Adam Ringler, Ph.D.
I am a scientist at the Albuquerque Seismological Laboratory. I like to work on problems related to instrumentation and data quality. If you have any queries please don't hesitate to contact me.
Publications
Ringler, A. T., R. E. Anthony, R. C. Aster, C. J. Ammon, S. Arrowsmith, H. Benz, C. Ebeling, W. -Y. Kim, H. C. P. Lau, V. Lekić, P. G. Richards, D. P. Schaff, M. Vallée, and W. Yeck (2021). Achievements and prospects of global broadband seismic networks after 30 years of continuous geophysical observations, in review.
Ringler, A. T., R. E. Anthony, P. Davis, K. Hafner, R. Mellors, S. Schneider, and D. C. Wilson (2021). Improved resolution across the Global Seismographic Network: A new era in low-frequency seismology, in review.
Anthony, R. E., A. T. Ringler, and D. C. Wilson (2021). Seismic background noise levels across the Continental United States from USArray Transportable Array: The influence of geology and geopgraphy, in review.
Yang, Y., X. Song, and A. T. Ringler (2021). An evaluation of the timing accuracy of global and regional seismic stations and networks, Seis. Res. Lett., in press.
Wilson, D. C., E. Wolin, W. Yeck, R. E. Anthony, and A. T. Ringler (2021). Modeling seismic network detection thresholds using production picking algorithms, Seis. Res. Lett., in press.
Ringler, A. T. and R. E. Anthony (2021). Local variations in broadband sensor installations: Orientations, sensitivities, and noise levels, Pure Appl. Geophys., in press.
Zürn, W., T. Forbriger, R. Widmer-Schnidrig, P. Duffner, and A. T. Ringler (2021). Modeling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO - A reprise, Geophys. J. Int., DOI: 10.1093/gji/ggab336 [Link]
Ringler, A. T., D. B. Mason, G. Laske, T. Storm, and M. Templeton (2021). Why do my squiggles look funny? A gallery of compromised seismic signals, Seis. Res. Lett., DOI: 10.1785/0220210094 [Link]
Ringler, A. T., R. E. Anthony, C. A. Dalton, and D. C. Wilson (2021). Rayleigh-wave amplitude uncertainty across the Global Seismographic Network and potential implications for global tomography, Bull. Seis. Soc. Amer., 111 (3), 1273-1292. [Link]
Ringler, A. T., R. E. Anthony, D. C. Wilson, D. Auerbach, S. Bargabus, P. Davis, M. Gunnels, K. Hafner, J. F. Holland, A. Kearns, and E. Klimczak (2021). A review of timing accuracy across the Global Seismographic Network, Seis. Res. Lett., 92 (4), 2270-2281 [Link]
Anthony, R. E., A. T. Ringler, M. DuVernois, K. R. Anderson, and D. C. Wilson (2021). Six decades of seismology at South Pole, Antarctica: Current limitiations and future opportunities to facilitate new geophysical observations, Seis. Res. Lett., 92 (5), 2718-2735. [Link]
Tape, C., A. T. Ringler, and D. L. Hampton (2020). Recording the Aurora at seismometers across Alaska, Seis. Res. Lett., 91 (6), 3039-3053. [Link]
Alejandro, A. C. B., A. T. Ringler, D. C. Wilson, R. E. Anthony, and S. V. Moore (2020). Towards understanding relationships between atmo
Science and Products
Improvements in absolute seismometer sensitivity calibration using local earth gravity measurements
Noise reduction in long‐period seismograms by way of array summing
Potential improvements in horizontal very broadband seismic data in the IRIS/USGS component of the Global Seismic Network
Strong-motion observations of the M 7.8 Gorkha, Nepal, earthquake sequence and development of the N-shake strong-motion network
A quick SEED tutorial
Upgrade of the New China Digital Seismograph Network
Self-noise models of five commercial strong-motion accelerometers
The data quality analyzer: a quality control program for seismic data
Uncertainty estimates in broadband seismometer sensitivities using microseisms
Seismometer Self-Noise and Measuring Methods
Seismic Station Installation Orientation Errors at ANSS and IRIS/USGS Stations
Obtaining changes in calibration-coil to seismometer output constants using sine waves
Science and Products
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Filter Total Items: 70
Improvements in absolute seismometer sensitivity calibration using local earth gravity measurements
The ability to determine both absolute and relative seismic amplitudes is fundamentally limited by the accuracy and precision with which scientists are able to calibrate seismometer sensitivities and characterize their response. Currently, across the Global Seismic Network (GSN), errors in midband sensitivity exceed 3% at the 95% confidence interval and are the least‐constrained response parameterAuthorsRobert E. Anthony, Adam T. Ringler, David C. WilsonNoise reduction in long‐period seismograms by way of array summing
Long‐period (>100 s period) seismic data can often be dominated by instrumental noise as well as local site noise. When multiple collocated sensors are installed at a single site, it is possible to improve the overall station noise levels by applying stacking methods to their traces. We look at the noise reduction in long‐period seismic data by applying the time–frequency phase‐weighted stackingAuthorsAdam T. Ringler, David C. Wilson, Tyler Storm, Benjamin T. Marshall, Charles R. Hutt, Austin HollandPotential improvements in horizontal very broadband seismic data in the IRIS/USGS component of the Global Seismic Network
