Application of waveform tomography to a crooked-line 2D land seismic data set

Abstract

Onshore hydrocarbon exploration in the back-arc region of Japan suffers from seismic imaging challenges. Seismic data are typically acquired in a difficult environment: along a crooked line over a rough surface topography underlain by severe near-surface weathering layers. The area is geologically complex, and prestack depth processing is desirable. However, the data quality is suboptimal at near offsets, and it hinders the migration velocity analysis required for depth processing. Thus, a geologic interpretation needs to rely on time-processing results. Nevertheless, some data sets are rich in low-frequency components and contain clear refracted and wide-angle reflected waves, both of which are favorable conditions for the application of waveform tomography. We have used one such data set, and we determined the applicability of waveform tomography to estimate the P-wave velocity distribution. To mitigate difficulties in the data, we carefully optimized waveform tomography strategies and diligently validated the results. We evaluated the effects of the crooked line by conducting waveform tomography in 2D and 2.5D. We determined that for this data set, the effect of the crooked line was not substantial enough to require the 2.5D implementation, but it was large enough to require careful trace editing. We also minimized the effects of large variations in amplitudes due to near-surface effects and source and receiver characteristics, primarily by fitting phase components of the data during waveform tomography, but also by adjusting the amplitudes in a surface-consistent manner to stabilize the source estimation process. The final velocity model delineated known shallow sedimentary structures, but it also revealed deep large-thrust structures critical to the structural understanding of the area. The result confirmed the capability of waveform tomography to yield a reliable velocity model, and it also opened up the possibility of applications of depth processing in the region.