Título:	A statistics-based descriptor for automatic classification of scatterers in seismic sections
Autor(es):	Maciel, Susanne Tainá Ramalho Biloti, Ricardo
ORCID:	https://orcid.org/0000-0002-6800-0002 https://orcid.org/0000-0002-5186-9705
Assunto:	Difração Radar de penetração no solo
Data de publicação:	Set-2020
Editora:	Society of Exploration Geophysicists
Referência:	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.
Abstract:	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.
DOI:	https://doi.org/10.1190/geo2018-0673.1
Versão da editora:	https://library.seg.org/doi/abs/10.1190/geo2018-0673.1
Aparece nas coleções:	Artigos publicados em periódicos e afins

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