Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

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

We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …

Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm
for solving problems relating to differential equations. Compared to classical numerical …

Frequency-domain wavefield solutions corresponding to the anisotropic acoustic wave
equation can be used to describe the anisotropic nature of the Earth. To solve a frequency …

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 …

Pressure prediction has long been one of subject of research focuses in petroleum geology
and exploration, but is traditionally limited to moderately overpressured formations due to …

Solving the wave equation to obtain wavefield solutions is an essential step in illuminating
the subsurface using seismic imaging and waveform inversion methods. Here, we utilize a …

高效高精度的反演算法在重力大数据时代背景下显得尤为重要, 受深度学习卓越的非线性映射
能力的启发, 本文提出了一种基于深度学习的重力异常及重力梯度异常的联合反演方法 …

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization
problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented …