Geology topics

Adam T. Ringler

Publications and source records attributed to Adam T. Ringler.

At least 73 records · Page 4Linked to original sources

USGS2018

Do low-cost seismographs perform well enough for your network? An overview of laboratory tests and field observations of the OSOP Raspberry Shake 4D

Seismologists have recently begun utilizing low-cost nodal sensors in dense deployments to sample the seismic wavefield at unprecedented spatial resolution. Earthquake Early Warning Systems (EEWS) and other monitoring networks (e.g. wastewater injection) would additionally benefit from network densification; however, current nodal systems lack power systems and/or real-time data transmission required for these applications. A candidate sensor for these networks may instead be a low-cost, all-in-one package such as the OSOP Raspberry Shake 4D (RS-4D). The RS-4D includes a vertical component geophone, 3-component accelerometer, digitizer, and near real-time miniSEED data transmission, and costs only a few hundred dollars per unit. Here, we step through instrument testing of three RS-4Ds at the Albuquerque Seismological Laboratory. We find the geophones have sensitivities constrained to within 4% of nominal, but that they have relatively high self-noise levels compared to the broadband sensors typically used in seismic networks. To demonstrate the impact this would have on characterizing nearby events, we estimate local magnitudes of earthquakes in Oklahoma using Trillium Compact broadband sensor data from U.S. Geological Survey (USGS) aftershock deployments as well as 23 Raspberry Shakes operated by hobbyists and private owners within Oklahoma. We find that for ML 2.0-4.0 earthquakes at distances of 20-100 km from seismic stations, the Raspberry Shakes require events of magnitude ~0.3 larger than the broadband sensors in order to reliably estimate ML at a given distance from the epicenter. We conclude that RS-4Ds are suitable for densifying backbone networks designed for studies of local and regional events.

Seismological Research Letters↗

USGS2018

Observations of rotational motions from local earthquakes using two temporary portable sensors in Waynoka, Oklahoma

Characterizing rotational motions from earthquakes at local distances has the potential to improve earthquake engineering and seismic gradiometry by better characterizing the complete seismic wavefield. Applied Technology Associates (ATA) has developed a proto‐seismic magnetohydrodynamic (SMHD) three‐component rotational rate sensor. We deploy two ATA rotational rate sensors at a temporary aftershock station in Waynoka, Oklahoma. From 27 April to 6 June 2017, we recorded the translational and rotational motions of 155 earthquakes of ML≥2.0 within 220 km of the station. Using the recorded events, we compare peak ground rotation rate (⁠ PGω˙ ⁠) with peak ground velocity (PGV) and with peak ground acceleration (PGA). Our results support previously identified potential relationships between the two quantities. We also compare peak ground rotations (⁠ PGω ⁠) as a function of seismic moment and distance. We found that PGω˙ decays with an exponent of approximately −4.0km−1 for both horizontal and vertical components. On the other hand, PGA decays with an exponent of approximately −1.8km−1 for all components. We compute apparent phase velocity directly from the rotational data for both horizontally polarized shear waves (SH; 379m/s with a standard deviation of 114m/s ⁠) and vertically polarized compression and shear waves (P‐SV; 387m/s with a standard deviation of 121m/s ⁠). Finally, by comparing various rotational and translational components, we look at potential implications for estimating local event source parameters. We found that the absolute correlation of nearby earthquakes decays at a rate of approximately 0.39/km for rotational sensors. This decay rate of absolute correlation is faster on translational sensors with a decay rate of 0.44/km. The latter may help in identifying phenomena such as repeating earthquakes by using differences in correlations as a function of distance and how these differences compare with translational correlations.

Oklahoma↗

USGS2018

Sensor suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite

To standardize parameters used in seismometer testing and calibration and to make these algorithms accessible to the seismological community, we have developed a new seismometer testing software package called Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. This software is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. Our goal is not to be all‐inclusive but instead to focus on a few of the instrumentation tests we view as critical when verifying a sensor’s performance. The tests include self‐noise, relative azimuth, relative gain, and estimation of the poles and zeros. 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). The software has been made available on GitHub 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 among different tests with similar processing methods being used for both.

