Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

Seismic inversion is one of the fundamental techniques for solving geophysics problems. To
obtain the elastic parameters or petrophysical parameters, it is necessary to establish a …

Mineral prospectivity mapping (MPM) is designed to reduce the exploration search space by
combining and analyzing geological prospecting big data. Such geological big data are too …

Numerical simulations of seismic wave propagation in heterogeneous 3-D media are central
to investigating subsurface structures and understanding earthquake processes, yet are …

Full-waveform inversion (FWI) is an efficient technique for capturing the subsurface physical
features by iteratively minimizing the misfit between simulated and observed seismograms …

Seismic inversion, complemented by machine learning algorithms, significantly improves the
accuracy and efficiency of subsurface parameter estimation from seismic data. In this …

Forward modeling of seismic waves using physics-informed neural networks (PINNs) has
attracted much attention. However, a notable challenge arises when modeling seismic wave …

Accurate ocean environment perception is crucial for weather and climate prediction.
Environmental limitations and deployment costs constrain satellite and buoy real-time …

As deep learning (DL) gains popularity for its ability to make accurate predictions in various
fields, its applications in geosciences are also on the rise. Many studies focus on achieving …