Implicit Neural Representation for Elastic Full‐Waveform Inversion.
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| Title: | Implicit Neural Representation for Elastic Full‐Waveform Inversion. |
|---|---|
| Authors: | Berti, Sean1 (AUTHOR) sean.berti@dst.unipi.it, Aleardi, Mattia1 (AUTHOR), Stucchi, Eusebio1 (AUTHOR) |
| Source: | Geophysical Prospecting. May2026, Vol. 74 Issue 4, p1-15. 15p. |
| Subject Terms: | *Elastic waves, *Surface waves (Fluids), *Seismic traveltime inversion, *Geophysical observations, *Automatic differentiation, *Imaging systems in seismology |
| Abstract: | Full‐waveform inversion (FWI) has become a cornerstone for high‐resolution seismic imaging, yet it remains computationally demanding and sensitive to initial model assumptions and noise. Recent advances have shown that representing the subsurface model, using implicit neural representations (INRs), can provide compact, continuous and differentiable parameterizations that improve convergence and reduce overfitting. In this study, we extend the INR‐based FWI framework to the elastic regime, with a focus on near‐surface applications and the inversion of surface waves. In particular, we performed the inversion of both synthetic and field surface wave datasets. Our method leverages Deepwave for elastic wave simulation and gradient computation via automatic differentiation. In the synthetic test, we compare the performance obtained using different INR architectures to find the optimal configuration. For the field dataset inversion instead, we compare our results with those obtained using a standard deterministic FWI approach, highlighting its superior robustness with respect to initialization. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194013171 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Implicit Neural Representation for Elastic Full‐Waveform Inversion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Berti%2C+Sean%22">Berti, Sean</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sean.berti@dst.unipi.it</i><br /><searchLink fieldCode="AR" term="%22Aleardi%2C+Mattia%22">Aleardi, Mattia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stucchi%2C+Eusebio%22">Stucchi, Eusebio</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Geophysical+Prospecting%22">Geophysical Prospecting</searchLink>. May2026, Vol. 74 Issue 4, p1-15. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Elastic+waves%22">Elastic waves</searchLink><br />*<searchLink fieldCode="DE" term="%22Surface+waves+%28Fluids%29%22">Surface waves (Fluids)</searchLink><br />*<searchLink fieldCode="DE" term="%22Seismic+traveltime+inversion%22">Seismic traveltime inversion</searchLink><br />*<searchLink fieldCode="DE" term="%22Geophysical+observations%22">Geophysical observations</searchLink><br />*<searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br />*<searchLink fieldCode="DE" term="%22Imaging+systems+in+seismology%22">Imaging systems in seismology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Full‐waveform inversion (FWI) has become a cornerstone for high‐resolution seismic imaging, yet it remains computationally demanding and sensitive to initial model assumptions and noise. Recent advances have shown that representing the subsurface model, using implicit neural representations (INRs), can provide compact, continuous and differentiable parameterizations that improve convergence and reduce overfitting. In this study, we extend the INR‐based FWI framework to the elastic regime, with a focus on near‐surface applications and the inversion of surface waves. In particular, we performed the inversion of both synthetic and field surface wave datasets. Our method leverages Deepwave for elastic wave simulation and gradient computation via automatic differentiation. In the synthetic test, we compare the performance obtained using different INR architectures to find the optimal configuration. For the field dataset inversion instead, we compare our results with those obtained using a standard deterministic FWI approach, highlighting its superior robustness with respect to initialization. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194013171 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/1365-2478.70168 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Elastic waves Type: general – SubjectFull: Surface waves (Fluids) Type: general – SubjectFull: Seismic traveltime inversion Type: general – SubjectFull: Geophysical observations Type: general – SubjectFull: Automatic differentiation Type: general – SubjectFull: Imaging systems in seismology Type: general Titles: – TitleFull: Implicit Neural Representation for Elastic Full‐Waveform Inversion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Berti, Sean – PersonEntity: Name: NameFull: Aleardi, Mattia – PersonEntity: Name: NameFull: Stucchi, Eusebio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00168025 Numbering: – Type: volume Value: 74 – Type: issue Value: 4 Titles: – TitleFull: Geophysical Prospecting Type: main |
| ResultId | 1 |