Seismological Research Letters↗

USGS2018

The widespread influence of Great Lakes microseisms across the United States revealed by the 2014 polar vortex

During the winter of 2014, a weak polar vortex brought record cold temperatures to the north‐central (“Midwest”) United States, and the Great Lakes reached the highest extent of ice coverage (92.5%) since 1979. This event shut down the generation of seismic signals caused by wind‐driven wave action within the lakes (termed “lake microseisms”), giving an unprecedented opportunity to isolate and characterize these novel signals through comparison with nonfrozen time periods. Using seismic records at 72 broadband stations, we observe Great Lakes microseism signals at distances >300 km from the lakes. In contrast to conventional oceanic microseisms, there is no clear relationship between the frequency content of the seismic signals (observed from ~0.5–5‐s period) and the dominant swell period or resonance periods of the lakes based on their bathymetric profiles. Thus, the exact generation mechanism is not readily explained by conventional microseism theory and warrants further investigation.

Geophysical Research Letters↗

USGS2018

Laboratory tests of three Z‐Land Fairfield Nodal 5‐Hz, three‐component sensors

We conduct a number of laboratory tests at the Albuquerque Seismological Laboratory to verify the self‐noise and fidelity in which 3 three‐component Fairfield Nodal Z‐Land, Generation 2, 5‐Hz sensors are able to record seismic signals. In addition to the incoherent self‐noise of the sensors, we estimate the sensitivity of the units in digital volts/m/s, the damping, and the free period. These three parameters allow us to completely characterize the response of the instruments. We find that the responses of all components match a mean‐derived response to within 5% of amplitude and 0.03 radians in phase. This close agreement suggests that for most applications a nominal response is suitable. We also checked the timing of the units as compared to a Quanterra Q330HR and found good agreement up to 200samples/s . Finally, we compared the results of our noise tests on these sensors to a couple of nodal sensors recently deployed at the Community Wavefield Demonstration Experiment in north‐central Oklahoma and found that local site noise and not the sensor self‐noise is a fundamental limiter in the resolution of these deployed sensors at frequencies above ∼0.1Hz .

Seismological Research Letters↗

USGS2017

Effects of thermal variability on broadband seismometers: Controlled experiments, observations, and implications

Isolating seismic instruments from temperature fluctuations is routine practice within the seismological community. However, the necessary degree of thermal stability required in broadband installations to avoid generating noise or compromising the fidelity in the seismic records is largely unknown and likely application dependent. To quantify the temperature sensitivity of seismometers over a broad range of frequencies, we artificially induced local temperature changes on three different models of seismometers to measure the effect of thermal variations on seismometer output. We found that diurnal temperature changes above 0.002°C root mean square (rms) showed significant changes in velocity and acceleration output in comparison to thermally stable reference measurements. We also found that sensor incoherent self‐noise increased with temperature variation; these increases in noise can be modeled as 1 / f "> 1 / f noise (pink noise), and are unlikely to be easily corrected for. These experimental results are compared with the data from Incorporated Research Institutions for Seismology (IRIS) U.S. Geological Survey (USGS) Global Seismographic Network (GSN) station TUC (Tucson, Arizona). This station is well instrumented with temperature sensors and has three different broadband seismometers, each of which uses a different method of thermal isolation. We show that the water bricks and borehole installations give ample temperature attenuation to thermally isolate seismometers from diurnal thermal variability that would compromise seismic data. We find that seismometer installations that provide thermal stability below 0.002°C rms could help to improve long‐period vertical seismic data across the GSN by decreasing temperature‐driven 1 / f "> 1 / f noise.

Bulletin of the Seismological Society of America↗

USGS2017

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 parameter in seismic recording systems. We explore a new methodology utilizing precise absolute Earth gravity measurements to determine the midband sensitivity of seismic instruments. We first determine the absolute sensitivity of Kinemetrics EpiSensor accelerometers to 0.06% at the 99% confidence interval by inverting them in a known gravity field at the Albuquerque Seismological Laboratory (ASL). After the accelerometer is calibrated, we install it in its normal configuration next to broadband seismometers and subject the sensors to identical ground motions to perform relative calibrations of the broadband sensors. Using this technique, we are able to determine the absolute midband sensitivity of the vertical components of Nanometrics Trillium Compact seismometers to within 0.11% and Streckeisen STS‐2 seismometers to within 0.14% at the 99% confidence interval. The technique enables absolute calibrations from first principles that are traceable to National Institute of Standards and Technology (NIST) measurements while providing nearly an order of magnitude more precision than step‐table calibrations.

