Deepwater Hydrocarbon Seep Detection: Tools and

Techniques using Multibeam Echosounders

Garrett Mitchell1, Jim Gharib2, David Millar3

1Project Geoscientist, Fugro Marine GeoServices, Inc. 6100 Hillcroft, Houston, Texas 77274

Email:gmitchell@fugro.com Phone: + 1 (713) 778-6880 2Global Product Line Manager for Seep Studies, Fugro Marine GeoServices, Inc. Houston, Texas 3President, 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 Fugros Global

Center of Excellence for Seep Studies in Houston, Texas since 2013.

mailto:gmitchell@fugro.com

U.S. Hydro 2017

1

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 waves 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. GeoHabs 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 Fugros 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 Earths 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

http://geohab.org/

U.S. Hydro 2017

2

et al., 2006; Solomon et al., 2009), seafloor morphology (Len 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). High-

resolution marine geophysical techniques are used to detect seeps by exploiting their acoustically-

reflective 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 (

U.S. Hydro 2017

3

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 high-

resolution 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-and-

geochemical-campaigns#tabbed1

https://www.fugro.com/our-services/marine-site-characterisation/marine-geotechnical/seep-hunting-and-geochemical-campaigns#tabbed1https://www.fugro.com/our-services/marine-site-characterisation/marine-geotechnical/seep-hunting-and-geochemical-campaigns#tabbed1

U.S. Hydro 2017

4

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

U.S. Hydro 2017

5

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.

U.S. Hydro 2017

6

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.

U.S. Hydro 2017

7

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 fine-

tune 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.

U.S. Hydro 2017

8

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 Kongsbergs 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.

U.S. Hydro 2017

9

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 high-

sounding 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

U.S. Hydro 2017

10

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.

U.S. Hydro 2017

11

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.

U.S. Hydro 2017

12

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/

https://www.fugro.com/media-centre/fugro-world/article/worlds-largest-offshore-seep-hunting-surveyhttp://www.tgs.com/News/2017/TGS_announces_new_Multibeam_project_in_U_S__Gulf_of_Mexico/

U.S. Hydro 2017

13

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

U.S. Hydro 2017

14

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.

U.S. Hydro 2017

15

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

U.S. Hydro 2017

16

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

U.S. Hydro 2017

17

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 Eriks 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 Bobs 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 fine-scale 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.

U.S. Hydro 2017

18

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 factory-

installed 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

U.S. Hydro 2017

19

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.

U.S. Hydro 2017

20

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. Cariss AVG algorithm

diminishes the areal extent and strength of the high backscatter feature both along- and across-

track. 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.

U.S. Hydro 2017

21

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 post-

calibrated 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 post-

calibration 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.

U.S. Hydro 2017

22

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 well-

placed 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 anomalys 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).

U.S. Hydro 2017

23

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.

U.S. Hydro 2017

24

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 off-

nadir 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.

U.S. Hydro 2017

25

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.

U.S. Hydro 2017

26

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.

U.S. Hydro 2017

27

Sources Abrams, M. A. (1996). Distribution of subsurface hydrocarbon seepage in near-surface marine

sediments in D. Schumacher and M. A. Abrams, eds., Hydrocarbon migration and its

near-surface expression: AAPG Memoir 66, p. 1-14.

Abrams, M. A. (2005). Significance of hydrocarbon seepage relative to petroleum generation and

entrapment. Marine and Petroleum Geology, 22(4), 457-477.

Abrams, M. A., & Dahdah, N. F. (2011). Surface sediment hydrocarbons as indicators of

subsurface hydrocarbons: Field calibration of existing and new surface geochemistry

methods in the Marco Polo area, Gulf of Mexico. AAPG bulletin, 95(11), 1907-1935.

Aminzadeh, F., Berge, T. B., & Connolly, D. L. (Eds.). (2013). Hydrocarbon seepage: from

source to surface. Society of Exploration Geophysicists and American Association of

Petroleum Geologists.

Augustin, J. M., & Lurton, X. (2005, June). Image amplitude calibration and processing for

seafloor mapping sonars. In Oceans 2005-Europe (Vol. 1, pp. 698-701). IEEE.

