Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Journal Article · Fri Oct 01 00:00:00 EDT 2021 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2021.3114101· OSTI ID:1827579

Lin, Youzuo ^[1]; Feng, Shihang ^[1]; Wohlberg, Brendt Egon ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Fossil Energy (FE)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1827579
• Report Number(s): LA-UR--21-20004
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 60; ISSN 0196-2892
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
data augmentation
earth sciences
multiscale analysis
scientific deep learning
seismic full-waveform inversion (FWI)
style transfer