Bulletin of the Seismological Society of America↗

USGS2017

Detection and characterization of pulses in broadband seismometers

Pulsing - caused either by mechanical or electrical glitches, or by microtilt local to a seismometer - can significantly compromise the long‐period noise performance of broadband seismometers. High‐fidelity long‐period recordings are needed for accurate calculation of quantities such as moment tensors, fault‐slip models, and normal‐mode measurements. Such pulses have long been recognized in accelerometers, and methods have been developed to correct these acceleration steps, but considerable work remains to be done in order to detect and correct similar pulses in broadband seismic data. We present a method for detecting and characterizing the pulses using data from a range of broadband sensor types installed in the Global Seismographic Network. The technique relies on accurate instrument response removal and employs a moving‐window approach looking for acceleration baseline shifts. We find that pulses are present at varying levels in all sensor types studied. Pulse‐detection results compared with average daily station noise values are consistent with predicted noise levels of acceleration steps. This indicates that we can calculate maximum pulse amplitude allowed per time window that would be acceptable without compromising long‐period data analysis.

Bulletin of the Seismological Society of America↗

USGS2017

Repeatability of testing a small broadband sensor in the Albuquerque Seismological Laboratory Underground Vault

Variability in seismic instrumentation performance plays a fundamental role in our ability to carry out experiments in observational seismology. Many such experiments rely on the assumed performance of various seismic sensors as well as on methods to isolate the sensors from nonseismic noise sources. We look at the repeatability of estimating the self‐noise, midband sensitivity, and the relative orientation by comparing three collocated Nanometrics Trillium Compact sensors. To estimate the repeatability, we conduct a total of 15 trials in which one sensor is repeatedly reinstalled, alongside two undisturbed sensors. We find that we are able to estimate the midband sensitivity with an error of no greater than 0.04% with a 99th percentile confidence, assuming a standard normal distribution. We also find that we are able to estimate mean sensor self‐noise to within ±5.6 dB with a 99th percentile confidence in the 30–100‐s‐period band. Finally, we find our relative orientation errors have a mean difference in orientation of 0.0171° from the reference, but our trials have a standard deviation of 0.78°.

Bulletin of the Seismological Society of America↗

USGS2017

Characterizing local variability in long‐period horizontal tilt noise

Horizontal seismic data are dominated by atmospherically induced tilt noise at long periods (i.e., 30 s and greater). Tilt noise limits our ability to use horizontal data for sensitive seismological studies such as observing free earth modes. To better understand the local spatial variability of long‐period horizontal noise, we observe horizontal noise during quiet time periods in the Albuquerque Seismological Laboratory (ASL) underground vault using four small‐aperture array configurations. Each array comprises eight Streckeisen STS‐2 broadband seismometers. We analyze the spectral content of the data using power spectral density and magnitude‐squared coherence (γ2‐coherence). Our results show a high degree of spatial variability and frequency dependence in the long‐period horizontal wavefield. The variable nature of long‐period horizontal noise in the ASL vault suggests that it might be highly local in nature and not easily characterized by simple physical models when overall noise levels are low, making it difficult to identify locations in the vault with lower horizontal noise. This variability could be limiting our ability to apply coherence analysis for estimating horizontal sensor self‐noise and could also complicate various indirect methods for removing long‐period horizontal noise (e.g., collocated rotational sensor or microbarograph).

New Mexico↗

USGS2017

Broadband seismic noise attenuation versus depth at the Albuquerque Seismological Laboratory

