The massive accumulation of available multi-modal mineral exploration data for most
metallogenic belts worldwide provides abundant information for the discovery of mineral …

Exploration seismology uses reflected and refracted seismic waves, emitted from a
controlled (active) source into the ground, and recorded by an array of seismic sensors …

Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …

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 …

Horizon extraction and fault detection are essential in seismic interpretation and are closely
related to each other. Most existing methods tend to deal with these two tasks …

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

Physics-informed neural networks (PINNs) have recently been utilized to tackle wave
equation-based forward and inverse problems. However, they encounter challenges in …

Recently, a physics-informed neural network (PINN) has been adopted to solve partial
differential equation-based forward and inverse problems. Compared with numerical …

Microseismic monitoring plays an essential role for reservoir characterization and
earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity …

The warming and thawing of permafrost are the primary factors that impact the stability of
embankments in cold regions. However, due to uncertainties in thermal boundaries and soil …