Optimizing Neural Network Efficiency by Neuron Pruning

Abstract

This study primarily focuses on two main objectives.  The primary objective is to develop an appropriate model for accurately predicting the missing shear wave data in the Volve Oil Field located in the North Sea. By employing the Multi-Layer Perceptron Regression and modifying the neural network structure, the first goal attains a prediction accuracy of 0.943 for missing S-Wave log data. Furthermore, the objective of the study is to enhance the precision of forecasting incomplete S-Wave log data by optimising the structure of the artificial neural network, using neuron pruning techniques based on sensitivity analysis. This optimisation leads to a heightened accuracy rate of 0.9609. The effectiveness of these pruning strategies is clearly evident in their demonstrated capacity to improve the accurate prediction of missing data in the sonic wave log.