Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion Article Swipe
Jesper Dramsch
,
G. Côrte
,
Hamed Amini
,
Colin MacBeth
,
Mikael Lüthje
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3997/2214-4609.201901967
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3997/2214-4609.201901967
Geoscience data often have to rely on strong priors in the face of\nuncertainty. Additionally, we often try to detect or model anomalous sparse\ndata that can appear as an outlier in machine learning models. These are\nclassic examples of imbalanced learning. Approaching these problems can benefit\nfrom including prior information from physics models or transforming data to a\nbeneficial domain. We show an example of including physical information in the\narchitecture of a neural network as prior information. We go on to present\nnoise injection at training time to successfully transfer the network from\nsynthetic data to field data.\n
Related Topics
Concepts
Computer science
Outlier
Inversion (geology)
Anomaly detection
Deep learning
Synthetic data
Artificial neural network
Machine learning
Artificial intelligence
Transfer of learning
Data modeling
Prior probability
Noisy data
Noise (video)
Data mining
Bayesian probability
Geology
Paleontology
Database
Structural basin
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.3997/2214-4609.201901967
- OA Status
- green
- Cited By
- 1
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2935117054
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2935117054Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3997/2214-4609.201901967Digital Object Identifier
- Title
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Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation InversionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Jesper Dramsch, G. Côrte, Hamed Amini, Colin MacBeth, Mikael LüthjeList of authors in order
- Landing page
-
https://doi.org/10.3997/2214-4609.201901967Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1904.02254Direct OA link when available
- Concepts
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Computer science, Outlier, Inversion (geology), Anomaly detection, Deep learning, Synthetic data, Artificial neural network, Machine learning, Artificial intelligence, Transfer of learning, Data modeling, Prior probability, Noisy data, Noise (video), Data mining, Bayesian probability, Geology, Paleontology, Database, Structural basin, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2020: 1Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 10, 85 |
| abstract_inverted_index.try | 16 |
| abstract_inverted_index.data | 1, 52, 88 |
| abstract_inverted_index.face | 11 |
| abstract_inverted_index.from | 47 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.rely | 5 |
| abstract_inverted_index.show | 57 |
| abstract_inverted_index.that | 23 |
| abstract_inverted_index.time | 81 |
| abstract_inverted_index.These | 33 |
| abstract_inverted_index.field | 90 |
| abstract_inverted_index.model | 20 |
| abstract_inverted_index.often | 2, 15 |
| abstract_inverted_index.prior | 45, 71 |
| abstract_inverted_index.these | 40 |
| abstract_inverted_index.appear | 25 |
| abstract_inverted_index.detect | 18 |
| abstract_inverted_index.models | 49 |
| abstract_inverted_index.neural | 68 |
| abstract_inverted_index.priors | 8 |
| abstract_inverted_index.strong | 7 |
| abstract_inverted_index.data.\n | 91 |
| abstract_inverted_index.domain. | 55 |
| abstract_inverted_index.example | 59 |
| abstract_inverted_index.machine | 30 |
| abstract_inverted_index.models. | 32 |
| abstract_inverted_index.network | 69, 86 |
| abstract_inverted_index.outlier | 28 |
| abstract_inverted_index.physics | 48 |
| abstract_inverted_index.examples | 35 |
| abstract_inverted_index.learning | 31 |
| abstract_inverted_index.physical | 62 |
| abstract_inverted_index.problems | 41 |
| abstract_inverted_index.training | 80 |
| abstract_inverted_index.transfer | 84 |
| abstract_inverted_index.anomalous | 21 |
| abstract_inverted_index.including | 44, 61 |
| abstract_inverted_index.injection | 78 |
| abstract_inverted_index.learning. | 38 |
| abstract_inverted_index.Geoscience | 0 |
| abstract_inverted_index.imbalanced | 37 |
| abstract_inverted_index.Approaching | 39 |
| abstract_inverted_index.information | 46, 63 |
| abstract_inverted_index.are\nclassic | 34 |
| abstract_inverted_index.information. | 72 |
| abstract_inverted_index.sparse\ndata | 22 |
| abstract_inverted_index.successfully | 83 |
| abstract_inverted_index.transforming | 51 |
| abstract_inverted_index.Additionally, | 13 |
| abstract_inverted_index.a\nbeneficial | 54 |
| abstract_inverted_index.benefit\nfrom | 43 |
| abstract_inverted_index.present\nnoise | 77 |
| abstract_inverted_index.from\nsynthetic | 87 |
| abstract_inverted_index.of\nuncertainty. | 12 |
| abstract_inverted_index.the\narchitecture | 65 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.51682824 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |