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ARXIV:2603.15354 · GEOPHYSICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15354GEOPHYSICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration.
Opportunity summary
Pain A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progress, but existing generative models…
Seismic inversion is a core problem in geophysical exploration, where traditional methods suffer from high computational costs and are susceptible to initial model dependence. In recent years, deep generative model-based seismic inversion methods have…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This paper proposes an end-to-end fast seismic inversion method based on Conditional Rectified Flow[1], which designs a dedicated seismic encoder to extract multi-scale seismic…
Geophysical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration.
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10.48550/arXiv.2603.15354A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration.
Abstract
Seismic inversion is a core problem in geophysical exploration, where traditional methods suffer from high computational costs and are susceptible to initial model dependence. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progress, but existing generative models struggle to balance sampling efficiency and inversion accuracy. This paper proposes an end-to-end fast seismic inversion method based on Conditional Rectified Flow[1], which designs a dedicated seismic encoder to extract multi-scale seismic features and adopts a layer-by-layer injection control strategy to achieve fine-grained conditional control. Experimental results demonstrate that the proposed method achieves excellent inversion accuracy on the OpenFWI[2] benchmark dataset. Compared with Diffusion[3,4] methods, it achieves sampling acceleration; compared with InversionNet[5,6,7] methods, it achieves higher accuracy in generation. Our zero-shot generalization experiments on Marmousi[8,9] real data further verify the practical value of the method. Experimental results show that the proposed method achieves excellent inversion accuracy on the OpenFWI benchmark dataset; compared with Diffusion methods, it achieves sampling acceleration while maintaining higher accuracy than InversionNet methods; experiments based on the Marmousi standard model further verify that this method can generate high-quality initial velocity models in a zero-shot manner, effectively alleviating the initial model dependency problem in traditional Full Waveform Inversion (FWI), and possesses industrial practical value.
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PROBLEM
A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progress, but existing generative models struggle...
METHOD
Seismic inversion is a core problem in geophysical exploration, where traditional methods suffer from high computational costs and are susceptible to initial model dependence. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progres...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This paper proposes an end-to-end fast seismic inversion method based on Conditional Rectified Flow[1], which designs a dedicated seismic encoder to extract multi-scale seismic features and adopts a layer...
WHY NOW
Geophysical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progress, but existing generative models struggle to balance sampling efficiency and inversion accuracy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Seismic inversion is a core problem in geophysical exploration, where traditional methods suffer from high computational costs and are susceptible to initial model dependence. In recent years, deep generative model-based seismic inversion methods have achieved remarkable progress, but existing generative models struggle to balance sampling efficiency and inversion accuracy.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This paper proposes an end-to-end fast seismic inversion method based on Conditional Rectified Flow[1], which designs a dedicated seismic encoder to extract multi-scale seismic features and adopts a layer-by-layer injection control strategy to achieve fine-grained conditional control.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Geophysical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A fast seismic inversion method leveraging Conditional Rectified Flow to enhance accuracy and efficiency in geophysical exploration.
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Geophysical AI
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Commercial read
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