Seismic noise induced by atmospheric processes such as wind and pressure changes can be a major contributor to the background noise observed in many seismograph stations, especially those installed at or near the surface. Cultural noise such as vehicle traffic or nearby buildings with air handling equipment also contributes to seismic background noise. Such noise sources fundamentally limit our ability to resolve earthquake‐generated signals. Many previous seismic noise versus depth studies focused separately on either high‐frequency ( > 1    Hz "> > 1 Hz ) or low‐frequency ( < 0.05    Hz "> < 0.05 Hz ) bands. In this study, we use modern high‐quality broadband (BB) and very broadband (VBB) seismometers installed at depths ranging from 1.5 to 188 m at the Albuquerque Seismological Laboratory to evaluate noise attenuation as a function of depth over a broad range of frequencies (0.002–50 Hz). Many modern seismometer deployments use BB or VBB seismometers installed at various depths, depending on the application. These depths range from one‐half meter or less in aftershock study deployments, to one or two meters in the Incorporated Research Institutions for Seismology Transportable Array (TA), to a few meters (shallow surface vaults) up to 100 m or more (boreholes) in the permanent observatories of the Global Seismographic Network (GSN). It is important for managers and planners of these and similar arrays and networks of seismograph stations to understand the attenuation of surface‐generated noise versus depth so that they can achieve desired performance goals within their budgets as well as their frequency band of focus. The results of this study will assist in decisions regarding BB and VBB seismometer installation depths. In general, we find that greater installation depths are better and seismometer emplacement in hard rock is better than in soil. Attenuation for any given depth varies with frequency. More specifically, we find that the dependence of depth will be application dependent based on the frequency band and sensitive axes of interest. For quick deployments (like aftershock studies), 1 m may be deep enough to produce good data, especially when the focus is on vertical data where temperature stability fundamentally limits the low‐frequency noise levels and little low‐frequency data will be used. For temporary (medium‐term) deployments (e.g., TA) where low cost can be very important, 2–3 m should be sufficient, but such shallow installations will limit the ability to resolve low‐frequency signals, especially on horizontal components. Of course, one should try for maximum burial depth within the budget when there is interest in using the data for low‐frequency applications. For long‐term deployments like the permanent observatories of the GSN and similar networks, 100–200 m depth in hard rock is desirable to achieve lowest noise, although 30–60 m may be acceptable.

Bulletin of the Seismological Society of America↗

USGS2016

Noise 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 stacking method of Schimmel and Gallart (2007) as well as the phase‐weighted stacking (PWS) method of Schimmel and Paulssen (1997) to four collocated broadband sensors installed in the quiet Albuquerque Seismological Laboratory underground vault. We show that such stacking methods can improve vertical noise levels by as much as 10 dB over the mean background noise levels at 400 s period, suggesting that greater improvements could be achieved with an array involving multiple sensors. We also apply this method to reduce local incoherent noise on horizontal seismic records of the 2 March 2016 M w 7.8 Sumatra earthquake, where the incoherent noise levels at very long periods are similar in amplitude to the earthquake signal. To maximize the coherency, we apply the PWS method to horizontal data where relative azimuths between collocated sensors are estimated and compared with a simpler linear stack with no azimuthal rotation. Such methods could help reduce noise levels at various seismic stations where multiple high‐quality sensors have been deployed. Such small arrays may also provide a solution to improving long‐period noise levels at Global Seismographic Network stations.

Bulletin of the Seismological Society of America↗

USGS2015

Potential 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 longer made or supported by the original manufacturer. We investigate the incoherent noise of horizontal‐component sensors, where coherent signals among sensors have been removed, giving an upper bound on the self‐noise of both the STS‐1 and STS‐2 horizontal components. Our findings suggest that a well‐installed STS‐2 could potentially produce data with similar or better incoherent noise levels to that of a horizontal‐component STS‐1. Along with our experimental investigation, we compare background noise levels for a calendar year at Incorporated Research Institutions for Seismology/U.S. Geological Survey network stations, which comprise approximately two‐thirds of the GSN, with collocated STS‐1 and STS‐2 seismometers. The use of an STS‐2‐class of sensor (flat to velocity to 120 s period) to acquire low‐frequency data in surface‐vault installations would allow network operators to focus more attention on improving vertical data. In order to deal with the difference in instrument response shapes between the two instruments, we detail two different time‐domain filters that would allow users to convert broadband STS‐2 data into very broadband data with a response similar to that of an STS‐1 (flat to velocity to 360 s period). We conclude that the complexity of the current primary horizontal vault sensors in the GSN may not be necessary until we are better able to isolate surface horizontal sensors from various noise sources.

Seismological Research Letters↗

USGS2015

Strong-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 preliminary analysis of spectral accelerations, and peak ground acceleration and velocity for earthquakes up to M 7.3 from the QCN stations, as well as preliminary analysis of the mainshock recording from the NetQuakes station. We show that mainshock peak accelerations were lower than expected and conclude the Kathmandu Valley experienced a pervasively nonlinear response during the mainshock. Phase picks from the QCN and NetQuakes data are also used to improve aftershock locations. This study confirms the utility of QCN instruments to contribute to ground-motion investigations and aftershock response in regions where conventional instrumentation and open-access seismic data are limited. Initial pilot installations of QCN instruments in 2014 are now being expanded to create the Nepal–Shaking Hazard Assessment for Kathmandu and its Environment (N-SHAKE) network.

