Synthetic seismic data generation with deep learning
| Autor: | Giacomo Roncoroni, Luca Bortolussi, N. Bienati, C. Fortini, Michele Pipan |
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| Přispěvatelé: | Roncoroni, G., Fortini, C., Bortolussi, L., Bienati, N., Pipan, M. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computational complexity theory Artificial neural network Synthetic seismogram Test data generation business.industry Computer science Deep learning Seismic modelling Inverse problem 010502 geochemistry & geophysics 01 natural sciences Synthetic data Geophysics Recurrent neural network Reflection seismic Machine learning Artificial intelligence business Algorithm 0105 earth and related environmental sciences |
| Popis: | We study the applicability of deep learning (DL) methods to generate acoustic synthetic data from 1D models of the subsurface. We designed and implemented a Neural Network (NN) and we trained it to generate synthetic seismograms (common shot gathers) from 1-D velocity models on two different datasets: one obtained from published results and the other generated by Finite Differences (FD) numerical simulation. We furthermore compared the results from the proposed model with the published one. Moreover, we tried to to add more flexibility to this methodology by allowing change of wavelet and the acquisition geometry. We obtained good results in terms of both computation efficiency and quality of prediction. The main potentialities of the work are the low computational cost, a high prediction speed and the possibility to solve complex non-linear problems without knowing the physical law behind the phenomenon, which could led great advantages in the solution also of the inverse problem . DL to generate 1-D acoustic synthetic seismograms without solving wave equation Solution to the 1-D problem through custom Recurrent Neural Network Retraining strategy to improve flexibility and applicability Computational complexity analysis. |
| Databáze: | OpenAIRE |
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