Erratum to an evaluation of the timing accuracy of global and regional seismic stations and networks
No abstract available.
Geology topics
Publications and source records attributed to Adam T. Ringler.
No abstract available.
The U.S. Geological Survey (USGS) Global Seismographic Network (GSN) Program operates two thirds of the GSN, a network of state‐of‐the‐art, digital seismological and geophysical sensors with digital telecommunications. This network serves as a multiuse scientific facility and a valuable resource for research, education, and monitoring. The other one third of the GSN is funded by the National Science Foundation (NSF), and the operations of this component are overseen by EarthScope. This collaboration between the USGS, EarthScope, and NSF has allowed for the development and operations of the GSN to be a truly multiuse network that provides near real‐time open access data, facilitating fundamental discoveries by the Earth science community, supporting the earthquake hazards mission of the USGS, benefitting tsunami monitoring by the National Oceanic and Atmospheric Administration, and contributing to nuclear test monitoring and treaty verification. In this article, we describe the installation and evolution of the seismic networks operated by the USGS that ultimately led to the USGS portion of the GSN (100 stations under network codes IU, IC, and CU) as they are today and envision technological advances and opportunities to further improve the utility of the network in the future. This article focuses on the USGS‐operated component of the GSN; a companion article on the GSN stations funded by the NSF and operated by the Cecil and Ida Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California at San Diego by Davis et al. (2023) appears in this volume.
Ocean waves excite continuous globally observable seismic signals. We use data from 52 globally distributed seismographs to analyze the vertical component primary microseism wavefield at 14–20 s period between the late 1980s and August 2022. This signal is principally composed of Rayleigh waves generated by ocean wave seafloor tractions at less than several hundred meters depth, and is thus a proxy for near-coastal swell activity. Here we show that increasing seismic amplitudes at 3 σ significance occur at 41 (79%) and negative trends occur at 3 σ significance at eight (15%) sites. The greatest absolute increase occurs for the Antarctic Peninsula with respective acceleration amplitude and energy trends ( ± 3 σ ) of 0.037 ± 0.008 nm s −2 y −1 (0.36 ± 0.08% y −1 ) and 4.16 ± 1.07 nm 2 s −2 y −1 (0.58 ± 0.15% y −1 ), where percentage trends are relative to historical medians. The inferred global mean near-coastal ocean wave energy increase rate is 0.27 ± 0.03% y −1 for all data and is 0.35 ± 0.04% y −1 since 1 January 2000. Strongly correlated seismic amplitude station histories occur to beyond 50 ∘ of separation and show regional-to-global associations with El Niño and La Niña events.
An increase in seismic stations also having microbarographs has led to increased interest in the field of seismoacoustics. A review of the recent advances in this field can be found in Dannemann Dugick et al. (2023) . The goal of this note is to draw the attention of the readers of Dannemann Dugick et al. (2023) to several additional interactions between the solid Earth and atmosphere that have not been classically considered in the field of seismoacoustics. The 15 January 2022 Hunga Tonga–Hunga Ha‘api eruption produced acoustic gravity waves that were recorded globally. For example, the Lamb wave from this eruption produced early‐arriving and long‐lasting tsunami waves. This eruption also provided globally recorded coupling of atmospheric modes with solid Earth modes, providing another example of the complex interactions that can occur at the boundary between the atmosphere and the solid Earth. Even in the absence of large atmospheric signals, collocated pressure sensors at seismic stations can be a useful tool for estimating the local substructure, such at V S 30 , the average shear velocity of the upper 30 m. Finally, at low frequencies, it is possible to use pressure records to correct out atmospheric disturbances recorded on seismometers. We briefly review the aforementioned, nontraditional seismoacoustic topics that we feel are important to consider as part of the full suite of interactions occurring between the solid Earth and atmosphere.
