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Tyler Storm

Publications and source records attributed to Tyler Storm.

9 recordsLinked to original sources
USGS2025

U.S. Geological Survey geomagnetic variometer data: Capitalizing on seismic infrastructure

The U.S. Geological Survey’s Geomagnetism Program is collaborating with the Earthquake Hazards Program and Global Seismographic Network Program to densify magnetic field observations. This collaboration focuses on the installation of magnetometers, or magnetic variometers, at existing seismic stations. Along with improving the density of space weather observations for hazard monitoring, these data can be used to correct colocated magnetic field induced noise in seismic data. Such corrections are especially useful during time periods of large magnetic storms where the magnetic field‐induced instrument noise can be of similar amplitude to earthquake ground‐motion records.

contiguous United States↗
USGS2021

Why do my squiggles look funny? A gallery of compromised seismic signals

Seismic instruments are highly sensitive and capable of recording a large range of different Earth signals. The high sensitivity of these instruments also makes them prone to various failures. Although many failures are very obvious, such as a dead channel, there are other more subtle failures that easily go unnoticed by both network operators and data users. This work documents several different types of failure modes in which the instrument is no longer faithfully recording ground‐motion data. Although some of these failure modes make the data completely unusable, there are also a number of failures in which the data can still be used for certain applications. Of course, the ideal situation is to identify as soon as possible when data become compromised and to have the network operator fix the station. However, knowing how the data became compromised can also help data users to identify if the data can still be used for their particular application. This work in no way attempts to exhaustively document recording failures but rather to communicate examples and equip the reader with ways of identifying failure modes.

Seismological Research Letters↗
USGS2019

Calibration analysis and noise estimates of WWSSN Station ALQ (Albuquerque, New Mexico)

World‐Wide Standardized Seismograph Network (WWSSN) records contain daily calibration pulses that can be used to retrieve the magnification as well as the response of the instrument for a given day record. We analyze a select number of long‐period vertical (LPZ) records from WWSSN station ALQ (Albuquerque, New Mexico). Although we find that the response of this instrument varies widely throughout time, we show that in most cases, we are able to estimate a pole‐zero response that has a root mean square error of &lt; 10 % "> <10% ⁠ . The variability of responses in historical networks has been reported elsewhere because a large number of adjustments of the galvanometer and seismometer were necessary. Using our derived responses, we estimate noise levels for a number of 1 hr digitized traces. We find that noise levels for the LPZ ALQ records are lower than previously reported, suggesting that these records can resolve smaller amplitude signals than previously suggested. We also find that although care must be taken in the digitization and calibration analysis, we are not limited by noise introduced in the digitization process. Finally, we see that to retrieve the full secondary microseism amplitude, we must use both the LPZ and short‐period components because the LPZ component is limited by system noise at periods less than &#x223C; 5 &#x2009;&#x2009; s "> ∼5 s ⁠ .

New Mexico↗
USGS2019

Rapid station and network quality analysis for temporary deployments

Seismic station data quality is commonly defined by metrics such as data completeness or background seismic noise levels in specific frequency bands. However, for temporary networks such as aftershock deployments or induced seismicity monitoring, the most critical metric is often how well the station performs when recording events of interest. A timely measure of station performance can be used for real-time network maintenance and to help make decisions about which stations may need to be moved or are redundant. We develop new event-based methods to assess station and network performance, including estimating network magnitude of completeness, determining station signal-to-noise ratios as a function of earthquake magnitude, and computing relative station amplitudes. At times, a complete catalog of local seismic events may not exist, such as in an aftershock deployment where hundreds to thousands of small earthquakes may be happening and catalog generation efforts cannot keep up. To overcome this, we use an envelope of the average energy recorded by the network to identify events of interest. We find that the log amplitude of events identified using this technique scales linearly with local earthquake magnitudes. This suggests that this approach can be used to determine seismicity rates and detection thresholds.

Seismological Research Letters↗
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↗
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↗
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&thinsp;&thinsp;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↗