Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm

Machine learning provides numerous data-driven tools for automatic pattern recognition. Even though various algorithms such as neural networks and support vector machines have been widely applied, it is still necessary to explore new paradigms and algorithms to improve the machine learning assisted seismic interpretation. Random Forest (RF) is a widely used ensemble algorithm, however, only limited studies of random forest in the seismic application were published. In this article, the methodology of random forest is introduced systematically. Meanwhile, to solve the problem of hyper-parameter determination, we propose an improved algorithm named Pruning Random Forest (PRF). To reveal the advantages of PRF in terms of predictive performance, robustness, and feature selection compared with support vector machine, neural network, and decision tree, several well-designed experiments are executed based on the seismic data of the western Bohai Sea of China. The potential and advantages of random forest in the present case are confirmed by various experiments, which substantiates that the proposed pruning random forest algorithm provides a reliable alternative way for further machine learning assisted seismic interpretation.