Application of machine learning classification algorithms to predict probability of channel presence from seismic data

The article analyzes the application of various classification algorithms (Random Forest, Extra Trees, Gradient Boosting, Multi-layer Perceptron, Gaussian Naive Bayes, Voting Classifier) based on supervised machine learning, both for individual seismic attributes (amplitude, phase, frequency) and for a set of seismic attributes calculated in the interval of channel system development for probabilistic assessment of presence of channels. The object of the study is the deposits of the Tyumen suite of a group of deposits located in the Khanty-Mansiysk Autonomous Okrug. The purpose of this work is to find the most effective seismic attributes and machine learning classification methods to optimize the methodology and improve the accuracy of forecasting the probability of channel system presence.