arXiv:2606.14139v1 Announce Type: new Abstract: Full waveform inversion (FWI) recovers subsurface velocity from seismic recordings by solving a severely ill-posed, nonconvex PDE-constrained optimization. Classical regularizers stabilize the inversion but fail to reproduce realistic geological structures; recent diffusion-prior methods improve realism at the cost of a fragile trade-off between data fidelity and prior consistency. We propose Decoupled Latent Optimization (DLO), which relaxes the standard latent-optimization formulation into a quadratic-penalty objective over an auxiliary physica
The continuous evolution of AI in scientific computing drives innovation in challenging inverse problems like full waveform inversion, where traditional methods struggle with realism.
This development offers a more robust and realistic approach to subsurface imaging, which is critical for resource exploration and geological understanding, by integrating advanced AI techniques.
The proposed Decoupled Latent Optimization (DLO) method provides a more stable and realistic inversion process compared to previous AI-enhanced or classical regularized methods, improving the utility of diffusion models in scientific fields.
- · Geophysical exploration companies
- · AI researchers in scientific computing
- · Energy sector
- · Developers of less robust traditional FWI methods
Improved accuracy and efficiency in subsurface imaging for oil and gas or geothermal exploration.
Faster and more reliable discovery of natural resources, influencing energy supply chains.
Potential for broader application of DLO-like techniques to other ill-posed scientific inverse problems, accelerating discovery in diverse fields.
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Read at arXiv cs.LG