arXiv:2607.04982v1 Announce Type: cross Abstract: High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this end, we present a diffusion-guided framework for structurally preconditioned velocity-model reconstruction from sparse well-log information. The proposed approach combines plane-wave PDE regularization, structurally preconditioned inversion, and measur
The continuous advancements in AI, particularly diffusion models, are finding new applications in complex scientific and engineering domains like subsurface imaging, pushing the boundaries of traditional methods.
Improved subsurface velocity models are critical for more efficient and accurate energy exploration, resource management, and geological risk assessment, directly impacting global energy supply chains and infrastructure projects.
This AI-driven approach offers higher resolution and more constrained velocity models from sparse data, potentially reducing exploration costs and risks while improving the success rate of drilling operations.
- · Energy exploration companies
- · Geophysical services providers
- · AI/ML software developers
- · Governments with resource extraction industries
- · Companies reliant on less precise traditional geophysical methods
More accurate subsurface imaging will lead to better informed decisions in oil, gas, and geothermal exploration and production.
Reduced exploration costs and improved success rates could make marginal fields economically viable, impacting global energy supply dynamics.
The application of similar AI techniques might extend to other complex inverse problems in scientific imaging, accelerating discoveries in various fields.
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Read at arXiv cs.AI