Sensor Suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite

In order to allow the casual user (geophysicists without expertise in instrumentation) to quickly and consistently determine several parameters critical to determining seismometer health, we have developed a new seismometer testing software package called: Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. The package is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. The sensor tests, which include computing sensor self-noise, relative gain, azimuth, and processing calibrations to determine poles and zeros, can be calculated in a standardized way so that results can be directly compared between tests and between different groups. For the self-noise and the relative azimuth, we also include three component versions of these tests to allow for the case of sensors with potentially different orientations (e.g. boreholes). Our goal is to focus on a few of the instrumentation tests we view as critical when verifying a sensor’s performance. The package is extremely flexible so that it can be used to troubleshoot issues with a single sensor or to compute multi-component self-noise of several sensors in a laboratory setting. The software has been made available on GitHub (https://github.com/usgs/asl-sensor-suite) with the hope that it will be useful for other seismologists who need to quickly verify various sensor parameters without having to write their own versions of the algorithms. Furthermore, by using a common platform and processing algorithms it becomes possible to compare results between different tests and between different groups with similar processing methods being used for both. CAPTION: Upper Panel Power Spectral Density (PSD) estimates (solid lines) for the vertical components of a Nanometrics Trillium 360 sensor (green), as well as the primary KS-54000 (red, location code 00) at IRIS/USGS network (network code IU) station ANMO (Albuquerque, New Mexico), and the secondary sensor at ANMO (location code 10) a Nanometrics Trillium 120. The selfnoise estimates are shown as dashed spectra of slightly darker color. We have included the Peterson (1993) New Low/High-Noise Model (NLNM/NHNM) in black for reference. Lower Panel Azimuth estimate of the IRIS/USGS (network code IU) station WVT (Waverly, Tennessee). The azimuth of the primary Streckeisen STS-6 sensor (location code 00) horizontal components (LH1, red; LH2, blue) were estimated using a co-located Trillium compact (green) where the sensor was oriented to North using a gyroscopic compass. The azimuth of the STS-6 was found to be 340 degrees (left). The time windows used for this estimate are shown on the right.

[1]  Charles R. Hutt,et al.  Guidelines for Standardized Testing of Broadband Seismometers and Accelerometers , 2010 .

[2]  D. Wilson,et al.  Improvements in Absolute Seismometer Sensitivity Calibration Using Local Earth Gravity Measurements , 2017 .

[3]  C. R. Hutt,et al.  Characterizing Local Variability in Long‐Period Horizontal Tilt Noise , 2017 .

[4]  H. Murakami,et al.  Systematic monitoring of instrumentation health in high-density broadband seismic networks , 2015, Earth, Planets and Space.

[5]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[6]  A. T. Ringler,et al.  Relative azimuth inversion by way of damped maximum correlation estimates , 2012, Comput. Geosci..

[7]  J. Peterson,et al.  Observations and modeling of seismic background noise , 1993 .

[8]  Barry N. Taylor,et al.  Guidelines for Evaluating and Expressing the Uncertainty of Nist Measurement Results , 2017 .

[9]  Miaki Ishii,et al.  An Assessment of the Accuracy of GSN Sensor Response Information , 2005 .

[10]  C. R. Hutt,et al.  Effects of thermal variability on broadband seismometers: Controlled experiments, observations, and implications , 2017 .

[11]  Charles R. Hutt,et al.  Data Quality of Seismic Records from the Tohoku, Japan, Earthquake as Recorded across the Albuquerque Seismological Laboratory Networks , 2012 .

[12]  Kasey Aderhold,et al.  Data Quality of Collocated Portable Broadband Seismometers Using Direct Burial and Vault Emplacement , 2015 .

[13]  G. Ekström,et al.  Measurements of Seismometer Orientation at USArray Transportable Array and Backbone Stations , 2008 .

[14]  L. Gary Holcomb,et al.  A Direct Method for Calculating Instrument Noise Levels in Side-by-Side Seismometer Evaluations , 1989 .

[15]  Daniel E. McNamara,et al.  Ambient Noise Levels in the Continental United States , 2004 .

[16]  Franc Runovc,et al.  Seismometer self-noise estimation using a single reference instrument , 2011, Journal of Seismology.

[17]  Charles R. Hutt,et al.  Estimating Pole–Zero Errors in GSN‐IRIS/USGS Network Calibration Metadata , 2012 .

[18]  Reinoud Sleeman,et al.  A PDF Representation of the STS‐2 Self‐Noise Obtained from One Year of Data Recorded in the Conrad Observatory, Austria , 2012 .

[19]  Reinoud Sleeman,et al.  Three-Channel Correlation Analysis: A New Technique to Measure Instrumental Noise of Digitizers and Seismic Sensors , 2006 .

[20]  Mark A. Zumberge,et al.  Performance of an Optical Seismometer from 1 μHz to 10 Hz , 2014 .

[21]  Charles R. Hutt,et al.  Self-Noise Models of Seismic Instruments , 2009 .