Full-intensity waveform inversion

Liu, Yike; He, Bin; Lu, Hui; Zhang, Zhendong; Xie, Xiao‐Bi; Zheng, Yingcai

doi:10.1190/geo2017-0682.1

Cited by 31 publications

(14 citation statements)

References 35 publications

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“…It is recognized that FWI is sensitive to inaccurate starting models and the lack of low‐frequency data due to the cycle‐skipping problem. To mitigate the problem of cycle‐skipping, substantial efforts have been made to establish a reliable starting model for FWI, such as emphasizing low‐wavenumber updates (Alkhalifah, ; Kazei & Alkhalifah, ; Wu & Alkhalifah, ; Xie, ; Yao et al, ), modulating low‐frequency components (Bharadwaj et al, ; Bozdağ et al, ; Chi et al, ; Liu et al, ; Shin & Cha, ), and building more effective objective functions (Leeuwen & Mulder, ; Yi et al, ; Zhang et al, ; Zhang & Alkhalifah, ).…”

Section: Introductionmentioning

confidence: 99%

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

Liu

2020

JGR Solid Earth

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The accuracy of the background velocity analysis is critical to image the subsurface structure in seismology. Wave‐equation migration velocity analysis using source‐domain common‐image gathers requires picking the relative moveouts based on semblance analysis. However, the picking process is complex and time‐consuming. To avoid picking and maintain efficiency in generating CIGs, an automatic wave‐equation migration velocity analysis method is proposed utilizing the focusing properties of the Radon‐domain common‐image gathers. Seismic events in the Radon‐domain common‐image gathers appear focused if an accurate migration velocity model is used; otherwise, the resultant events will be unfocused. The objective function is defined in the Radon domain to measure focus of the common‐image gathers automatically. The proposed migration velocity analysis method links the defocusing information to the migration velocity update under the assumption that model perturbations only result in slope shifts in the common‐image gathers. This method provides a reasonably accurate background velocity model for imaging and full‐waveform inversion without requiring an accurate initial velocity model. Tests on synthetic and field data demonstrate the effectiveness of the method. This method is tolerable to both inaccurate initial velocity models and the lack of low‐frequency data.

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Section: Introductionmentioning

confidence: 99%

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

Liu

2020

JGR Solid Earth

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“…Hu et al [12] proposed a waveform mode decomposition method to recover the low frequency components of the observed data. Liu et al [13] fitted the intensity of the observed and synthetic data, and found that sufficient low-frequencies in the intensity data can help FWI avoid cycle skipping. Sun and Demanet [14] used high frequency signals as training set and used deep learning based on the convolutional neural network to extrapolate low frequencies.…”

Section: Introductionmentioning

confidence: 99%

Convolution Coding and Amplitude Attenuation-Based Full Waveform Inversion

et al. 2020

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Full waveform inversion (FWI) is a powerful method to reconstruct the properties of the subsurface media. However, the standard FWI is a non-unique and ill-posed inversion problem, which requires proper techniques to avoid cycle skipping phenomena. Sufficient low-frequencies in the observed data and a good initial model are helpful to mitigate cycle skipping problem, but they are hard to be provided in real cases. Therefore, to reduce the non-linearity and improve the convergence of FWI, we developed a novel approach inspired by using the convolutional neural network to mitigate the cycle skipping problem. We use the 1-dimentional (1-D) convolution kernels of different lengths to convolve each seismic trace of the synthetic and observed data to extract the different features of each time sample, and then we use the Sigmoid function to encode each time sample of the synthetic and observed data according to the polarity of the features. By comparing the coding similarity for the time sample of the synthetic and observed data at the corresponding time, we can identify which part of the synthetic data is well matched with the observed data and which part is mismatched. For the mismatched synthetic data, we attenuate them to reduce their interference on the gradient, thereby the cycle skipping problem can be mitigated. In this case, we use the global-correlation misfit function which behaves better in mitigating the interference of the incorrect amplitude information and highlighting the phase information with weaker non-linearity. In addition, the convolution coding and amplitude attenuation-based method has a strong anti-noise capability and can be combined with the encoded multisource scheme to save the computational costs. Marmousi model tests demonstrate that convolution coding and amplitude attenuation-based FWI behaves better than the standard FWI in generating convergent inverted result. INDEX TERMS Amplitude attenuation, coding, convolution, full waveform inversion.

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“…Recently, lots of efforts have been made to find better objective functions that are immune from cycle skipping (Liu et al, 2018;Zhang et al, 2018a;Wu et al, 2019;Yi et al, 2019). The conventional wiggle-to-wiggle subtraction based measurement fails FWI when the predicted and observed data exceed the half-cycle limit (Virieux and Operto, 2009).…”

Section: Introductionmentioning

confidence: 99%

Regularized elastic full-waveform inversion using deep learning

Zhang

Alkhalifah

2019

GEOPHYSICS

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Obtaining high-resolution models of the earth, especially around the reservoir, is crucial to properly image and interpret the subsurface. We have developed a regularized elastic full-waveform inversion (FWI) method that uses facies as the prior information. Deep neural networks (DNNs) are trained to estimate the distribution of facies in the subsurface. Here, we use facies extracted from wells as the prior information. Seismic data, well logs, and interpreted facies have different resolution and illumination to the subsurface. Besides, a physical process, such as anelasticity in the subsurface, is often too complicated to be fully considered. Therefore, there are often no explicit formulas to connect the data coming from different geophysical surveys. A deep-learning method can find the statistically correct connection without the need to know the complex physics. In our deep-learning scheme, we specifically use it to assist the inverse problem instead of the widely used labeling task. First, we conduct an adaptive data-selection elastic FWI using the observed seismic data and obtain estimates of the subsurface, which do not need to be perfect. Then, we use the extracted facies information from the wells and force the estimated model to fit the facies by training DNNs. In this way, a list of facies is mapped to a 2D or 3D inverted model guided mainly by the structure features of the model. The multidimensional distribution of facies is used either as a regularization term or as an initial model for the next waveform inversion. Our method has two main features: (1) It applies to any kind of distribution of data samples and (2) it interpolates facies between wells guided by the structure of the estimated models. Results with synthetic and field data illustrate the benefits and limitations of this method.

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Full-intensity waveform inversion

Cited by 31 publications

References 35 publications

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

Convolution Coding and Amplitude Attenuation-Based Full Waveform Inversion

Regularized elastic full-waveform inversion using deep learning

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