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New ROMNet method combines machine learning and reduced order modeling for seismic imaging

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ROMNet, a novel approach that combines reduced order modeling (ROM) with machine learning to improve waveform inversion for seismic imaging. This method aims to reduce the computational cost and complexity associated with traditional full waveform inversion (FWI) by using a neural network to map ROM matrices to wave speeds more efficiently. ROMNet was tested using synthetic data and the GeoFWI dataset, showing competitive performance against other deep learning-based FWI techniques like Fourier-DeepONet and InversionNet. AI

IMPACT This hybrid approach could accelerate seismic data processing and improve the accuracy of subsurface imaging for applications like earthquake analysis and resource exploration.

RANK_REASON The item is an academic paper detailing a new methodology for waveform inversion. [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 →

New ROMNet method combines machine learning and reduced order modeling for seismic imaging

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The item is an academic paper detailing a new methodology for waveform inversion. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Liliana Borcea, Alexander Mamonov, Kui Ren, Haizhao Yang, Chugang Yi ·

    ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion

    arXiv:2608.25160v1 Announce Type: cross Abstract: Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user controlled sensors. We consider this inverse problem for acoustic waves and an activ…

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