Rayleigh-wave multicomponent crosscorrelation-based source strength distribution inversions. Part 2: a workflow for field seismic data
Rayleigh-wave multicomponent crosscorrelation-based source strength distribution inversions. Part 2: a workflow for field seismic data
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DOI：
10.1093/gji/ggaa284
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发表时间：
2020-09
影响因子：
2.8
通讯作者：
Zongbo Xu;T. Mikesell;J. Umlauft;G. Gribler
中科院分区：
文献类型：
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作者：
Zongbo Xu;T. Mikesell;J. Umlauft;G. Gribler
环境地震源分布（例如位置和强度）的估计可以帮助研究地震源机制和地下结构调查。人们可以通过将全波形反演（FWI）理论应用于地震（噪声）互相关来反演环境地震（噪声）源分布。这种估计方法特别适用于没有明显体波到的地震记录。在反演前需要进行数据预处理，但一些常用于背景噪声层析成像的预处理方法会使背景（噪声）源分布估计产生偏差，不应用于FWI。考虑到这一点，我们提出了一个完整的工作流程，从原始地震噪声记录，通过预处理程序的反演。我们提出的工作流程与现场数据的例子在Hartoušov，捷克共和国，地震源是CO2脱气区在地球表面（即一个martole或mofette）。讨论了在处理和反演过程中，速度模型不准确、滞弹性和阵列灵敏度等因素对反演结果的影响。所提出的工作流程可以用于跨不同尺度的现场数据的多组分数据。
Estimation of ambient seismic source distributions (e.g. location and strength) can aid studies of seismic source mechanisms and subsurface structure investigations. One can invert for the ambient seismic (noise) source distribution by applying full-waveform inversion (FWI) theory to seismic (noise) crosscorrelations. This estimation method is especially applicable for seismic recordings without obvious body-wave arrivals. Data pre-processing procedures are needed before the inversion, but some pre-processing procedures commonly used in ambient noise tomography can bias the ambient (noise) source distribution estimation and should not be used in FWI. Taking this into account, we propose a complete workflow from the raw seismic noise recording through pre-processing procedures to the inversion. We present the workflow with a field data example in Hartoušov, Czech Republic, where the seismic sources are CO2 degassing areas at Earth’s surface (i.e. a fumarole or mofette). We discuss factors in the processing and inversion that can bias the estimations, such as inaccurate velocity model, anelasticity and array sensitivity. The proposed workflow can work for multicomponent data across different scales of field data.