15 citations · 24 across the 10 of their papers we have counts for
10 papers
Artificial Intelligence for Subsurface Imaging Understanding: A Decade Review of Challenges, Methods, Benchmarks, and Outlook
Yimin Dou, Xinming Wu, Hui Gao +9
Subsurface imaging interpretation bridges observed geophysical data and quantitative geological models, supporting hydrocarbon exploration, CO2 storage assessment, and geohazard mo…
Implicit Structural Modeling via Generative Diffusion Frameworks
Yimin Dou, Xinming Wu, Zhixiang Guo +2
Implicit structural modeling can support understanding subsurface spatial configurations, revealing patterns of geological evolution, and enabling quantitative simulation of geolog…
Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
Yimin Dou, Xinming Wu, Hui Gao +1
Relative Geologic Time (RGT) estimation from seismic data is a cornerstone of subsurface structural modeling, depositional evolution analysis, and reservoir characterization, suppo…
Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems
Hui Gao, Xinming Wu, Jiarun Yang +2
Artificial intelligence (AI) has been increasingly applied to various geophysical scenarios, yet its practical deployment remains limited by scarce field labels, pronounced synthet…
Intelligent Traffic Monitoring with Distributed Acoustic Sensing
Dongzi Xie, Xinming Wu, Zhixiang Guo +3
Distributed Acoustic Sensing (DAS) is promising for traffic monitoring, but its extensive data and sensitivity to vibrations, causing noise, pose computational challenges. To addre…
SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction
Xu Si, Xinming Wu, Hanlin Sheng +2
Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labele…
Seismic Foundation Model (SFM): a new generation deep learning model in geophysics
Hanlin Sheng, Xinming Wu, Xu Si +3
While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geop…
Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)
Xu Si, Xinming Wu, Zefeng Li +2
Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdepende…
Deep learning based sferics recognition for AMT data processing in the dead band
Enhua Jiang, Rujun Chen, Xinming Wu +3
In the audio magnetotellurics (AMT) sounding data processing, the absence of sferic signals in some time ranges typically results in a lack of energy in the AMT dead band, which ma…
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
Luming Liang, Sen Deng, Lionel Gueguen +3
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer…