Full waveform inversion based on a local traveltime correction and zero-mean cross-correlation-based misfit function

Dong, Shiqi; Han, Liguo; Hu, Yong; Yin, Yuchen

doi:10.1007/s11600-019-00388-x

Cited by 8 publications

(2 citation statements)

References 51 publications

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“…Wang et al (2016) used dynamic time warping (DTW), which can detect the travel-time difference between synthetic and observed data to help FWI avoid cycle skipping. Dong S. et al, 2020 proposed a local travel time correction approach to decrease travel-time differences between waveforms to improve waveform matching. Chen et al (2022) proposed a penalized differential DTW misfit function to further identify the travel-time difference between observed and synthetic data.…”

Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on dynamic data matching of convolutional wavefields

et al. 2023

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Cycle skipping problem caused by the absent of low frequencies and inaccurate initial model makes full waveform inversion (FWI) deviate from the true model. A novel method is proposed to mitigate cycle skipping phenomenon by dynamic data matching which improves the matching of synthetic and observed events to regulate the updating of initial model in a correct direction. 1-dimentional (1-D) Gaussian convolutional kernels with different lengths are used to extract features of each time sample in each trace which represents the integrated properties of wavefield at different time ranges centered on each time sample. According to the minimum Euclidean distance of the features, the optimally matched pairs of time samples in the observed and synthetic trace can be found. A constraint evaluates the reliability of dynamic matching by attenuating the amplitude of synthetic data according to the values of traveltime differences between each pairs of optimally matched time samples is proposed to improve the accuracy of data matching. In addition, Gaussian kernels have the capability to extract features of time samples contaminated by strong noises accurately to improve the robustness of the propose method further. The selection scheme of optimal parameters is discussed and concluded to ensure the convergence of the proposed method. Numerical tests on Marmousi model verify the feasibility of the propose method. The proposed method provides a new approach to tackle the convergence problem of FWI when using the field seismic data.

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Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on dynamic data matching of convolutional wavefields

et al. 2023

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“…Wang et al [18] used the dynamic warping technique, which can detect the traveltime difference between the synthetic and observed data, to help FWI avoid cycle skipping. Dong et al [19] proposed a local traveltime time correction approach to decrease the traveltime differences between waveforms at different time to improve the matching between waveforms. Hu et al [20] measured the differences between the weighted local correlation-phase of the synthetic and observed data to make the inversion process more linear.…”

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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A new multi-scale signed direct envelope inversion with more accurate amplitude polarity information

Luo

Chen

et al. 2020

Acta Geophys.

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Full waveform inversion based on a local traveltime correction and zero-mean cross-correlation-based misfit function

Cited by 8 publications

References 51 publications

Full waveform inversion based on dynamic data matching of convolutional wavefields

Full waveform inversion based on dynamic data matching of convolutional wavefields

Convolution Coding and Amplitude Attenuation-Based Full Waveform Inversion

A new multi-scale signed direct envelope inversion with more accurate amplitude polarity information

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