A modified nonstretching NMO correction using matching-pursuit algorithm

2014 ◽  
Author(s):  
Xiaohui Yang ◽  
Siyuan Cao ◽  
Qiong Liu ◽  
Yuzhou Wang ◽  
Dian Yuan
Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. U9-U18 ◽  
Author(s):  
Bo Zhang ◽  
Kui Zhang ◽  
Shiguang Guo ◽  
Kurt J. Marfurt

Wide-azimuth, long-offset surveys are becoming increasingly common in unconventional exploration plays where one of the key routine processes is maintaining data fidelity at far offsets. The conventional NMO correction that processes the data sample-by-sample results in the well-known decrease of frequency content and amplitude distortion through stretch, which lowers the seismic resolution and hinders [Formula: see text] and amplitude variation with offset and azimuth (AVAz) analysis of the long-offset signal. To mitigate the stretch typically associated with large offsets, we use a matching-pursuit-based normal moveout correction (MPNMO) to reduce NMO-stretch effects in long-offset data. MPNMO corrects the data wavelet-by-wavelet rather than sample-by-sample, thereby avoiding stretch. We apply our technique (1) to a set of synthetic gathers and (2) as part of a residual velocity analysis workflow to a prestack time-migrated data volume acquired over the Northern Chicontepec Basin, Mexico. Test results show that MPNMO can produce relatively nonstretched events and generate higher temporal resolution prestack gathers.


2018 ◽  
Vol 173 ◽  
pp. 03073
Author(s):  
Liu Yang ◽  
Ren Qinghua ◽  
Xu Bingzheng ◽  
Li Xiazhao

In order to solve the problem that the wideband compressive sensing reconstruction algorithm cannot accurately recover the signal under the condition of blind sparsity in the low SNR environment of the transform domain communication system. This paper use band occupancy rates to estimate sparseness roughly, at the same time, use the residual ratio threshold as iteration termination condition to reduce the influence of the system noise. Therefore, an ICoSaMP(Improved Compressive Sampling Matching Pursuit) algorithm is proposed. The simulation results show that compared with CoSaMP algorithm, the ICoSaMP algorithm increases the probability of reconstruction under the same SNR environment and the same sparse degree. The mean square error under the blind sparsity is reduced.


2018 ◽  
Vol 67 (9) ◽  
pp. 2058-2068 ◽  
Author(s):  
Carlos Morales-Perez ◽  
Jose Rangel-Magdaleno ◽  
Hayde Peregrina-Barreto ◽  
Juan Pablo Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez

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