Berge, T. B. (2013). Hydrocarbon Seeps: Recognition and Meaning. In Hydrocarbon Seepage:

From Source to Surface (pp. 1-7). Society of Exploration Geophysicists and American

Association of Petroleum Geologists.

Bernard, B. B., Brooks, J. M., Baillie, P., Decker, J., Teas, P. A., & Orange, D. L. (2008).

Surface Geochemical Exploration and Heat Flow Surveys in Fifteen (15) Frontier

Indonesian Basins. In International Petroleum Technology Conference. International

Petroleum Technology Conference

Beaudoin, J.D., Johnson, P.D., Lurton, X., and Augustin, J. (2012). R/V Falkor Multibeam

Echosounder System Review. UNH-CCOM/JHC Technical Report 12-001. 58 pp.

http://mac.unols.org/sites/mac.unols.org/files/20120904_Falkor_EM710_EM302_report.

pdf

Brooks, J.M., C. Fisher, H. Roberts, B. Bernard, I. McDonald, R. Carney, S. Joye, E. Cordes, G.

Wolff, E. Goehring (2014) Investigations of chemosynthetic communities on the lower

continental slope of the Gulf of Mexico: Volume I: Final report. U.S. Dept. of the

Interior, Bureau of Ocean Energy Management, Gulf of Mexico OCS Region, New

Orleans, LA. OCS Study BOEM 2014-650. 560 pp.

Brown, C.J. and P. Blondel (2009) Developments in the application of multibeam sonar

backscatter for seafloor habitat mapping. Applied Acoustics, 70(10), 1242-1247.

http://dx.doi.org/10.1016/j.apacoust.2008.08.004

Canet, C., Prol-Ledesma, R. M., Escobar-Briones, E., Mortera-Gutirrez, C., Lozano-Santa Cruz,

R., Linares, C., ... & Morales-Puente, P. (2006). Mineralogical and geochemical

characterization of hydrocarbon seep sediments from the Gulf of Mexico. Marine and

Petroleum Geology, 23(5), 605-619. http://dx.doi.org/10.1016/j.marpetgeo.2006.05.002

Chiocci, F.L., Cattaneo, A. & Urgeles, R. (2011) Seafloor mapping for geohazard assessment:

state of the art. Marine Geophysical Research 32: 1. http://dx.doi.org/10.1007/s11001-

011-9139-8

Coleman, D. F., & Ballard, R. D. (2001). A highly concentrated region of cold hydrocarbon

seeps in the southeastern Mediterranean Sea. Geo-Marine Letters, 21(3), 162-167.

Cordes, E. E., Carney, S. L., Hourdez, S., Carney, R. S., Brooks, J. M., & Fisher, C. R. (2007).

Cold seeps of the deep Gulf of Mexico: community structure and biogeographic

comparisons to Atlantic equatorial belt seep communities. Deep Sea Research Part I:

http://mac.unols.org/sites/mac.unols.org/files/20120904_Falkor_EM710_EM302_report.pdfhttp://mac.unols.org/sites/mac.unols.org/files/20120904_Falkor_EM710_EM302_report.pdfhttp://dx.doi.org/10.1016/j.apacoust.2008.08.004http://dx.doi.org/10.1016/j.marpetgeo.2006.05.002http://dx.doi.org/10.1007/s11001-011-9139-8http://dx.doi.org/10.1007/s11001-011-9139-8

U.S. Hydro 2017

28

Oceanographic Research Papers, 54(4), 637-653.

http://dx.doi.org/10.1016/j.dsr.2007.01.001

Cordes, E. E., Bergquist, D. C., & Fisher, C. R. (2009). Macro-ecology of Gulf of Mexico cold

seeps. Annual Review of Marine Science, 1, 143-168.

http://dx.doi.org/10.1146/annurev.marine.010908.163912

Cordes, E. E., Becker, E. L., Hourdez, S., & Fisher, C. R. (2010). Influence of foundation

species, depth, and location on diversity and community composition at Gulf of Mexico

lower-slope cold seeps. Deep Sea Research Part II: Topical Studies in Oceanography,

57(21), 1870-1881. http://dx.doi.org/10.1016/j.dsr2.2010.05.010

De Beukelaer, S. M., MacDonald, I. R., Guinnasso, N. L., & Murray, J. A. (2003). Distinct side-

scan sonar, RADARSAT SAR, and acoustic profiler signatures of gas and oil seeps on

the Gulf of Mexico slope. Geo-Marine Letters, 23(3-4), 177-186.

de Moustier, C. (2015) Review of Fugro Americas EM302 Sea Trials Data Report (June 26-30).

