activity
20232026
most citedSeismic Foundation Model (SFM): a new generation deep learning model in geophysics

15 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

physics.geo-ph2026

Massive-scale unlabeled field and labeled synthetic seismic datasets of global shelf-edge clinothems

Hui Gao, Xinming Wu, Jintao Li +2

Seismic stratigraphic interpretation of shelf-edge clinothems is essential for revealing tectonic evolution, paleoclimate change, depositional dynamic conditions, and hydrocarbon g…

physics.geo-ph2025★ 2 cited

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding

Yimin Dou, Xinming Wu, Nathan L Bangs +4

Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate mode…

physics.geo-ph2025

On the workflow, opportunities and challenges of developing foundation model in geophysics

Hanlin Sheng, Xinming Wu, Hang Gao +4

Foundation models, as a mainstream technology in artificial intelligence, have demonstrated immense potential across various domains in recent years, particularly in handling compl…

cs.CV2025

CMEdataset Advancing China Map Detection and Standardization with Digital Image Resources

Yan Xu, Zhenqiang Zhang, Zhiwei Zhou +3

Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map compliance. However, there is curr…

physics.geo-ph2023★ 15 cited

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…