Processing of anisotropic data in the τ-p domain: II — Common-conversion-point sorting

Geophysics ◽  
2005 ◽  
Vol 70 (4) ◽  
pp. D29-D36 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
P Wave ◽  
Data Driven ◽  
Common Source ◽  
Converted Wave ◽  
Conversion Point ◽  
Sorting Data ◽  
Arbitrary Strength ◽  
Data Driven Approach ◽  
P Domain ◽  
Time Offset

Common-conversion-point (CCP) sorting of P-SV converted-wave data is conventionally done by first sorting data into common asymptotic-conversion-point (CACP) gathers and then computing the involved CCP shifts from analytic approximations. I explore an alternative method where the latter step is replaced by an entirely data-driven approach. Moveout curves of correlated P-P and P-SV reflections in collocated CMP and CACP gathers are first scanned for points of equal slowness. A common-source slowness indicates that the downgoing branches of the P-P and P-SV waves overlap if the conversion occurs at the reflecting interface. The P-SV conversion point is then assumed to be situated underneath the associated P-P wave midpoint. A migration of amplitudes from CACP to CCP gathers is straightforward once the exact CCP position is known. This data-driven approach requires kinematic information only and is exact for laterally homogeneous media with arbitrary strength of anisotropy if horizontal symmetry planes are present at all depths. Both time-offset and τ-p domain implementations are possible, although the latter are preferred.

Data-driven, target-oriented, kinematic prediction and subtraction of multiples from pure and mode-converted multicomponent data

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V105-V114 ◽  
Author(s):  
Juanjuan Cao ◽  
George A. McMechan
Keyword(s):  
Time Window ◽  
P Wave ◽  
Data Driven ◽  
Propagation Path ◽  
Common Source ◽  
Converted Wave ◽  
Before And After ◽  
Explicit Consideration ◽  
Data Adaptive ◽  
Polarity Correction

Most multiple removal algorithms focus on multiples of primary P-wave reflections; removal of multiples of converted reflections have not received comparable attention, so explicit consideration is overdue. A target-oriented algorithm predicts converted wave multiples by coupling apparent slownesses, and then subtracts them from elastic common-source data in a data-adaptive window. Prediction is based on matching apparent slownesses in common-source and common-receiver gathers at all source and receiver locations along the propagation path. Predictions use only offset and traveltime, of the primary pure and converted waves that produce the multiples, picked from common-source gathers, and the slownesses calculated from them. Higher-order multiples can be predicted by repeating this process to match slownesses at a sequence of alternating source and receiver locations in turn. Primary reflections (e.g., SS, SP, and PS) that are considered to be noise, can also be subtracted. The predictions are data-driven and require no velocities, angles, reflector orientations or free-surface topography. Any single component (usually vertical) may be used to identify and pick the traveltimes. The resulting predictions are also valid for all other components. The subtraction involves flattening the predicted time trajectory of the multiple, followed by trace averaging to estimate the local wavelet at each location in a moving trace and time window that contains the wavelet of the multiple. The subtraction is data-adaptive, and implicitly involves amplitude and phase information, so separate or prior estimation of the source time or directivity functions is not required. Two synthetic examples showed that the slowness-based algorithm is successful in predicting and reducing converted wave multiples in an elastic medium. Migrated P-wave subsurface images are generated before and after multiple removal to evaluate the performance. Polarity correction of the horizontal component (either before or after subtraction) ensures coherent stacking.

S-Zero Stack: A converted wave processing to extract subsurface density information

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. N1-N10
Author(s):  
Keshan Zou
Keyword(s):  
Incident Angle ◽  
Shear Velocity ◽  
P Wave ◽  
Density Contrast ◽  
Richards Equation ◽  
Maximum Magnitude ◽  
S Wave ◽  
Converted Wave ◽  
Amplitude Variation With Offset ◽  
Conversion Point

Analyzing the Aki-Richards equation for converted waves, I found that it is possible to decouple the effect of density contrast from that of shear velocity contrast. The two terms were mixed when the P-wave incident angle was less than 30°, but they started to separate at a middle angle range (approximately 40°). The term related to S-wave velocity contrast reached zero at an incident angle around 60°. However, the other term, which was related to the density contrast, did not reverse polarity until 90°. Furthermore, this density term reached almost the maximum (magnitude) around 60°. Based on those characteristics, I designed a new method called “S-Zero Stack” to capture the density contrast reliably at the subsurface interface without going to inversion. S-Zero Stack captured subsurface density anomalies using a special stacking method. It is simple but robust, even when there is noise in the common-conversion-point gathers. Combined with the traditional P-wave amplitude-variation-with-offset technique, S-Zero Stack of PS-waves may help discriminate commercial gas from fizz in gas sand and could be a useful tool in shale gas exploration to locate lower-density anomalies (sweet spots).

