Deep-pretrained-FWI: combining supervised learning with physics-informed neural network Article Swipe
Ana Paula Oliveira Muller
,
C. R. Bom
,
Jessé C. Costa
,
Matheus Klatt
,
Elisângela L. Faria
,
Marcelo P. de Albuquerque
,
Márcio P. de Albuquerque
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.02338
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.02338
An accurate velocity model is essential to make a good seismic image. Conventional methods to perform Velocity Model Building (VMB) tasks rely on inverse methods, which, despite being widely used, are ill-posed problems that require intense and specialized human supervision. Convolutional Neural Networks (CNN) have been extensively investigated as an alternative to solve the VMB task. Two main approaches were investigated in the literature: supervised training and Physics-Informed Neural Networks (PINN). Supervised training presents some generalization issues since structures, and velocity ranges must be similar in training and test set. Some works integrated Full-waveform Inversion (FWI) with CNN, defining the problem of VMB in the PINN framework. In this case, the CNN stabilizes the inversion, acting like a regularizer and avoiding local minima-related problems and, in some cases, sparing an initial velocity model. Our approach combines supervised and physics-informed neural networks by using transfer learning to start the inversion. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. We show that transfer learning reduces the uncertainties of the process, accelerates model convergence, and improves the final scores of the iterative process.
Related Topics
Concepts
Convolutional neural network
Maxima and minima
Artificial intelligence
Computer science
Artificial neural network
Machine learning
Test set
Inverse problem
Inversion (geology)
Generalization
Supervised learning
Pattern recognition (psychology)
Algorithm
Mathematics
Structural basin
Paleontology
Biology
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.02338
- https://arxiv.org/pdf/2212.02338
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310830173
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310830173Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2212.02338Digital Object Identifier
- Title
-
Deep-pretrained-FWI: combining supervised learning with physics-informed neural networkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-05Full publication date if available
- Authors
-
Ana Paula Oliveira Muller, C. R. Bom, Jessé C. Costa, Matheus Klatt, Elisângela L. Faria, Marcelo P. de Albuquerque, Márcio P. de AlbuquerqueList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.02338Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2212.02338Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2212.02338Direct OA link when available
- Concepts
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Convolutional neural network, Maxima and minima, Artificial intelligence, Computer science, Artificial neural network, Machine learning, Test set, Inverse problem, Inversion (geology), Generalization, Supervised learning, Pattern recognition (psychology), Algorithm, Mathematics, Structural basin, Paleontology, Biology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.were | 59 |
| abstract_inverted_index.with | 96, 161 |
| abstract_inverted_index.(CNN) | 43 |
| abstract_inverted_index.(FWI) | 95 |
| abstract_inverted_index.(VMB) | 19 |
| abstract_inverted_index.Model | 17 |
| abstract_inverted_index.based | 158 |
| abstract_inverted_index.being | 27 |
| abstract_inverted_index.case, | 109 |
| abstract_inverted_index.final | 196 |
| abstract_inverted_index.human | 38 |
| abstract_inverted_index.local | 121 |
| abstract_inverted_index.model | 3, 191 |
| abstract_inverted_index.since | 77 |
| abstract_inverted_index.solve | 52 |
| abstract_inverted_index.start | 146 |
| abstract_inverted_index.task. | 55 |
| abstract_inverted_index.tasks | 20 |
| abstract_inverted_index.trend | 173 |
| abstract_inverted_index.used, | 29 |
| abstract_inverted_index.using | 142, 154 |
| abstract_inverted_index.works | 91 |
| abstract_inverted_index.Neural | 41, 68 |
| abstract_inverted_index.acting | 115 |
| abstract_inverted_index.cases, | 127 |
| abstract_inverted_index.image. | 11 |
| abstract_inverted_index.issues | 76 |
| abstract_inverted_index.model. | 132 |
| abstract_inverted_index.neural | 139 |
| abstract_inverted_index.ranges | 81 |
| abstract_inverted_index.scores | 197 |
| abstract_inverted_index.simple | 165 |
| abstract_inverted_index.which, | 25 |
| abstract_inverted_index.widely | 28 |
| abstract_inverted_index.(PINN). | 70 |
| abstract_inverted_index.capture | 169 |
| abstract_inverted_index.despite | 26 |
| abstract_inverted_index.initial | 130, 176 |
| abstract_inverted_index.intense | 35 |
| abstract_inverted_index.inverse | 23 |
| abstract_inverted_index.methods | 13 |
| abstract_inverted_index.perform | 15 |
| abstract_inverted_index.problem | 100 |
| abstract_inverted_index.reduced | 163 |
| abstract_inverted_index.reduces | 184 |
| abstract_inverted_index.require | 34 |
| abstract_inverted_index.seismic | 10 |
| abstract_inverted_index.similar | 84 |
| abstract_inverted_index.sparing | 128 |
| abstract_inverted_index.Building | 18 |
| abstract_inverted_index.Networks | 42, 69 |
| abstract_inverted_index.Velocity | 16 |
| abstract_inverted_index.accurate | 1 |
| abstract_inverted_index.approach | 134, 157 |
| abstract_inverted_index.avoiding | 120 |
| abstract_inverted_index.combines | 135 |
| abstract_inverted_index.defining | 98 |
| abstract_inverted_index.improves | 194 |
| abstract_inverted_index.learning | 144, 183 |
| abstract_inverted_index.methods, | 24 |
| abstract_inverted_index.networks | 140 |
| abstract_inverted_index.obtained | 153 |
| abstract_inverted_index.presents | 73 |
| abstract_inverted_index.problems | 32, 123 |
| abstract_inverted_index.process, | 189 |
| abstract_inverted_index.process. | 201 |
| abstract_inverted_index.training | 65, 72, 86, 160 |
| abstract_inverted_index.transfer | 143, 182 |
| abstract_inverted_index.velocity | 2, 80, 131, 172 |
| abstract_inverted_index.Inversion | 94 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.ill-posed | 31 |
| abstract_inverted_index.iterative | 200 |
| abstract_inverted_index.Supervised | 71 |
| abstract_inverted_index.approaches | 58 |
| abstract_inverted_index.framework. | 106 |
| abstract_inverted_index.integrated | 92 |
| abstract_inverted_index.inversion, | 114 |
| abstract_inverted_index.inversion. | 148 |
| abstract_inverted_index.stabilizes | 112 |
| abstract_inverted_index.supervised | 64, 136, 156 |
| abstract_inverted_index.accelerates | 190 |
| abstract_inverted_index.alternative | 50 |
| abstract_inverted_index.extensively | 46 |
| abstract_inverted_index.iterations. | 178 |
| abstract_inverted_index.literature: | 63 |
| abstract_inverted_index.pre-trained | 150 |
| abstract_inverted_index.regularizer | 118 |
| abstract_inverted_index.specialized | 37 |
| abstract_inverted_index.structures, | 78 |
| abstract_inverted_index.Conventional | 12 |
| abstract_inverted_index.convergence, | 192 |
| abstract_inverted_index.investigated | 47, 60 |
| abstract_inverted_index.supervision. | 39 |
| abstract_inverted_index.Convolutional | 40 |
| abstract_inverted_index.Full-waveform | 93 |
| abstract_inverted_index.uncertainties | 186 |
| abstract_inverted_index.generalization | 75 |
| abstract_inverted_index.minima-related | 122 |
| abstract_inverted_index.Physics-Informed | 67 |
| abstract_inverted_index.physics-informed | 138 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.63036281 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |