Stable explicit wavefield extrapolation using recursive operators

2002 ◽  
Author(s):  
Thang Nguyen ◽  
John Castagna ◽  
Richard Day
Keyword(s):  
Wavefield Extrapolation ◽  
Recursive Operators

Tutorial on rotational seismology and its applications in exploration geophysics

Geophysics ◽  
2017 ◽  
Vol 82 (5) ◽  
pp. W17-W30 ◽  
Author(s):  
Zhenhua Li ◽  
Mirko van der Baan
Keyword(s):  
Ground Motion ◽  
Rotational Motion ◽  
Moment Tensor ◽  
Spatial Gradient ◽  
Rotational Seismology ◽  
Rotational Ground Motion ◽  
Reflection Seismic ◽  
Full Analysis ◽  
Pressure Changes ◽  
Wavefield Extrapolation

Traditionally, seismological interpretations are based on the measurement of only translational motions, such as particle displacement, velocity, and/or acceleration, possibly combined with pressure changes; yet theory indicates that rotational motions should also be observed for a complete description of all ground motions. The recent and ongoing development of rotational sensors renders a full analysis of the translational and rotational ground motion possible. We have developed the basic mathematical theory related to rotational motion. And we also evaluated several instruments used to directly measure the rotational ground motion, which may be applicable for exploration geophysics. Finally, we made several applications of rotational motion in exploration geophysics, namely, (1) P- and S-wavefield separation, (2) wavefield reconstruction, (3) ground-roll removal, (4) microseismic event localization and reflection seismic migration by wavefield extrapolation, and (5) moment tensor inversion. The cited research shows that in particular, the information on the spatial gradient of the wavefield obtained by rotational sensors is beneficial for many purposes. This tutorial is meant to (1) enhance familiarity with the concept of rotational seismology, (2) lead to additional applications, and (3) fast track the continued development of rotational sensors for global and exploration geophysical use.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

3D passive wavefield imaging using the energy norm

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. KS13-KS27 ◽  
Author(s):  
Daniel Rocha ◽  
Paul Sava ◽  
Jeffrey Shragge ◽  
Ben Witten
Keyword(s):  
Wave Mode ◽  
Source Location ◽  
Time Step ◽  
Elastic Wave Equation ◽  
Imaging Condition ◽  
Mode Decomposition ◽  
Passive Seismic ◽  
Anisotropic Elastic ◽  
Wavefield Extrapolation ◽  
Multicomponent Data

In passive seismic monitoring of microseismicity, full-wavefield imaging offers a robust approach for the estimation of source location and mechanism. With multicomponent data and the full 3D anisotropic elastic wave equation, the coexistence of P- and S-modes at the source location in time-reversal wavefield extrapolation allows the development of imaging conditions that identify the source position and radiation pattern. We have developed an imaging condition for passive wavefield imaging that is based on energy conservation and is related to the source mechanism. Similar to the correlation between the decomposed P- and S-wavefields — the most common imaging condition used in passive elastic wavefield imaging — our proposed imaging condition compares the different modes present in the displacement field producing a strong and focused correlation at the source location without costly wave-mode decomposition at each time step. Numerical experiments demonstrate the advantages of the proposed imaging condition (compared to PS correlation with decomposed wave modes), its sensitivity with respect to velocity inaccuracy, and its quality and efficacy in estimating the source location.

DATA ALLOCATION STRATEGIES FOR PARALLEL IMAGE PROCESSING ALGORITHMS

1995 ◽  
Vol 09 (04) ◽  
pp. 615-634 ◽  
Author(s):  
VIRGINIE MARION-POTY ◽  
SERGE MIGUET
Keyword(s):  
Parallel Implementation ◽  
Image Data ◽  
Data Allocation ◽  
Load Imbalance ◽  
Parallel Image Processing ◽  
Parallel Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Recursive Operators ◽  
Allocation Strategies

This paper discusses several data allocation strategies used for the parallel implementation of basic imaging operators. It shows that depending on the operator (sequential or parallel, with regular or irregular execution time), the image data must be partitioned in very different manners: The square sub-domains are best adapted for minimizing the communication volume, but rectangles can perform better when we take into account the time for constructing messages. Block allocations are well adapted for inherently parallel operators since they minimize interprocessor interactions, but in the case of recursive operators, they lead to nearly sequential executions. In this framework, we show the usefulness of block-cyclic allocations. Finally, we illustrate the fact that allocating the same amount of image data to each processor can lead to severe load imbalance in the case of some operators with data-dependant execution times.

Compressed wavefield extrapolation

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. SM77-SM93 ◽  
Author(s):  
Tim T. Lin ◽  
Felix J. Herrmann
Keyword(s):  
Wave Propagation ◽  
Compressed Sensing ◽  
Signal Recovery ◽  
New Paradigm ◽  
New Approach ◽  
Measurement Basis ◽  
Recent Developments ◽  
Data Volume ◽  
Extrapolation Problem ◽  
Wavefield Extrapolation

An explicit algorithm for the extrapolation of one-way wavefields is proposed that combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3D. By using ideas from compressed sensing, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by nonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can be successfully recovered when the measurement basis is incoherent with the representa-tion in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can be successfully extrapolated in the modal domain, despite evanescent wave modes. The degree to which the wavefield can be recovered depends on the number of missing (evanescent) wavemodes and on the complexity of the wavefield. A proof of principle for the compressed sensing method is given for inverse wavefield extrapolation in 2D, together with a pathway to 3D during which the multiscale and multiangular properties of curvelets, in relation to the Helmholz operator, are exploited. The results show that our method is stable, has reduced dip limitations, and handles evanescent waves in inverse extrapolation.

GPU implementation of minimal dispersion recursive operators for reverse time migration

2015 ◽  
Author(s):  
Allon Bartana* ◽  
Dan Kosloff ◽  
Brandon Warnell ◽  
Chris Connor ◽  
Jeff Codd ◽  
...  
Keyword(s):  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Recursive Operators ◽  
Gpu Implementation
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