# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
###### Abstract  
Seafloor soil classification is essential for marine engineering, environmental monitoring, and geological surveys. Traditional classification methods, such as physical sampling and acoustic backscatter analysis, have inherent limitations, including spatial constraints and inconsistencies in distinguishing sediments with similar acoustic properties. This study uses frequency-dependent acoustic reflection coefficients to investigate a novel spectral-based approach to seabed soil classification. Experiments were conducted in a controlled aquatic environment to isolate the spectral characteristics of two soil types: poorly graded sand (SP) and poorly graded gravel (GP). The research employed calibrated transducers to measure reflection coefficients across the 100-400 kHz frequency range, allowing for a comparative spectral analysis between the two sediments. The results demonstrate that SP and GP exhibit distinct spectral fingerprints, with SP showing higher reflectance across all measured frequencies, while GP displays a more variable spectral response. These findings suggest that frequency-dependent reflectance provides a more sensitive and accurate classification criterion than conventional backscatter intensity analysis. By eliminating environmental variability and focusing on intrinsic soil properties, this study establishes a foundation for automated, non-invasive classification methods that could be integrated into machine learning frameworks for real-time seabed characterization. The proposed methodology enhances the precision of remote sensing techniques and presents significant advantages in offshore engineering, environmental monitoring, and hydrographic surveys. Future research should extend this approach to diverse sediment types and open marine environments to refine and validate its applicability in real-world scenarios. acoustic reflection; seafloor classification; frequency-dependent analysis; spectral soil characterization; remote sensing; marine engineering +
Footnote †: journal: Applied sciences  
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through extensive field research that physical sampling methods allow precise grain-size distribution analysis and detailed soil characterization. They further showed that acoustic backscatter data can reveal seabed composition patterns, highlighting the potential of remote acoustic sensing as a complementary tool for broader seabed classification. These constraints prompted the development of remote sensing techniques, specifically acoustic methods, for more efficient seafloor classification. A notable effort to expand acoustic seafloor classification capabilities involved using side-scan sonar (SSS), which provides extensive spatial coverage of the seafloor and is commonly used in habitat mapping applications. The Coastal Water Habitat Mapping (CWHM) project, as reviewed by [PERSON] and [PERSON] [11], emphasized the potential of SSS as a cost-effective tool for large-scale seabed classification. Unlike single-beam systems, SSS generates high-resolution imagery that can infer seafloor characteristics based on the acoustic texture. However, despite its advantages in providing broad coverage, [PERSON] and [PERSON] [11] highlighted the key limitations of SSS, particularly its dependence on visual interpretation rather than direct quantitative classification. They noted that while SSS is effective for identifying large-scale geological structures, it struggles to distinguish between sediment types with similar acoustic signatures. Moreover, its performance is highly influenced by factors such as grazing angle, water column properties, and seafloor roughness, leading to inconsistencies in classification. These findings underscore the ongoing challenge of achieving reliable sediment differentiation using acoustic backscatter alone and highlight the need for complementary classification methods that integrate spectral or statistical approaches.