Seismological Research Letters↗

USGS2015

A 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 important that different data providers follow a common format. The Standard for the Exchange of Earthquake Data (SEED) provides one such format for storing seismic and other geophysical data. The SEED format is widely used in earthquake seismology; however, SEED and its structure can be difficult for many first-time users (ourselves included). Below is a quick tutorial that outlines the basic structure of SEED format. This write-up is in no way intended to replace the comprehensive SEED manual ( Ahern et al. , 2009 ), and instead of going into the details of any specific part of the SEED format we refer the reader to the manual for additional details. The goal of this write-up is to succinctly explain the basic structure of SEED format as well as the associated jargon, as most commonly used now, in a colloquial way so that novice users of SEED can become more familiar with the format and its application quickly. Our goal is to give the reader the necessary background so that when problems or questions about SEED format arise they will have some understanding of where they should look for more details or from where the problem might be stemming. As a secondary goal, we hope to help the reader become familiar with the SEED manual ( Ahern et al. , 2009 ), which contains detailed information about all aspects of the SEED format.

Seismological Research Letters↗

USGS2015

Upgrade of the New China Digital Seismograph Network

No abstract available.

Seismological Research Letters↗

USGS2015

Self-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/s 2 peak). Such instruments have played a fundamental role in improving our understanding of earthquake source physics (Bock etal. , 2011), earthquake engineering (Youd et al. , 2004), and regional seismology (Zollo et al. , 2010). Although strong‐motion accelerometers tend to have higher noise levels than high‐quality broadband velocity seismometers, their higher clip‐levels provide linear recordings at near‐field sites even for the largest of events where a collocated broadband sensor would no longer be able to provide onscale recordings (Clinton and Heaton, 2002). Recently, the seismological community has begun to make use of strong‐motion accelerometer data even in the absence of large ground motions (e.g., Tibuleac et al. , 2011). The noise floor of the instruments often limits the usefulness of strong‐motion accelerometer data in such studies, because it obscures first arrivals or can make the traces dominated by noise. When a strong‐motion accelerometer is deployed in a quiet setting, the noise floors of the digitizer and the accelerometer tend to dominate the other noise sources (Cauzzi and Clinton, 2013). This situation is unlike that using broadband sensors, in which site conditions are typically the largest contributing source of noise in seismic data, especially at long periods (Wilson et al. , 2002). With the widespread deployment of strong‐motion accelerometers recorded on high resolution digitizers, it is now possible to get continuous high‐rate acceleration data in which the digitizer noise is not the dominant noise source (Cauzzi and Clinton, 2013). To better characterize the noise of a number of commonly deployed accelerometers in a standardized way, we conducted noise measurements on five different models of strong‐motion accelerometers. Our study was limited to traditional accelerometers (Fig. 1) and is in no way exhaustive.

Seismological Research Letters↗

USGS2015

The 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 automated manner. The DQA consists of a metric calculator, a PostgreSQL database, and a Web interface: The metric calculator, SEEDscan, is a Java application that reads and processes miniSEED data and generates metrics based on a configuration file. SEEDscan compares hashes of metadata and data to detect changes in either and performs subsequent recalculations as needed. This ensures that the metric values are up to date and accurate. SEEDscan can be run as a scheduled task or on demand. The PostgreSQL database acts as a central hub where metric values and limited station descriptions are stored at the channel level with one-day granularity. The Web interface dynamically loads station data from the database and allows the user to make requests for time periods of interest, review specific networks and stations, plot metrics as a function of time, and adjust the contribution of various metrics to the overall quality grade of the station. The quantification of data quality is based on the evaluation of various metrics (e.g., timing quality, daily noise levels relative to long-term noise models, and comparisons between broadband data and event synthetics). Users may select which metrics contribute to the assessment and those metrics are aggregated into a “grade” for each station. The DQA is being actively used for station diagnostics and evaluation based on the completed metrics (availability, gap count, timing quality, deviation from a global noise model, deviation from a station noise model, coherence between co-located sensors, and comparison between broadband data and synthetics for earthquakes) on stations in the Global Seismographic Network and Advanced National Seismic System.

Computers & Geosciences↗