Výsledky vyhledávání - "Danilo R. Velis"

Seismic data summarization with content-aware resizing

Autor: Julián Luis Gómez, Danilo R. Velis

Publikováno v: GEOPHYSICS. 87:IM133-IM141

Seam carving (SC) is a computer vision algorithm that resizes natural images by removing their least informative regions. We adapt SC as a novel application to reduce the size of 2D and 3D seismic data with amplitude preservation. Unlike decimation a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5af93523ff8436299b284129bbe14c58
https://doi.org/10.1190/geo2021-0439.1

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Data-driven edge detectors for seismic data interpretation

Autor: Lucia E. Nicolosi Gelis, Danilo R. Velis, Julián L. Gómez

Publikováno v: GEOPHYSICS. 86:O59-O68

We have developed a novel method to assist in seismic interpretation. The algorithm learns data-driven edge detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d5cf10552c736a8ea84ae449a48728f1
https://doi.org/10.1190/geo2020-0759.1

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Simulated annealing velocity analysis: Automating the picking process

Autor: Danilo R. Velis

Publikováno v: GEOPHYSICS. 86:V119-V130

We have developed an automated method for velocity picking that allows us to estimate appropriate velocity functions for the normal moveout correction of common-depth-point (CDP) gathers, valid for either hyperbolic or nonhyperbolic trajectories. In

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::83aad8f2a284c471bbdf602c8e3194ab
https://doi.org/10.1190/geo2020-0323.1

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Microseismic denoising assessment by polarization histograms

Autor: Juan I. Sabbione, Julián L. Gómez, German I. Brunini, Danilo R. Velis

Publikováno v: GEOPHYSICS. 86:KS11-KS22

We have performed a comparison of microseismic data denoising methods based on their effect on the polarization attributes of 3C microseismic signals. The compared denoising methods include the classic band-pass filtering, and three recently proposed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b74e4efa4726018831f3bfbcf964863c
https://doi.org/10.1190/geo2020-0130.1

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Footprint removal from seismic data with residual dictionary learning

Autor: Danilo R. Velis, Julián L. Gómez

Publikováno v: GEOPHYSICS. 85:V355-V365

Dictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows for the removal of random nois

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3732b7a1cddfa25105af433895582632
https://doi.org/10.1190/geo2019-0482.1

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Automatic well tying and wavelet phase estimation with no waveform stretching or squeezing

Autor: Daniel O. Pérez, Danilo R. Velis, Gabriel Ricardo Gelpi

Publikováno v: GEOPHYSICS. 85:D83-D91

Well-to-seismic tie and wavelet phase estimation are closely related steps that link geology and seismic data. Usually, these processes are solved separately or alternately, diminishing the quality of the well-tying procedure. Apart from trying diffe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c05913993665d8ecd3403f881eb1a29a
https://doi.org/10.1190/geo2019-0284.1

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Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms

Autor: Julián L. Gómez, Danilo R. Velis, Juan I. Sabbione

Publikováno v: GEOPHYSICS. 85:V1-V10

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5577f4ea769e5c854721215ceac389b8
https://doi.org/10.1190/geo2019-0099.1

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