Abstract
How to improve the prediction accuracy of compressional wave speed has always been one of the basic research subjects in geoacoustics study field. Due to the stability of granularity, whether in the laboratory or in the seabed environment, the regression relationship between compressional wave speed and granularity is an important sound speed inversion method. Machine Learning (ML) provides a new solution for more efficient sound speed prediction systems. In this study, two ML algorithm, Random forest (RF) and Support Vector Regression (SVR), combined with nine granularity parameters (mean grain size, median grain size, skewness, kurtosis, sorting coefficient, gravel, sand, silt, and clay content respectively.) to analysis the effect of granularity property on sound speed. As a result, the sound speed-granularity predictive models were established, and the sound speed accuracy obtained based on the predictive models are higher than that of the regression equations, and the RF model has a higher accuracy than the SVR model. Based on the RF predictive model, the feature selection was conducted and the results show that the most influential parameter of granularity is mean grain size. Furthermore, the RF model can also predict the sound speed with high precision in the absence of partial parameters, which can be a useful tool for ocean engineering and seismic inversion.
Highlights
The sediment acoustic properties study is important in the underwater acoustic and geo-engineering field
The comparisons indicated that the correlation coefficient of Random forest (RF) and Support Vector Regression (SVR) model is higher than regression equations, and the standard deviation (STDEV) and root mean square error (RMSE) are lower than regression equations, which means that both RF and SVR have a higher accuracy than regression equations
The two Machine Learning (ML)(RF and SVR) algorithm were used with nine granularity indexes as inputs to establish a model for predicting the sound speed in seafloor sediment
Summary
The sediment acoustic properties study is important in the underwater acoustic and geo-engineering field. In underwater acoustic and seafloor classification measurements, the compressional wave speed (hereafter referred to as the sound speed) is dependent on the properties of the sedimentary system. Many factors can affect the sound speed of unconsolidated seafloor sediments, including the physical properties, sedimentary environment, The associate editor coordinating the review of this manuscript and approving it for publication was Xiang Huang. The physical properties are considered the most important [10], [17], especially the granularity, which is described by factors such as the mean grain size, median grain size, grain fraction, and sorting coefficient. As the grain size keep unchanged when the sediment was collected from the seafloor to the laboratory, the relationship between sound speed and granularity has long been an important topic of sediment acoustic study [2]
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