Deep Neural Network-based Current and Voltage Prediction Models for Digital Measuring Unit of Capacitive Resistivity Underground Imaging Transmitter Subsystem

Abstract

Real-time monitoring of output electrical parameters of the transmitted signals in a capacitive resistivity underground imaging system is necessary because these are significant in the calculation of underground resistivity, however, machine learning has not yet been applied in this application to improve the accuracy of measurement. This study aims to develop and select the best prediction models that can be implemented for a digital measuring unit suitable for capacitive resistivity underground imaging. Three deep neural network models namely Elman recurrent neural network (ERNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were explored to build prediction models for the current and voltage of the transmitter circuit. The prediction models' performance was assessed using mean squared error (MSE), which is reduced to its absolute lowest value. The result shows that the best-trained models for current and voltage prediction are the ERNN models with configurations of 900-600-500 hidden neurons network with training MSE of 9.82 X 10 -9 and the configured 1300-1000-900 hidden neurons with training MSE of 0.465, respectively. With the help of the prediction models, it would be possible to measure current and voltage output accurately, allowing simultaneous data acquisition while avoiding the need for a separate measuring device.