Advances in Exploration Geophysics: Integrating Machine Learning with Geophysical Methods

Abstract

This has revolutionized exploration geophysics with the inclusion of machine learning, especially deep learning, with conventional geophysical methods. The paper discusses how machine learning influences the efficiency and accuracy in seismic imaging, gravity and magnetic data inversion, environmental monitoring, extraterrestrial resource exploration, and remote sensing applications. The study confirms that machine learning algorithms improve imaging accuracy in complex geological settings, optimize inversion processes for gravity and magnetic data, enhance real-time environmental monitoring, and advance extraterrestrial resource exploration through a comprehensive literature review and quantitative data analysis. Moreover, the integration of machine learning with remote sensing significantly boosts geophysical data analysis and interpretation. Despite these successes, significant challenges persist with variability of data, algorithm adaptation, and computational cost. Results illustrate the transformative nature of machine learning for geophysics, highlighting the need for future research that will bridge the existing limitations into its wider applicability in geological and extraterrestrial environments.