Impedance inversion of pre-stack seismic data in the depth domain*
Jiang Wei, Chen Xue Hua, Zhang Jie, Luo Xin, Dan Zhi Wei, and Xiao Wei
1. State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China.
2. Key Laboratory of Earth Exploration & Information Techniques of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China.
3. Data Processing Company, Geophysical-China Oilfi eld Services Limited, Zhanjiang 524057, China.
Abstract：
The extensive application of pre-stack depth migration has produced huge volumes of seismic data, which allows for the possibility of developing seismic inversions of reservoir properties from seismic data in the depth domain. It is difficult to estimate seismic wavelets directly from seismic data due to the nonstationarity of the data in the depth domain. We conduct a velocity transformation of seismic data to make the seismic data stationary and then apply the ridge regression method to estimate a constant seismic wavelet. The estimated constant seismic wavelet is constructed as a set of space-variant seismic wavelets dominated by velocities at different spatial locations. Incorporating the weighted superposition principle, a synthetic seismogram is generated by directly employing the space-variant seismic wavelets in the depth domain. An inversion workflow based on the model-driven method is developed in the depth domain by incorporating the nonlinear conjugate gradient algorithm, which avoids additional data conversions between the time and depth domains. The impedance inversions of the synthetic and field seismic data in the depth domain show good results, which demonstrates that seismic inversion in the depth domain is feasible. The approach provides an alternative for forward numerical analyses and elastic property inversions of depth-domain seismic data. It is advantageous for further studies concerning the stability, accuracy, and efficiency of seismic inversions in the depth domain.
2019, Vol. 16 
