Seismic Inversion with Deep Neural Networks: a Feasibility Analysis
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Authors
Puzyrev, Vladimir
Egorov, Anton
Pirogova, Anastasia
Elders, Christopher
Otto, Claus
Date
2019Type
Conference Paper
Metadata
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Puzyrev, V. and Egorov, A. and Pirogova, A. and Elders, C. and Otto, C. 2019. Seismic Inversion with Deep Neural Networks: a Feasibility Analysis. In: 81st EAGE Conference and Exhibition 2019, 3rd Jun 2019, London, UK.
Source Conference
81st EAGE Conference and Exhibition 2019
Faculty
Faculty of Science and Engineering
School
School of Earth and Planetary Sciences (EPS)
Collection
Abstract
We investigate deep learning approaches to inversion of a 1D model of the subsurface using synthetic surface seismic and VSP data. Several deep neural networks based on three different architectures are developed and tested. The matrix propagator technique is used to generate the synthetic data for network training. The pre-trained deep neural networks can instantly predict velocity models from new data in a single step. The synthetic datasets used in training can be extended by adding random noise to the existing data, thus making the method closer to real-world conditions.