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

针对塔里木, 四川和鄂尔多斯盆地海相深层油气富集机理与关键工程技术基础研究的需求,
国家自然科学基金委员会于2020 年初启动了企业创新发展联合基金集成项目 …

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 (LF) signal content in seismic data as well as a realistic initial model are key
ingredients for robust and efficient full-waveform inversions (FWIs). However, acquiring LF …

The natural damping effect of the Earth typically results in significant distortion of seismic
waveforms, which greatly diminishes the precise of subsequent processes such as …

In this study, we developed a novel 18-layers residual full convolutional neural network
(18RFCN) for audio magnetotellurics (AMT) data inversion. Different from traditional …

Full waveform inversion (FWI) is capable of generating high‐resolution subsurface
parameter models, but it is susceptible to cycle‐skipping when the data lack low‐frequency …

Deep learning is a promising approach to velocity model building because it has the
potential of processing large seismic surveys with minimal resources. By leveraging large …

Seismic inversion is an indispensable part of the earth exploration to precisely obtain the
properties of subsurface media based on seismic data. However, the lack or inaccuracy of …

Machine-learning (ML) applications in seismic exploration are growing faster than
applications in other industry fields, mainly due to the large amount of acquired data for the …