FGSI PO#03-57004-3345

Digby, A., Puentes, V. and Len, J. (2016) Cold Seeps Associated with Structured Benthic

Communities: More Accurate Identification and Evaluation Using a New Multibeam

Survey Methodology in the Offshore Southern Colombian Caribbean. International

Journal of Geosciences, 7, 761-774. http://dx.doi.org/10.4236/ijg.2016.75058

Fonesca, L., and L. Mayer (2007) Remote estimation of surficial seafloor properties

through the application Angular Range Analysis to multibeam sonar data. Marine

Geophysical Research, 28, 119-126. http://dx.doi.org/10.1007/s11001-007-9019-4

Fisher, Ch., Roberts, H., Cordes, E. and Bernard, B. (2007) Cold Seeps and Associated

Communities of the Gulf of Mexico. Oceanography, 20, 69-79.

http://dx.doi.org/10.5670/oceanog.2007.12

Garcia-Pineda, O., MacDonald, I., Zimmer, B., Shedd, B., & Roberts, H. (2010). Remote-

sensing evaluation of geophysical anomaly sites in the outer continental slope, northern

Gulf of Mexico. Deep Sea Research Part II: Topical Studies in Oceanography, 57(21),

1859-1869.

Gee, L., McKenna, L., & Beaudoin, J. (2014). New Tools for Water Column Feature Detection,

Extraction and Analysis FMMidwater Interactively Visualizes Time-Varying Geospatial

Data. Sea Technology, 55(10), 27-30.

Hovland, M. (2007). Discovery of prolific natural methane seeps at Gullfaks, northern North

Sea. Geo-Marine Letters, 27(2-4), 197-201.

Innangi, S., Bonanno, A., Tonielli, R., Gerlotto, F., Innangi, M., & Mazzola, S. (2016). High

resolution 3-D shapes of fish schools: A new method to use the water column backscatter

from hydrographic MultiBeam Echo Sounders. Applied Acoustics, 111, 148-160.

Johansen, C., Todd, A. C., & MacDonald, I. R. (2017). Time series video analysis of bubble

release processes at natural hydrocarbon seeps in the Northern Gulf of Mexico. Marine

and Petroleum Geology, 82, 21-34.

Johnson, J. E., Goldfinger, C., & Suess, E. (2003). Geophysical constraints on the surface

distribution of authigenic carbonates across the Hydrate Ridge region, Cascadia margin.

Marine Geology, 202(1), 79-120.

Judd, A., & Hovland, M. (2009). Seabed fluid flow: the impact on geology, biology and the

marine environment. Cambridge University Press.

Kluesner, J.W., E.A. Silver, N.L. Bangs, K.D. McIntosh, J. Gibson, D. Orange, C.R. Ranero, and

http://dx.doi.org/10.1016/j.dsr.2007.01.001http://dx.doi.org/10.1146/annurev.marine.010908.163912http://dx.doi.org/10.1016/j.dsr2.2010.05.010http://dx.doi.org/10.4236/ijg.2016.75058http://dx.doi.org/10.1007/s11001-007-9019-4http://dx.doi.org/10.5670/oceanog.2007.12

U.S. Hydro 2017

29

R. von Huene. (2013), High density of structurally controlled, shallow to deep water fluid

indicators imaged offshore Costa Rica, Geochem. Geophys, Geosys., 14,

http://dx.doi:10.1002/ggge.20058

Korneliussen, R.J., Heggelund, Y., Eliassen, I.K., ye, O.K., Knutsen, T., Dalen, J., 2009.

Combining multibeam-sonar and multifrequency-echosounder data: examples

of the analysis and imaging of large euphausiid schools. ICES J. Mar. Sci. 66,

991-997. http://doi.org/10.1093/icesjms/fsp092

Lamarche, G., Lurton, X., Verdier. A., and Augustin, J. (2011) Quantitative characterisation of

seafloor substrate and bedforms using advanced processing of multibeam backscatter

Application to Cook Strait, New Zealand. Continental Shelf Research 31 S93-S109.

