This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

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

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 …

Accurate prediction of groundwater level (GWL) over a period of time is of great importance
for groundwater resources management. Machine learning techniques due to their great …

Faults divide the strata into discontinuous blocks, making describing the spatial relationship
between geological bodies more difficult. However, the literature still lacks simplification …

Borehole data are essential for conducting precise urban geological surveys and large-
scale geological investigations. Traditionally, explicit modelling and implicit modelling have …

Geological modeling commonly results in a single prescribed geometric representation of
the subsurface with no consideration of uncertainties. Accounting for uncertainties is of …

Implicit structural modeling using sparse and unevenly distributed data is essential for
various scientific and societal purposes ranging from natural source exploration to …

The cross section is the basic data for building 3D geological models. It is inefficient to draw
a large number of cross sections to build an accurate model. This paper reports the use of …

The performance of regional groundwater level (GWL) prediction model hinges on
understanding intricate spatiotemporal correlations among monitoring wells. In this study, a …