# Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
###### Abstract  
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. marine survey; acoustic reflection; spectral analysis; sediments identification +
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been limited to side-scan sonar data, which are inherently limited by the scale of the features they capture, as noted by [27]. Moreover, it should be noted that a single swath of acoustic data may comprise multiple soil types, limiting such methods' applicability. While they may yield valuable insights when applied to large, relatively homogeneous sea bottom regions, they may be less effective when dealing with more complex, heterogeneous areas containing various soil types, such as rock, sand, and clay. Artificial intelligence (AI) techniques have significantly enhanced acoustic data classification in marine science, providing a more objective and efficient approach to seabed mapping. A study by [35] utilized hybrid artificial neural networks incorporating a self-organizing feature map and learning vector quantization to classify four manganese nodule-bearing sites using angular backscatter intensity as the learning and classification features. The resulting model achieved classification accuracies ranging from 87 to 95%. Additionally, refs. [36; 37; 38; 39] utilized a feedforward neural network and convolutional neural networks to predict source charge and bottom composition (mud and sand combinations) for sus charges. The features varied from simulated pressure time series or extracted features, such as peak level, integrated level, signal length, and decay time, to simulated peak pressure and backscatter intensity. The accuracies of the models varied from 84% to 97% for the convolutional neural network used in [37]. These studies illustrate AI algorithms' potential in classifying acoustic data for marine soil characterization. The classification of soil based on multibeam echo sounder (MBES) data has been the focus of recent studies [40; 41; 42]. One such study, [40], applied support vector machine classification to MBES bathymetric and backscatter data to classify mud, sand, and gravel, achieving an accuracy of 90%. Similarly, [41] utilized deep neural networks to classify the same materials from bathymetric and backscatter data that were reduced using fuzzy ranking. This resulted in an accuracy of 86% for soil classification. Another study [42] achieved a classification accuracy of 93% for mud, sand, and gravel combinations using deep learning with backscatter bathymetry and the angular response and mosaic texture of MBES data. These studies demonstrate the efficacy of AI techniques, specifically support vector machines and deep neural networks, in classifying soil based on MBES data.