One of the most prominent challenges related to legacy seismic data is determining how these data can be appropriately used in modern research applications. The wide variety of instrumentation used in the analog era, the format of recording on paper wrapped around a helicorder drum, and limited metadata information introduces ambiguities that are not typical of modern digital data. Therefore, techniques must be developed to help characterize uncertainties in legacy data. This article presents an analysis that compares corecorded signals from two instruments—a Trillium Compact or Press‐Ewing (PE) seismometer for sensing ground motion and two recording systems: a modern Q330 digitizer or heated‐stylus system. Analyses of the recordings in both time and frequency domains indicate time uncertainty on the order of one second, identify a flat response in a 10–60 s band for the PE and drum recorder, and highlight how specific features of scans and paper seismograms (e.g., repeated portions of scans and line thickness) can cause timing jumps or reduced trace amplitude.
Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time‐tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground‐motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.
Records of pressure variations on seismographs were historically considered unwanted noise; however, increased deployments of collocated seismic and acoustic instrumentation have driven recent efforts to use this effect induced by both wind and anthropogenic explosions to invert for near‐surface Earth structure. These studies have been limited to shallow structure because the pressure signals have relatively short wavelengths (<∼300 m). However, the 2022 eruption of Hunga Tonga–Hunga Ha’apai (also called “Hunga”) volcano in Tonga generated rare, globally observed, high‐amplitude infrasound signals with acoustic wavelengths of tens of kilometers. In this study, we examine the acoustic‐to‐seismic coupling generated by the Hunga eruption across 82 Global Seismographic Network (GSN) stations and show that ground motion amplitudes are related to upper (0 to ∼5 km) crust material properties. We find high (>0.8) correlations between pressure and vertical component ground motion at 83% of the stations, but only 30% of stations show this on the radial component, likely due to complex tilt effects. We use average elastic properties in the upper 5.2 km from the CRUST1.0 model to estimate vertical seismic/acoustic coupling coefficients ( S V / A ">
Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.
The eruption of the submarine Hunga Tonga-Hunga Haʻapai (Hunga Tonga) volcano on 15 January 2022, was one of the largest volcanic explosions recorded by modern geophysical instrumentation. The eruption was notable for the broad range of atmospheric wave phenomena it generated and for their unusual coupling with the oceans and solid Earth. The event was recorded worldwide across the Global Seismographic Network (GSN) by seismometers, microbarographs and infrasound sensors. The broad-band instrumentation in the GSN allows us to make high fidelity observations of spheroidal solid Earth normal modes from this event at frequencies near 3.7 and 4.4 mHz. Similar normal mode excitations were reported following the 1991 Pinatubo (Volcanic Explosivity Index of 6) eruption and were predicted, by theory, to arise from the excitation of mesosphere-scale acoustic modes of the atmosphere coupling with the solid Earth. Here, we compare observations for the Hunga Tonga and Pinatubo eruptions and find that both strongly excited the solid Earth normal mode 0 S 29 (3.72 mHz). However, the mean modal amplitude was roughly 11 times larger for the 2022 Hunga Tonga eruption. Estimates of attenuation ( Q ) for 0 S 29 across the GSN from temporal modal decay give Q = 332 ± 101, which is higher than estimates of Q for this mode using earthquake data ( Q = 186.9 ± 5). Two microbarographs located at regional distances (<1000 km) to the volcano provide direct observations of the fundamental acoustic mode of the atmosphere. These pressure oscillations, first observed approximately 40 min after the onset of the eruption, are in phase with the seismic Rayleigh wave excitation and are recorded only by microbarographs in proximity (<1500 km) to the eruption. We infer that excitation of fundamental atmospheric modes occurs within a limited area close to the site of the eruption, where they excite select solid Earth fundamental spheroidal modes of similar frequencies that are globally recorded and have a higher apparent Q due to the extended duration of atmospheric oscillations.
