This study describes an automated data quality verification procedure supported by a database of power spectral densities (PSD) estimates for geophysical waveform data. The Royal Netherlands Meteorological Institute (KNMI) manages a 100-TB archive of continuous geophysical data collected from accelerometers, geophones, broadband seismometers, and infrasonic arrays deployed across the continental and Caribbean Netherlands. This rapidly expanding network at a scale of over 700 instruments makes the manual evaluation of data quality impractical and must be supported by data policies and automated methods. A technique is presented to compress and store PSD estimates in a database with a storage footprint of less than 0.05% of the raw data archive. Every week, the instrument performance is validated by comparing statistical properties of its latest monthly probabilistic PSD distribution to strict quality metrics. The criteria include thresholds based on global noise models, datalogger quantization noise models, constraints imposed by ambient noise conditions, and confidence intervals based on PSD estimates calculated from validated archived data. When a threshold is crossed, the station operator is alerted of the suspected degraded instrument performance, severely limiting the required amount of manual labor and associated human errors. The automated PSD assessment technique is applicable to accelerometers, geophones, broadband seismometers, infrasonic stations, and is demonstrated to be extendable to hydrophones, gravimeters, tiltmeters, and Global Navigation Satellite System receivers. The approach is therefore suitable for other geophysical monitoring infrastructures, for example, observational networks dedicated to continuous volcano monitoring. It is shown that it possible to detect degraded instrument performance that may otherwise remain undetected.

中文翻译：

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通过功率谱密度估计的自动分析对地球物理仪器进行性能评估

本研究描述了由地球物理波形数据的功率谱密度 (PSD) 估计数据库支持的自动数据质量验证程序。荷兰皇家气象研究所 (KNMI) 管理着一个 100 TB 的连续地球物理数据档案，这些数据是从部署在荷兰大陆和加勒比海地区的加速度计、地震检波器、宽带地震仪和次声阵列收集的。这个规模超过 700 台仪器的快速扩展网络使得手动评估数据质量变得不切实际，必须得到数据策略和自动化方法的支持。提出了一种技术来压缩和存储 PSD 估计值在一个数据库中，其存储空间小于原始数据存档的 0.05%。每周，该仪器的性能通过将其最新的每月概率 PSD 分布的统计特性与严格的质量指标进行比较来验证。这些标准包括基于全局噪声模型的阈值、数据记录器量化噪声模型、环境噪声条件施加的约束以及基于从验证存档数据计算得出的 PSD 估计值的置信区间。当超过阈值时，台站操作员会收到疑似仪器性能下降的警报，从而严重限制了所需的体力劳动和相关的人为错误。自动 PSD 评估技术适用于加速度计、地震检波器、宽带地震仪、次声站，并被证明可扩展到水听器、重力仪、倾斜仪和全球导航卫星系统接收器。因此，该方法适用于其他地球物理监测基础设施，例如专门用于持续火山监测的观测网络。结果表明，可以检测到可能无法检测到的劣化仪器性能。