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.

中文翻译：

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地震相引导高精度地质异常识别方法及应用


流行的地质异常（如断层、河道、溶洞、裂缝等）识别方法，如相干立方法、相似法、似然法等，应用于目标层位平坦化地震数据，通常可以得到较高精度的地质异常识别结果。 ，与使用目标层位未平坦地震数据的对应数据进行比较。然而，由于地质构造（或地震数据）的复杂性和目标层位的层位跟踪精度，这些方法在实现高精度地质异常识别结果方面仍面临巨大挑战。为了最大限度地减少地质结构的复杂性和目标层位的层位跟踪精度对地质异常识别的影响，从而获得高精度的地质异常识别结果，为基于深度学习的地质异常识别方法提供精确的标签，我们提出基于地震相概念和互相关算法的地震相引导高精度地质异常识别方法（FHGI）。 FHGI包含FHGI流程图、地震相引导逐道高精度地质异常识别因子计算（FTGC）；其中FTGC由基于目标层位的地震数据平坦化（THF）、地震相引导的目标道二维次震数据集生成（FTG）、基于互相关算法的目标层位进一步平坦化（CFA）和基于互相关系数的高精度地质异常识别因子计算（CGC）。 THF旨在减少地质结构复杂性的影响，并为FTG提供输入3D地震数据。 FTG旨在自动生成目标道对应的二维地震数据集，从而进一步降低地质结构复杂性的影响，为CFA提供输入二维地震数据集。 CFA以FTG结果中的目标轨迹作为互相关函数计算的参考，然后利用它们进一步平坦化FTG结果中的目标地平线，从而最小化目标地平线跟踪精度的影响，并提供CGC 的输入二维次震数据集。 CGC以CFA结果中的目标轨迹作为互相关系数计算的参考，然后用于高精度地质异常识别因子计算，从而提供高精度地质异常识别结果。公共合成地震数据集和实际的 3D 地震数据集示例表明，FHGI 作为地质异常识别技术具有巨大潜力。