Part I — CRS stack: Global optimization of the 2D CRS-attributes

Garabito, German; Stoffa, Paul L.; Lucena, L. S.; Cruz, João Carlos Ribeiro

doi:10.1016/j.jappgeo.2012.07.005

Cited by 33 publications

(18 citation statements)

References 15 publications

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“…*The running time for the full search is the approximated running time using very fast simulated annealing heuristic. We assumed 300 semblance evaluations for each parameter pair, which is consistent with the results in Garabito et al (2012). window function used in the GST/QST approach smoothes the parameter estimates, which makes it harder to get good estimates in regions with rapidly changing events.…”

Section: Experiments On Real Datamentioning

confidence: 95%

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Fast and robust common-reflection-surface parameter estimation

et al. 2018

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The common-reflection-surface (CRS) method offers a stack with higher signal-to-noise ratio at the cost of a timeconsuming semblance search to obtain the stacking parameters. We have developed a fast method for extracting the CRS parameters using local slope and curvature. We estimate the slope and curvature with the gradient structure tensor and quadratic structure tensor on stacked data. This is done under the assumption that a stacking velocity is already available. Our method was compared with an existing slopebased method, in which the slope is extracted from prestack data. An experiment on synthetic data shows that our method has increased robustness against noise compared with the existing method. When applied to two real data sets, our method achieves accuracy comparable with the pragmatic and full semblance searches. Our method has the advantage of being approximately two and four orders of magnitude faster than the semblance searches.

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Section: Experiments On Real Datamentioning

confidence: 95%

“…1 brute-force approach, but they are not guaranteed to find the global optimum. The most popular are simulated annealing (Müller, 2003;Garabito et al, 2012;Minato et al, 2012) and differential evolution (Barros et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fast and robust common-reflection-surface parameter estimation

et al. 2018

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“…To circumvent these problems, while keeping a reasonable computational complexity, Garabito et al (2012) proposed a global optimization strategy based on the very-fast simulated annealing (VFSA) algorithm (Sen and Stoffa, 2013) to estimate all the CRS parameters in a single search procedure. The details of the method are beyond the scope of this paper.…”

Section: Global Searchmentioning

confidence: 99%

“…We use b = 0 (the correct value of the parameter) to make the visualization easier. However, plots of semblance varying the three parameters have been shown in (Garabito et al, 2012). In this figure, we also show the estimates obtained by the sequential search and the global search.…”

Section: Global Searchmentioning

confidence: 99%

“…This procedure is repeated sequentially, until all parameters have been estimated. As an alternative, the CRS parameters may be estimated using a global optimization strategy, which searches simultaneously for the combination of all the parameters with the largest coherency (Garabito et al, 2012). This strategy yields better results, at the cost of higher computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of the Parameter Estimation Strategy in the CRS Method

Barros

Ferrari

Krummenauer

et al. 2013

13th International Congress of the Brazilian Geophysical Society &Amp; EXPOGEF, Rio De Janeiro, Brazil, 26–29 August 2013

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The common-reflection surface (CRS) method is a powerful alternative to the traditional commonmidpoint (CMP) stacking, as it enables stacking using more traces than CMP. The CRS method is based on a traveltime that depends on three parameters in the 2D case, and in eight parameters in the 3D case. Traditionally, to keep computational costs low, these parameters are estimated based on a sequence of single-parameter searches, in which each parameter is estimated in turn, while keeping the others fixed. Alternatively, a global optimization method can be used to estimate all parameters simultaneously. In this paper, we analyze the impact of using such a global method. We show that the resulting image may be much improved. We also show that the parameters estimated using the traditional search may not be accurate, and may not result in a large coherence when used in the full CRS traveltime.

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