# Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
###### Abstract
By incorporating features such as bathymetric and backscatter data, as well as angular response and mosaic texture, the accuracy of soil classification has significantly improved. These findings have important implications for the mapping and characterizing of seabed environments, highlighting the potential of AI techniques for future research in marine science. Upon analysis of the current state-of-the-art literature, it is evident that the features utilized in machine learning approaches for the topic of this article rely heavily on essential characteristics such as backscatter intensity as a function of the angle of incidence, mosaic texture, and bathymetry [35; 36; 39; 40; 41; 42]. While these features have proven effective in seabed mapping and classification, it is essential to note that actual time series or spectra backscattered from a point on the seafloor have rarely been utilized. However, a study by [43] demonstrated the potential of using the frequency domain representation of the time series of a reflected chirp sub-bottom profiler signal for distinguishing between sand and sandstone. This approach showed promising results, as the number of crossings of the spectrum at 1/16 of the maximal normalized power classifier demonstrated the ability to assess the probability for sand or sandstone with over 80% certainty in over 75% of the cases. Incorporating this approach into machine learning models for seabed mapping and classification may provide a more comprehensive understanding of the seafloor structures and their associated ecological communities, ultimately leading to the better management and preservation of marine resources. Future research should explore the potential of utilizing time series or spectrum data in machine learning approaches to characterize marine environments, as it may lead to significant advancements in the field. The central hypothesis of the research [43] was that the spectral features of acoustic signals reflected from sand and sandstone sea bottoms are due to essential dissimilarities in the physical properties of these two media. These properties include low-size morphology at the top of both sediment types, and a discrepancy of several meters (depending on the wave period) beneath the reflector boundary. As these singularities are significantly different between sand and sandstone, they are expected to affect the acoustic signals reflected from the top of the sea bottom, thus affecting spectral parameters such as amplitude, main frequency, the frequency-dependent reflection coefficient, number of spikes, etc. However, the main disadvantage of the presented method was the qualitative choice of the spectral parameters used for classification resulting in less-than-optimal accuracy. The present study used the discrete values of the reflected time series and spectra obtained by [43] as training sets for two logistic regression models [44]. This machine learning technique was found to be effective for classifying sand and sandstone. It was shown to equal the results of much more complicated machine learning methods, such as convolutional neural networks, used in previous studies [35; 36; 37; 38; 39; 40; 41; 42]. The paper aims to demonstrate that logistic regression models can ensure accurate man-independent promising results (in terms of accuracy) for soil categorization at the sea bottom. As shown below, the suggested method allows us to accurately classify the sand and sandstone, making it a viable and practical option for categorizing these seabed sediment types. The paper consists of five main sections: Section 2 considers the method of the study, including a description of the experimental setup and data preparation methodology; Section 3 portrays time series and spectral data; Section 4 presents the results of the logistic regression modeling; and Sections 5 and 6 present our discussion and conclusions.