Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

In recent years, supervised-deep-learning methods have shown some advantages over
conventional methods in seismic data denoising, such as higher signal-to-noise ratio after …

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 …

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 …

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to
reliable subsurface properties. However, it is challenging to acquire field data with an …

Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect
fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic …

Solving the wave equation is one of the most (if not the most) fundamental problems we face
as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation …

Building high-fidelity velocity models for salt structures is a valuable and difficult problem in
seismic exploration. Acoustic-based full-waveform inversion (FWI) methods usually produce …

We propose to invert reservoir porosity from poststack seismic data using an innovative
approach based on deep-learning methods. We develop an unsupervised approach to …

Building realistic and reliable models of the subsurface is the primary goal of seismic
imaging. We have constructed an ensemble of convolutional neural networks (CNNs) to …