Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile Article Swipe
Maurizio Ercoli
,
Filippo Carboni
,
Assel Akimbekova
,
R. Carbonell
,
Massimiliano Rinaldo Barchi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/feart.2023.1119554
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/feart.2023.1119554
Legacy seismic reflection data constitute infrastructure of tremendous value for basic research. This is especially relevant in seismically hazardous areas, as such datasets can significantly contribute to the seismotectonic characterization of the region. The quality of the data and the resulting image can be effectively improved by using modern tools, such as pre-conditioning techniques and seismic attributes. The latter are extensively used by the hydrocarbon exploration industry, but are still only poorly applied to the study of active faults. Pre-conditioning filters are effective in removing random noise, which hampers the detection of subtle geologic structures (i.e., normal faults). In this study, a workflow including pre-conditioning and extraction of seismic attributes is used to improve the quality of the CROP-04 deep seismic reflection profile. CROP-04 was acquired in the 1980s across the Southern Apennines mountain range, one of the most hazardous seismically active regions in Italy. The results show the capacity of this method to extract, from low-resolution legacy data, subtle seismic fabrics that correspond to a dense network of fault sets. These seismic signatures and the enhanced discontinuities disrupting the reflections, which were invisible in the original data, correlate well with the main regional normal faults outcropping at the surface. Moreover, the data reveal higher structural complexity, due to many secondary synthetic and antithetic structures, knowledge of which is useful in modeling of the local and regional distribution of the deformation and potentially in guiding future field mapping of active faults. This proposed approach and workflow can be extended to seismotectonic studies of other high-hazard regions worldwide, where seismic reflection data are available.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/feart.2023.1119554
- https://www.frontiersin.org/articles/10.3389/feart.2023.1119554/pdf
- OA Status
- gold
- Cited By
- 11
- References
- 181
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362467443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362467443Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/feart.2023.1119554Digital Object Identifier
- Title
-
Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profileWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-31Full publication date if available
- Authors
-
Maurizio Ercoli, Filippo Carboni, Assel Akimbekova, R. Carbonell, Massimiliano Rinaldo BarchiList of authors in order
- Landing page
-
https://doi.org/10.3389/feart.2023.1119554Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/feart.2023.1119554/pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/feart.2023.1119554/pdfDirect OA link when available
- Concepts
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Geology, Seismology, Classification of discontinuities, Reflection (computer programming), Workflow, Fault (geology), Computer science, Database, Mathematics, Programming language, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
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181Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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