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2D diffusion models enable efficient 3D seismic velocity modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel depth-progressive framework for building 3D seismic velocity models using 2D generative diffusion models. This approach maps the depth dimension of 3D velocity patches to the channel dimension of a 2D U-Net, enabling sequential synthesis of patches from shallow to deep. The method conditions deeper predictions on shallower information and geophysical constraints, significantly reducing computational costs compared to traditional 3D formulations while maintaining high accuracy across various geological settings. AI

IMPACT This method offers a more computationally efficient approach to building 3D seismic velocity models, potentially accelerating geophysical analysis and subsurface exploration.

RANK_REASON Academic paper detailing a novel methodology for seismic velocity model building. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

2D diffusion models enable efficient 3D seismic velocity modeling

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Academic paper detailing a novel methodology for seismic velocity model building. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shijun Cheng, Randy Harsuko, Mohammad H. Taufik, Tariq Alkhalifah ·

    Depth-progressive 3D seismic velocity model building via 2D generative diffusion models

    arXiv:2610.14812v1 Announce Type: cross Abstract: Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, largely due to the prohibitive memory and training cost of full 3D formulations. …

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