allow for massive oversampling of soundings with significant
overlap that allowed us to examine our “2X” technique that we use to improve the quality of the
backscatter and increase the geologic resolvability to account for nadir and slope artifacts.
Processing settings within each software were examined to evaluate how varying each would affect
the backscatter signature over the seep, i.e. cleaning the dataset prior to backscatter processing,
using a reference surface to account for changes in slope geometry, use of time series vs. beam
average, use of AVG with varying window sizes, among other settings. These backscatter analyses
are supplemented with georeferenced seafloor imagery to ground-truth and assess the observed
acoustic reflectivity patterns. The line geometry allowed for us to map the known plume emission
sites at various take-off angles to test the far-nadir limits of detectability of plumes before using
the EM302 for exploratory surveying of the Southern Gulf of Mexico and Caribbean Basins.
The first survey of GC600 was designed with a line spacing of 950 m and with an obtained
swath width of 4,500 m, provided 80% overlap between adjacent lines (Figure 9). Seafloor
backscatter was processed in Caris Geocoder, Fledermaus Geocoder (FMGT), and Kongsberg
Poseidon software, gridded at 5 m, and imported into ArcGIS for analysis. Plumes were extracted
from midwater backscatter data using Fledermaus Midwater (FMMW) software using the
automated Feature Detector toolkit (Gee et al., 2014).
Figure 9. Line plan for the pre- and post-calibration survey at GC600. 3X coverage was acquired directly over the NW-SE
trending plume ridge. Mapped plume emission sites from the first survey allowed for specific take-off angle detectability to be
analyzed.
The backscatter normalization of the EM302 is intended to balance the reflectivity intensity
across sectors, pings, swaths, and modes. Through a manual and iterative process, the method
adjusts the beam pattern coefficients embedded in the BSCorr.txt file to normalize intensity values
over sectors. Prior to the start of the normalization, a copy of the existing BSCorr.txt was
downloaded from the Kongsberg Power Unit (PU) and an expendable bathythermograph (XBT)
and sound velocimeter (XSV) were acquired at the site. Accurate salinity (mean value representing
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the water column) is critical for the calculation of the absorption coefficient which is necessary for
determining backscatter intensity. The absorption profile is used to estimate transmission loss in
the water column. Two reciprocal lines over a flat, uniform seafloor approximately 2,000 m deep,
appropriate for both Deep and Very Deep Modes of the EM302, were run at 6 kts using the SIS
acquisition settings intended for the seep survey. A line heading of 0°/180° was perpendicular to
slope to avoid potential across-track intensity changes. Select direction of line to limit impact of
motion on vessel (pitch, roll, and heading). The length of the line included several thousand pings,
large enough to provide a robust statistical analysis of the reflectivity dataset (Augustin and Lurton,
2005). The Angular Response Curve (ARC) was extracted using all pings from these raw
Kongsberg. all files and analyzed to determine the residual intensity offset values for each sector
in both Deep and Very Deep Modes. Using Excel, the Median Reflectivity (dB) vs. Transmit Angle
(°) is plotted and corrections for each swath (fore and aft) are obtained by holding mid-swath
sectors constant and adjusting offset adjacent ones until seamless. The sector dB offsets were
added to the corresponding sector values in the BSCorr.txt file. After this file was modified and
uploaded back into SIS, the process was repeated using the reciprocal line for QC and a validation
line was acquired to confirm to absence of the banding artifact. Figure 10 shows a flow chart of
the steps involved in the normalization process.
Figure 10. Flow chart of the backscatter normalization process. Adopted from Orange and Kennedy (2015)..
Following the backscatter normalization, a second survey was carried out over GC600 to
evaluate the results, the value of 2X in improving the geologic resolvability when comparing the
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data to the 1 m gridded AUV backscatter dataset, and assess near-seafloor plume detectability in
the outer beams. The line plan was slightly offset from the original lines in the pre-calibration
survey (Figure 9) and were acquired in both Deep and Very Deep Modes. The overlap between
adjacent lines provided 3X coverage over the main ridge containing the plume emission sites. The
seafloor backscatter data was gridded at 5 m, 2.5 m, and 1 m and the improvement in resolution
was analyzed relative to the 1 m ECOGIG AUV dataset. To test water column detection limits of
the EM302, plume emission sites along the ridge were imaged at 10-12°, 32-34°, and 45° take-off
angles. Water column data were processed using Feature Detection in FMMW with normalization,
threshold, and despeckle filters. A cluster analysis was performed to enhance the midwater
backscatter signal of the plumes and improve interpretation of emission sites (Gee et al., 2014).
