A method for non-orthogonal seismic polarization-vector separation

Lei, Jun

doi:10.1111/j.1365-246x.2005.02709.x

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…The abovementioned vector wavefield separation methods all imply that the P-waves and S-waves are orthogonally polarized, and this assumption is generally not valid for an anisotropic medium [82]. For the wavefield separation problem of non-orthogonal P-waves and S-waves in an anisotropic medium, Lei [33] and Lu et al [34] discussed this problem from the perspective of natural earthquakes and exploration seismology and provided a solution. The basic idea of the solution is to apply a rotational transform to transform the vertical and horizontal components based on the Cartesian coordinate system to the affine coordinate system composed of the wave vector directions of realistic P-waves and S-waves, thereby achieving separation of the P-wave and S-wave.…”

Section: The Vector Separating Methods Of the P-wave And S-wavementioning

confidence: 99%

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Discussions on the Processing of the Multi-Component Seismic Vector Field

Wang

Sun

et al. 2019

Applied Sciences

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Multi-component seismic data contain a great deal of vector field information that reflects the situation of the underground medium. However, the processing methods used for multi-component seismic data are still being developed, and effectively retaining and using this information is the difficulty and the focus of the task. Currently, the main-stream processing techniques of multi-component seismic data treat the individual components independently as a scalar field; in this way, they do not excavate the vector features of the wavefield, thus restricting the potential utilities of the effective information. Research into processing methods that are suitable for use with the vector field, which can better retain and use the orientations and the relative amplitude relationship between multi-component seismic data, is urgently needed and represent an important direction for the current development of multi-component seismic data processing techniques. In this paper, we introduce and summarize several existing vector pre-processing techniques, including polarization filtering, de-noising using vector order statistics, group sparse representation, and vector separation of compressional waves and shear waves, to help scholars develop more effective vector field processing methods and to promote the development of vector processing techniques for multi-component seismic data.

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Section: The Vector Separating Methods Of the P-wave And S-wavementioning

confidence: 99%

“…Separation of the P-waves and S-waves is a special de-noising processing, which is very important in processing multi-component seismic data. The vector separation of P-waves and S-waves proposed by Lei [33], Lu et al [34], and Li [35] can retain the intrinsic properties of a vector wavefield.…”

Section: Introductionmentioning

confidence: 99%

Discussions on the Processing of the Multi-Component Seismic Vector Field

Wang

Sun

et al. 2019

Applied Sciences

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“…Polarization analysis was further treated in other works and utilized for phase identification (e.g., Vidale, 1986;Jurkevics, 1988;Roberts et al, 1989;Cichowicz, 1993;Chael, 1997;Wang and Teng, 1997;Bai and Kennett, 2000). More recently, Lei (2005) treated P and S separation using a nonorthogonal seismic polarization scheme.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Automatic Detectors of P and S Waves Using Singular Value Decomposition

Kurzon¹,

Vernon²,

Rosenberger³

et al. 2014

Bulletin of the Seismological Society of America

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We implement a new method for automatic detection of P and S phases using singular value decomposition (SVD) analysis. The method is based on the realtime iteration algorithm of Rosenberger (2010) for the SVD of three-component seismograms. The algorithm identifies the apparent incidence angle by applying SVD and separates the waveforms into their P and S components. We apply the algorithm to filtered waveforms and then either set detectors on the incidence angle and singular values or apply signal-to-noise ratio (SNR) detectors for P and S picking on the filtered and SVD-separated channels. The Anza Seismic Network and the recent portable deployment in the San Jacinto fault zone area provide a very dense seismic network for testing the detection algorithm in a diverse setting, including events with different source mechanisms, stations with different site characteristics, and ray paths that diverge from the approximation used in the SVD algorithm. A 2-30 Hz Butterworth band-pass filter gives the best performance for a large variety of events and stations. We use the SVD detectors on many events and present results from the complex and intense aftershock sequence of the M w 5.2 June 2005 event. This sequence was thoroughly reviewed by several analysts, identifying 294 events in the first hour, all located in a dense cluster around the mainshock. We used this dataset to fine-tune the automatic SVD detection, association, and location, achieving a 37% automatic identification and location of events. All detected events fall within the dense cluster, and there are no false events. An ordinary SNR detector does not exceed 11% success and has a wider spread of locations (not within the reviewed cluster). The preknowledge of the phases picked (P or S) by the SVD detectors significantly reduces the noise created by phase-blind SNR detectors.

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Separation of fast and slow shear waves and prediction of fracture parameters based on non-orthogonal assumptions

Luo,

Yang,

Zhou

et al. 2024

Geophysical Journal International

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Summary Natural fractures play a significant role in oil and gas reservoirs. Accurate predictions of fracture parameters are vital in reservoir prediction and oil and gas development. The birefringent phenomenon of shear waves in fractured media makes shear wave splitting (SWS) analysis an important tool in formulating fracture predictions. The traditional SWS analysis method is based on an orthogonal assumption of fast and slow shear waves. However, in an orthotropic medium composed of a background vertical transversely isotropic medium and a set of vertical fractures, fast and slow shear waves are not necessarily orthogonal. This causes the traditional SWS analysis method to fail. To solve this problem, we proposed an SWS analysis algorithm with a non-orthogonal assumption of fast and slow shear waves in this study. First, we introduced a parameter (difference angle) to characterise the angle between slow shear waves and the normal polarisation directions of the fast shear waves. Subsequently, based on the traditional two-parameter scanning algorithm, a parameter was added to facilitate three-parameter scanning. In addition, we derived an expression for the two-parameter scanning objective function using the non-orthogonal assumption. Two-parameter scanning can accurately extract fast and slow wave time delay data, but it cannot determine an accurate fast shear wave polarisation direction. Therefore, we optimised the three-parameter scanning algorithm as follows: first, we used two-parameter scanning to obtain the fast and slow wave time delays and then performed further scanning to determine the polarisation direction of the fast shear wave and difference angle. The optimisation algorithm significantly improved the computational efficiency. Subsequently, we tested the accuracy of this method using synthetic single-trace and three-component vertical seismic profile data. We demonstrated the implementation process of the three-parameter scanning method using actual data, separated fast and slow shear waves, and predicted fracture parameters. The final fracture parameters were verified.

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A method for non-orthogonal seismic polarization-vector separation

Cited by 7 publications

References 30 publications

Discussions on the Processing of the Multi-Component Seismic Vector Field

Discussions on the Processing of the Multi-Component Seismic Vector Field

Real-Time Automatic Detectors of P and S Waves Using Singular Value Decomposition

Separation of fast and slow shear waves and prediction of fracture parameters based on non-orthogonal assumptions

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