Geology topics

Austin Holland

Publications and source records attributed to Austin Holland.

7 recordsLinked to original sources

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

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↗

USGS2016

PhasePApy: A robust pure Python package for automatic identification of seismic phases

We developed a Python phase identification package: the PhasePApy for earthquake data processing and near‐real‐time monitoring. The package takes advantage of the growing number of Python libraries including Obspy. All the data formats supported by Obspy can be supported within the PhasePApy. The PhasePApy has two subpackages: the PhasePicker and the Associator, aiming to identify phase arrival onsets and associate them to phase types, respectively. The PhasePicker and the Associator can work jointly or separately. Three autopickers are implemented in the PhasePicker subpackage: the frequency‐band picker, the Akaike information criteria function derivative picker, and the kurtosis picker. All three autopickers identify picks with the same processing methods but different characteristic functions. The PhasePicker triggers the pick with a dynamic threshold and can declare a pick with false‐pick filtering. Also, the PhasePicker identifies a pick polarity and uncertainty for further seismological analysis, such as focal mechanism determination. Two associators are included in the Associator subpackage: the 1D Associator and 3D Associator, which assign phase types to picks that can best fit potential earthquakes by minimizing root mean square (rms) residuals of the misfits in distance and time, respectively. The Associator processes multiple picks from all channels at a seismic station and aggregates them to increase computational efficiencies. Both associators use travel‐time look up tables to determine the best estimation of the earthquake location and evaluate the phase type for picks. The PhasePApy package has been used extensively for local and regional earthquakes and can work for active source experiments as well.

Seismological Research Letters↗

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

Efforts to monitor and characterize the recent increasing seismicity in central Oklahoma

The sharp increase in seismicity over a broad region of central Oklahoma has raised concerns regarding the source of the activity and its potential hazard to local communities and energy-industry infrastructure. Efforts to monitor and characterize the earthquake sequences in central Oklahoma are reviewed. Since early 2010, numerous organizations have deployed temporary portable seismic stations in central Oklahoma to record the evolving seismicity. A multiple-event relocation method is applied to produce a catalog of central Oklahoma earthquakes from late 2009 into early 2015. Regional moment tensor (RMT) source parameters were determined for the largest and best-recorded earthquakes. Combining RMT results with relocated seismicity enabled determination of the length, depth, and style of faulting occurring on reactivated subsurface fault systems. It was found that the majority of earthquakes occur on near-vertical, optimally oriented (northeast-southwest and northwest-southeast) strike-slip faults in the shallow crystalline basement. In 2014, 17 earthquakes occurred with magnitudes of 4 or larger. It is suggested that these recently reactivated fault systems pose the greatest potential hazard to the region.

Oklahoma↗