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SubsurfaceGen generates realistic seismic data for ML-based FWI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SubsurfaceGen, a GPU-accelerated tool for generating realistic, field-scale 3D velocity models and seismic data. This new system addresses limitations in existing datasets for machine learning approaches to full waveform inversion (FWI). The accompanying dataset includes 4,276 2D velocity slices and seismic data from 42 diverse geological settings, designed to improve ML-based FWI for applications like carbon sequestration and hydrocarbon exploration. AI

IMPACT Enables more realistic training data for ML models in subsurface imaging, potentially improving accuracy in energy exploration and hazard assessment.

RANK_REASON The cluster contains an academic paper detailing a new method and dataset for machine learning applications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SubsurfaceGen generates realistic seismic data for ML-based FWI

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The cluster contains an academic paper detailing a new method and dataset for machine learning applications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joseph Stitt, Pratik Rathore, Madeleine Udell, Ching-Yao Lai ·

    SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

    arXiv:2605.30541v1 Announce Type: new Abstract: Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration to earthquake hazard assessment. Machine learning approaches to FWI need field-…

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