Implementation of random forest in geo-acoustic study

Abstract

The correlation between sediment sound velocity (V) and physical properties has been studied for 60 years using empirical formulas and found to be difficult to predict V accurately. Random forest (RF) is a scientific discipline and a method of data analysis that automates analytical model building. Here, we present the implementation of the RF algorithm in V prediction and sediment classification. The goal of this study is to establish a predictive model based on RF using multiple physical parameters (mean grain size, porosity, wet bulk density, and water content). Compared to empirical formulas, the average error of RF velocity is only 0.95%, ranging from 0.03% to 2.73%, which has improved the accuracy of V prediction. We also used Mean Decrease Impurity importance to evaluate the importance of a variable and found that the most important feature in the predictive model is the mean grain size. The classification model based on RF reaching up to 75% accuracy in the dataset. Multiple features, such as physical properties, sedimentary environment, and sediment source, affect the geo-acoustic properties of sediments. The next goal is to use multiple features to improve the model and further improve the accuracy of sound velocity prediction and sediment classification.