Generalized staining algorithm for seismic modeling and migration

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. T17-T26 ◽  
Author(s):  
Qihua Li ◽  
Xiaofeng Jia

The staining algorithm is introduced to improve the signal-to-noise ratio (S/N) of poorly illuminated subsurface structures in seismic imaging. However, the amplitudes of the original and the stained wavefield, i.e., the real and the imaginary wavefields, differ by several orders of magnitude, and the waveform of the stained wavefield may be greatly distorted. We have developed a generalized staining algorithm (GSA) to achieve amplitude preservation in the stained wavefield. The real wavefield and the stained wavefield propagate in the same velocity model. A source wavelet is used as the source of the real wavefield; however, the real wavefield is extracted from the stained area as the source of the stained wavefield. The GSA maintains some properties of the original staining algorithm. The stained wavefield is synchronized with the real wavefield, and it contains only information relevant to the target region. By imaging with the stained wavefield, we obtain higher S/Ns in images of target structures. The most significant advantage of our method is the amplitude preservation of the stained wavefield, which means that this method could potentially be used in quantitative illumination analysis and velocity model building. The GSA could be adopted easily for frequency-domain wavefield propagators and time-domain propagators. Furthermore, the GSA can generate any number of stained wavefields. Numerical experiments demonstrate these features of the GSA, and we apply this method in target-oriented modeling and imaging as well as obtaining amplitude-preserved stained wavefields and higher S/Ns in images of target structures.

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. S121-S129 ◽  
Author(s):  
Bo Chen ◽  
Xiaofeng Jia

In seismic migration, some structures such as those in subsalt shadow zones are not imaged well. The signal in these areas may be even weaker than the artifacts elsewhere. We evaluated a method to significantly improve the signal-to-noise ratio (S/N) in poorly illuminated areas of the model. We constructed a “phantom” wavefield: an extension of the wavefield to the complex domain. The imaginary wavefield was synchronized with the real wavefield, but it contained only the events relevant to a target region of the model, which was specified using a staining algorithm. The real wavefield interacted with the entire model. However, all structures except for the target were transparent to the imaginary wavefield, which is excited only when the real wavefront arrives at the target structure. The real and the imaginary source wavefields were crosscorrelated with the regular receiver wavefield. The results were revealed in two images: the conventional reverse time migration image and an image of the target region only. Synthetic experiments showed that the S/N of the target structures was improved significantly, with other structures effectively muted.


Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB191-WB207 ◽  
Author(s):  
Yaxun Tang ◽  
Biondo Biondi

We present a new strategy for efficient wave-equation migration-velocity analysis in complex geological settings. The proposed strategy has two main steps: simulating a new data set using an initial unfocused image and performing wavefield-based tomography using this data set. We demonstrated that the new data set can be synthesized by using generalized Born wavefield modeling for a specific target region where velocities are inaccurate. We also showed that the new data set can be much smaller than the original one because of the target-oriented modeling strategy, but it contains necessary velocity information for successful velocity analysis. These interesting features make this new data set suitable for target-oriented, fast and interactive velocity model-building. We demonstrate the performance of our method on both a synthetic data set and a field data set acquired from the Gulf of Mexico, where we update the subsalt velocity in a target-oriented fashion and obtain a subsalt image with improved continuities, signal-to-noise ratio and flattened angle-domain common-image gathers.


2021 ◽  
Vol 40 (6) ◽  
pp. 460-463
Author(s):  
Lionel J. Woog ◽  
Anthony Vassiliou ◽  
Rodney Stromberg

In seismic data processing, static corrections for near-surface velocities are derived from first-break picking. The quality of the static corrections is paramount to developing an accurate shallow velocity model, a model that in turn greatly impacts the subsequent seismic processing steps. Because even small errors in first-break picking can greatly impact the seismic velocity model building, it is necessary to pick high-quality traveltimes. Whereas various artificial intelligence-based methods have been proposed to automate the process for data with medium to high signal-to-noise ratio (S/N), these methods are not applicable to low-S/N data, which still require intensive labor from skilled operators. We successfully replace 160 hours of skilled human work with 10 hours of processing by a single NVIDIA Quadro P6000 graphical processing unit by reducing the number of human picks from the usual 5%–10% to 0.19% of available gathers. High-quality inferred picks are generated by convolutional neural network-based machine learning trained from the human picks.


Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. S151-S155 ◽  
Author(s):  
Mikhail Baykulov ◽  
Stefan Dümmong ◽  
Dirk Gajewski

A processing workflow was introduced for reflection seismic data that is based entirely on common-reflection-surface (CRS) stacking attributes. This workflow comprises the CRS stack, multiple attenuation, velocity model building, prestack data enhancement, trace interpolation, and data regularization. Like other methods, its limitation is the underlying hyperbolic assumption. The CRS workflow provides an alternative processing path in case conventional common midpoint (CMP) processing is unsatisfactory. Particularly for data with poor signal-to-noise ratio and low-fold acquisition, the CRS workflow is advantageous. The data regularization feature and the ability of prestack data enhancement provide quality control in velocity model building and improve prestack depth-migrated images.


Author(s):  
M. Pouget ◽  
C. Beigbeder ◽  
F. Gamar-Sadat ◽  
H. Prigent ◽  
M. Drubigny ◽  
...  

2019 ◽  
Vol 38 (3) ◽  
pp. 214-219 ◽  
Author(s):  
Dhananjay Tiwari ◽  
Jian Mao ◽  
James Sheng

The application of full-waveform inversion (FWI) to bring high resolution to the velocity model is becoming a standard approach in the velocity model-building workflow. Diving wave FWI in conjunction with reflection FWI (RFWI) has been widely used in the Gulf of Mexico (GOM) to optimize the suprasalt model. Accuracy of a velocity model from tomography is dependent on residual moveout (RMO) picking accuracy. In a good signal-to-noise ratio area, the confidence of RMO picking is high. But gathers in areas affected by gas exhibit poor event continuity, which makes it difficult to get accurate RMO picks. In such a geologic regime, FWI can improve the velocity model and therefore the final image quality. There are two main components of a velocity model from the GOM area: the first is the sediment, and the second is salt geometry. In the beginning of the model-building cycle, it is most likely that salt geometry is not accurately defined. This inaccuracy leads to a big mismatch between synthetic and observed data for both diving wave FWI and RFWI. One way to handle this situation is to start with the salt model and iteratively adjust the salt interpretation as FWI model building progresses from lower to higher frequencies. Another approach could be eliminating the salt-related energy from the input and then using the sediment-only model for FWI. We are proposing a desalt approach in which we try to eliminate or reduce the salt-related energy from the input data and then use a sediment-only velocity model as a starting model for the entire suprasalt FWI workflow. We will present a case study in which, by adapting the desalt workflow, we could manage to do more FWI iterations by eliminating salt interpretation.


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