Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements
Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements
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DOI：
10.1111/j.1365-246x.2007.03374.x
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发表时间：
2007-06-01
影响因子：
2.8
通讯作者：
Yang, Y.
中科院分区：
文献类型：
--
作者：
Bensen, G. D.;Ritzwoller, M. H.;Yang, Y.
背景噪声层析成像是地震学研究的一个新兴领域。本文介绍了环境噪声数据处理的现状，因为它已经在过去几年的发展，并打算通过突出的例子来解释和证明这一发展。环境噪声数据处理过程分为四个主要阶段：（1）单站数据准备，（2）互相关和时间叠加，（3）频散曲线的测量（对群速度和相速度进行频率-时间分析）和（4）质量控制，包括误差分析和可接受测量的选择。本文所述的程序不仅被设计为提供可靠的测量，而且是灵活的，适用于各种各样的观测环境，以及完全自动化。对于自动化数据处理过程，数据质量控制措施对于识别和拒绝不良测量以及计算可接受测量的质量保证统计数据特别重要。判断质量的主要度量标准是稳定性，即在获得测量结果的条件下测量结果对扰动的鲁棒性。特别是时间重复性，是可靠性的一个重要指标，在我们的评估中被提升到一个很高的位置，因为我们将季节重复性等同于测量不确定性。代理曲线所观察到的信噪比平均测量的不确定性显示承诺提供有用的预期测量误差估计的情况下，所需的时间子集的长时间序列。
Ambient noise tomography is a rapidly emerging field of seismological research. This paper presents the current status of ambient noise data processing as it has developed over the past several years and is intended to explain and justify this development through salient examples. The ambient noise data processing procedure divides into four principal phases: (1) single station data preparation, (2) cross-correlation and temporal stacking, (3) measurement of dispersion curves (performed with frequency-time analysis for both group and phase speeds) and (4) quality control, including error analysis and selection of the acceptable measurements. The procedures that are described herein have been designed not only to deliver reliable measurements, but to be flexible, applicable to a wide variety of observational settings, as well as being fully automated. For an automated data processing procedure, data quality control measures are particularly important to identify and reject bad measurements and compute quality assurance statistics for the accepted measurements. The principal metric on which to base a judgment of quality is stability, the robustness of the measurement to perturbations in the conditions under which it is obtained. Temporal repeatability, in particular, is a significant indicator of reliability and is elevated to a high position in our assessment, as we equate seasonal repeatability with measurement uncertainty. Proxy curves relating observed signal-to-noise ratios to average measurement uncertainties show promise to provide useful expected measurement error estimates in the absence of the long time-series needed for temporal subsetting.