Backscatter acquisition parameters and processing software settings were analyzed in order
to optimize the MBES for detecting and delineating seafloor seeps during the GC600 surveys. Best
practices for commercial seep hunting dictate that acquisition settings and processing procedure
remain constant throughout the survey to ensure a consistent product (Rice et al., 2015). We
developed a best practices acquisition guideline for seep detection using the EM302 that we used
for the Gigante and Otos seep surveys:





To maximize ping rate and sounding density dual-pinging Deep Mode on the EM302 is
entirely used throughout the survey in 500 – 3,000 m water depth to ensure high-density
along-track data density and faster survey speeds. Step changes in acoustic backscatter
intensity of up to 5 dB may be observed when changing depth mode leading to “patchwork”
quality backscatter mosaics.
A fixed FM-enabled dual-swath mode is preferable for backscatter data density in deep
waters (3,000 m +) using Deep Mode.
Sector coverage settings in the SIS Runtime parameters use an Auto angular coverage
mode for improved bottom detection with high density equidistant beam spacing and a max
swath width of 68°/68°.
Pitch and yaw stabilization are turned on with a head tilt of 1° to 3° to mitigate/prevent
“Erik’s Horns.”
In the Filter and Gains tab, the penetration filter and sector tracking are off. Using a tilted
head, a penetration filter will create a false bottom detection that creates a very low
backscatter signature dubbed “Bob’s Blobs.” Sector tracking normalizes the backscatter
intensity across the swath in real-time during acquisition but does not record the process
and irreversibly alters the file.
We developed a best practices processing guideline for seep detection using the EM302 that we
used for the Gigante and Otos seep surveys:



Daily sound velocity profiles and salinity measurements are performed using CTD, XBT,
and XSV casts and independently calculated on a QC spreadsheet. This check alleviates
potential poor-quality data resulting from bad profiles before they are added into SIS.
Bathymetry editing is light to avoid over-smoothing of data to aid interpretation of finescale features (Orange et al., 2010). Hillshaded relief is created with azimuths of 45°, 135°,
225°, 315° with a low artificial sun grazing angle to highlight fine-scale features.
Use of three backscatter processing software packages assists with interpretation.
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Individual settings for each of the three packages used:
Caris Geocoder v. 9
 Geocoder processing algorithm
 Time series with anti-aliasing
 Auto Gain Correction
 Auto TVG
 AVG Adaptive 300
 Avoid despeckle – this functionality averages anomalous backscatter data with
surrounding cells
 Use a cleaned reference surface
 Full blend gridding method
Fledermaus Geocoder v. 7.5
 Tx/Rx Power Gain Correction
 AVG Adaptive 300
 Use a cleaned reference surface
Poseidon v. 2.4
 2D interpolating filter of 9
 Footprint size of 50%
Seafloor backscatter data are processed by line (-5 to -65 dB intensity range, red = high reflectivity,
blue = low reflectivity) and imported into ArcGIS with a custom model builder script. Mosaics are
generated using the mean functionality in Image Analysis. Analysis of various processing settings
in each software was performed using remotely-acquired data from GC600 and ECOGIG AUV
backscatter imagery to compare settings and software packages. The high-resolution Eagle Ray
AUV 1 m backscatter imagery was used a “gold standard” to compare the software processing
analyses to ignoring fine-scale differences due to frequency and penetration differences between
the 200 kHz and 30 kHz MBES.
Results and Discussion
Backscatter Intensity Normalization
Intensity imbalances were observed between swaths (aft and fore in dual-pinging Deep Mode),
sectors, and modes and the BSCorr was manually modified to normalize these differences. The
normalization was performed on a flat seafloor devoid of any naturally-occurring features to
eliminate potential errors in both Deep and Very Deep Modes. Deep mode dual-swath has eight
transmit sectors (central four transmit sectors are continuous wave (CW) with the outer two sectors
powered by linearly frequency modulated (LFM) pulses) each with a different acoustic frequency
for both fore and aft swaths. Each of the sectors has an individual beam pattern coefficient that
normalizes backscatter intensity offsets across sectors, swaths, and modes contained in the factoryinstalled BSCorr.txt file. Very Deep Mode operates in the same manner but with six sectors
operating in LFM pulses.
The magnitude of the banding artifacts was similar to many of the anomalous backscatter
anomalies that will mask potential hydrocarbon seeps (Figure 11). An observed sector imbalance
of up to 3 dB was observed across sectors in Deep Mode and banding is especially pronounced
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along the port side sectors. The port side is generally lower intensity that exhibited discrete bands
of higher intensity. The starboard side appeared to have higher overall intensity across a flat a
featureless seafloor. Within Deep Mode, there are visible > 1 dB differences between fore and aft
swaths leading to a pixelated along-track banding pattern. A significant visible difference between
modes is observed in the data with up to a 5 dB variation between Deep and Very Deep Mode
(Figure 11).
