Processing Ambient Noise Data Using Phase Cross‐Correlation and Application Toward Understanding Spatiotemporal Environmental Effects
Processing Ambient Noise Data Using Phase Cross‐Correlation and Application Toward Understanding Spatiotemporal Environmental Effects
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DOI：
10.1029/2023jf007091
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
H. B. Woo;S. Bilek;J. Gochenour;R. Grapenthin;A. Luhmann;J. Martin
中科院分区：
文献类型：
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作者：
H. B. Woo;S. Bilek;J. Gochenour;R. Grapenthin;A. Luhmann;J. Martin
背景噪声干涉测量法的典型使用集中于用于地下结构的勘探和其它应用的较长周期（>1 s）波，而非常浅的结构和一些环境地震学应用可受益于使用较短周期（<1 s）波。通过比较两种方法，传统的基于振幅的互相关和线性叠加（TCC‐Lin）和最近开发的相位互相关和时频相位加权叠加（PCC‐PWS）方法，利用在非均匀岩溶含水层系统中收集的合成和真实的数据，我们探索了短周期环境噪声干涉测量确定浅层地震速度结构的潜力。我们的研究结果表明，PCC-PWS方法在提取短周期波速方面比TCC-Lin方法更有效，特别是当使用在包含复杂浅层结构（如本文研究的岩溶含水层系统）的区域中收集的数据时。除了计算互相关函数的不同方法外，我们还研究了信噪比和站对之间传播的波长数量的相对重要性，以确定数据/解决方案的质量。我们发现，波长数较低的3对网络平均群速度曲线的影响最大。最后，我们测试了用于创建最终经验绿色函数的堆栈数量的敏感性，发现与TCC‐Lin方法相比，PCC‐PWS方法需要大约一半数量的互相关函数来生成可靠的速度曲线。当可用的数据收集时间有限时，这是PCC-PWS方法的一个重要优势。
Typical use of ambient noise interferometry focuses on longer period (>1 s) waves for exploration of subsurface structure and other applications, while very shallow structure and some environmental seismology applications may benefit from use of shorter period (<1 s) waves. We explore the potential for short‐period ambient noise interferometry to determine shallow seismic velocity structures by comparing two methodologies, the conventional amplitude‐based cross‐correlation and linear stacking (TCC‐Lin) and a more recently developed phase cross‐correlation and time‐frequency phase‐weighted‐stacking (PCC‐PWS) method with both synthetic and real data collected in a heterogeneous karst aquifer system. Our results suggest that the PCC‐PWS method is more effective in extracting short‐period wave velocities than the TCC‐Lin method, especially when using data collected in regions containing complex shallow structures such as the karst aquifer system investigated here. In addition to the different methodologies for computing the cross correlation functions, we also examine the relative importance of signal‐to‐noise ratio and number of wavelengths propagating between station pairs to determine data/solution quality. We find that the lower number of wavelengths of 3 has the greatest impact on the network‐averaged group velocity curve. Lastly, we test the sensitivity of the number of stacks used to create the final empirical Green's function, and find that the PCC‐PWS method required about half the number of cross‐correlation functions to develop reliable velocity curves compared to the TCC‐Lin method. This is an important advantage of the PCC‐PWS method when available data collection time is limited.