In the field of geophysics, big data, AI, and inverse problems involve cross-disciplinary integration of computer science, mathematics, statistics, and geophysics. This approach enables the development of accurate subsurface property models by analyzing vast amounts of geophysical data. Traditionally, various geophysical methods were used to study geological anomalies, but the use of big data and AI has shown potential to enhance this process. This talk will introduce both model-driven and data-driven inverse problems and explain how optimizing algorithms are used to solve for physical properties of the earth's subsurface from geophysical data collected at the surface.