Figure 11. Unnormalized seafloor backscatter with strong sector banding (adopted from Orange and Kennedy, 2015). SIS
display Deep vs Very deep (Line_0000) showing a large step change (> 5dB difference) when changing modes and > 1 dB
differences in intensity between fore and aft swaths.
Figures 12 show the results of the normalization using angular response curves (ARC).
Line_0000 was collected with a heading of 0° at 6 kts over the selected area. The fore and aft
swaths were balanced using Line_0000. Figure 12A shows the ARC generated from the pings
along this line. Swath 1 uncorrected aft is shown in blue and swath 2 uncorrected fore is in green.
The apparent tilt shown in the port sectors is contributing to the relatively lower backscatter
intensity compared to the starboard side (refer to Figure 11). During the normalization procedure,
a -1 dB adjustment was applied to the fore swath to normalize the fore to the aft swaths.
Line_0001 was collected at a 180° heading back over Line_0000 evaluated the ARC in
Very Deep Mode. Figure 12B shows the normalization of Very Deep Mode where blue is the
uncorrected and the 1st iteration of the corrected ARC is shown, by sector, in alternating red and
green for differentiation. The corrections for each sector are located in the table on the graph. After
normalizing between sectors and fore and aft swaths in Deep Mode during Line_0000, and across
sectors in Very Deep Mode during the reciprocal Line_0001, a normalization across modes was
performed. This is a relative normalization and Very Deep was chosen as the standard ARC
requiring a bulk shift of -5.25 dB applied to the swath-balanced Deep Mode (Figure 12C). Figures
12D and 12E show the results of the shift per swath where 12D shows the corrected aft (swath 1)
with the corrected Very Deep and 12E shows the corrected fore (swath 2) with the corrected Very
Deep Mode.
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Figure 12. Angular response curves (ARC) for the normalization.
Figure 13 shows the results of the pre- and post-calibration lines run over GC600. Initial
efforts into reducing the sector banding using AVG was discussed prior to the normalization. This
survey is regional-scale and there was concern after analyzing AVG that this would not only
potentially wipe out small seep anomalies, but also affect the aesthetic quality of the final mosaics.
AVG corrects for the change in backscatter strength as a function of angle-of-insonification. The
AVG algorithm in Geocoder computes an average signal level over a specific range of grazing
angles and fits a curve to the average across-track variation. The user specifies the number of pings
along track to average over. The larger the number of pings specified, the more the imagery is
potentially smeared along-track. The first column of Figure 13 shows the pre-calibration Line 001
processed with Caris Geocoder (Adaptive AVG 300), FMGT (Adaptive AVG 300), and Poseidon.
Poseidon does not have AVG functionality. In the Caris Geocoder example, the portside banding
is eliminated but with a reduced intensity signal over the NW-SE trending seep ridge. The AVG
algorithm used in Caris Geocoder eliminates the sector intensity differences but also much of the
anomalous high backscatter signal on the hardgrounds along ridge. Caris’s AVG algorithm
diminishes the areal extent and strength of the high backscatter feature both along- and acrosstrack. The ECOGIG Eagle Ray AUV 1 m backscatter data is shown for control. In the FMGT
example for the pre-calibrated line, use of AVG removes almost all of the across-track step changes
in acoustic intensity but smears pixel clusters of anomalously high backscatter along-track. Sector
banding is severe in Poseidon and without a normalization this dataset would be difficult to
interpret.
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Column 2 shows the equivalent calibrated line (same azimuth – Line 147) using AVG
(Adaptive 300) in Caris Geocoder and FMGT. It appears AVG has adequately suppressed the
sector imbalance in the Geocoder examples (Line 001 vs. Line 147), at least on a localized scale.
Slight differences can be seen along the seep ridge line where higher intensities exist in the postcalibrated lines. This clearer delineation of the hardground and seep-influenced seafloor is the
objective of balancing the sectors before a seep survey. The sector balancing normalization results
are most apparent in the Poseidon example. The iterative balancing alleviated the strong port side
sector and normalized the overall intensities across the entire swath (recall that the port side had
the more severe high banding surrounded by overall lower intensities while the starboard side was
generally higher). A strong nadir strip is apparent in the Poseidon calibrated backscatter image.
This region of intense specular reflection is dampened by use of AVG in FMGT and Caris
Geocoder.
