Ocean-Bottom Seismometer Instrument Orientations via Automated Rayleigh-Wave Arrival-Angle Measurements
Ocean-Bottom Seismometer Instrument Orientations via Automated Rayleigh-Wave Arrival-Angle Measurements
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DOI：
10.1785/0120160165
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发表时间：
2017-04-01
影响因子：
3
通讯作者：
Laske, Gabi
中科院分区：
文献类型：
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作者：
Doran, Adrian K.;Laske, Gabi
美国海底地震仪 (OBS) 仪器池运行十多年后，仍然需要一致且准确的程序来确定被动源自由落体宽带 OBS 的水平地震仪组件相对于地理北方的方向。我们提出了一种新的基于 Python 的自动化高精度算法，用于在数据后处理过程中获取此信息。与之前的一些方法一样，我们的新方法 Doran-Laske-Orientation-Python (DLOPy) 基于测量远震地震的中周期表面波到达角。 DLOPy 的一个重要新方面是在设置分析窗口时参考现代全球色散图。我们在多个频率下重复测量，以降低横向异质结构中波传播的偏差。我们纳入了第一个小大圆弧和主要大圆弧的测量值，以进一步降低由不均匀的地理数据覆盖范围引起的偏差。我们通过使用来自全球地震网络仪器的数据（其方向有详细记录）的基准测试来证明我们的技术的高精度，这是针对一种成熟的“实践”但缓慢的技术。我们展示了所有 Cascadia Initiative 部署的结果，以及许多其他 OBS 实验。与其他广泛使用的自动化代码相比，DLOPy 需要更少的事件来实现相同或更好的准确性。这一优势对于持续时间短至几个月的 OBS 部署可能非常有利。我们的计算机代码可供下载。它需要最少的用户输入，并经过优化，可以处理通过地震学数据管理中心联合研究机构传播的数据。
After more than 10 years of the U.S. ocean-bottom seismometer (OBS) Instrument Pool operations, there is still need for a consistent and accurate procedure to determine the orientation of the horizontal seismometer components of passive-source free-fall broadband OBSs with respect to geographic north. We present a new Python-based, automated, and high-accuracy algorithm to obtain this information during postprocessing of the data. As with some previous methods, our new method Doran-Laske-Orientation-Python (DLOPy) is based on measuring intermediate-period surface-wave arrival angles from teleseismic earthquakes. A crucial new aspect of DLOPy is the consultation of modern global dispersion maps when setting up the analysis window. We repeat measurements at several frequencies to lower biases from wave propagation in laterally heterogeneous structure. We include measurements from the first minor and major great-circle arcs to further lower biases caused by uneven geographical data coverage. We demonstrate the high accuracy of our technique through benchmark tests against a well-established "hands-on" but slow technique using data from instruments of the Global Seismographic Network for which orientations are well documented. We present results for all Cascadia Initiative deployments, along with a number of other OBS experiments. Compared to other widely used automated codes, DLOPy requires fewer events to achieve the same or better accuracy. This advantage may be greatly beneficial for OBS deployments that last as short as a few months. Our computer code is available for download. It requiresminimal user input and is optimized to work with data disseminated through the Incorporated Research Institutions for Seismology Data Management Center.