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Learning Velocity Model for Complex Media with Deep Convolutional Neural Networks

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Abstract

The paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used to describe the media. We used an open-source dataset of velocity distributions to compare the presented results with the previous works directly. Forward modeling is performed using the grid-characteristic numerical method. The inverse problem is solved using deep convolutional neural networks. Modifications for a baseline UNet architecture are proposed to improve both structural similarity index measure and quantitative correspondence of the velocity profiles with the ground truth. We evaluate our enhancements and demonstrate the statistical significance of the results.

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Funding

AV and AS are supported by RFBR project 18-29-02127 for their work on establishing a numerical pipeline and creating the dataset. LG is supported by the Basic Research Program at the National Research University Higher School of Economics for his work on forward-modeling, and inverse problem approaches. AU is supported by RSF project 19-71-30020 for his work on ablation study.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Russia

    A. S. Stankevich, I. O. Nechepurenko, A. V. Shevchenko, A. E. Ustyuzhanin & A. V. Vasyukov

  2. HSE University, 109028, Moscow, Russia

    L. I. Gremyachikh & A. E. Ustyuzhanin

Authors
  1. A. S. Stankevich
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  2. I. O. Nechepurenko
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  3. A. V. Shevchenko
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  4. L. I. Gremyachikh
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  5. A. E. Ustyuzhanin
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  6. A. V. Vasyukov
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Corresponding authors

Correspondence to A. S. Stankevich, I. O. Nechepurenko, A. V. Shevchenko, L. I. Gremyachikh, A. E. Ustyuzhanin or A. V. Vasyukov.

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Stankevich, A.S., Nechepurenko, I.O., Shevchenko, A.V. et al. Learning Velocity Model for Complex Media with Deep Convolutional Neural Networks. Lobachevskii J Math 45, 336–345 (2024). https://doi.org/10.1134/S1995080224010499

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