Abstract
This article presents a methodology to identify key studies using machine learning (ML) in geophysics. We created a comprehensive database of fundamental articles for a systematic review. The main goal is to classify the applications and methods of ML in geophysics through a modified PRISMA approach. The study results offer current insights into the trends and developments of ML applications in geophysics. This article presents a systematic method for identifying and analyzing significant studies that apply ML in geophysics. We created a detailed database of essential articles relevant to this field. Using a modified PRISMA guideline, we conducted a thorough review to evaluate and categorize the literature. The main goal of this review is to provide a clear classification of ML applications and methods in geophysics. We documented how ML techniques are used for various geophysical problems, including data analysis, pattern recognition, and predictive modeling. This classification helps clarify the range of ML applications in geophysics and highlights the specific methods and tools used. Our findings offer an updated view of current trends and developments in ML applications within geophysics. By analyzing the progress of ML and their effectiveness in geophysical applications, this study reveals emerging trends and suggests future research directions.