# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
###### Abstract
[13] found that a fundamental challenge remained: the acoustic response was still influenced by factors external to the sediment properties, such as water column variations and incident angle dependencies. In summary, seafloor soil classification is critical for marine engineering, environmental monitoring, and geological surveys. Traditional classification methods, such as physical sampling and acoustic backscatter analysis, face persistent challenges. Physical sampling methods, including grab sampling and core drilling, provide high-resolution grain-size distribution data but are time-consuming, labor-intensive, and limited in spatial coverage [10]. Acoustic-based techniques have been developed as efficient remote-sensing alternatives, including side-scan sonar (SSS) and multibeam echo sounders (MBESs). Still, they rely primarily on backscatter intensity, which is affected by environmental variables such as grazing angles, water column properties, and sediment roughness [11; 14]. As a result, these methods struggle to differentiate sediments with similar grain sizes and composition, leading to classification inconsistencies [15; 17]. Recent research has explored spectral-based methods as an alternative to seabed classification. Studies by [21; 22] have demonstrated the potential of spectral acoustic analysis to distinguish between sand and sandstone using frequency-dependent backscatter characteristics. However, prior studies have focused on differentiating rock from sediment rather than more subtly classifying soils with similar grain sizes but different grading properties. Additionally, many spectral studies lack controlled experimental conditions, making it difficult to isolate the intrinsic acoustic response of sediments from external environmental influences. Hence, a systematic investigation is needed to assess whether spectral acoustic analysis can reliably classify sedimentary soils with subtle physical differences. Specifically, the research should focus on isolating intrinsic spectral characteristics in controlled environments and establishing spectral fingerprints for different soil types. The primary objective of this study is to develop a non-invasive, spectral-based approach for soil classification in aquatic environments. This study aims to achieve the following:  
* Establish distinct spectral fingerprints for poorly graded sand (SP) and poorly graded gravel (GP) using frequency-dependent acoustic reflection coefficients. * Evaluate the reliability of spectral analysis in distinguishing soil types with similar grain sizes but different grading properties. Develop a controlled experimental methodology that eliminates environmental variables and isolates sediment-specific spectral responses. Lay the foundation for automated classification by providing spectral data that can be integrated with machine learning models for seabed characterization. By achieving these objectives, this research will advance seabed classification techniques, providing a more accurate and reproducible alternative to traditional acoustic intensity-based methods. To achieve the outlined goal, this study will undertake the following key tasks:  
Experimental Setup in a Controlled Environment  
Conduct spectral acoustic measurements in a freshwater tank to eliminate water column distortions. Use calibrated transducers to measure frequency-dependent acoustic reflection in the 100-400 kHz range. Comparative Spectral Analysis of SP and GP Soils  
Measure and analyze the spectral reflection properties of poorly graded sand (SP) and poorly graded gravel (GP). Identify and quantify the spectral differences between the two soil types. Validation of Spectral Signatures for Classification  
Evaluate the repeatability and robustness of spectral fingerprints. Assess whether the spectral differences provide a reliable classification criterion. Assessment of Potential for Automated Classification  
Explore how spectral reflection coefficients can be used as input features for machine learning models in future classification frameworks.