The crossdip correction as a tool to improve imaging of crooked line seismic data: A case study from the post-glacial Burträsk fault, Sweden

Abstract

Abstract. Understanding the development of post-glacial faults and their associated seismic activity is crucial for risk assessment in Scandinavia. However, imaging these features and their geological environment is complicated due to special challenges of their hardrock setting, such as weak impedance contrasts, sometimes high noise levels and crooked acquisition lines. A crooked line geometry can cause time shifts that seriously de-focus and deform reflections containing a crossdip component. Advanced processing methods like swath 3D processing and 3D pre-stack migration can, in principle, handle the crooked line geometry, but may fail when the noise level is too high. For these cases, the effects of reflector crossdip can be compensated for by introducing a linear correction term into the standard processing flow. However, existing implementations of the crossdip correction rely on a slant stack approach which can, for some geometries, lead to a duplication of reflections. Here we present a module for the crossdip correction that avoids the reflection duplication problem by shifting the reflections prior to stacking. Based on tests with synthetic data, we developed an iterative processing scheme where a sequence consisting of crossdip correction, velocity analysis and DMO correction is repeated until the stacked image converges. Using our new module to reprocess a reflection seismic profile over the post-glacial Burträsk Fault in Northern Sweden increased the image quality significantly. Strike and dip information extracted from the crossdip analysis helped to interpret a set of southeast dipping reflections as shear zones belonging to the regional scale Burträsk Shear Zone (BSZ), implying that the BSZ itself is not a vertical, but a southeast dipping feature. Our results demonstrate that the crossdip correction is a highly useful alternative to more sophisticated processing methods for noisy datasets. This highlights the often underestimated potential of rather simple, but noise-tolerant methods, in processing hardrock seismic data.