Reflection waveform inversion based on full-band seismic data reconstruction for salt structure inversion

2019 ◽  
Vol 220 (1) ◽  
pp. 235-247
Author(s):  
Guoxin Chen ◽  
Shengchang Chen ◽  
Wencai Yang

SUMMARY Salt structures are high potential targets for oil and gas exploration. However, large-scale salt domes with irregular surfaces pose significant challenges for velocity model building. For full waveform inversion, in the absence of a high-fidelity initial model, the success of the inversion depends on low-frequency seismic data, which are scarce in the exploration data sets. This paper presents a new idea to solve the problem of salt structure velocity modelling. First, we propose an envelope-based full-band seismic data reconstruction algorithm. The smoothness of envelope is used to segment the events in seismic data, and the phase independence of envelope is used for the identification of the seismic event's arrival-time to obtain the apparent reflection sequences of the subsurface. Full-band seismic data are obtained by convolving the apparent reflection sequence with full-band source. Window averaging function and threshold strategy are used to ensure the accuracy of seismic event segmentation and the stability of the algorithm when dealing with noisy data. Then the multiscale reflection waveform inversion based on reconstructed data is proposed for salt structure velocity building. The numerical experiment results of the Sigbee2A model demonstrate the performance of the inversion algorithm in the case where the seismic data lack low-frequency components and contain noise. The limitations of the algorithm have also been analysed and studied.

Author(s):  
Feng Qian ◽  
Cangcang Zhang ◽  
Lingtian Feng ◽  
Cai Lu ◽  
Gulan Zhang ◽  
...  

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.


Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. R199-R206 ◽  
Author(s):  
Wansoo Ha ◽  
Changsoo Shin

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.


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