CSEM Optimization Using the Correspondence Principle Article Swipe
Adriany Valente
,
Deivid Nascimento
,
Jessé C. Costa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14198846
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14198846
Traditionally, 3D modeling of marine controlled-source electromagnetic (CSEM) data (in the frequency domain) involves high-memory demand, requiring solving a large linear system for each frequency. To address this problem, we propose to solve Maxwell’s equations in a fictitious dielectric medium with time-domain finite-difference methods, with the support of the correspondence principle. As an advantage of this approach, we highlight the possibility of its implementation for execution with GPU accelerators, in addition to multi-frequency data modeling with a single simulation. Furthermore, we explore using the correspondence principle to the inversion of CSEM data by calculating the gradient of the least-squares objective function employing the adjoint-state method to establish the relationship between adjoint fields in a conductive medium and their counterparts in the fictitious dielectric medium, similar to the approach used in forward modeling. We validate this method through 2D inversions of three synthetic CSEM datasets, computed for a simple model consisting of two resistors in a conductive medium, a model adapted from a CSEM modeling and inversion package, and the last one based on a reference model of turbidite reservoirs on the Brazilian continental margin. We also evaluate the differences between the results of inversions using the steepest descent method and our proposed momentum method, comparing them with the limited-memory BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm (L-BFGS-B). In all experiments, we use smoothing by model reparameterization as a strategy for regularizing and stabilizing the iterations throughout the inversions. The results indicate that, although it requires more iterations, our modified momentum method produces the best models, which are consistent with results from the L-BFGS-B algorithm and require less storage per iteration.
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- article
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- en
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- https://doi.org/10.3390/app14198846
- OA Status
- gold
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- 10
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https://doi.org/10.3390/app14198846Digital Object Identifier
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CSEM Optimization Using the Correspondence PrincipleWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-01Full publication date if available
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Adriany Valente, Deivid Nascimento, Jessé C. CostaList of authors in order
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https://doi.org/10.3390/app14198846Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app14198846Direct OA link when available
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Computer scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.turbidite | 177 |
| abstract_inverted_index.conductive | 114, 155 |
| abstract_inverted_index.consistent | 253 |
| abstract_inverted_index.consisting | 149 |
| abstract_inverted_index.dielectric | 38, 122 |
| abstract_inverted_index.fictitious | 37, 121 |
| abstract_inverted_index.frequency. | 24 |
| abstract_inverted_index.inversions | 138, 193 |
| abstract_inverted_index.iteration. | 265 |
| abstract_inverted_index.iterations | 230 |
| abstract_inverted_index.principle. | 50 |
| abstract_inverted_index.reservoirs | 178 |
| abstract_inverted_index.throughout | 231 |
| abstract_inverted_index.(L-BFGS-B). | 212 |
| abstract_inverted_index.Maxwell’s | 33 |
| abstract_inverted_index.calculating | 93 |
| abstract_inverted_index.continental | 182 |
| abstract_inverted_index.differences | 188 |
| abstract_inverted_index.high-memory | 14 |
| abstract_inverted_index.inversions. | 233 |
| abstract_inverted_index.iterations, | 242 |
| abstract_inverted_index.possibility | 60 |
| abstract_inverted_index.simulation. | 78 |
| abstract_inverted_index.stabilizing | 228 |
| abstract_inverted_index.time-domain | 41 |
| abstract_inverted_index.Furthermore, | 79 |
| abstract_inverted_index.counterparts | 118 |
| abstract_inverted_index.experiments, | 215 |
| abstract_inverted_index.regularizing | 226 |
| abstract_inverted_index.relationship | 108 |
| abstract_inverted_index.accelerators, | 68 |
| abstract_inverted_index.adjoint-state | 103 |
| abstract_inverted_index.least-squares | 98 |
| abstract_inverted_index.Traditionally, | 0 |
| abstract_inverted_index.correspondence | 49, 84 |
| abstract_inverted_index.implementation | 63 |
| abstract_inverted_index.limited-memory | 208 |
| abstract_inverted_index.electromagnetic | 6 |
| abstract_inverted_index.multi-frequency | 72 |
| abstract_inverted_index.controlled-source | 5 |
| abstract_inverted_index.finite-difference | 42 |
| abstract_inverted_index.reparameterization | 221 |
| abstract_inverted_index.(Broyden–Fletcher–Goldfarb–Shanno) | 210 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.70823285 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |