The field of geophysics is on the cusp of a remarkable paradigm shift, transitioning from traditional physics-based methods and embracing the power of data-driven innovations. This shift is driven primarily by the latest advancements in sensing technologies coupled with machine learning. Multiphysics and distributed acquisition enable the collection of larger, more diverse data sets with increasing spatiotemporal resolution, leaving traditional analysis methods insufficient to harvest information efficiently. Advanced machine learning and new statistical frameworks can extract actionable insights from these increasingly complex data sets. This special section brings to the forefront innovations in seismic analysis, processing, subsurface characterization, and distributed acoustic sensing (DAS) to demonstrate how data-centric approaches reshape the field through new levels of accuracy, efficiency, and adaptability.
Ning et al. (Mon,) studied this question.