Preference of echo features for classification of seafloor sediments using neural networks

De, Chanchal; Chakraborty, Bhaskar

doi:10.1007/s11001-010-9101-1

Cited by 7 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, a first-order correction was applied to remove the influence of the depth on the time spread. The time spread of the echo envelope was multiplied by a factor h ref /h, where h ref is a reference depth of 50 m (approximate average of all the spot depths) and h is the depth at the position of the individual echo data (De and Chakraborty, 2010). The procedure followed is equivalent to the depthdependent correction described by van Walree et al (2005).…”

Section: Depth-dependent Correctionmentioning

confidence: 99%

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

Haris

Chakraborty

2014

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. Dual-frequency echo-envelope data acquired using the normal-incidence single-beam echosounder system (SBES) have been examined to study its scale invariant properties. The scaling and multifractality of the SBES echo envelopes (at 33 and 210 kHz) were validated by applying a stochastic-based multifractal analysis technique. The analyses carried out substantiate the hierarchy of multiplicative cascade dynamics in the echo envelopes, demonstrating a first-order multifractal phase transition. The resulting scale invariant parameters (α, C 1 , and H ) establish gainful information that can facilitate distinctive delineation of the sediment provinces in the central part of the western continental shelf of India. The universal multifractal parameters among the coarse and fine sediments exhibit subtle difference in α and H , whereas the codimension parameter C 1 representing the sparseness of the data varies. The C 1 values are well clustered at both the acoustic frequencies, demarcating the coarse and fine sediment provinces. Statistically significant correlations are noticeable between the computed C 1 values and the ground truth sediment information. The variations in the multifractal parameters and their behavior with respect to the ground truth sediment information are in good corroboration with the previously estimated sediment geoacoustic inversion results obtained at the same locations.

show abstract

Section: Depth-dependent Correctionmentioning

confidence: 99%

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

Haris

Chakraborty

2014

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

show abstract

“…In their recent research, De and Chakraborty (2010, 2011, 2012 discussed the estimation of mean grain sizes of mixed seabed sediments using dual-frequency single beam data and neural network, but they did not use multi-beam echo sounder BS data to do seabed mixed sediment classification.…”

Section: Introductionmentioning

confidence: 99%

Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

Tang

Lei

et al. 2014

Marine Georesources & Geotechnology

View full text Add to dashboard Cite

The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay.

show abstract

Estimation of mean grain size of seafloor sediments using neural network

Chakraborty

2012

Mar Geophys Res

View full text Add to dashboard Cite

Preference of echo features for classification of seafloor sediments using neural networks

Cited by 7 publications

References 22 publications

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

Estimation of mean grain size of seafloor sediments using neural network

Contact Info

Product

Resources

About