Prediction of Rock and Mineral from Sound Navigation and Ranging Waves using Artificial Intelligence Techniques

Abstract

Since sound waves penetrate the sea more deeply than radar and light waves, SONAR (Sound Navigation and Ranging) is used to explore and map the ocean. When working in the mining industry, engineers may find SONAR to be an invaluable tool for helping them visualize the location of rocks and minerals by charting frequency signals. Assuming an object is within the sound pulse's range, the sound pulse will reflect off the target and send an echo in the direction of the sonar transmitter if the target is within the range of the sound pulse. The transmitter uses the power source to receive signals and figure out how strong the signals are. It establishes the pause in time between the generation of the pulse and the receiving of its corresponding signal. It analyzes the duration between the emission of the pulse and its matching reception, which estimates the distance and location of the matter. Engineers determine the item using an audio wave. With the assistance of AI, the process of evaluating, organizing, and identifying the item is going to be circumvented in order to accomplish the objectives of immediately identifying the item based on the scheduled bandwidths. In this proposed method, PCA and t-SNE are employed to extract features. Utilizing classification approaches such as Logistic Regression and Random Forest Tree, an accuracy of 72% and 91%, respectively, was attained. Similarly, CNN and LSTM models are also employed and finally they have yielded an accuracy of about 80.77% and 99% respectively.