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An empirical study of large-scale data-driven full waveform inversion.

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Big data significantly enhances deep learning models for seismic full waveform inversion (FWI). Training on large, diverse datasets improves accuracy and generalization, showing that model capacity must scale with data size for optimal results.

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Area of Science:

  • Geophysics
  • Machine Learning
  • Data Science

Background:

  • Deep learning models show promise for solving complex geophysical problems like full waveform inversion (FWI).
  • The impact of large-scale, diverse datasets on deep learning for FWI remains under-explored.
  • OPENFWI provides a valuable resource for investigating big data's role in FWI.

Purpose of the Study:

  • To empirically evaluate the effect of big data on deep learning models applied to the FWI problem.
  • To quantify performance improvements in FWI using large, multi-structural synthetic datasets.
  • To determine the relationship between model capacity and dataset size for optimal FWI performance.

Main Methods:

  • Deep learning models were trained and evaluated on 10 2D subsets of the OPENFWI dataset, totaling 470,000 seismic data and velocity map pairs.
  • Performance was assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Structural Similarity Index (SSIM).
  • Experiments included comparisons between training on combined datasets versus individual subsets and leave-one-out generalization tests, alongside varying model capacities.

Main Results:

  • Training on the combined OPENFWI dataset improved MAE by 13.03%, MSE by 7.19%, and SSIM by 1.87% compared to split datasets.
  • Leave-one-out generalization tests showed average improvements of 28.60% in MAE, 21.55% in MSE, and 8.22% in SSIM.
  • Increasing model capacity alongside data size yielded significant performance gains, with the largest model outperforming the smallest by 20.06% (MAE), 13.39% (MSE), and 0.72% (SSIM).

Conclusions:

  • Big data, particularly large and diverse synthetic datasets like OPENFWI, demonstrably enhances deep learning model performance for full waveform inversion.
  • Optimal performance in data-driven FWI requires a synergistic scaling of model capacity with the size and complexity of the training dataset.
  • This study validates the effectiveness of big data in improving FWI accuracy and generalization, paving the way for more robust geophysical subsurface imaging.