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Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data

Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase velocity-frequency panels in a data-driven manner using deep neural networks. This is a different approach from many recent works that attempt to estimate velocity from directly reflected body waves or guided waves. A secondary objective is to analyze the influence on the prediction accuracy of various commonly employed deep learning practices, such as transfer learning and data augmentations. Through numerical experiments on synthetic data as well as a real geophysical example, we demonstrate that transfer learning as well as data augmentations are helpful when using deep learning for velocity estimation. A third and final objective is to study lack of generalization of deep learning models for out-of-distribution (OOD) data in the context of our problem, and present a novel approach to tackle it. We propose a domain adaptation network for training deep learning models that uses a priori knowledge on the range of velocity values in order to constrain mapping of the output. The final comparison on field data, which was not part of the training data, show the deep neural network predictions compare favorably with a conventional velocity model estimation obtained with a dispersion curve inversion workflow.

Ketersediaan
287551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
16 hlm PDF, 12.932 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.3, December 2022
Subjek
U-net
Near-surface
Dispersion curve
Rayleigh wave velocity
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data
    Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase velocity-frequency panels in a data-driven manner using deep neural networks. This is a different approach from many recent works that attempt to estimate velocity from directly reflected body waves or guided waves. A secondary objective is to analyze the influence on the prediction accuracy of various commonly employed deep learning practices, such as transfer learning and data augmentations. Through numerical experiments on synthetic data as well as a real geophysical example, we demonstrate that transfer learning as well as data augmentations are helpful when using deep learning for velocity estimation. A third and final objective is to study lack of generalization of deep learning models for out-of-distribution (OOD) data in the context of our problem, and present a novel approach to tackle it. We propose a domain adaptation network for training deep learning models that uses a priori knowledge on the range of velocity values in order to constrain mapping of the output. The final comparison on field data, which was not part of the training data, show the deep neural network predictions compare favorably with a conventional velocity model estimation obtained with a dispersion curve inversion workflow.
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