Converted‐wave reflection seismology over inhomogeneous, anisotropic media

Geophysics ◽  
1999 ◽  
Vol 64 (3) ◽  
pp. 678-690 ◽  
Author(s):  
Leon Thomsen
Keyword(s):  
Velocity Ratio ◽  
Anisotropic Media ◽  
P Wave ◽  
Image Point ◽  
Physical Parameters ◽  
Pure Mode ◽  
Good Precision ◽  
Converted Wave ◽  
Wave Data ◽  
Conversion Point

Converted‐wave processing is more critically dependent on physical assumptions concerning rock velocities than is pure‐mode processing, because not only moveout but also the offset of the imaged point itself depend upon the physical parameters of the medium. Hence, unrealistic assumptions of homogeneity and isotropy are more critical than for pure‐mode propagation, where the image‐point offset is determined geometrically rather than physically. In layered anisotropic media, an effective velocity ratio [Formula: see text] (where [Formula: see text] is the ratio of average vertical velocities and γ2 is the corresponding ratio of short‐spread moveout velocities) governs most of the behavior of the conversion‐point offset. These ratios can be constructed from P-wave and converted‐wave data if an approximate correlation is established between corresponding reflection events. Acquisition designs based naively on γ0 instead of [Formula: see text] can result in suboptimal data collection. Computer programs that implement algorithms for isotropic homogeneous media can be forced to treat layered anisotropic media, sometimes with good precision, with the simple provision of [Formula: see text] as input for a velocity ratio function. However, simple closed‐form expressions permit hyperbolic and posthyperbolic moveout removal and computation of conversion‐point offset without these restrictive assumptions. In these equations, vertical traveltime is preferred (over depth) as an independent variable, since the determination of the depth is imprecise in the presence of polar anisotropy and may be postponed until later in the flow. If the subsurface has lateral variability and/or azimuthal anisotropy, then the converted‐wave data are not invariant under the exchange of source and receiver positions; hence, a split‐spread gather may have asymmetric moveout. Particularly in 3-D surveys, ignoring this diodic feature of the converted‐wave velocity field may lead to imaging errors.

Some comments on common-asymptotic-conversion-point (CACP) sorting of converted-wave data in isotropic, laterally inhomogeneous media

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. U29-U36 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
Wave Velocity ◽  
Velocity Ratio ◽  
P Wave ◽  
Pure Mode ◽  
S Wave ◽  
Converted Wave ◽  
Wave Data ◽  
Conversion Point ◽  
Isotropic Media ◽  
Zero Offset

Common-midpoint (CMP) sorting of pure-mode data in arbitrarily complex isotropic or anisotropic media leads to moveout curves that are symmetric around zero offset. This greatly simplifies velocity determination of pure-mode data. Common-asymptotic-conversion-point (CACP) sorting of converted-wave data, on the other hand, only centers the apexes of all traveltimes around zero offset in arbitrarily complex but isotropic media with a constant P-wave/S-wave velocity ratio everywhere. A depth-varying CACP sorting may therefore be required to position all traveltimes properly around zero offset in structurally complex areas. Moreover, converted-wave moveout is nearly always asymmetric and nonhyperbolic. Thus, positive and negative offsets need to be processed independently in a 2D line, and 3D data volumes are to be divided in common azimuth gathers. All of these factors tend to complicate converted-wave velocity analysis significantly.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

A Mostly Data-Driven Approach to Inverse Text Normalization

2017 ◽  
Author(s):  
Ernest Pusateri ◽  
Bharat Ram Ambati ◽  
Elizabeth Brooks ◽  
Ondrej Platek ◽  
Donald McAllaster ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Text Normalization
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