Seismic Facies-Guided High-Precision Geological Anomaly Identification Method and Application

The popular geological anomaly (such as fault, river course, cave, and crack) identification methods, such as coherence cube, semblance, likelihood, and others, usually can achieve higher precision geological anomaly identification results when applied to the target horizon flattened seismic data, comparing to their counterparts using the target horizon-unflattened seismic data. However, these methods still face great challenges in achieving high-precision geological anomaly identification results, due to the complexity of the geological structure (or the seismic data) and the horizon tracking accuracy of the target horizon. To minimize the impact of the complexity of geological structure and the horizon tracking accuracy of the target horizon in geological anomaly identification, thereby obtaining high-precision geological anomaly identification results and providing precise labels for deep-learning-based geological anomaly identification methods, we propose a seismic facies-guided high-precision geological anomaly identification method (FHGI), basing on the concept of seismic facies and the cross-correlation algorithm. FHGI contains the flowchart of FHGI, and the seismic facies-guided trace-by-trace high-precision geological anomaly identification factor calculation (FTGC); in which FTGC consists of the target horizon-based seismic data flattening (THF), the seismic facies-guided target trace 2-D subseismic dataset generation (FTG), the cross-correlation algorithm-based target horizon further flattening (CFA), and the cross-correlation coefficient-based high-precision geological anomaly identification factor calculation (CGC). The THF aims to reduce the impact of the complexity of the geological structure and provide the input 3-D seismic data for the FTG. FTG aims to automatically generate the 2-D subseismic dataset corresponding to the target trace, thereby further reducing the impact of the complexity of the geological structure and providing the input 2-D subseismic dataset for CFA. CFA takes the target trace in the result of FTG as the reference for cross-correlation functions calculation and then uses them to further flatten the target horizon in the result of FTG, thereby minimizing the impact of the horizon tracking accuracy of the target horizon and providing the input 2-D subseismic dataset for CGC. CGC takes the target trace in the result of CFA as the reference for cross-correlation coefficient calculation and then uses them for high-precision geological anomaly identification factor calculation, thereby providing high-precision geological anomaly identification results. A public synthetic seismic dataset and actual 3-D seismic dataset examples demonstrate that FHGI has great potential as a technique for geological anomaly identification.