Channel detection using the self-adaptive generalized S-transform
Naihao Liu, Bo Zhang, Jinghuai Gao, Yijie Zhang, Xiudi Jiang · 2018
Achieving a proper time-frequency (TF) resolution is the key to extract information from seismic data using TF algorithms and characterize reservoir properties using decomposed frequency components. The generalized Stransform (GST) is one of the most widely used TF algorithms. However, it is difficult to choose an optimized parameter set for the whole seismic data set. In this paper, we propose to set parameters of the GST adaptively using the instantaneous frequency (IF) of seismic traces. We name the proposed workflow as the self-adaptive generalized S-transform (SAGST). To demonstrate the validity and effectiveness of the proposed SAGST, we apply it to field data to detect channels. Real data examples illustrate that SAGST can research a better TF resolution. Presentation Date: Thursday, October 18, 2018 Start Time: 8:30:00 AM Location: 209A (Anaheim Convention Center) Presentation Type: Oral