Application of the 3D common‐reflection‐surface stack workflow in a crystalline rock environment - Ahmed, K; Schwarz, B; Gajewski, D (European Association of Geoscientists and Engineers, 2015)
Seismic data from crystalline or hardrock environments usually exhibit a poor signal‐to‐noise ratio due to low impedance contrasts in the subsurface. Moreover, instead of continuous reflections, we observe a lot of steeply dipping events resembling parts of diffractions. The conventional seismic processing (common midpoint stack and dip moveout) is not ideally suited for imaging such type of data. Common‐reflection‐surface stack processing considers more traces during the stack than common midpoint processing, and the resulting image displays a better signal‐to‐noise ratio. In the last decade, the common‐reflection‐surface stack method was established as a powerful tool to provide improved images, especially for low‐fold or noise‐contaminated data. The common‐reflection‐surface stack and all attributes linked to it are obtained using a coherence‐based automatic data‐driven optimization procedure. In this work we applied the common‐reflection‐surface stack workflow to 3D crystalline rock seismic data, which were acquired near Schneeberg, Germany, for geothermal exploration. The common‐reflection‐surface stack itself provided an image of good signal‐to‐noise ratio. However, for data from environments with low acoustic impedance and poor velocity information, coherence, which is automatically obtained in the optimization procedure, provides an alternative way to image the subsurface. Despite the reduced resolution, for these data, the coherence image provided the best results for an initial analysis. Utilized as a weight, the coherence attribute can be used to further improve the quality of the stack. By combining the benefits of a decreased noise level with the high‐resolution and high‐interference properties of waveforms, we argue that these results may provide the best images in an entirely data‐driven processing workflow for the Schneeberg data.