Column 3 shows FMGT and Caris Geocoder without the use of AVG in pre- and postcalibration lines. Both of Line 001 and 147 are dominated by the nadir strip. In the pre-calibration
lines, this strip in addition to the banding, creates almost a useless product for interpretation.
Normalizing balances the sectors, but AVG is clearly needed to suppress the nadir strip during
post-processing. Column 4 shows the effect of the sector balancing without the use of AVG. While
the sector banding artifact is reduced the pronounced nadir stripe has an intensity magnitude equal
to the seep feature leading to potential missed targets. The images indicate that a backscatter
normalization performed in acquisition with the use of AVG in processing is optimal for
identification of anomalous backscatter areas. Note that unbalanced backscatter affects water
column interpretability. Severe sector misalignment is observed in the water column data that
would likely lead to missed bubble cluster reflectors in the dataset. In addition, automated plume
extraction tools require a clean dataset and a sharp contrast between reflector and background
intensity.
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Figure 13. Results from the surveys over GC600 show pre- and post-calibration results.
2X Seafloor Backscatter Acquisition
Both surveys over GC600 showed prolific plumes in the water column regardless of the intensity
normalization. During an exploratory survey, the presence of plumes in the water column would
signify active seepage in the area and therefore focus our seafloor interpretations near the suspected
emission site. Interpreted plumes in the water column will validate areas of anomalously high
seafloor backscatter as seep-related features. While there is plume positional uncertainty involved
in addition to the highly ephemeral nature of plume emission sites, we will begin to analyze the
seafloor backscatter patterns near the emission site. At a site like GC600, a 2X survey would be
run over the site to sharpen the high backscatter anomalies and clean up the dataset. While time
consuming and therefore expensive to run additional lines, geoscientists need to balance wellplaced cores with the cost of coring operations. During exploratory surveys with a hull-mounted
system, we would not know if this high acoustic return was due to exposed carbonate pavement or
scattered shells in hydrocarbon-soaked sediment ideal for coring. 2X would allow for a cleaner
delineation of the anomaly’s extent for interpretation of the seep. After a 2X survey, the decision
to core directly into the high backscatter anomaly versus taking a more conservative and cautious
approach by coring on the peripheral of the anomaly is as much philosophy, experience, and
science. Interpreting if the backscatter anomaly is related to hardness of the seafloor, roughness of
the seafloor, volumetric scattering, acquisition and seafloor slope geometry needs to be analyzed
with other datasets to mitigate the risk of a bent core barrel (Orange et al., 2010).
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Figure 14. Poseidon post-calibration seafloor backscatter showing 1X, 2X, and 3X.
Figure 14 shows a post-calibrated line processed with Poseidon. The benefits of oversampling
are clearly shown between the 1X-2X-3X coverage. The central 2X imagery suppresses the
artificially-elevated nadir strip while 3X helps define both the seep delineation and lessens the
noisy specular reflection. Of the three processing packages used during the seep surveys, only
Poseidon offers a pixel averaging functionality during mosaic generation within the software
package. Averaging the pixel values of overlapping cells is the fundamental principal behind 2X.
Averaging pixel values can suppress random noise and enhance the signal of an area of anomalous
backscatter. Caris HIPS and FMGT do not have this averaging functionality so each line will be
processed individually and then averaged in the mosaic with ArcGIS Image Analysis.
Figure 15 shows the results evaluating if geologic resolution increases with oversampling and
a decrease in cell grid size. Seafloor backscatter imagery is typically gridded at 5 m regardless of
water depth during our seep surveys. In 1,250 m water depth, would 2-3X coverage allow for the
imagery to be gridded at a higher resolution? On the top row Panel A, is the 5 m gridded 3X. Panel
B is gridded at 2.5 m, Panel C at 1 m, and Panel D is the Eagle Ray AUV 1 m dataset for
comparison. There are slight differences between the 5 m to 1 m grids with increasing amounts of
speckle apparent along the highly reflective plume ridge. Better delineation is negligible when
compared to the 200 kHz AUV dataset. Keep in mind that the AUV data is high frequency and is
likely mapping the upper few cm of sediment while the 30 kHz at an altitude of 1,250 m is
penetrating > 1 m. The center row (Panels E to H) shows the differences in Poseidon from 1X
coverage to 3X coverage compared to the AUV data. Progressively clearer delineation is noted
from 1X to 3X coverage highlighting the effectiveness of increasing geologic resolution of the
seep features. The bottom row (Panels I to L) shows the differences in processing packages for 3X
acquisition. Panel I is Caris Geocoder showing the muted reflective response along the seep ridge
and Panel J shows the FMGT 3X imagery product. Both of these Geocoder products used Adaptive
AVG with a window size of 300. Poseidon 3X is shown in Panel K and has an overall consistent
background reflectivity across the swath.
