New research uses machine learning to better understand subsurface geology

Los Alamos researchers are the first to successfully demonstrate a machine-learning-based seismic imaging technique applying to real data. Once this model is fully trained, it can significantly reduce the computation time and yield more accurate models of subsurface geology.

Accurately and efficiently characterizing subsurface geology is crucial for various applications, such as energy exploration, civil infrastructure, and groundwater contamination and remediation. The standard approach to obtaining this information is through computational seismic imaging, which involves reconstructing an image of subsurface structures from measurements of natural or artificially produced seismic waves.

Inspired by recent successes in applying deep learning to computer vision and medical problems, deep-learning-based data-driven methods have been applied to seismic imaging problems. Several encoder-decoder networks have been developed to reconstruct the subsurface structure from seismic data. Those deep-learning models are end-to-end, meaning that they use the seismic waveform data as the input and directly output its corresponding subsurface structure.

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