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首页> 外文期刊>Pure and Applied Geophysics >Calculation of Station-Representative Isotropic Receiver Functions
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Calculation of Station-Representative Isotropic Receiver Functions

机译:站代表各向同性接收器功能的计算

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摘要

The estimation of one-dimensional (1-D) isotropic structures is routine work in most receiver function (RF) analyses that generally use a reference radial RF (RRF) for each station. However, the assumption of negligible back-azimuthal dependencies in a set of RRFs for a station may not be valid because of anisotropic layering, dipping structures, or incorrect sensor orientations. This work presents a comprehensive procedure to obtain a station-representative isotropic RRF, which can be applied automatically to prepare RRF data. The method incorporates a harmonic stripping method with a grid-search for sensor orientations. An optimum angle for the sensor orientation is determined by searching for the angle that minimizes the energy in the tangential component RF (TRF). For each searched angle, possible effects by anisotropy and dipping structures are iteratively suppressed by an inversion process to exclude two- and four-robe back-azimuthal patterns. The performance of the method was first confirmed with a test using a set of highly noisy composite RRFs and TRFs. The method was then applied to RF data from the southern Korean Peninsula and southwestern Japan. Obtained isotropic RRFs and measured station orientations were found to be reliable in comparisons with results from neighboring stations and previous studies. As an automatized routine pre-process, the obtained isotropic RRF data are particularly useful for estimating 1-D isotropic structures in migration or inversion studies, which are potentially affected by back-azimuthal dependencies in RF data calculated through conventional averaging methods.
机译:一维(1-D)各向同性结构的估计是在大多数接收器功能(RF)分析中的常规工作,其通常用于每个站的参考径向RF(RRF)。然而,由于各向异性分层,浸渍结构或传感器方向不正确的传感器方向,在车站的一组RRF中的可忽略的后方位角依赖性的假设可能无效。这项工作提出了一种综合的程序,可以获得站代表的各向同性RRF,可以自动应用以准备RRF数据。该方法包括具有网格搜索的谐波剥离方法,用于传感器方向。通过搜索最小化切向分量RF(TRF)中的能量的角度来确定传感器方向的最佳角度。对于每个搜索的角度,通过反转过程迭代地抑制各向异性和浸渍结构的可能效果,以排除两个和四个长袍反向方案。首先使用一组高度嘈杂的复合RRF和TRF进行测试首先通过测试证实该方法的性能。然后将该方法应用于来自南朝鲜半岛和日本西南部的RF数据。发现了获得的各向同性RRF和测量的站取向在与邻近站和先前研究的结果的比较中可靠。作为自动化常规预处理,所获得的各向同性RRF数据对于估计迁移或反转研究中的1-D各向同性结构特别有用,这可能受到通过传统平均方法计算的RF数据中的反向方依赖性的影响。

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