Minimum semblance: A new coherence measure to improve resolution of semblance sections

Kamioka, Daniela M.; Camargo, Alexandre William; Novais, Amélia; Schleicher, Jörg; Santos, Lúcio Tunes

doi:10.1190/sbgf2015-261

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…To define minimum semblance, Kamioka et al (2015) suggested to use the minimum value of several semblance calculations over a second, outer time window with size 2K + 1, i.e., S min = min…”

Section: Minimum Semblancementioning

confidence: 99%

“…AB semblance, introduced by Sarkar et al (2001Sarkar et al ( , 2002 and implemented by Fomel (2009), is interpreted as a correlation measure with an amplitude trend and is notably interesting for data presenting polarity reversal. Minimum semblance as introduced by Kamioka et al (2015) increases the resolution of the latter while preserving its advantages, including robustness and low computational cost.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time window-size dependence analysis for the minimum semblance approach

Kamioka¹,

Novais²,

Schleicher³

et al. 2016

Proceedings of the 7 Simpósio Brasileiro De Geofísica

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“…To define minimum semblance, Kamioka et al (2015) suggested to use the minimum value of several semblance calculations over a second, outer time window with size 2K + 1, i.e., S min = min…”

Section: Minimum Semblancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time window-size dependence analysis for the minimum semblance approach

Kamioka¹,

Novais²,

Schleicher³

et al. 2016

Proceedings of the 7 Simpósio Brasileiro De Geofísica

View full text Add to dashboard Cite

show abstract

Minimum semblance weighted stacking with polarity correction for surface microseismic data processing

Zhang

Chen

et al. 2019

The Leading Edge

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Diffraction-stack-based algorithms are the most popular microseismic location methods for surface microseismic data. They can accommodate microseismic data with low signal-to-noise ratio by stacking a large number of traces. However, changes in waveform polarity across the receiver line due to source mechanisms may prevent stacking methods from locating the true source. Imaging functions based on simple stacks have low resolution, producing large uncertainty in the final location result. To solve these issues, we introduce a minimum semblance weighted stacking method with polarity correction, which uses an amplitude trend least-squares fitting algorithm to correct the polarity across the receiver line. We adapt the semblance weighted stacking for better coherency measure to improve the imaging resolution. Moreover, the minimum semblance is used to further improve the resolution of location results. Application to both synthetic and real data sets demonstrates good performance of our proposed location method.

show abstract

Minimum semblance: A new coherence measure to improve resolution of semblance sections

Cited by 2 publications

References 6 publications

Time window-size dependence analysis for the minimum semblance approach

Time window-size dependence analysis for the minimum semblance approach

Minimum semblance weighted stacking with polarity correction for surface microseismic data processing

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