The Albuquerque Seismological Laboratory Data Quality Analyzer

The U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL) has several efforts underway to improve data quality at its stations. The Data Quality Analyzer (DQA) is one such development. The DQA is designed to characterize station data quality in a quantitative and automated manner. Station quality is based on the evaluation of various metrics, such as timing quality, noise levels, sensor coherence, and so on. These metrics are aggregated into a measurable grade for each station. The DQA consists of a website, a metric calculator (Seedscan), and a PostgreSQL database. The website allows the user to make requests for various time periods, review specific networks and stations, adjust weighting of the station's grade, and plot metrics as a function of time. The website dynamically loads all station data from a PostgreSQL database. The database is central to the application; it acts as a hub where metric values and limited station descriptions are stored. Data is stored at the level of one sensor's channel per day. The database is populated by Seedscan. Seedscan reads and processes miniSEED data, to generate metric values. Seedscan, written in Java, compares hashes of metadata and data to detect changes and perform subsequent recalculations. This ensures that the metric values are up to date and accurate. Seedscan can be run in a scheduled task or on demand by way of a config file. It will compute metrics specified in its configuration file. While many metrics are currently in development, some are completed and being actively used. These include: availability, timing quality, gap count, deviation from the New Low Noise Model, deviation from a station's noise baseline, inter-sensor coherence, and data-synthetic fits. In all, 20 metrics are planned, but any number could be added. ASL is actively using the DQA on a daily basis for station diagnostics and evaluation. As Seedscan is scheduled to run every night, data quality analysts are able to then use the website to diagnose changes in noise levels or other anomalous data. This allows for errors to be corrected quickly and efficiently. The code is designed to be flexible for adding metrics and portable for use in other networks. We anticipate further development of the DQA by improving the existing web-interface, adding more metrics, adding an interface to facilitate the verification of historic station metadata and performance, and an interface to allow better monitoring of data quality goals.