Automatic Extraction of Thickness Information from Sub-Surface Acoustic Measurements of Manganese Crusts

Neettiyath, Umesh; Thornton, Blair; Sangekar, Mehul; Ishii, Kazuo; Sato, Terukazu; Bodenmann, Adrian; Ura, Tamaki

doi:10.1109/oceanse.2017.8084917

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…Acoustic reflections made over seafloor sections classified as crust are used to estimate a thickness value [38]. The acoustic measurements are corrupted by noise generated by scattering, multipath reflections, and local inclusions in the crust layer.…”

Section: B Acoustic Data Interpretationmentioning

confidence: 99%

Deep-Sea Robotic Survey and Data Processing Methods for Regional-Scale Estimation of Manganese Crust Distribution

Neettiyath

Thornton

Sangekar

et al. 2021

IEEE J. Oceanic Eng.

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Manganese crusts (Mn-crusts) are a type of mineral deposit that exists on the surface of seamounts and guyots at depths of >800 m. We have developed a method to efficiently map their distribution using data collected by autonomous underwater vehicles and remotely operated vehicles. Volumetric measurements of Mn-crusts are made using a high-frequency subsurface sonar and a 3-D visual mapping instrument mounted on these vehicles. We developed an algorithm to estimate Mn-crust distribution by combining continuous subsurface thickness measurements with the exposed surface area identified in 3-D maps. This is applied to data collected from three expeditions at Takuyo Daigo seamount at depths of ∼1400 m. The transects add to ∼11 km in length with 12 510 m 2 mapped. The results show that 52% of the surveyed area is covered by Mn-crusts with a mean thickness of 69.6 mm. The mean Mn-crust occurrence is 69.6 kg/m 2 with a maximum of 204 kg/m 2 in the mapped region. The results are consistent with estimates made from samples retrieved from the area, showing more detailed distribution patterns and having significantly lower uncertainty bounds for regional-scale Mn-crust inventory estimation.

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Section: B Acoustic Data Interpretationmentioning

confidence: 99%

Deep-Sea Robotic Survey and Data Processing Methods for Regional-Scale Estimation of Manganese Crust Distribution

Neettiyath

Thornton

Sangekar

et al. 2021

IEEE J. Oceanic Eng.

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“…It is very common to measure the thickness of the target by using the reflected mechanical waves. Such methods have been widely used in multiple applications, such as geological exploration [13,14] and medical acoustics [15,16,17]. In this study, we emphasize on the work for improving the performance of the thickness measurements, considering that PPPAAP17 is a parametric array acoustic probe designed for the application of estimating the resources of CRCs.…”

Section: Introductionmentioning

confidence: 99%

China’s First Demonstration of Cobalt-rich Manganese Crust Thickness Measurement in the Western Pacific with a Parametric Acoustic Probe

Hong

Feng

Huang

et al. 2019

Sensors

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Cobalt-rich manganese crusts (CRCs) are important as a potential mineral source that could occur throughout the Pacific on seamounts, ridges, and plateaus. We built a prototype parametric acoustic probe to complete the task of in-situ thickness measurements to estimate the volumetric distribution of deep-sea mineral. The prototype is designed with dual-channels for receiving the primary and secondary signal, which lays a foundation for improving the thickness extraction algorithm. Considering that the signal quality is degraded by the system interference and ambient noise, some improvements to the algorithm are proposed by including the wavelet-based envelope extraction method and the adaptive estimation strategy based on the dual-channel information. Additionally, wavelet regression is applied to reduce the measuring noise assuming that the CRCs have local thickness invariability. The algorithm is suitable for the CRCs with the structure of the multilayers at the top surface and one single layer at the bottom surface. A laboratory experiment is performed to validate the effectiveness of the algorithm. The experiments carried out on the China Ocean 51th voyage in the Western Pacific Ocean on Aug 30, 2018, are described and the data obtained by using the sit-on-bottom stationary measurement are processed to validate the design of the prototype.

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Evaluation of the recognition of Cobalt-Rich Manganese Crusts based on Deep Learning Networks with physical phantoms

Wang

Hong

Feng

et al. 2020

Global Oceans 2020: Singapore – U.S. Gulf Coast

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Automatic Extraction of Thickness Information from Sub-Surface Acoustic Measurements of Manganese Crusts

Cited by 8 publications

References 7 publications

Deep-Sea Robotic Survey and Data Processing Methods for Regional-Scale Estimation of Manganese Crust Distribution

Deep-Sea Robotic Survey and Data Processing Methods for Regional-Scale Estimation of Manganese Crust Distribution

China’s First Demonstration of Cobalt-rich Manganese Crust Thickness Measurement in the Western Pacific with a Parametric Acoustic Probe

Evaluation of the recognition of Cobalt-Rich Manganese Crusts based on Deep Learning Networks with physical phantoms

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