15 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
Hang Gao, Xinming Wu, Luming Liang +4
Seismic geobody interpretation is crucial for structural geology studies and various engineering applications. Existing deep learning methods show promise but lack support for mult…
Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis
Zhixiang Guo, Xinming Wu, Luming Liang +3
We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, large neural networks trained on massive datasets, excel in diverse tasks with remar…
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…