S-S imaging with vertical-force sources

We show examples of S-S images created from multicomponent seismic data generated by vertical-force sources that can be quite useful to seismic interpreters. Two source types are used: vertical vibrators and shot-hole explosives. We first discuss S-S images made from data generated by a vertical vibrator and recorded with vertical receiver arrays of 3C geophones. We next show images extracted from surface-based 3C geophones deployed around this VSP well as a 3D seismic grid. The energy sources used to generate these surface 3D seismic data were shot-hole explosives. In all data examples, we observe that each type of vertical-force source (vertical vibrator and shot-hole explosive) produces abundant direct-S energy on radial and transverse geophones. We find only minimal amounts of P-wave energy on transverse-receiver data. In contrast, radial-receiver data have significant P-wave events intermingled with radial-S events. The minimal amount of P-wave noise on transverse-receiver data makes it easier to study S-S wave physics and to create S-S images with transverse-S data. The data examples focus on transverse-S data created by vertical-force sources because interpreters will find it more convenient to process and use this S-mode. Subsequent publications will assign equal weight to radial-S and transverse-S data.

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Inversion and interpretation of a 3D seismic data set from the Ouachita Mountains, Oklahoma

Geophysics ◽

10.1190/1.3073005 ◽

2009 ◽

Vol 74 (2) ◽

pp. B37-B45 ◽

Cited By ~ 3

Author(s):

Abuduwali Aibaidula ◽

George McMechan

Keyword(s):

Seismic Data ◽

Acoustic Impedance ◽

3D Visualization ◽

Wave Impedance ◽

P Wave ◽

3D Seismic ◽

S Wave ◽

Ouachita Mountains ◽

Data Set ◽

3D Seismic Data

Acoustic impedance inversion (AI) and simultaneous angle-dependent inversion (SADI) of a 3D seismic data set characterize reservoirs of Mississippian Morrowan age in the triangle zone of the frontal Ouachita Mountains, Oklahoma. Acoustic impedance of the near-angle seismic data images the 3D spatial distributions of Wapanucka limestone and Cromwell sandstone. Lamé [Formula: see text] ([Formula: see text] and [Formula: see text]) and [Formula: see text] sections are derived from the P-wave and S-wave impedance ([Formula: see text] and [Formula: see text]) sections produced by the SADI. Lithology is identified from the gamma logs and [Formula: see text]. The [Formula: see text], [Formula: see text], and [Formula: see text] are interpreted in terms of a hydrocarbon distribution pattern. The [Formula: see text] is used to identify high [Formula: see text] regions that are consistent with high sand/shale ratio. The estimated impedances and derived Lamé parameter sections are consistent with the interpretation that parts of the Wapanucka limestone and Cromwell sandstone contain potential gas reservoirs in fault-bounded compartments. The Cromwell sandstone contains the main inferred reservoirs; the two largest of these are each [Formula: see text] in pore volume. The inversion results also explain the observed low production in previous wells because those did not sample the best compartments. We propose a single new well location that would penetrate both reservoirs; 3D visualization facilitates this recommendation.

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P‐wave anisotropy from azimuthal velocity and AVO using wide‐azimuth 3D seismic data

10.1190/1.1851109 ◽

2004 ◽

Cited By ~ 2

Author(s):

Ahmed M. Al‐Marzoug ◽

Fernando A. Neves ◽

Jung J. Kim ◽

Edgardo L. Nebrija

Keyword(s):

Seismic Data ◽

Azimuthal Velocity ◽

P Wave ◽

3D Seismic ◽

3D Seismic Data

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Exploration and application of vector processing technology of S-wave 3D seismic data

10.1190/segam2021-3594152.1 ◽

2021 ◽

Author(s):

Gong Ting ◽

Wang Zhaolei ◽

Gu Xiaodi ◽

Luo Wenshan ◽

Gao Xianwei ◽

...

Keyword(s):

Seismic Data ◽

Processing Technology ◽

3D Seismic ◽

S Wave ◽

Vector Processing ◽

3D Seismic Data

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Multispectral coherence: Which decomposition should we use?

Interpretation ◽

10.1190/int-2019-0124.1 ◽

2020 ◽

Vol 8 (1) ◽

pp. T115-T129 ◽

Cited By ~ 3

Author(s):

Bin Lyu ◽

Jie Qi ◽

Fangyu Li ◽

Ying Hu ◽

Tao Zhao ◽

...

Keyword(s):

Maximum Entropy ◽

Seismic Data ◽

Decomposition Methods ◽

Small Scale ◽

Equal Weight ◽

3D Seismic ◽

Data Set ◽

Spectral Components ◽

Data Volume ◽

3D Seismic Data

Seismic coherence is commonly used to delineate structural and stratigraphic discontinuities. We generally use full-bandwidth seismic data to calculate coherence. However, some seismic stratigraphic features may be buried in this full-bandwidth data but can be highlighted by certain spectral components. Due to thin-bed tuning phenomena, discontinuities in a thicker stratigraphic feature may be tuned and thus better delineated at a lower frequency, whereas discontinuities in the thinner units may be tuned and thus better delineated at a higher frequency. Additionally, whether due to the seismic data quality or underlying geology, certain spectral components exhibit higher quality over other components, resulting in correspondingly higher quality coherence images. Multispectral coherence provides an effective tool to exploit these observations. We have developed the performance of multispectral coherence using different spectral decomposition methods: the continuous wavelet transform (CWT), maximum entropy, amplitude volume technique (AVT), and spectral probe. Applications to a 3D seismic data volume indicate that multispectral coherence images are superior to full-bandwidth coherence, providing better delineation of incised channels with less noise. From the CWT experiments, we find that providing exponentially spaced CWT components provides better coherence images than equally spaced components for the same computation cost. The multispectral coherence image computed using maximum entropy spectral voices further improves the resolution of the thinner channels and small-scale features. The coherence from AVT data set provides continuous images of thicker channel boundaries but poor images of the small-scale features inside the thicker channels. Additionally, multispectral coherence computed using the nonlinear spectral probes exhibits more balanced and reveals clear small-scale geologic features inside the thicker channel. However, because amplitudes are not preserved in the nonlinear spectral probe decomposition, noise in the noisier shorter period components has an equal weight when building the covariance matrix, resulting in increased noise in the generated multispectral coherence images.

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