ISPAQ v3.0 – The IRIS System for Portable Assessment of Quality Updated
ISPAQ is a command-line Python client that calculates seismic quality metrics for miniSEED-format data (either local files or through FDSN web services). Using the same algorithms as the IRIS MUSTANG quality assurance system, over 40 metrics are available including simple data statistics, data availability, signal anomalies, metadata checks, miniSEED flag counts, Power Spectral Densities (PSD), Probability Density Functions (PDF), and more. ISPAQ is available at https://github.com/iris-edu/ispaq.
New Features and Updates
A new version of ISPAQ was recently released in November 2021. Version 3.0 includes a number significant improvements including:
1. An integrated SQLite database
In addition to writing metrics to CSV text files, ISPAQ now has an option to store metric values in an included SQLite database. This database provides easier access for metric retrieval and analysis, and it can also be used to import metric values into QuARG, the Quality Assurance Report Generator tool.
2. Addition of the MUSTANG metrics sample_rate_channel, sample_rate_resp, and max_range
sample_rate_channel and sample_rate_resp are metrics that identify data that has a sample rate inconsistent with its metadata. sample_rate_channel compares the sample rate recorded in the miniSEED header with that in the channel metadata; sample_rate_resp compares the data sample rate with the sample rate predicted from its amplitude-frequency response curve.
max_range computes the difference between the maximum and minimum sample value within a 300-second rolling window and reports the largest daily value. It’s goal is to detect stations with large recurrent signals that are not extremely long period, which may be useful in the context of earthquake early warning systems.
3. Access to the IRIS PH5 archive
IRIS maintains an archive of PASSCAL HDF5 (PH5) datasets available through FDSN web services at http://service.iris.edu/ph5ws, and ISPAQ can now access these web services to calculate metrics. MUSTANG also routinely calculates metrics for this data, but it may not have metrics available for all channel types of interest. ISPAQ can calculate metrics for channels that do not have MUSTANG metric values (if the specific metric algorithm allows that channel type).
4. New Jupiter Notebook tutorials
ISPAQ v3 includes Jupiter Notebook tutorials for calculating metrics, using the SQLite database, and customizing plots of PSDs and PDFs.
5. Code port to Python3
Previous ISPAQ versions were written in Python2. Porting the code to Python3 improves its maintainability and access to current versions of dependencies.
6. Bug fixes and usage improvements
ISPAQ v3 has improved logging output, ability to record percent_availability=0 for days without data when using local files, inclusion of data quality code for PSD and PDF output, and more.
- Linux or macOS operating system
– Anaconda or Miniconda
ISPAQ was written by Jonathan Callahan (Mazama Science) and the IRIS Quality Assurance Team.
by Gillian Sharer (IRIS Data Management Center)