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A. T. Ringler

Publications and source records attributed to A. T. Ringler.

9 recordsLinked to original sources
USGS2012

Data quality of seismic records from the Tohoku, Japan earthquake as recorded across the Albuquerque Seismological Laboratory networks

Great earthquakes recorded across modern digital seismographic networks, such as the recent Tohoku, Japan, earthquake on 11 March 2011 (M w = 9.0), provide unique datasets that ultimately lead to a better understanding of the Earth's structure (e.g., Pesicek et al. 2008) and earthquake sources (e.g., Ammon et al. 2011). For network operators, such events provide the opportunity to look at the performance across their entire network using a single event, as the ground motion records from the event will be well above every station's noise floor.

Seismological Research Letters↗
USGS2012

Estimating pole/zero errors in GSN-IRIS/USGS network calibration metadata

Mapping the digital record of a seismograph into true ground motion requires the correction of the data by some description of the instrument's response. For the Global Seismographic Network (Butler et al., 2004), as well as many other networks, this instrument response is represented as a Laplace domain pole–zero model and published in the Standard for the Exchange of Earthquake Data (SEED) format. This Laplace representation assumes that the seismometer behaves as a linear system, with any abrupt changes described adequately via multiple time-invariant epochs. The SEED format allows for published instrument response errors as well, but these typically have not been estimated or provided to users. We present an iterative three-step method to estimate the instrument response parameters (poles and zeros) and their associated errors using random calibration signals. First, we solve a coarse nonlinear inverse problem using a least-squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least-squares best-fit Laplace pole–zero–gain model. Third, by applying the central limit theorem, we estimate the errors in this pole–zero model by solving the inverse problem at each frequency in a two-thirds octave band centered at each best-fit pole–zero frequency. This procedure yields error estimates of the 99% confidence interval. We demonstrate the method by applying it to a number of recent Incorporated Research Institutions in Seismology/United States Geological Survey (IRIS/USGS) network calibrations (network code IU).

Bulletin of the Seismological Society of America↗
USGS2012

Relative azimuth inversion by way of damped maximum correlation estimates

Horizontal seismic data are utilized in a large number of Earth studies. Such work depends on the published orientations of the sensitive axes of seismic sensors relative to true North. These orientations can be estimated using a number of different techniques: SensOrLoc (Sensitivity, Orientation and Location), comparison to synthetics (Ekstrom and Busby, 2008), or by way of magnetic compass. Current methods for finding relative station azimuths are unable to do so with arbitrary precision quickly because of limitations in the algorithms (e.g. grid search methods). Furthermore, in order to determine instrument orientations during station visits, it is critical that any analysis software be easily run on a large number of different computer platforms and the results be obtained quickly while on site. We developed a new technique for estimating relative sensor azimuths by inverting for the orientation with the maximum correlation to a reference instrument, using a non-linear parameter estimation routine. By making use of overlapping windows, we are able to make multiple azimuth estimates, which helps to identify the confidence of our azimuth estimate, even when the signal-to-noise ratio (SNR) is low. Finally, our algorithm has been written as a stand-alone, platform independent, Java software package with a graphical user interface for reading and selecting data segments to be analyzed.

Computers & Geosciences↗
USGS2011

Some possible causes of and corrections for STS-1 response changes in the Global Seismographic Network

The Global Seismographic Network (GSN) (Figure 1) plays a key role in providing seismic data for global earthquake monitoring ( e.g. , Benz et al. 2005), earthquake science ( e.g. , Tsai et al. 2005), and studies of Earth structure ( e.g. , Dalton et al. 2008). One of the key GSN design goals is to "provide high fidelity digital recordings of all teleseismic ground motions (adequate to resolve at or near ambient noise up to the largest teleseismic signals over the bandwidth from free oscillations (10 -4 Hz) to teleseismic body waves (up to approximately 15 Hz))" (GSN ad hoc Design Goals Subcommittee 2002). To help meet this goal, Streckeisen STS-1 seismometers were deployed at 80 GSN stations. Some of the GSN sensors have been deployed for more than 25 years. Several recent studies (Davis et al. 2005; Ekström et al. 2006; Davis and Berger 2007) have examined the question of overall calibration of the GSN. Ekström et al. (2006) indicated that a number of sites showed anomalous responses and suggested a gradual decay in the sensitivity. We have investigated the anomalous responses at several GSN sites. At least some of the problems observed by Ekström et al. (2006) may be attributed to humid air leaking into the feedback electronics of the STS-1 seismometers, which produces lower than normal sensitivities near the long-period corner of the instrument (360 seconds period). It appears that even though the feedback electronics boxes are designed to be sealed, water vapor can penetrate their interior after they have been exposed to highly humid seismometer vault air for extended periods. Highly humid air was also found to be present inside some STS-1 bell-jars (especially horizontal instruments) after loss of vacuum, resulting in corrosion and leakage between electrical conductors in connectors.

Seismological Research Letters↗
USGS2011

Seismically observed seiching in the Panama Canal

A large portion of the seismic noise spectrum is dominated by water wave energy coupled into the solid Earth. Distinct mechanisms of water wave induced ground motions are distinguished by their spectral content. For example, cultural noise is generally <1 s period, microseisms dominate the seismic spectrum from periods of 2 to 20 s, and the Earth's "hum" is in the range of 50 to 600 s. We show that in a large lake in the Panama Canal there is an additional source of long-period noise generated by standing water waves, seiches, induced by disturbances such as passing ships and wind pressure. We compare seismic waveforms to water level records and relate these observations to changes in local tilt and gravity due to an oscillating seiche. The methods and observations discussed in this paper provide a first step toward quantifying the impact of water inundation as recorded by seismometers. This type of quantified understanding of water inundation will help in future estimates of similar phenomena such as the seismic observations of tsunami impact. Copyright 2011 by the American Geophysical Union.

