Deep learning-based identification method for fracture-cavity bodies using angle-domain data

Abstract

Unlike horizontal layered sedimentary reservoirs, fracture-cavity bodies are primarily characterized by vertical strike-slip faulting and dissolution, leading to distinct seismic wave imaging features at different incidence angles. This paper proposes a novel approach for fracture-cavity bodies identification by leveraging angle-domain seismic information, with a focus on the utilization of large-angle data By systematically analyzing seismic data partitioned into small-angle (0–6°), medium-angle (7–26°), and large-angle (27–36°) domains, this work broadens the framework of deep learning-based identification for fracture-cavity systems and enriches seismic interpretation methodologies for complex geological bodies. First, forward modeling and comparative analysis of real seismic data validate that large-angle seismic data exhibit superior capability in resolving fracture-cavity structures. Subsequently, deep learning models are employed to extract seismic signatures of these reservoirs. Field applications demonstrate that conventional methods exhibit limited accuracy due to noise interference, whereas large-angle seismic data provide superior resolution in characterizing fracture-cavity geometries, offering more comprehensive spatial representations than traditional migration datasets. In summary, this research establishes a new strategy for identifying fracture-cavity bodies using large-angle seismic information, providing a transferable reference for analogous geological settings.