Wave-equation migration velocity analysis using plane-wave common-image gathers

Abstract

Wave-equation migration velocity analysis (WEMVA) based on subsurface-offset, angle domain, or time-lag common-image gathers (CIGs) requires significant computational and memory resources because it computes higher dimensional migration images in the extended image domain. To mitigate this problem, we have developed a WEMVA method using plane-wave CIGs. Plane-wave CIGs reduce computational cost and memory storage because they are directly calculated from prestack plane-wave migration and the number of plane waves is often much smaller than the number of shots. In the case of an inaccurate migration velocity, the moveout of plane-wave CIGs is automatically picked by a semblance analysis method, which is then linked to the migration velocity update by a connective function. Numerical tests on two synthetic data sets and a field data set validate the efficiency and effectiveness of this method.

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

“…Although the additional computational cost that arise from the Radon transformation are negligible, the memory storage of SCIGs is huge when hundreds or even thousands of sources are used. To overcome this problem, we can use a plane‐wave domain imaging condition (Liu & Liu, ; Zhang et al, ) that can be incorporated in a new objective function based on the corresponding CIGs (Guo & Schuster, ). In addition, we used RTM to obtain SCIGs, which can be replaced by other migration schemes to further improve efficiency.…”

“…Here, only the 2D case is considered, thus x = ( x , z ), where x and z represent the horizontal and vertical locations, respectively. To estimate the accuracy of the velocity model, the flatness of the SCIGs are often measured by picking the relative moveout (AI‐Yahya, ; Guo & Schuster, ). Although the picking process is largely automated, manual intervention is sometimes unavoidable and time‐consuming (Luo et al, ).…”

“…Alternatively, WEMVA is more robust than FWI, and it has been widely applied where the initial velocity model is far from the true one. It provides a very good migration velocity for RTM (AI‐Yahya, ; Biondi & Symes, ; Guo & Schuster, ; Sava et al, ; Symes, ). Conventional WEMVA methods update the migration velocity model by measuring the flatness of the common‐image gathers (CIGs) and minimizing the nonflatness in their objective functions.…”

“…Developing a fully automatic migration velocity analysis technique is important for imaging and inversion in reflection seismology. To avoid picking and keep high efficiency in generating the CIGs (Guo & Schuster, ; Sun & Alkhalifah, ), we have introduced a WEMVA method in the Radon domain, which is denoted as RWEMVA. This process is based on the focusing properties of Radon‐domain common‐image gathers (RCIGs).…”

Multiparameter deblurring filter and its application to elastic migration and inversion

“…The marine data are treated as the negative of the average of the normal stress components (Lu et al, 2009). The elastic P-and S-wave velocity tomograms (Guo and Schuster, 2017) shown in Figure 13 are used as the migration velocity models. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 This paper presented here as accepted for publication in Geophysics prior to copyediting and composition.…”