Open AccessExtended Kalman Filter deconvolution for extracting accurate seismic reflectivity
Author(s) -
Wilmer Téllez,
Ovidio Almanza,
Luis Alfredo Montes Vides
Publication year - 2022
Publication title -
boletín de geología
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.246
H-Index - 7
eISSN - 2145-8553
pISSN - 0120-0283
DOI - 10.18273/revbol.v44n1-2022007
Subject(s) - deconvolution , seismic trace , seismic inversion , reflectivity , kalman filter , blind deconvolution , computer science , inversion (geology) , synthetic seismogram , reliability (semiconductor) , algorithm , wiener deconvolution , geology , seismology , artificial intelligence , optics , wavelet , azimuth , power (physics) , physics , quantum mechanics , tectonics
Deconvolution attempts compensating for the distortions affecting a recorded seismogram, increasing its bandwidth and extracting subsurface reflectivity from such seismic trace. The estimated reflectivity needs the highest reliability and resolution because of its subsequent use in the pre-stack seismic processing sequence and seismic inversion. We implemented the predictive deconvolution algorithms, the homomorphic Phase Inversion, and the Extended Kalman Filtering. Their application to synthetic traces extracted reflectivity whose comparison with well-bore allowed comparing the reliability between methods. The algorithms applied to an offshore record provided results whose comparison permitted to analyze the impact of the deconvolution assumptions on each method performance.