The Streckeisen STS‐1 has been the primary vault‐type seismometer used in the over‐150‐station Global Seismographic Network (GSN). This sensor has long been known for its outstanding vertical, very long‐period (e.g., >100 s period), and low‐noise performance, although the horizontal long‐period noise performance is less well known. The STS‐1 is a limited, important resource, because it is no longAuthorsAdam T. Ringler, J.M. Steim, T Zandt, Charles R. Hutt, David C. Wilson, Tyler StormStrong-motion observations of the M 7.8 Gorkha, Nepal, earthquake sequence and development of the N-shake strong-motion network
We present and describe strong-motion data observations from the 2015 M 7.8 Gorkha, Nepal, earthquake sequence collected using existing and new Quake-Catcher Network (QCN) and U.S. Geological Survey NetQuakes sensors located in the Kathmandu Valley. A comparison of QCN data with waveforms recorded by a conventional strong-motion (NetQuakes) instrument validates the QCN data. We present preliminaryAuthorsAmod Dixit, Adam T. Ringler, Danielle F. Sumy, Elizabeth S. Cochran, Susan E. Hough, Stacey Martin, Steven Gibbons, James H. Luetgert, John Galetzka, Surya Shrestha, Sudhir Rajaure, Daniel E. McNamaraA quick SEED tutorial
Introduction A number of different government-funded seismic data centers offer free open-access data (e.g., U.S. Geological Survey, National Earthquake Information Center, the Incorporated Research Institutions for Seismology (IRIS), and Data Management System), which can be freely downloaded and shared among different members of the community (Lay, 2009). To efficiently share data, it is importaAuthorsAdam T. Ringler, John R. EvansUpgrade of the New China Digital Seismograph Network
No abstract available.AuthorsD. Anderson, J. Anderson, D. Ford, Lind S. Gee, G. Gyure, Charles R. Hutt, E. Kromer, B. Marshall, K. Persefield, Adam T. Ringler, M. Sharratt, Tyler Storm, David C. Wilson, D. Yang, Z. ZhengSelf-noise models of five commercial strong-motion accelerometers
Strong‐motion accelerometers provide onscale seismic recordings during moderate‐to‐large ground motions (e.g., up to tens of m/s2 peak). Such instruments have played a fundamental role in improving our understanding of earthquake source physics (Bocketal., 2011), earthquake engineering (Youdet al., 2004), and regional seismology (Zollo et al., 2010). Although strong‐motion accelerometers tend to hAuthorsAdam T. Ringler, John R. Evans, Charles R. HuttThe data quality analyzer: a quality control program for seismic data
The U.S. Geological Survey's Albuquerque Seismological Laboratory (ASL) has several initiatives underway to enhance and track the quality of data produced from ASL seismic stations and to improve communication about data problems to the user community. The Data Quality Analyzer (DQA) is one such development and is designed to characterize seismic station data quality in a quantitative and automateAuthorsAdam T. Ringler, M.T. Hagerty, James F. Holland, A. Gonzales, Lind S. Gee, J.D. Edwards, David C. Wilson, Adam BakerUncertainty estimates in broadband seismometer sensitivities using microseisms
The midband sensitivity of a seismic instrument is one of the fundamental parameters used in published station metadata. Any errors in this value can compromise amplitude estimates in otherwise high-quality data. To estimate an upper bound in the uncertainty of the midband sensitivity for modern broadband instruments, we compare daily microseism (4- to 8-s period) amplitude ratios between the vertAuthorsAdam T. Ringler, Tyler L. Storm, Lind S. Gee, Charles R. Hutt, David C. WilsonSeismometer Self-Noise and Measuring Methods
Seismometer self-noise is usually not considered when selecting and using seismic waveform data in scientific research as it is typically assumed that the self-noise is negligibly small compared to seismic signals. However, instrumental noise is part of the noise in any seismic record, and in particular, at frequencies below a few mHz, the instrumental noise has a frequency-dependent character andAuthorsAdam T. Ringler, R. Sleeman, Charles R. Hutt, Lind S. GeeSeismic Station Installation Orientation Errors at ANSS and IRIS/USGS Stations
Many seismological studies depend on the published orientations of sensitive axes of seismic instruments relative to north (e.g., Li et al., 2011). For example, studies of the anisotropic structure of the Earth’s mantle through SKS‐splitting measurements (Long et al., 2009), constraints on core–mantle electromagnetic coupling from torsional normal‐mode measurements (Dumberry and Mound, 2008), andAuthorsAdam T. Ringler, Charles R. Hutt, K. Persfield, Lind S. GeeObtaining changes in calibration-coil to seismometer output constants using sine waves
The midband sensitivity of a broadband seismometer is one of the most commonly used parameters from station metadata. Thus, it is critical for station operators to robustly estimate this quantity with a high degree of accuracy. We develop an in situ method for estimating changes in sensitivity using sine‐wave calibrations, assuming the calibration coil and its drive are stable over time and temperAuthorsAdam T. Ringler, Charles R. Hutt, Lind S. Gee, Leo D. Sandoval, David C. Wilson - Software