A statistics-based descriptor for automatic classification of scatterers in seismic sections

Maciel, Susanne Tainá Ramalho; Biloti, Ricardo

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Campo DC	Valor	Idioma
dc.contributor.author	Maciel, Susanne Tainá Ramalho	-
dc.contributor.author	Biloti, Ricardo	-
dc.date.accessioned	2021-01-25T18:15:33Z	-
dc.date.available	2021-01-25T18:15:33Z	-
dc.date.issued	2020-09	-
dc.identifier.citation	MACIEL, Susanne; BILOTI, Ricardo. A statistics-based descriptor for automatic classification of scatterers in seismic sections. Geophysics, v. 85, n. 5, 2020. DOI: https://doi.org/10.1190/geo2018-0673.1. Disponível em: https://library.seg.org/doi/abs/10.1190/geo2018-0673.1.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/39955	-
dc.language.iso	Inglês	pt_BR
dc.publisher	Society of Exploration Geophysicists	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	A statistics-based descriptor for automatic classification of scatterers in seismic sections	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Difração	pt_BR
dc.subject.keyword	Radar de penetração no solo	pt_BR
dc.identifier.doi	https://doi.org/10.1190/geo2018-0673.1	pt_BR
dc.relation.publisherversion	https://library.seg.org/doi/abs/10.1190/geo2018-0673.1	pt_BR
dc.description.abstract1	Discontinuities and small structures induce diffractions on seismic or ground-penetrating radar (GPR) acquisitions. Therefore, diffraction images can be used as a tool to access valuable information concerning subsurface scattering features, such as pinch outs, fractures, and edges. Usually, diffraction-imaging methods operate on diffraction events previously detected. Pattern-recognition methods are efficient to detect, image, and characterize diffractions. The use of this kind of approach, though, requires a numerical description of image points on a seismic section or radargram. We have investigated a new descriptor for seismic/GPR data that distinguishes diffractions from reflections. The descriptor consists of a set of statistical measures from diffraction operators sensitive to kinematic and dynamic aspects of an event. We develop experiments in which the proposed descriptor was incorporated into a pattern-recognition routine for diffraction imaging. The obtained method is useful for performing the automatic classification of image points using supervised and unsupervised algorithms, as a complementary step to Kirchhoff imaging. We also develop a new type of filtering, designed to address anomalies on the diffraction operators caused by interfering events. We evaluate the method using synthetic seismic data and real GPR data. Our results indicate that the descriptor correctly discriminates diffractions and shows promising results for low signal-to-noise-ratio situations.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-6800-0002	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-5186-9705	pt_BR
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