Autor: |
S. Angio, S. Hagedorn, I. Mikhalev, S. Hou, H. Hoeber, A. Clowes |
Rok vydání: |
2019 |
Předmět: |
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Zdroj: |
81st EAGE Conference and Exhibition 2019 Workshop Programme. |
DOI: |
10.3997/2214-4609.201901965 |
Popis: |
Summary We propose a hybrid analytics and machine learning approach for large-scale surface wave inversion (SWI) for shear-wave velocities in the shallow overburden. A sparse grid of 1D velocity models are inverted using analytic optimization. Then, a deep neural network (DNN) with three hidden layers is trained using a spatially sparse subset of the data and non-linear inversion results. Finally, we use the DNN to predict the velocity model for the whole survey. This approach is demonstrated on a real high density land project. In comparison to the purely analytical approach, the hybrid analytic-ML method estimates a more reliable shear velocity model over the whole survey with significant reduction in computing time. We end with a discussion around the potential of this type of method for other geophysical inverse problems and seismic processing. |
Databáze: |
OpenAIRE |
Externí odkaz: |
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