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Figure 15. Analysis of gridding resolution (top row), 1X-3X coverage (middle row), and processing software (bottom row).
Detection Limits of Midwater Plume Mapping
During the first survey over GC600, plume emission sites were mapped in FMMW and the second
survey was designed to run over the emission sites at various take-off angles. The objective of the
test was to evaluate the detectability of plumes in the far outer beams. Previous work by Weber et
al. (2012) consistently detected seeps with an EM302 over a swath width that was roughly twice
the water depth. Figure 16 shows the survey design in plan view with the near seafloor pick
representing the plume emissions sites. Below is a table containing values estimating the far offnadir limits. Take-off angles in excess of 50° were plumes imaged occurred in three lines
coinciding the estimation made by Weber et al. (2012). While reverberation masked many of the
far off-nadir seeps making it difficult to extract using threshold filtering and other automated
procedures, these plumes are visible and can be mapped by manual interpretive geopicking. Note
that most of these far off-nadir plumes are not visible in FMMW stacked view. The data file must
be viewed in beam fan view to see these plumes. This small diagnostic test provided information
that was latter used to plan line spacing ion unexplored frontier areas in the Southern Gulf of
Mexico.
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Figure 16. Analysis of EM302 midwater detection limits.
Conclusion
Prior to commencing the Gigante multibeam seep survey, Fugro performed a seep calibration over
GC600 using a newly-installed Kongsberg EM302 multibeam. The multibeam system had a faulty
factory-installed BSCorr.txt and required a backscatter intensity normalization to balance the
acoustic intensity across pings, swaths, and modes. The EM302 collected lines over GC600 before
the normalization and after to assess the magnitude of potential interference caused by the banding
artifact over a well-studied hydrocarbon seep. In addition to the intensity normalization, the
effectiveness of 2X oversampling and off-nadir midwater plume detection limits were evaluated.
The study found that the backscatter normalization was effective in eliminating the banding
artifacts. The intensity magnitude of the striping artifacts in many areas were as elevated as
backscatter anomalies interpreted to be seep-related hard hardgrounds. Uncalibrated backscatter
will affect intensity in both seafloor and midwater imagery, two datasets primarily used for seep
detection and characterization. Seeps are acoustically-reflective features and will be potentially
overlooked if normalization is not performed prior to a large survey such as Gigante and Otos. The
use of AVG in post-processing suppressed specular reflection in the nadir strip, another source of
interference in the imagery datasets. The study indicates that issues related to data quality (such as
a bad BSCorr.txt file) should be corrected in acquisition and not relied on in the post-processing
pipeline, a suggestion made in the Lurton and Lamarche report (2015). AVG functionality helps
but will potentially wipe-out or lessen many of the small characteristic high backscatter anomalies
that the seep survey is seeking for coring targets.
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Backscatter normalization should be performed when a multibeam is installed to fine-tune
the BSCorr.txt values, when along-track banding is noticed, and after transducer maintenance and
cleaning. Time-varying drift of backscatter intensity has been observed during long periods of ship
operations because of biofouling on the transducer.
2X acquisition is a simple technique used to enhance the SNR in multibeam reflectivity
data. It minimizes artificial acoustic returns that result from topographically-varied areas,
suppresses noise resulting from specular reflection due to angle of insonification, and will enhance
or saturate areas that are hard on the seafloor aiding seep detection. This technique enhances
delineation of hydrocarbon seeps that will assist USBL-navigated coring and ultimately providing
a greater success of obtaining a full core of sediment saturated with seep fluids. Figure 17 shows
a perspective view of the high-resolution ECOGIG AUV dataset (1 m) with plume locations and
a 5 mm orthomosaic of seafloor imagery acquired from the ECOGIG Mola Mola AUV at an
altitude of 3 m above the seafloor. A 3D perspective of a Poseidon 3X imagery product is shown
to compare the resolution of a hull-mounted EM302 with near-seafloor datasets. By using 2X+
acquisition coverage with a MBES optimized for seep detection, remotely-acquired seafloor
imagery data quality can be improved leading to better interpretation of resolvability of features.
Figure 17. 3D perspective of GC600 showing ECOGIG's AUV data with Poseidon 3X seafloor imagery. Through optimization of
acquisition and processing settings, a comprehensive backscatter intensity normalization, and 2X oversampling, remote seafloor
backscatter imagery can be vastly improved.
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