基于广义 Curvelet 变换和相空间特征聚类的 VSP 波场分离方法

Vertical Seismic Profiling (VSP) contains a large amount of stratum geological information and is a bridge between reflection seismic data and well logging data. VSP data is high-resolution and rich in information, which contains upgoing waves, downgoing waves, transition waves and other types of wavefields. The VSP data is always used for the calibration of the geological reflection interface and the inversion of the medium attenuation parameters. How to effectively separate wavefield is one of the keys to applying VSP data in oil and gas exploration. The Generalized Curvelet Transform(GCT) is a multi-scale and multi-directional phase space transform, which can extract characteristics of the wavefield in phase space. In this paper, we focus on the upgoing and downgoing waves separation of VSP wavefield and propose a VSP wavefield separation method based on GCT and phase space feature clustering. Firstly, it uses the GCT to extract features from the VSP wavefield and constructs a feature data set incorporating phase space angular information. Further, the K-means clustering algorithm is used to classify the wavefield features in the phase space. Finally, the inverse transform is performed to the classifications to achieve the adaptive separation of the upgoing and downgoing waves of the VSP data. In order to verify the effectiveness of the method, the proposed method is applied to the wavefield separation of the synthetic data and field VSP data, and compared with the commonly used F-K transform-based wavefield separation method. The results show that the proposed method has high angular resolution, strong anti-noise ability, and well fidelity and amplitude preserving, which can provide important basic data for the subsequent imaging and the analysis of stratigraphic characterizations.