arXiv:2604.07421v3 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill-posed. While deep learning approaches promise efficiency, existing Convolutional Neural Networks (CNNs) and single-paradigm Neural Operators (NOs) struggle with one fundamental issue: frequency entanglement of multi-scale geological features. To address this challenge, we propose Spectral-Preserving Adaptive MoE (SPAMoE), a novel spectrum-aware framework for solving inverse problems with complex
The continuous evolution of deep learning applied to complex scientific problems drives the need for more specialized and efficient AI architectures to overcome current limitations.
Improving Full-waveform inversion (FWI) with AI directly impacts industries reliant on high-resolution subsurface imaging, potentially accelerating resource discovery and seismic hazard assessment.
This new hybrid operator framework, SPAMoE, suggests a potential improvement in the accuracy and efficiency of deep learning for inverse problems by better handling multi-scale features, shifting how complex geophysical data is processed.
- · Oil and Gas Industry
- · Geophysical Survey Companies
- · Deep Learning Research Facilities
- · AI compute infrastructure providers
- · Traditional FWI methodologies
- · Companies relying on less efficient imaging techniques
More accurate and faster subsurface models enable better decision-making in resource exploration and environmental monitoring.
Reduced operational costs and risks in industries like energy, potentially leading to increased global resource availability and sustainability.
The principles of 'spectrum-aware hybrid operators' could be generalized to other scientific inverse problems, accelerating discovery in fields beyond geophysics.
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Read at arXiv cs.LG