Statistical grouping of acoustic reflection profiles

Milligan, S. D.

doi:10.1121/1.382045

Cited by 16 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Milligan et al 2 showed that in their experiment the largest characteristic root of the covariance matrix accounted for 97% of the observed variance. This means that almost all of pure reflectivity of the bottom can be reconstructed from the first principal component.…”

Section: ͑1͒mentioning

confidence: 98%

“…This is especially true when a pressure reflection coefficient of the bottom is directly measured. Even the statistical grouping method of Milligan et al 2 needed such correction to obtain mean acoustic reflection. The KL transform dictates that if X consists of M traces equal to within a scale factor, X can be perfectly reconstructed by the first eigenimage 1 u 1 v 1 T .…”

Section: ͑1͒mentioning

confidence: 99%

“…Earlier studies used monochromatic sonar data for the direct measurement of a pressure reflection coefficient of the sea floor. 1 Milligan et al 2 introduced a statistical approach using the Karhunen-Loeve ͑KL͒ transform. Instead of measuring the reflection coefficient, they utilized the entire acoustic echo signal from the sea floor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seabed classification from acoustic profiling data using the similarity index

Kim

Chang

Jou

et al. 2002

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

We introduce the similarity index (SI) for the classification of the sea floor from acoustic profiling data. The essential part of our approach is the singular value decomposition of the data to extract a signal coherent trace-to-trace using the Karhunen-Loeve transform. SI is defined as the percentage of the energy of the coherent part contained in the bottom return signals. Important aspects of SI are that it is easily computed and that it represents the textural roughness of the sea floor as a function of grain size, hardness, and a degree of sediment sorting. In a real data example, we classified a section of the sea floor off Cheju Island south of the Korean Peninsula and compared the result with the sedimentology defined from direct sediment sampling and side scan sonar records. The comparison shows that SI can efficiently discriminate the bottom properties by delineating sediment-type boundaries and transition zones in more detail. Therefore, we propose that SI is an effective parameter for geoacoustic modeling.

show abstract

Section: ͑1͒mentioning

confidence: 98%

Section: ͑1͒mentioning

confidence: 99%

See 1 more Smart Citation

Seabed classification from acoustic profiling data using the similarity index

Kim

Chang

Jou

et al. 2002

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…These studies as well as the one to be reported used the Huntec deep tow seismic (DTS) system, consisting of a 400 J boomer that emits a broadband pulse of useful frequency range (1-12 kHz). The earlier studies of Dunsiger and MacIsaac and Parrott et al and the promising independent studies by Milligan et al (1978) and LeBlanc et al (1980) do not utilize the explicit frequency resolution possible with a broadband source.…”

Section: Introductionmentioning

confidence: 94%

Remote acoustic classification of marine sediments with application to offshore Newfoundland

Cochrane¹,

Dunsiger²

1983

Can. J. Earth Sci.

View full text Add to dashboard Cite

Shallow marine sediments can be remotely classified by the spatial correlation properties of their seismic reflection signatures provided one uses a highly repetitive broadband acoustic source. A classification scheme defined by three spatial coherence parameters is shown capable of automatically differentiating between several formations of unconsolidated sediments in a limited area of offshore Newfoundland. The consistency and generality of the technique are explored and comparisons with standard echograrn interpretation are made.Les formations marines skdimentaires h faible profondeur peuvent &tre classifikes B distance par corrklation dans l'espace de propriCtCs Cmanant des profils de sismique rkflexion pourvu que l'on utilise une source sonore h large bande fidklement rkpktitive. Une classification mktrique dkfinie par trois parametres de cohCrence spatiale permet automatiquement de distinguer entre elles plusieurs formations de skdiments nonconsolidCs dans une rCgion d'ttendue IimitCe sur I'offshore de Terre-Neuve. L'uniformitC des rCsultats et l'application gCnkraliske de la technique sont Ctudikes et des comparisons sont faites avec llinterprCtation d'kchogrammes types.[Traduit par le journal]Can.

show abstract

“…This is equivalent to methods used on echo sounder data for seabed classification (Milligan et al 1978). However, the clustering method is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Classification of acoustically stable areas using empirical orthogonal functions

et al. 2011

View full text Add to dashboard Cite

Sonar performance modeling is crucial for submarine and anti-submarine operations. The validity of sonar performance models is generally limited by environmental uncertainty, and particularly uncertainty in the vertical sound speed profile (SSP). Rapid environmental assessment (REA) products, such as oceanographic surveys and ocean models may be used to reduce this uncertainty prior to sonar operations. Empirical orthogonal functions (EOF) applied on the SSPs inherently take into account the vertical gradients and therefore the acoustic properties. We present a method that employs EOFs and a grouping algorithm to divide a large group of SSPs from an ocean model simulation into smaller groups with similar SSP characteristics. Such groups are henceforth called acoustically stable groups. Each group represents a subset in space and time within the ocean model domain. Regions with low acoustic variability contain large and geographically contiguous acoustically stable groups. In contrast, small or fragmented acoustically stable groups are found in regions with high acoustic variability. The main output is a map of the group distribution. This is a REA product in itself, but the map may also be used as a planning aid for REA survey missions.

show abstract

Statistical grouping of acoustic reflection profiles

Cited by 16 publications

References 0 publications

Seabed classification from acoustic profiling data using the similarity index

Seabed classification from acoustic profiling data using the similarity index

Remote acoustic classification of marine sediments with application to offshore Newfoundland

Classification of acoustically stable areas using empirical orthogonal functions

Contact Info

Product

Resources

About