Abstract
Multi-component seismic data contain a great deal of vector field information that reflects the situation of the underground medium. However, the processing methods used for multi-component seismic data are still being developed, and effectively retaining and using this information is the difficulty and the focus of the task. Currently, the main-stream processing techniques of multi-component seismic data treat the individual components independently as a scalar field; in this way, they do not excavate the vector features of the wavefield, thus restricting the potential utilities of the effective information. Research into processing methods that are suitable for use with the vector field, which can better retain and use the orientations and the relative amplitude relationship between multi-component seismic data, is urgently needed and represent an important direction for the current development of multi-component seismic data processing techniques. In this paper, we introduce and summarize several existing vector pre-processing techniques, including polarization filtering, de-noising using vector order statistics, group sparse representation, and vector separation of compressional waves and shear waves, to help scholars develop more effective vector field processing methods and to promote the development of vector processing techniques for multi-component seismic data.
Highlights
The three-component seismic data in a three-dimensional Descartes system simultaneously record the linear motion of the particle in the vertical and horizontal directions as well as retain a relatively complete record of seismic wave translations
Diallo et al [43] and Ma [44] proposed to adaptively select the length of the time window according to the instantaneous frequency of the signal; Rene et al [45], Morozov and Smithson [46], Vidale [23], Schimmel and Gallart [47], and Lu et al [26] proposed the method of Complex Trace Analysis (CTA) based on the Hilbert transform to obtain the instantaneous polarization properties of the multi-component seismic signals
Many studies have revealed that multivariate order statistic filters perform better in color image processing because they treat the color image as vector data whereas other filtering methods treat the color image as scalar data [54]. These multivariate order statistic filters can be classified into several categories according to the ordering method for multivariate data, such as the multi-channel α-trimmed mean filter, multichannel Modified-Trimmed Mean (MTM) filter [55], Vector Median Filter (VMF) [56,57], and vector direction filter [58]
Summary
The three-component seismic data in a three-dimensional Descartes system simultaneously record the linear motion of the particle in the vertical and horizontal directions as well as retain a relatively complete record of seismic wave translations. The reverse time migration imaging technology of the elastic wave proposed by He et al [21] and Li et al [22] directly used the vector field as the input These vector processing techniques are still in the initial stages of research, and they are only sporadically distributed on a small number of independent links of the processing procedure for multi-component seismic data. Thereafter, polarization filtering was expanded and applied to suppress surface-waves in multi-component seismic data, separate fast and slow S-waves, and de-noise random signals. The random noise attenuation method proposed by Rodriguez et al [32], based on the 3C group sparsity constrained time-frequency transform, can process the multiple components simultaneously, and retain the relative amplitude relationship among the components and the weak effective signal well. The vector separation of P-waves and S-waves proposed by Lei [33], Lu et al [34], and Li [35] can retain the intrinsic properties of a vector wavefield
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