Machine Learning Approaches for Geophysical Data Analysis

Dear Colleagues,

Geophysical data lie in the central part of geophysical studies and provide the basis for gaining insights into the underlying fundamental principles. However, geophysical data are often complex, noisy and difficult to evaluate, making their analysis and interpretation very challenging. In addition, with the growing availability of geophysical data from different instruments and sources, the need for innovative data analysis methods has become increasingly pressing.

The recent surge in machine learning studies and applications shows it is a powerful tool for extracting valuable information and insights from complex datasets. Due to the nature of big data, machine learning provides a promising avenue for enhancing the accuracy and efficiency of geophysical data analysis. The use of machine learning in geophysics has already led to significant advances in the fields of seismology, geodesy, environmental science, and geo-energy exploration, among others.

In this context, we are pleased to announce a Special Issue on "Machine Learning Approaches for Geophysical Data Analysis". This Special Issue aims to explore the growing role of machine learning techniques in geophysics and their potential to transform the way geophysical data are analyzed and interpreted. This Special Issue seeks to bring together researchers from both geophysics and machine learning communities to share their expertise, present their research findings, and promote collaborations in this exciting and rapidly evolving field.

This Special Issue invites original research articles, reviews, and case studies that demonstrate the application of machine learning techniques in geophysical data processing and analysis, such as in the area of earthquake seismology, exploration geophysics, geothermal and carbon sequestration, geological mapping, environmental monitoring, and more. We invite all researchers working in related areas to submit their manuscripts and contribute to this Special Issue. Topics of interest for this Special Issue include but are not limited to:

• Machine learning for geophysical data interpretation;
• Data-driven geophysical imaging and inversion techniques;
• Feature extraction and dimensionality reduction for geophysical data;
• Data fusion and integration of different geophysical data via machine learning;
• Uncertainty quantification and data-driven modeling in geophysics;
• Deep learning for seismic interpretation and reservoir characterization;
• Machine learning for environmental monitoring and hazard assessment;
• Hybrid approaches combining machine learning with physics-based modeling;
• Machine learning for geospatial data analysis and integration;
• Machine learning for geophysical survey optimization;
• Machine learning for rock physics modeling;
• Transfer learning for geophysical data analysis.

We are confident that this Special Issue will provide a valuable and timely platform for researchers to share their latest findings and insights on the application of machine learning in geophysics. We look forward to receiving high-quality contributions that will help advances in this relevant field.

Dr. José A. Peláez
Dr. Peidong Shi
Prof. Dr. Sanyi Yuan
Guest Editors

Manuscript Submission Information

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