2019
DOI: 10.1111/1365-2478.12731
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Three‐parameter Radon transform in layered transversely isotropic media

Abstract: A B S T R A C TRadon transform is a powerful tool with many applications in different stages of seismic data processing, because of its capability to focus seismic events in the transform domain. Three-parameter Radon transform can optimally focus and separate different seismic events, if its basis functions accurately match the events. In anisotropic media, the conventional hyperbolic or shifted hyperbolic basis functions lose their accuracy and cannot preserve data fidelity, especially at large offsets. To a… Show more

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Cited by 6 publications
(4 citation statements)
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“…where, the long window length M = 5N and the short window length N = k/ i (t), k is the weight, and i (t) is the instantaneous dominant frequency of the i-th moment after the generalized S transform of the three-axis vibration signal [10], which refers to the frequency point with the highest energy among the wide spectrum corresponding to each moment after the generalized S transform. With the optimal matching impedance, in the near field of the blasting point, the moment the first break arrives the instantaneous dominant frequencies of the three axes are consistent [11].…”
Section: ) Extract the Arrival Time Information Of The First Breakmentioning
confidence: 81%
“…where, the long window length M = 5N and the short window length N = k/ i (t), k is the weight, and i (t) is the instantaneous dominant frequency of the i-th moment after the generalized S transform of the three-axis vibration signal [10], which refers to the frequency point with the highest energy among the wide spectrum corresponding to each moment after the generalized S transform. With the optimal matching impedance, in the near field of the blasting point, the moment the first break arrives the instantaneous dominant frequencies of the three axes are consistent [11].…”
Section: ) Extract the Arrival Time Information Of The First Breakmentioning
confidence: 81%
“…Figure 8d shows the results. Figure 8e and 8f show the separated primary P and PS reflections, using an anisotropic Radon transform (Abedi et al, 2019c). To summarize, Figure 8 shows a CMP processed for P and PS primary reflection data, with potential application for stacking and AVO analysis of each wave mode.…”
Section: Applicationsmentioning
confidence: 99%
“…Reconstruction of the missing data parts from the existing data is based on assuming a form of continuity for specific seismic events, using simplifying assumptions about the events' shape, or finding such continuity from the existing data itself. Classical data reconstruction methods use prediction filters in the frequency domain (e.g., [1]- [3]), curvelet transform (e.g., [4], [5]), seislet transform (e.g., [6], [7]), Radon transform (e.g., [8]- [11]), and inversion (e.g., [12], [13]). Recent data reconstruction methods that use data-driven approaches include dictionary learning (e.g., [14], [15]), support vector regression (e.g., [16], [17]), and deep learning (e.g., [18]- [24]).…”
Section: Introductionmentioning
confidence: 99%