Acoustic Classification with Single-Beam Echosounders: Processing Methods and Theory for Isolating Effects of the Seabed on Echoes

Biffard, Ben; Preston, J.M.; Chapman, N. Ross

doi:10.1109/oceans.2007.4449250

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…Many studies, such as those performed by [8], [9], [10], [11], [12], [13] and others showed that manipulating some properties of the bottom reflected signal (eg. scattered energy, frequency spectrum and wavelet shape) allows the prediction of seafloor properties, related to the bottom and subbottom sedimentary or rocky layers.…”

Section: Introductionmentioning

confidence: 99%

Case study on sediment classification using seismic attributes and 2d high-resolution seismic data

Santana

Neto

2013

2013 IEEE/OES Acoustics in Underwater Geosciences Symposium

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This work has the purpose to understand the relation that are between seismic attributes extracted from 2D high-resolution seismic data and the sediment surveyed. To accomplish this goal, we performed a research using 2D highresolution seismic data acquired on the Continental Shelf of the Antarctic Peninsula, nearby the South Shetland Islands, and sediment samples acquired with gravity corer overlapping each seismic line surveyed. The geologic samples were used as control samples to make the binding between seismic traces -sediment cores and to fulfill the sediment classification software training outline. One computational script was written to extract the seismic attributes from the seismic data and in sequence the attributes were statistically analyzed. The attributes were used as variables during the statistical process and they were: Amplitude (AMP), Instantaneous Phase (PHI), Instantaneous Frequency (FREQ), Envelope (ENV), Time Derivative of the Envelope (DTENV), Second Derivative of the Envelope (DT2ENV) and Acceleration of Phase (ACPHI). The statistical analyses were: Principal Components (PCA), Dendrograms and K-MeansClassification. Four kinds of seismic attributes groups were coherently formed on statistical analyses. In theory basis, the PHI shouldn't be grouped with any other attribute because of its physical behavior completely distinct from the others attributes here studied. The sets of attributes formed that presented ACPHI were absolutely coherent, in a way that all others attributes grouped within ACPHI present the same physical and spatial behavior such as DTENV, AMP, ENV and FREQ, this is due to the absorption of both the amplitude and frequency of the wave propagated through the medium. Therefore, it is believed that both more coherent sets of attributes are AMP and ENV identified by all the statistical analyses and DTENV and ACPHI formed by the PCA and Dendrograms analyses, and are both well supported by the other analyses that assured an intrinsic physical and spatial relation between the grouped attributes. The subsequent step on the Semi-Automatic Sediment Classification Process using 2D high-resolution seismic data were to prepare the set of attributes that grouped coherently, to identify the range of values of each attribute grouped that correspond to the location of each core sample, locate on the reflectors in the seismic section where those ranges of values from the set of attributes are found at the same place and finally suppose that on these regions identified by the script we have the same kind of sediment found on the geological samples.

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Section: Introductionmentioning

confidence: 99%

Case study on sediment classification using seismic attributes and 2d high-resolution seismic data

Santana

Neto

2013

2013 IEEE/OES Acoustics in Underwater Geosciences Symposium

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“…This comment letter was written to address the claim in [1] that the classification method therein could "be used as an alternative to the Quester Tangent Corporation (QTC) commercial software." The only references in [1] to commercial software are from 1999 and 2001 and there have been many advances [2]- [8] implemented since then, particularly in depth compensation and objective clustering. In this comment letter, we review these two, compare them to the methods presented in [1], and add some other comments.…”

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confidence: 99%

“…An echo starts when the initial part of the transmit pulse reflects from the seabed and returns to the transducer; it ends when the final part of the transmit pulse returns to the transducer after reflecting from the deepest significant scatterer in the sediment at the edge of the beamwidth [2], [3]. One contribution to echo duration is the spreading time along the seabed from the point of first contact to the beamwidth edge, and this contribution is proportional to water depth.…”

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confidence: 99%

“…Amiri-Simkooei et al [1] so is inappropriate for the depth-independent contributions. An algorithm that considers the nature of each contribution is based on the standard echo length (SEL) for the pulse length and beamwidth of the sounder in use and for a selected sediment [2]. For typical echoes in [1], SEL resampling ratios (time compression or expansion) are much smaller than the ratio of depths because the depth-independent contributions are larger than the spreading time.…”

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confidence: 99%

“…This is not always true. Details of SEL-based depth compensation have been published in [2]- [4]; objective clustering has been described in [5] and [7]; a list of features was presented in [8]; and a full summary of QTC classification methods can be found in [7].…”

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Comments on “Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification” by A. R. Amiri-Simkooei, M. Snellen, and D. G. Simons

Preston

Biffard

2012

IEEE J. Oceanic Eng.

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Acoustic seabed classification with single-beam sounders is both an established process and a research topic. A recent paper (IEEE JOURNAL OF OCEANIC ENGINEERING, vol. 36, no. 2, pp. 259-271, Apr. 2011) summarizes some popular techniques, demonstrating that they can generate realistic sediment maps. In the conclusion of that paper, it is claimed that the algorithms therein can substitute for a widely used commercial software suite. This claim is not correct. Among other issues, the techniques in that paper fall short of current practice in two areas: compensation for varying water depths and choosing the best number of clusters in unsupervised classification.Index Terms-Classification algorithms, principal component analysis (PCA), single-beam echo sounder.

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RETRACTED ARTICLE: Research on North Gulf distributed big data submarine 3D terrain computing system based on remote sensing and multi-beam

Dong

Zhang

et al. 2019

Soft Comput

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Acoustic Classification with Single-Beam Echosounders: Processing Methods and Theory for Isolating Effects of the Seabed on Echoes

Cited by 12 publications

References 6 publications

Case study on sediment classification using seismic attributes and 2d high-resolution seismic data

Case study on sediment classification using seismic attributes and 2d high-resolution seismic data

Comments on “Principal Component Analysis of Single-Beam Echo-Sounder Signal Features for Seafloor Classification” by A. R. Amiri-Simkooei, M. Snellen, and D. G. Simons

RETRACTED ARTICLE: Research on North Gulf distributed big data submarine 3D terrain computing system based on remote sensing and multi-beam

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