Seismic trace interpolation in the <i>f‐x‐y</i> domain

Wang, Yanghua

doi:10.1190/1.1500385

Cited by 85 publications

(38 citation statements)

References 10 publications

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“…The inner eye-wall appears relatively independent from the outer eye-wall with a diameter of 50 km at t = 14 h. It should be noted there are cyclonic vortices located on the outer eye-wall PV ring. A previous study (Wang 2002) also indicates mesoscale vortexes may exist near the eye-wall of hurricanes.…”

Section: (B) Formation Of Spiral Rain Bands Between Concentric Eye-wamentioning

confidence: 99%

“…Could a mesoscale model reproduce the large outer eye-wall, inner eye-wall and spiral rain band between them? Spiral rain bands near hurricane eye-walls have been recently explained by vortex Rossby wave theory (Guinn and Schubert 1993;Möller andMontgomery 1999, 2000;Wang 2002). After MacDonald (1968) hypothesized that the spiral rain bands in hurricanes are associated with Rossby-type waves, vortex Rossby waves in hurricanes have been studied using both observational data (Reasor et al 2000) and simplified numerical models (Melander et al 1987;Carr and Williams 1989;Sutyrin 1989;Shapiro and Montgomery 1993;Smith and Montgomery 1995).…”

Section: Introductionmentioning

confidence: 99%

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Interaction between concentric eye‐walls in super typhoon Winnie (1997)

Zhang

Kuo

Chen

2005

Quart J Royal Meteoro Soc

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SUMMARYSuper typhoon Winnie had one of the largest eye-walls ever recorded in the western Pacific Ocean. The diameter of the outer eye-wall reached 370 km when Winnie passed through Okinawa on 16 August 1997. Observations revealed a 24-hour intensity cycle of concentric eye-walls after the large eye-wall formed. Using the Penn State University/National Center for Atmospheric Research mesoscale model MM5 with 3 km grid horizontal spacing on the finest nested mesh, Winnie was successfully simulated in terms of concentric eye-walls, spiral rain bands in the moat and the cycle of concentric eye-walls.The life cycle of concentric eye-walls is found to be associated with vortex Rossby wave propagation in the moat. The propagation properties of the PV (rainfall) spiral bands are found to be consistent with predictions using vortex Rossby theory. The initial inward vorticity gradient at the inner edge of the outer eye-wall and outward vorticity gradient at the outer edge of the inner eye-wall support inward and outward PV (rainfall) spiral bands, respectively, in the moat at the lower level. At the middle level, only an outward vorticity gradient exists at the outer edge of the inner eye-wall, which permits the development of an outward PV band in the moat at initial time. The inward spiral PV (rain) band moves faster than the outward PV (rain) band and both of them propagate cyclonically. At 09 h, when the fast inward PV band catches up to the slow outward PV band, the slow outward rain band is replaced by the fast inward rain band, resulting in a well-developed and independent inner eye-wall at 14 h. After the vorticity gradient near the inner eye-wall becomes stronger than that near the outer eye-wall at lower level, the whole convective core is attracted to the outer eye-wall. It finally merges with it at 37 h, leaving a weak vortex in the centre. The concentric eye-walls of Winnie interact with each other through vortex Rossby waves.

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Section: (B) Formation Of Spiral Rain Bands Between Concentric Eye-wamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interaction between concentric eye‐walls in super typhoon Winnie (1997)

Zhang

Kuo

Chen

2005

Quart J Royal Meteoro Soc

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“…It is observed that the function ) , ( x d ω is strictly sinusoidal and predictable in the x direction. Consequently, the trace can be predicted by the preceding or following traces with a spatial prediction filter (Spitz 1991, Wang 1999, 2002. .11) shows that the prediction filter in the x − direction is the complex conjugate of the filter in the x + direction.…”

Section: Appendix: Fx Prediction Filteringmentioning

confidence: 99%

Multichannel algorithms for seismic reflectivity inversion

Wang

2016

Journal of Geophysics and Engineering

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Seismic reflectivity inversion is a deconvolution process for quantitatively extracting the reflectivity series and depicting the layered subsurface structure. The conventional method is a single channel inversion and cannot clearly characterise stratified structures, especially from seismic data with low signal-to-noise ratio. Because it is implemented on a trace-by-trace basis, the continuity along reflections in the original seismic data is deteriorated in the inversion results. We propose here multichannel inversion algorithms that apply the information of adjacent traces during seismic reflectivity inversion. Explicitly, we incorporate a spatial prediction filter into the conventional Cauchy-constrained inversion method. We verify the validity and feasibility of the method using field data experiments and find an improved lateral continuity and clearer structures achieved by the multichannel algorithms. Finally, we compare the performance of three multichannel algorithms and merit the effectiveness based on the lateral coherency and structure characterisation of the inverted reflectivity profiles, and the residual energy of the seismic data at the same time.

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“…In order to merge it with a neighboring modern 3D seismic dataset, I attempt first to regularize traces spatially and then to produce a refined output using a f -x-y domain trace interpolation method that is suitable for a spatially aliased data set (Wang, 2002). For this very specific data set, trace regularization is actually accomplished in two steps.…”

Section: Real Data Examplementioning

confidence: 99%

“…For instance, in seismic tomography, one commonly defines the cost function using the L 2 norm (the Euclidean norm) in the data space, incorporating the L 2 norm in the model space. In addition, one uses the L 2 norm of the model derivatives, attempting to penalize large spatial derivatives and to produce a smoothed model (Tarantola, 1987;Wang, 2003). The objective of this paper, however, is precisely opposite, that is, to find a sparse model with minimum structure.…”

Section: Introductionmentioning

confidence: 99%

Sparseness‐constrained least‐squares inversion: Application to seismic wave reconstruction

Wang¹

2003

GEOPHYSICS

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The spectrum of a discrete Fourier transform (DFT) is estimated by linear inversion, and used to produce desirable seismic traces with regular spatial sampling from an irregularly sampled data set. The essence of such a wavefield reconstruction method is to solve the DFT inverse problem with a particular constraint which imposes a sparseness criterion on the least-squares solution. A working definition for the sparseness constraint is presented to improve the stability and efficiency. Then a sparseness measurement is used to measure the relative sparseness of the two DFT spectra obtained from inversion with or without sparseness constraint. It is a pragmatic indicator about the magnitude of sparseness needed for wavefield reconstruction. For seismic trace regularization, an antialiasing condition must be fulfilled for the regularizing trace interval, whereas optimal trace coordinates in the output can be obtained by minimizing the distances between the newly generated traces and the original traces in the input. Application to real seismic data reveals the effectiveness of the technique and the significance of the sparseness constraint in the least-squares solution.

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Seismic trace interpolation in the f‐x‐y domain

Cited by 85 publications

References 10 publications

Interaction between concentric eye‐walls in super typhoon Winnie (1997)

Interaction between concentric eye‐walls in super typhoon Winnie (1997)

Multichannel algorithms for seismic reflectivity inversion

Sparseness‐constrained least‐squares inversion: Application to seismic wave reconstruction

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