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multichannel processing

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Journal Article
Journal: Geophysics
Published: 01 December 1998
Geophysics (1998) 63 (6): 1971–1985.
... spectrum. This information can be extracted using multichannel processing procedures. The observed spectrum is considered as the sum of three terrestrial and three background component spectra, which are determined through suitable airborne and ground calibrations. The background components can...
Journal Article
Journal: Geophysics
Published: 01 June 1970
Geophysics (1970) 35 (3): 461–470.
.... This appears to provide a motive for developing multichannel processes which expand further our processing capabilities beyond the essentially single channel ones now in use. The present study evaluates the multichannel processing potential afforded by present day seismic digital field recording systems...
Series: Society of Exploration Geophysicists Geophysical References Series
Published: 01 January 1990
DOI: 10.1190/1.9781560802440.ch1
EISBN: 9781560802440
... processing. Stacking, the multichannel process introduced first, was soon followed by poststack multichannel processes such as velocity filtering (Fail and Grau, 1963; Embree et al., 1963) and migration. Prestack multichannel processes became more feasible and even mandatory when the increase in number...
Series: Society of Exploration Geophysicists Geophysical References Series
Published: 01 January 1990
DOI: 10.1190/1.9781560802440
EISBN: 9781560802440
Journal Article
Journal: The Leading Edge
Published: 01 October 2005
The Leading Edge (2005) 24 (10): 984–989.
...Ray Abma; Nurul Kabir Abstract The purpose of this article is to illustrate the characteristics of various interpolation methods. Seismic data need to be interpolated when the spatial sampling of acquired data is coarser than the spatial sampling required for a multichannel process...
FIGURES | View All (25)
Journal Article
Journal: Interpretation
Published: 14 September 2020
Interpretation (2020) 8 (4): T793–T801.
..., the single-trace process can have a lack of lateral information in the squeezed results and lead to some discontinuous geologic information that will mislead the interpreter. Thus, to improve the stability of SSGST, we have developed a multichannel seismic trace squeezing method. Multichannel SSGST (MSSGST...
Journal Article
Published: 01 September 2005
Journal of Environmental and Engineering Geophysics (2005) 10 (3): 307–322.
...Julian Ivanov; Choon B. Park; Richard D. Miller; Jianghai Xia Abstract Accurate estimation of the fundamental-mode dispersion curve is the most critical processing step of many shallow surface-wave methods. Use of multichannel analysis of surface waves (MASW), has proven very effective...
FIGURES | View All (9)
Journal Article
Journal: Geophysics
Published: 14 March 2011
Geophysics (2011) 76 (2): U1–U11.
... . Oppenheim A. V. Schafer R. W. , 1989 . Discrete-time signal processing : Prentice Hall . Park C. B. Miller R. D. Xia J. , 1999 , Multichannel analysis of surface waves : Geophysics , 64 , no. 3 , 800 – 808 , doi: 10.1190/1.1444590 . Santamarina J. C...
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Image
The inverted result of the synthetic record test: (a) multichannel inverted AI profile, (b) the partial inverted results with single-trace processing in the left panel and multichannel processing in the right pane, (c) the comparison of AI curves at CDP 240 (left panel) and 800 (right panel), and (d) the original record and the reconstructed record at CDP 240 (top panel) and 800 (bottom panel).
Published: 03 December 2018
Figure 2. The inverted result of the synthetic record test: (a) multichannel inverted AI profile, (b) the partial inverted results with single-trace processing in the left panel and multichannel processing in the right pane, (c) the comparison of AI curves at CDP 240 (left panel) and 800 (right
Journal Article
Journal: Geophysics
Published: 03 May 2013
Geophysics (2013) 78 (3): S157–S164.
...Samuel H. Gray ABSTRACT Seismic migration is a multichannel process, in which some of the properties depend on various grid spacings. First, there is the acquisition grid, which actually consists of two grids: a grid of source locations and, for each source location, a grid of receiver locations...
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Journal Article
Journal: Geophysics
Published: 01 October 1989
Geophysics (1989) 54 (10): 1306–1317.
... positioning, vertical plants, identical geophones, perfect ground coupling, etc.). Variations in receiver array response may degrade the effectiveness of multichannel processing and analysis schemes that rely upon channel-to-channel waveform constancy. In effect, array-response variation is a form of noise...
Image
Progressive multichannel correlation (PMCC) array processing and detection results for infrasound waves from the sixth test UNE17, recorded at CHNAR. Waveforms are band‐pass filtered from 0.5 to 5.0 Hz and aligned with respect to the seismic origin time of the test. The three panels above the waveforms display back‐azimuth, trace velocity, and relative amplitude of detected signals. The color version of this figure is available only in the electronic edition.
Published: 28 April 2022
Figure 7. Progressive multichannel correlation (PMCC) array processing and detection results for infrasound waves from the sixth test UNE17, recorded at CHNAR. Waveforms are band‐pass filtered from 0.5 to 5.0 Hz and aligned with respect to the seismic origin time of the test. The three panels
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r(x). The correlation processor then estimates the PDF and shaping parameter  (see Ⓔ equation S1) from the resulting time series. (Bottom) Correlation detector preprocessing results and a deterministic‐correlation parameter ρ∞ are input to a cone processor that evaluates an intermediate decision rule. This rule then computes the target data’s normalized projection s(x) onto a template‐waveform cone that is geometrically represented in Figure 3. This nonlinear transformation defines a mapping from r(x) to s(x) through function r(s) that defines (along with NE and ) the PDF (equation 19). The processor then uses a fixed false‐alarm‐on‐noise probability PrFA (lightly shaded area under ) to estimate threshold  and thereby form a decision rule for detecting target waveforms in x.
Published: 13 September 2016
Figure 4. Processing operations of a multichannel cone detector. (Top) a template is scanned against a longer, commensurate target data stream to produce a continuous, scalar correlation statistic r ( x ). The correlation processor then estimates the PDF and shaping parameter (see Ⓔ
Image
The land seismic section after inverse Q-filtering. Any improvement in the continuity of the events should be reliable because the inverse Q-filtering algorithm works trace by trace (i.e., is not a multichannel process).
Published: 02 June 2006
Figure 12. The land seismic section after inverse Q -filtering. Any improvement in the continuity of the events should be reliable because the inverse Q -filtering algorithm works trace by trace (i.e., is not a multichannel process).