Application of Envelope in Salt Structure Velocity Building: From Objective Function Construction to the Full-Band Seismic Data Reconstruction

Chen, Guoxin; Yang, Wayne; Chen, Sheng‐Chang; Liu, Yanan; Gu, Zheng

doi:10.1109/tgrs.2020.2978125

Cited by 23 publications

(3 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Migration velocity analysis (MVA) can recover the kinematic features of the model by evaluating the quality of the image through, for example, measuring the flatness of angle-domain common-image gathers [4], [5]. We can also extract specific features from the data to retrieve a good initial velocity model, like seismic envelope inversion [6], [7], [8], phase-based waveform inversion [9], [10]. Reflection-waveform inversion (RWI) is able to retrieve a smooth initial velocity model for FWI [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Song

Alkhalifah

2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented by solving for the frequency-domain representation of the wavefield using the finite-difference method. The approach requires matrix inversions and affords limited flexibility to accommodating irregular model geometries. On the other hand, physics-informed neural network (PINN) uses the underlying physical laws as loss functions to train the neural network (NN) to provide flexible continuous functional approximations of the solutions without matrix inversions. By including a dataconstrained term in the loss function, the trained NN can reconstruct a wavefield that simultaneously fits the recorded data and satisfies the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we rely on a small-size NN to predict the velocity using the reconstructed wavefield. In this velocity prediction NN, spatial coordinates are used as input data to the network and the scattered Helmholtz equation is used to define the loss function. After we train this network, we are able to predict the velocity in the domain of interest. We develop this PINN-based WRI method and demonstrate its potential using a part of the Sigsbee2A model and a modified Marmousi model. The results show that the PINN-based WRI is able to invert for a reasonable velocity with very limited iterations and frequencies, which can be used in a subsequent FWI application.

show abstract

Section: Introductionmentioning

confidence: 99%

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Song

Alkhalifah

2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In addition, low-frequency enhancement methods, when possible, can play an important role in mitigating cycle-skipping issues. Synthetic studies show that the signal envelope can produce ultra low-frequency content below the lowest physical frequency in the source spectrum, which can be exploited to improve the background velocity model (Bozdag et al 2011;Wu et al 2014;Hu et al 2019;Chen et al 2020). On the other hand, low-frequency extrapolation with multiple signal classification (Li & Demanet 2016) and deep learning training (Sun & Demanet 2020;Hu et al 2021) recently presents some encouraging synthetic and field data results for initial velocity design.…”

Section: Introductionmentioning

confidence: 99%

Localized adaptive waveform inversion: theory and numerical verification

Yong

Brossier

Métivier

et al. 2022

Geophysical Journal International

View full text Add to dashboard Cite

Summary Correctly interpreting phase events thanks to data processing techniques based on correlation or deconvolution has been the focus of numerous studies in the field of high-resolution seismic imaging using full-waveform inversion. To mitigate the non-convexity of the misfit function and the risk to converge towards non-informative local minima, correlation and deconvolution techniques make it possible to focus on phase information instead of amplitude information and to design more convex misfit function, alleviating the dependency of the full-waveform inversion process on the accuracy of initial models. Such techniques however rely on the assumption that the phase events can be compared one by one, or that all the phase events are shifted in time in a similar way. This assumption is not satisfied in practice, which limits the effectiveness of these correlation/deconvolution-based methods. To overcome the issue, we propose to account for the non-stationary relation between observed and predicted data through a local in-time deconvolution technique, based on time-frequency analysis of the signal using a Gabor transform. This makes it possible to estimate instantaneous time-shift between locally coherent phase events. This strategy generalizes the conventional normalized deconvolution technique, which has been popularized under the name of adaptive waveform inversion. To support the introduction of our novel method, we compare it with four misfit functions based respectively on classical cross-correlation, penalized cross-correlation, penalized deconvolution, and adaptive waveform inversion. We analyze the behavior of these methods on specific scenarios, and then propose a comparison on 2D synthetic benchmarks. We show how our “localized” adaptive waveform inversion applies in these realistic tests and overcomes some of the limitations of the aforementioned techniques.

show abstract

“…The envelope calculated by Hilbert transformation loses polarity information. The signed DEI method makes up for this disadvantage and improves the inversion quality of deep structures by adding polarity to envelope calculation [20]. Other improvements of the DEI method include multi-offset DEI strategy [21], joint instantaneous amplitude and phase DEI [22], and angle domain DEI [23].…”

Section: Introductionmentioning

confidence: 99%

Wavefield Decomposition-Based Direct Envelope Inversion and Structure-Guided Perturbation Decomposition for Salt Building

et al. 2021

View full text Add to dashboard Cite

Due to the large-scale and strong perturbation features of salt bodies, it is very difficult to complete a good salt building with the conventional full waveform inversion (FWI) method without low-frequency data and prior information. The direct envelope inversion (DEI) method is quite effective for salt building when seismic data lack low-frequency information. However, in the current DEI studies, the calculation of the envelope field, which needs a nonlinear envelope operator, does not consider the influences of wavefield overlapping, and the inversion quality of subsalt areas needs further improvements. In this paper, we analyze the effects of wavefield overlapping on envelope field calculation and propose a new envelope field calculation method based on wavefield decomposition. Then, we propose a wavefield decomposition-based direct envelope inversion (WDDEI) method, in which the gradient is calculated using the new envelope field. To improve the inversion quality of subsalt structures, we propose a structure-guided perturbation decomposition method, which can separate the strong scattering salt information from the DEI results with the help of reverse time migration images. Finally, numerical tests are conducted on a modified SEG/EAGE salt model to demonstrate the effectiveness and the antinoise performance of the proposed method.

show abstract

Application of Envelope in Salt Structure Velocity Building: From Objective Function Construction to the Full-Band Seismic Data Reconstruction

Cited by 23 publications

References 78 publications

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Localized adaptive waveform inversion: theory and numerical verification

Wavefield Decomposition-Based Direct Envelope Inversion and Structure-Guided Perturbation Decomposition for Salt Building

Contact Info

Product

Resources

About