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The present analysis of state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom are rarely used as features for machine learning models. The paper deals with the artificial intelligence techniques used to examine CHIRP-recorded data. The data were collected using a CHIRP sub-bottom profiler to study two sand bottom sites and two sandstone bottom sites in the offshore zone of Ashqelon City (Southern Israel). The first reflection time series and spectra of all the traces from the four sites generated two training and two test sets. Two logistic regression models were trained using the training sets and evaluated for accuracy using the test sets. The examination results indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms and the advantages of their combination with other types of data have great potential for future research and the enhancement of remote marine soil classification. [ABSTRACT FROM AUTHOR]

Modern studies which dealt with the frequency domain analysis showed that a frequency-domain approach has an essential advantage and mentioned an inner qualitative relationship between the subsurface structure and its frequency spectra. This paper deals with the acoustic spectral response of sand and sandstone sediments at the sea bottom. An acoustic data collection campaign was conducted over two sand sites and two sandstone sites. The analysis of the results shows that reflections of acoustic signals from sand and sandstone sea bottom are characterized by various spectral features in the 2.75–6.75 kHz range. The differences in acoustic response of sand and sandstone can be quantified by examining the maximal normalized reflected power, the mean frequency, and the number of crossings at different power levels. The statistical value distribution of these potential classifiers was calculated and analyzed. These classifiers, and especially the roughness of the spectrum quantified by the number of crossings parameter can give information to assess the probability for sand or sandstone based on the reflected spectra and be used for actual distinction between sand and sandstone in sub bottom profiler data collection campaigns. [ABSTRACT FROM AUTHOR]

The present analysis of state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom are rarely used as features for machine learning models. The paper deals with the artificial intelligence techniques used to examine CHIRP-recorded data. The data were collected using a CHIRP sub-bottom profiler to study two sand bottom sites and two sandstone bottom sites in the offshore zone of Ashqelon City (Southern Israel). The first reflection time series and spectra of all the traces from the four sites generated two training and two test sets. Two logistic regression models were trained using the training sets and evaluated for accuracy using the test sets. The examination results indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms and the advantages of their combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.

Modern studies which dealt with the frequency domain analysis showed that a frequency-domain approach has an essential advantage and mentioned an inner qualitative relationship between the subsurface structure and its frequency spectra. This paper deals with the acoustic spectral response of sand and sandstone sediments at the sea bottom. An acoustic data collection campaign was conducted over two sand sites and two sandstone sites. The analysis of the results shows that reflections of acoustic signals from sand and sandstone sea bottom are characterized by various spectral features in the 2.75–6.75 kHz range. The differences in acoustic response of sand and sandstone can be quantified by examining the maximal normalized reflected power, the mean frequency, and the number of crossings at different power levels. The statistical value distribution of these potential classifiers was calculated and analyzed. These classifiers, and especially the roughness of the spectrum quantified by the number of crossings parameter can give information to assess the probability for sand or sandstone based on the reflected spectra and be used for actual distinction between sand and sandstone in sub bottom profiler data collection campaigns.