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Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization
Version 1
: Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (08:43:40 CEST)
A peer-reviewed article of this Preprint also exists.
Kushnir, U.; Frid, V. Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization. Appl. Sci. 2023, 13, 8131. Kushnir, U.; Frid, V. Spectrum-Based Logistic Regression Modeling for the Sea Bottom Soil Categorization. Appl. Sci. 2023, 13, 8131.
Abstract
The paper deals with applying Artificial Intelligence techniques to examine CHIRP-recorded data in sand and sandstone sea-bottom sites. The provided analysis of the state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom were rarely used as the features for machine learning models. The results of the examination 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 its combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.
Keywords
marine survey; acoustic reflection; spectral analysis; sediments identification
Subject
Engineering, Marine Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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