Journal of Geophysical Research B: Solid Earth↗
USGS2011

A Comparison of seismic instrument noise coherence analysis techniques

The self-noise of a seismic instrument is a fundamental characteristic used to evaluate the quality of the instrument. It is important to be able to measure this self-noise robustly, to understand how differences among test configurations affect the tests, and to understand how different processing techniques and isolation methods (from nonseismic sources) can contribute to differences in results. We compare two popular coherence methods used for calculating incoherent noise, which is widely used as an estimate of instrument self-noise (incoherent noise and self-noise are not strictly identical but in observatory practice are approximately equivalent; Holcomb, 1989; Sleeman et al., 2006). Beyond directly comparing these two coherence methods on similar models of seismometers, we compare how small changes in test conditions can contribute to incoherent-noise estimates. These conditions include timing errors, signal-to-noise ratio changes (ratios between background noise and instrument incoherent noise), relative sensor locations, misalignment errors, processing techniques, and different configurations of sensor types.

Bulletin of the Seismological Society of America↗
USGS2010

Temporal variations in Global Seismic Stations ambient noise power levels

Recent concerns about time-dependent response changes in broadband seismometers have motivated the need for methods to monitor sensor health at Global Seismographic Network (GSN) stations. We present two new methods for monitoring temporal changes in data quality and instrument response transfer functions that are independent of Earth seismic velocity and attenuation models by comparing power levels against different baseline values. Our methods can resolve changes in both horizontal and vertical components in a broad range of periods (∼0.05 to 1,000 seconds) in near real time. In this report, we compare our methods with existing techniques and demonstrate how to resolve instrument response changes in long-period data (>100 seconds) as well as in the microseism bands (5 to 20 seconds). High quality broadband data recorded by the GSN are fundamental to characterizing a wide range of Earth science issues including: the size and rupture of large earthquakes ( e.g. , Tsai et al. 2005 ); imaging the interior of the Earth ( e.g. , Van der Hilst et al. 1997 ); tracking global climate variation ( Aster et al. 2008 ); and monitoring calving glaciers (Ekström et al. 2003 , 2006a ). Recent studies based on theoretical Earth models ( Ekström et al. 2006b ; Davis and Berger 2007 ) suggest that broadband seismometer gain levels can vary with time. This has also been confirmed, for the STS-1 sensor, experimentally ( Yuki and Ishihara 2002 ). It therefore has become necessary to systematically check for temporal changes in amplitude at GSN stations. Many of these changes are frequency-dependent in nature and not a priori predictable ( Ekström et al. 2006b ). Robust methods that can be applied to a large number of stations in a broad range of frequency bands are necessary.

Seismological Research Letters↗
USGS2010

Method for calculating self-noise spectra and operating ranges for seismographic inertial sensors and recorders

Understanding the performance of sensors and recorders is prerequisite to making appropriate use of them in seismology and earthquake engineering. This paper explores a critical aspect of instrument performance, the “self” noise level of the device and the amplitude range it can usefully record. Self noise limits the smallest signals, while instrument clipping level creates the upper limit (above which it either cannot produce signals or becomes unacceptably nonlinear). Where these levels fall, and the “operating range” between them, determines much of the instrument's viability and the applications for which it is appropriate. The representation of seismic-instrument self-noise levels and their effective operating ranges (cf., dynamic range) for seismological inertial sensors, recorders (data acquisition units, or DAUs), and integrated systems of sensors and recorders (data acquisition systems, or DASs) forces one to address an unnatural comparison between transient finite-bandwidth signals, such as earthquake records, and the instrument's self noise, an effectively stationary signal of infinite duration. In addition to being transient, earthquakes and other records of interest are characterized by a peak amplitude and generally a narrow, peaked spectral shape. Unfortunately, any power spectrum computed for such transient signals is ill defined, since the maximum of that spectrum depends strongly upon signal and record durations. In contrast, the noise floor of an instrument is approximately stationary and properly described by a power spectral density (PSD) or its root (rPSD). Put another way, earthquake records have units of amplitude ( e.g. , m/s 2 ) while PSDs have units of amplitude-squared per hertz ( e.g. , (m/s 2 ) 2 /Hz) and the rPSD has units of amplitude per root of hertz ( e.g. , (m/s 2 )/Hz 1/2 ). Thus, this incompatability is a conflict between earthquake (amplitude) and PSD (spectral density) units that requires one to make various assumptions before they can be compared. For purposes of instrument operational performance, we provide a means of evaluating signal and noise and the range between them in a manner representative of time-domain instrument performance. We call these “operating range diagrams” (ORDs), plots of instrument self noise and clipping level; the “operating range” is the range between these values. For frequency-domain performance we elect to show self noise as an rPSD that may be compared to another instrument's noise or to ambient Earth noise ( e.g. , Peterson 1993); however, to limit the number of arbitrary choices required to merge transient and stationary signals we do not compare the rPSD to transient signals in the frequency domain. Our solution for a time-domain comparison is not new but rather builds upon the consensus of the first and second Guidelines for Seismometer Testing workshops (Hutt et al. 2009) and long established practice in acoustics. We propose this method as a standard for characterizing seismic instruments, and it has been endorsed by the second workshop (Hutt et al. 2009, 2010) and the Advanced National Seismic System (ANSS) Working Group (2008) and recent ANSS procurement specifications.

Seismological Research Letters↗