Staining algorithm for seismic modeling and migration

2017

GEOPHYSICS

Self Cite

The staining algorithm is introduced to improve the signal-tonoise 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 frequencydomain 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.

Section: Wave Equation In the Complex Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalized staining algorithm for seismic modeling and migration

2017

GEOPHYSICS

Self Cite

“…Moreover, Wu et al (1996) used high-order finite difference operators to achieve reverse time migration in three dimensions, and compared the features of high-order and low-order schemes. To compensate for poorly imaged areas, Chen and Jia (2014) proposed a staining algorithm for seismic modeling and imaging. This method performs dye marking on the target area and defines the staining wavefield.…”

Section: Introductionmentioning

confidence: 99%

An efficient local imaging strategy based on illumination analysis with deep learning

Rong

2021

Front. Earth Sci.

Self Cite

We propose a deep-learning-based illumination analysis and efficient local imaging method. Based on the wavefield forward modeling, seismic illumination can intuitively express the energy propagation of direct waves, reflected waves, and transmitted waves, while it requires high calculation costs. We use a series of convolution operations in deep learning to establish the nonlinear relationship between the model and the illuminations to realize single-shot illumination result of the model. Stacking the single shot illumination results obtained by the network prediction can further help determine the target area. For the target area, we use a deep learning method to obtain the low illumination area of the geological model. Each shot has contribution to the low illuminated area; single shot is selected based on the contribution of the shot being greater than the average illuminance, and the low illumination area is imaged by reverse time migration on the selected shot gather. The trained convolutional neural network can help us quickly obtain the single shot illumination result of the model, which is convenient to analyze the energy distribution of various areas of geological model, and do further imaging for target areas. Using part of the shot gathers to image a local area can recover the complex geological structure of the area and improve the efficiency of reverse time migration especially for 3D problems. This method has universal applicability and is suitable for local imaging of various complex models such as subsalt areas and deep regions.

“…Staining algorithm (SA) (Chen and Jia, 2014;Li and Jia, 2017) is a processing technique to improves the image quality of poorly imaged areas, which has been successfully applied to the seismic migration. Similar to fate mapping in developmental biology (Dale and Slack, 1987;Ginhoux et al, 2010) which traces target cells and their progeny at later stages of development, SA traces and extracts those waves that pass through target structures.…”

Section: Introductionmentioning

confidence: 99%

A high-resolution weak-signal reconstruction method for electromagnetic wave modeling and reverse time migration of ground-penetrating radar data

SEG Technical Program Expanded Abstracts 2017

Sun

2017

Self Cite

Staining algorithm (SA) was proposed to extract waves that pass through the target area and image the target area with the extracted wave to obtain a higher image quality. In this study, we proposed an approach to reconstruct weak signals for electromagnetic (EM) wave modeling and established the correspondence between subsurface target structures and the reconstructed wavefield. When using the reconstructed partial wavefield in the reverse time migration (RTM) of ground-penetrating radar (GPR) data, we can mute nontarget information from the conventional RTM image and keep target related structures. Numerical experiments show that our method is able to extract low-energy waves under lossy layers. Furthermore, the constructed electromagnetic wavefield is actually a subset of the real electromagnetic wavefield. We applied this method to target-oriented imaging and obtained high-quality images of target structures in lossy media.