Is Multibeam Water Column Data Really Worth the Disk Space?

Gee, Lindsay; Doucet, Maurice; Parker, David; Weber, Tom; Beaudoin, Jonathan

doi:10.3990/2.236

Cited by 8 publications

(7 citation statements)

References 2 publications

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“…Use of these modules allows the operator to process the bathymetry, backscatter, and water column data using common registration and fuse them together for analysis in 4D (xyz, time) (Doucet et al 2009, Gee et al 2012. Use of these modules allows the operator to process the bathymetry, backscatter, and water column data using common registration and fuse them together for analysis in 4D (xyz, time) (Doucet et al 2009, Gee et al 2012.…”

Section: Methodsmentioning

confidence: 99%

A New Look at Seafloor Venting: Natural Gas Hydrate Derivatives

Barnard

Sager

Snow

et al. 2016

Day 3 Wed, May 04, 2016

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Natural leaks of multiphase hydrocarbons from the seafloor are widespread. Characterizing the temporal and spatial variability of these emissions provides baselines for comparison to the magnitude of anthropogenic leaks. Determining leakage rates will be important to our understanding the sustainable implementation of subsea infrastructure and the relative contribution of natural sources to global budgets. Multibeam echo soundings from cruise AT21-02 were used to define a region with several~600 to 900 m tall gas plumes in the water column directly above cratered hummocky regions of the sea floor with high backscatter in water depths of~1500 m over the Barbados Accretionary Complex. The relationship of seafloor morphologic features such as faults, craters, and mud volcanoes with vent-gas plumes in the water column is indicative of substantial emissions from this region. Disappearance of the acoustic plumes at~600 m is coincident with the top of CH 4 gas hydrate stability in the water column. In the plumes, natural gas hydrate shells that form at the gas-water interface and armor the gas from dissolution during ascent likely encapsulate bubbles. Ascent-driven pressure change in the bubbles causes shattering of shells forming gas hydrate shards that rise with the ascending plume and add to its acoustic reflection strength. The multiphase fluid consisting of gas, shelled bubbles, shards, and water changes character at the top of gas hydrate stability where the hydrate dissociates and dissolves. Differentiating sources and further characterizing emissions will provide baselines and also contribute to understanding the relative importance of the different emissions sources.

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Section: Methodsmentioning

confidence: 99%

A New Look at Seafloor Venting: Natural Gas Hydrate Derivatives

Barnard

Sager

Snow

et al. 2016

Day 3 Wed, May 04, 2016

View full text Add to dashboard Cite

show abstract

“…In (1), N is set as 2 or 3, which means that the feature prototype has two or three rectangles. The feature prototype is located at r i = (x, y, w, h, θ).…”

Section: Rectangular Haar-like Featurementioning

confidence: 99%

“…Moreover, the MWC data outside of the MSR is often removed because of the serious seafloor side lobes effect, and only the MWC data in the MSR can be used for the detection (Figures 16-18). The MWC data in the MSR are only adopted in the studies depicted in the literature [1,42,43]. To detect gas plumes in whole MWC images, the two sides of the MWC image and the outside of the MSR were chosen as the negative samples, depicted in Section 2.1.3, to overcome the effect on the correct detection rate of the proposed method.…”

Section: Effect Of the Mbes Measuring Model And Seabed Side Lobe Effectmentioning

confidence: 99%

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Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

et al. 2020

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The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

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“…The study investigates to identify kelp canopies by benefitting from water column returns of MBES system. One reason for the absence of seagrass studies with water column data can be described as the requirement of huge disk spaces which makes it challenging to collect, process and analyse the data (Gee, Doucet, Parker, Weber, & Beaudoin, 2012). There is currently a working group that aims to develop processing workflows and methodologies for water column data (Lamarche, Le Gonidec, Greinert, Lucieer, & Lurton, 2017).…”

Section: Datamentioning

confidence: 99%

A review of seagrass detection, mapping and monitoring applications using acoustic systems

Gümüşay

Bakırman

Kızılkaya

et al. 2018

European Journal of Remote Sensing

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Seagrass meadows are key elements of marine ecosystems as they affect the physical, chemical and biological environment and provide habitats for fish and invertebrates. Human activities have caused a deterioration in seagrass which has led to unstable benthic habitats; therefore, to prevent major decline, seagrass distribution must be mapped and monitored. Acoustic systems allow researchers, scientists and decision makers to collect highresolution datasets such as bathymetry, backscatter and sub-bottom profiles. These systems are able to characterise the properties of the seafloor including plants, sediments and habitats. In this review, we examine seagrass mapping, monitoring and detection applications using acoustic systems in the literature. Although there are various methodologies for data collection, processing, classification and validation, these are limited to certain seagrass species or study areas. Further worldwide research is required to achieve consistent seagrass detection systems with data acquisition, pre-processing, classification and post-processing.

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Is Multibeam Water Column Data Really Worth the Disk Space?

Cited by 8 publications

References 2 publications

A New Look at Seafloor Venting: Natural Gas Hydrate Derivatives

A New Look at Seafloor Venting: Natural Gas Hydrate Derivatives

Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

A review of seagrass detection, mapping and monitoring applications using acoustic systems

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