Leifer, I., Clark, J. F., & Chen, R. F. (2000). Modifications of the local environment by natural

marine hydrocarbon seeps. Geophysical Research Letters, 27(22), 3711-3714.

Leifer, I., Boles, J. R., Luyendyk, B. P., & Clark, J. F. (2004). Transient discharges from marine

hydrocarbon seeps: spatial and temporal variability. Environmental Geology, 46(8),

1038-1052.

Leifer, I., Luyendyk, B. P., Boles, J., & Clark, J. F. (2006). Natural marine seepage blowout:

Contribution to atmospheric methane. Global biogeochemical cycles, 20(3).

http://dx.doi.org/10.1029/2005GB002668

Len, R., Somoza, L., Medialdea, T., Gonzlez, F. J., Daz-del-Ro, V., Fernndez-Puga, M. C.,

... & Mata, M. P. (2007). Sea-floor features related to hydrocarbon seeps in deepwater

carbonate-mud mounds of the Gulf of Cdiz: from mud flows to carbonate precipitates.

Geo-Marine Letters, 27(2-4), 237-247.

Lucieer, V., Roche, M., Degrendele, K., Malik, M., Dolan, M. (2015) Chapter 3 Acquisition: Best practice guide. In: Lurton, X.; Lamarche, G. (Eds). Backscatter measurements by

seafloormapping sonars Backscatter measurements by seafloormapping sonars Guidelines and Recommendations p 53-78.

Lurton, X, (2010) An Introduction to Underwater Acoustics Principles and Applications. 669p.

Lurton, X.; Lamarche, G. (Eds) (2015) Backscatter measurements by seafloor-mapping sonars.

Guidelines and Recommendations. 200p.

http://geohab.org/wpcontent/uploads/2014/05/BSWGREPORT-MAY2015.pdf

MacDonald, I. R., J. F. Redly, Jr., S. E. Best, R Venkataramaiah, R. Sassen, N. L. Guinasso, Jr.,

and J. Amos, (1996), Remote sensing inventory of active oil seeps and chemosynthetic

communities in the northern Gulf of Mexico, in D. Schurnacher and M. A. Abrams, eds.,

Hydrocarbon migration and its near-surface expression: AAFG Memoir 66, p. 27-37.

MacDonald, I. R., Leifer, I., Sassen, R., Stine, P., Mitchell, R., & Guinasso, N. (2002). Transfer

of hydrocarbons from natural seeps to the water column and atmosphere. Geofluids, 2(2),

95-107.

McConnell, D. R., & Orange, D. L. (2014, November). Are Marine Geochemical Surveys

Unreliable? It Is All About Location. In EAGE Shallow Anomalies Workshop.

Milkov, A. V., Sassen, R., Apanasovich, T. V., & Dadashev, F. G. (2003). Global gas flux from

mud volcanoes: a significant source of fossil methane in the atmosphere and the ocean.

Geophysical Research Letters, 30(2). http://dx.doi.org/10.1029/2002GL016358

Orange, D.L., Yuna, Y., Maher, N., Barry, J. and Greene, G. (2002) Tracking California Seafloor

Seeps with Bathymetry. Continental Shelf Research, 22, 2273-2290.

http://dx.doi.org/10.1016/S0278-4343(02)00054-7

Orange, D. L., Teas, P. A., Decker, J., Baillie, P., GIlleran, P., & Levey, M. D. (2008, January).

http://dx.doi:10.1002/ggge.20058http://doi.org/10.1093/icesjms/fsp092http://dx.doi.org/10.1029/2005GB002668http://geohab.org/wpcontent/uploads/2014/05/BSWGREPORT-MAY2015.pdfhttp://dx.doi.org/10.1029/2002GL016358http://dx.doi.org/10.1016/S0278-4343(02)00054-7

U.S. Hydro 2017

30

The Utilisation of SeaSeep Surveys (a Defense/Hydrography Spin-Off) to Identify and

Sample Hydrocarbon Seeps in Offshore Frontier Basins. In International Petroleum

Technology Conference. International Petroleum Technology Conference.

