Sparse regularization inversion method for transient electromagnetic data and high-resolution prospection of subsurface targets Article Swipe
Zhiyong Zhou
,
Ke He
,
Sarah M. Kang
,
Junchen Zhang
,
Fengqin Chi
,
Jie Ren
,
Wei Zhang
,
Ning Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17068-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17068-0
Transient Electromagnetic Method (TEM) is a geophysical technique with great potential for high-resolution subsurface imaging. However, conventional inversion algorithms for TEM typically employ the L2-norm regularization in the objective function, which assumes a smoothly varying resistivity distribution. This assumption limits the resolution of sharp electrical interfaces. To address this issue, this paper implements an inversion algorithm that incorporates an L1-norm regularization term, promoting sparsity in the spatial gradient of the resistivity model and thereby enabling more accurate reconstruction of abrupt electrical boundaries under sufficient iteration. To overcome the numerical difficulties caused by the non-differentiability of the L1 norm at zero points, we adopt the Iteratively Reweighted Least Squares (IRLS) method within a Gauss-Newton (GN) inversion framework. In this approach, the L1 regularization problem is transformed into a sequence of weighted L2 regularization subproblems, each of which is solved using the GN method. This strategy retains the sparsity-promoting property of the L1 norm while effectively circumventing its computational challenges. Inversion results from both 1D and 2D synthetic models demonstrate that the proposed algorithm significantly improves the resolution of sharp resistivity transitions compared to traditional L2-norm regularization approaches. Furthermore, the method has been successfully applied to field TEM data from a real survey site, where it effectively identified underground coal mine voids and fault structures.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-17068-0
- https://www.nature.com/articles/s41598-025-17068-0.pdf
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413495238
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413495238Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-17068-0Digital Object Identifier
- Title
-
Sparse regularization inversion method for transient electromagnetic data and high-resolution prospection of subsurface targetsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-25Full publication date if available
- Authors
-
Zhiyong Zhou, Ke He, Sarah M. Kang, Junchen Zhang, Fengqin Chi, Jie Ren, Wei Zhang, Ning ChenList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-17068-0Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-17068-0.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41598-025-17068-0.pdfDirect OA link when available
- Concepts
-
Inversion (geology), Regularization (linguistics), Prospection, High resolution, Superresolution, Computer science, Geology, Transient (computer programming), Remote sensing, Artificial intelligence, Geomorphology, Image (mathematics), Geography, Structural basin, Operating system, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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