Velocity analysis using <i>AB</i> semblance

Fomel, Sergey

doi:10.1111/j.1365-2478.2008.00741.x

Cited by 137 publications

(74 citation statements)

References 17 publications

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“…We can simply pick up the maximum in the energy spectrum map ( , ) E j θ as the polarizing angle. In the other hand, similar to automatic velocity picking from semblance scans, we can use the automatic picking technology to obtain the polarizing angle (Harlan, 2001;Fomel, 2009). Once the polarization angles are determined, we can use the axes rotation equation 1 to obtain the P-and SV-wavefield.…”

Section: Note That Energy ( )mentioning

confidence: 99%

The application of polarizing filtering with floating coordinate system in 3D3C VSP wavefield separation

Shao

Wang

et al. 2011

SEG Technical Program Expanded Abstracts 2011

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Due to the diversity of measuring geometry, the wavefields from 3D3C vertical seismic profile (VSP) are often complex, and therefore wavefield separation is very important in the processing of VSP data. In this abstract, we analyzed the theory of polarizing filtering with floating coordinate system (PFFCS) and applied it to 3D3C VSP field dataset. We reviewed the methods of computing polarizing angle and described the algorithm of PFFCS. The polarizing angles of reflected wave computed by PFFCS vary with the traces and reflection layers. Depending on the direction of the concentrated reflected wave from the reflection point, the coordinate system is established. We set up the processing workflow according to characteristics of the field VSP dataset, effectively separated and obtained the all kinds of wavefield. Compared to the conventional polarizing filter, the PFFCS gives a better result for this field data.

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Section: Note That Energy ( )mentioning

confidence: 99%

The application of polarizing filtering with floating coordinate system in 3D3C VSP wavefield separation

Shao

Wang

et al. 2011

SEG Technical Program Expanded Abstracts 2011

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“…Here,

N = N (t_{0})

is a integer function belonging to interval

[- N_{\max}, N_{\max}]

with

false(2 N_{\max} + 1 false) Δ t_{0}

equal to the length of signal of the dominant frequency in the data set. In summary, after the estimation of the set of parameter

P (t_{0})

, the parameter

N (t_{0})

is estimated by a coherence analysis (Neidell and Taner ; Symes and Carazzone ; Fomel ) among

2 N_{\max} + 1

possibilities for each time sample. Note that this estimation of

N (t_{0})

is done automatically, sample‐by‐sample, without any picking procedure.…”

Section: Stretch‐free Generalized Normal Moveoutmentioning

confidence: 99%

Stretch‐free generalized normal moveout correction

Faccipieri

Coimbra

Bloot

2018

Geophysical Prospecting

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The effective application of normal moveout correction processes mainly depends on four factors: the chosen traveltime approximation, the stretching associated with the given traveltime, crossing events and phase changes, the last two being inherent to the seismic data. In this context, we conduct a quantitative analysis on stretching considering a general traveltime expression depending on half‐offset and midpoint coordinates. Through this analysis, we propose a mathematically proven procedure to eliminate stretching, which can be applied to any traveltime approximation. The proposed method is applied to synthetic and real data sets, considering different traveltime approximations and achieved complete elimination of stretching.

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“…However, the reflection events (indicted by arrows) in Fig. The AB semblance (Fomel 2009) can handle the AVO phenomena correctly, where A and B are the AVO intercept and gradient. This synthetic example shows that the similarity weighted k-means method can be used for velocity estimation accurately.…”

Section: Synthetic Examplesmentioning

confidence: 99%

Automatic stacking‐velocity estimation using similarity‐weighted clustering

Liu

et al. 2017

Geophysical Prospecting

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Local seismic event slopes contain subsurface velocity information and can be used to estimate seismic stacking velocity. In this paper, we propose a novel approach to estimate the stacking velocity automatically from seismic reflection data using similarity‐weighted k‐means clustering, in which the weights are local similarity between each trace in common midpoint gather and a reference trace. Local similarity reflects the local signal‐to‐noise ratio in common midpoint gather. We select the data points with high signal‐to‐noise ratio to be used in the velocity estimation with large weights in mapped traveltime and velocity domain by similarity‐weighted k‐means clustering with thresholding. By using weighted k‐means clustering, we make clustering centroids closer to those data points with large weights, which are more reliable and have higher signal‐to‐noise ratio. The interpolation is used to obtain the whole velocity volume after we have got velocity points calculated by weighted k‐means clustering. Using the proposed method, one obtains a more accurate estimate of the stacking velocity because the similarity‐based weighting in clustering takes into account the signal‐to‐noise ratio and reliability of different data points in mapped traveltime and velocity domain. In order to demonstrate that, we apply the proposed method to synthetic and field data examples, and the resulting images are of higher quality when compared with the ones obtained using existing methods.

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Velocity analysis using AB semblance

Cited by 137 publications

References 17 publications

The application of polarizing filtering with floating coordinate system in 3D3C VSP wavefield separation

The application of polarizing filtering with floating coordinate system in 3D3C VSP wavefield separation

Stretch‐free generalized normal moveout correction

Automatic stacking‐velocity estimation using similarity‐weighted clustering

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