接收函数的曲波变换去噪

Attenuation of noise in receiver functions using curvelet transform

压制横向非均匀地壳介质引起的散射波场对于基于水平分层介质模型的接收函数地壳结构成像及其地震各向异性研究至关重要.虽然通过数据叠加和低通滤波,在一定程度上能够压制散射波场,但也有可能导致不希望的波形畸变、信息丢失或数据分辨率降低.为了避免上述问题,本文将近年来快速发展的曲波变换理论用于远震接收函数的散射噪声压制.与勘探地震学不同,我们面临的主要问题在于地震台站和震源的空间缺失导致的接收函数空间不均匀采样.为此,我们将压缩感知理论与曲波变换去噪相结合,在对缺失数据进行波场重建的同时,实现散射噪声的压制.为了论证方法的可行性,本文进行了噪声压制和波场重建的理论检验.并将本文方法用于处理IRIS全球台网固定台站和川西台阵远震接收函数.结果表明:1)地壳介质横向不均匀引起的散射噪声可以得到有效压制,接收函数的信噪比得到提高,震相的可追踪性得到改善,从而利于进一步的接收函数反演和地震各向异性研究;2)缺失数据可以正确重建;3)本文的方法既可用于单台-多事件的数据集,也可用于单个事件-阵列观测的数据去噪,但单台-多事件数据集的结果优于阵列观测的情况.

Suppressing the scattering induced by the laterally heterogeneous media is an important problem for the imaging of the crustal structure and its anisotropy based on the stratified model from receiver functions.Although the scattering is able to be suppressed,to some degree,with stacking technique and low-pass filtering,these may lead to undesired waveform distortion,information loss or resolution reduction.To avoid these problems,we make use of the curvelet transform technique,which is developing rapidly in recent years,to reduce the scattering field in the receiver functions.Unlike exploration seismology,our major challenge is the uneven spatial sampling of the receiver functions,which is caused by the spatially incomplete distribution of stations and earthquake events.To overcome the difficulty,we incorporate the compressed sensing with the curvelet-based denoising to realize simultaneously the denoising and wavefield reconstruction.To verify our method,we test the denoising and wavefield reconstruction with synthetic receiver functions,and then apply our method to the observed data at one of the IRIS stations and the western Sichuan array,respectively.The results show：1）Our method iseffective in suppressing the scattering induced by the laterally heterogeneous media of the crust,so that the signal-to-noise ratio and the spatial traceability of the receiver functions are enhanced significantly;this may help the waveform imaging of the crustal structure and anisotropic parameters;2）Missing data can be reconstructed correctly;3）Our method can be applied to cases including single station and array observations,but it is more effective in the single station case than the array observations.