Artificial Intelligence Computational Methods for Geophysical Subsurface Imaging and Monitoring

The field of geophysics is uniquely positioned to benefit from the rapid evolution of machine learning (ML) techniques, which have shown significant promise in data processing, modeling, and interpretation across various scientific disciplines. With the vast and intricate datasets characteristic of geophysical research, often marked by noise, incompleteness, and heterogeneity, ML has emerged as a valuable tool. It facilitates the extraction of meaningful insights from complex data, thereby enhancing the quality of imaging and monitoring processes. Existing studies highlight the potential of ML to transform traditional geophysical methodologies by offering innovative means for data acquisition and analysis, yet challenges surrounding data integrity and model robustness remain areas of ongoing inquiry.

This research topic aims to investigate the current applications and frontier developments of machine learning within geophysics, with a particular focus on advancing subsurface imaging and monitoring. By welcoming contributions from fields such as seismology, exploration geophysics, geodynamics, and geodesy, this topic strives to present a comprehensive overview of how ML is reshaping geophysical research. Central goals include assessing the effectiveness of new deep learning architectures, integration of physical knowledge into ML methodologies, and addressing issues of uncertainty and model robustness. Furthermore, the broader implications of transparency and reproducibility in ML applications for geophysics are pivotal aspects to explore.

To gather further insights into the integration of ML in geophysical research, we welcome articles addressing, but not limited to, the following themes:

o Innovations in seismic imaging and monitoring

o Applications of ML in geodetic, gravitational, and magnetic studies

o ML-driven hazard assessment and inversion technologies

o Development of open-source software and training datasets

o Challenges and future directions for ML in geophysics

Contributions are encouraged from diverse article types, ensuring a rich dialogue that advances the understanding and application of ML in exploring the Earth's subsurface.