Orange, D. L., Teas, P. A., Decker, J., Baillie, P., & Johnstone, T. (2009). Using seaseep surveys

to identify and sample natural hydrocarbon seeps in offshore frontier basins. Proceedings,

Indonesian Petroleum Association.

Orange, D. L., Teas, P. A., & Decker, J. (2010, January). SS: Multibeam Backscatter-Insights

into Marine Geological Processes and Hydrocarbon Seepage. In Offshore Technology

Conference. Offshore Technology Conference.

Orange, D. and P. Kennedy. (2015) Systematic Errors in Multibeam Backscatter (Sector, Ping,

Starboard/Port, Mode, and Vessel). Recognizing the problems, understanding the

impacts, and resolving the issues. Kongsberg FEMME Conference.

Orange, D., P. Teas, J. Decker, J. Klusner, and A. Digby. (2015). Increasing Multibeam

Resolvability through Over-sampling. Kongsberg FEMME Conference.

Rice, G.; Degrendele, K.; Pallentin, A.; Roche, M.; Gutierrez, F. (2015) Chapter 5 Acquisition: Best practice guide. In: Lurton, X.; Lamarche, G. (Eds). Backscatter measurements by

seafloormapping sonars Backscatter measurements by seafloormapping sonars Guidelines and Recommendations p 107132.

Roberts, H. H., Hardage, B. A., Shedd, W. W., & Hunt Jr, J. (2006). Seafloor reflectivityan

important seismic property for interpreting fluid/gas expulsion geology and the presence

of gas hydrate. The Leading Edge, 25(5), 620-628.

Roberts, H., Carney, R., Kupchik, M., Fisher, C., Nelson, K., Becker, E., ... & Brooks, J. (2007).

ALVIN explores the deep northern Gulf of Mexico slope. Eos, Transactions American

Geophysical Union, 88(35), 341-342. http://doi.org/10.1029/2007EO350001

Roberts, H. H., Shedd, W., & Hunt, J. (2010). Dive site geology: DSV ALVIN (2006) and ROV

JASON II (2007) dives to the middle-lower continental slope, northern Gulf of Mexico.

Deep Sea Research Part II: Topical Studies in Oceanography, 57(21), 1837-1858.

Roberts, H. H., Feng, D., & Joye, S. B. (2010). Cold-seep carbonates of the middle and lower

continental slope, northern Gulf of Mexico. Deep sea research part II: Topical Studies in

Oceanography, 57(21), 2040-2054.

Sager, W. W., MacDonald, I. R., & Hou, R. (2004). Side-scan sonar imaging of hydrocarbon

seeps on the Louisiana continental slope. AAPG bulletin, 88(6), 725-746.

Skarke, A. Ruppel, C., Kodis, M., D. Brothers, and Lobecker, E. (2014) Widespread methane

leakage from the sea floor on the northern US Atlantic margin. Nature Geoscience, 7,

http://doi.org/10.1038/NGEO2232

Solomon, E. A., Kastner, M., MacDonald, I. R., & Leifer, I. (2009). Considerable methane fluxes

to the atmosphere from hydrocarbon seeps in the Gulf of Mexico. Nature Geoscience,

2(8), 561-565.

Trenkel, V. M., Mazauric, V., and Berger, L. 2008. The new fisheries multibeam echosounder

ME70: description and expected contribution to fisheries research. ICES Journal of

Marine Science, 65: 645655. https://doi.org/10.1093/icesjms/fsn051

Wang, B., Socolofsky, S. A., Breier, J. A., & Seewald, J. S. (2016). Observations of bubbles in

natural seep flares at MC 118 and GC 600 using in situ quantitative imaging. Journal of

Geophysical Research: Oceans, 121(4), 2203-2230.

Weber, T.C., Mayer, L., Beaudoin, J., Jerram, K., Malik, M., Shedd, B., Rice, G., (2012)

Mapping gas seeps with the deepwater multibeam echosounder on Okeanos

http://doi.org/10.1029/2007EO350001http://doi.org/10.1038/NGEO2232https://doi.org/10.1093/icesjms/fsn051

U.S. Hydro 2017

31

Explorer. Oceanography 25 (1-Supplement).