Seismic Inversion with Deep Neural Networks: a Feasibility Analysis
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...
| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/76413 |
| _version_ | 1848763676792717312 |
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| author | Puzyrev, Vladimir Egorov, Anton Pirogova, Anastasia Elders, Christopher Otto, Claus |
| author_facet | Puzyrev, Vladimir Egorov, Anton Pirogova, Anastasia Elders, Christopher Otto, Claus |
| author_sort | Puzyrev, Vladimir |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:07:15Z |
| format | Conference Paper |
| id | curtin-20.500.11937-76413 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:07:15Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-764132019-10-28T03:19:34Z Seismic Inversion with Deep Neural Networks: a Feasibility Analysis Puzyrev, Vladimir Egorov, Anton Pirogova, Anastasia Elders, Christopher Otto, Claus 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. 2019 Conference Paper http://hdl.handle.net/20.500.11937/76413 10.3997/2214-4609.201900765 restricted |
| spellingShingle | Puzyrev, Vladimir Egorov, Anton Pirogova, Anastasia Elders, Christopher Otto, Claus Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title | Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title_full | Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title_fullStr | Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title_full_unstemmed | Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title_short | Seismic Inversion with Deep Neural Networks: a Feasibility Analysis |
| title_sort | seismic inversion with deep neural networks: a feasibility analysis |
| url | http://hdl.handle.net/20.500.11937/76413 |