The U.S. Geological Survey (USGS) maintains an archive of 189,180 digitized scans of analog seismic records from the World‐Wide Standardized Seismograph Network (WWSSN). Although these scans have been made public, the archive is too large to manually review, and few researchers have utilized large numbers of these records. To facilitate further research using this historical dataset, we develop a simple convolutional neural network (CNN) that rapidly (∼4.75 s/film chip) classifies scanned film chip images (called “chips,” because they are individually cut segments of 70 mm film) into four categories of “interestingness” to earthquake seismologists based on the presence of earthquakes and other seismic signals in the record: “no interest,” “little interest,” “interest,” and “high interest.” The CNN, dubbed “Seismic Analog Record Network” (SARNet), can identify four types of seismic traces (“no events,” “minor events,” “major events,” and “errors”) in 200 × 200 pixel subcrops with an accuracy of 92% using a confidence threshold of 85%. SARNet then converts 100 random subcrops from each film chip into the overall classification of interestingness. In this task, SARNet performed as well as expert human classifiers in determining the film chip’s overall interest grade. Applying SARNet to 34,000 film chips in the WWSSN archive found that 21% of the images were of “high interest” and had an “indeterminate” rate of only 4%. Thus, the need for the manual review of images was reduced by 79%. Sorting of film chips derived from SARNet will expedite further exploration of the archive of digitized analog seismic records stored at the USGS.
Acoustic energy originating from explosions, sonic booms, bolides and thunderclaps have been recorded on seismometers since the 1950s. Direct pressure loading from the passing acoustic wave has been modelled and consistently observed to produce ground deformations of the near surface that have retrograde elliptical particle motions. In the past decade, increased deployments of colocated seismometers and infrasound sensors have driven efforts to use the transfer function between direct acoustic-to-seismic coupling to infer near-surface material properties including seismic velocity structure and elastic moduli. In this study, we use a small aperture (≈600 m) array of broadband seismometers installed in different manners and depths in both granite and sedimentary overburden to understand the fundamental nature and repeatability of seismic excitation from 1 to 15 Hz using horizontally propagating acoustic waves generated by 97 local (2–10 km) explosions. In agreement with modelling, we find that the ground motions induced by acoustic-to-seismic coupling attenuate rapidly with depth. We confirm the modelled relation between acoustic and ground motion amplitudes, but show that within one acoustic wavelength, the uncertainty in the transfer coefficient between seismic and acoustic energy at a given seismic station increases linearly with separation distance between the seismic and acoustic sensor. We attribute this observation to the rapid decorrelation of the infrasonic wavefield across small spatial scales and recommend colocating seismic and infrasound sensors for use in studies seeking to invert for near-surface material properties. Additionally, contrary to acoustic-to-seismic coupling theory and prior observations, we find that seismometers emplaced in granite do not record retrograde elliptical particle motions in response to direct pressure loading. We rule out seismometer tilt effects as a likely source of this observations and suggest that existing models of acoustic-to-seismic excitation may be too simplistic for seismometers placed in high rigidity materials.
The Global Seismographic Network (GSN)—a global network of ≈150 very broadband stations—is used by researchers to study the free oscillations of the Earth (≈0.3–10 mHz) following large earthquakes. Normal‐mode observations can provide information about the radial density and anisotropic velocity structure of the Earth (including near the core–mantle boundary), but only when signal‐to‐noise ratios at very low frequencies are sufficiently high. Most normal‐mode observations in the past three decades have been made using Streckeisen STS‐1 vault seismometers. However, these sensors are no longer being manufactured or serviced. Candidate replacement sensors, the Streckeisen STS‐6 and the Nanometrics T‐360GSN, have been recently installed in boreholes, postholes, and vaults at several GSN stations and GSN testbeds. In this study, we examine normal‐mode spectra following three M w "> M w 8 earthquakes in 2021 and from one M w "> M w 8.2 earthquake in 2014 to evaluate the change in GSN low‐frequency performance on the vertical component. From this analysis, we conclude that the number of GSN stations capable of resolving normal modes following M w "> M w 8 earthquakes has nearly doubled since 2014. The improved observational capabilities will help better understand the radial velocity and density estimates of the Earth.
Since 2004, the most complete estimate of background noise levels across the continental U.S. was attained using 61 broadband seismic stations to calculate power spectral density (PSD) probability density functions. To improve seismic noise estimates across the U.S., we examine vertical component seismic data from the EarthScope USArray Transportable Array seismic network that rolled across the U.S. and southeastern Canada between 2004 and 2015 and form a large (10 TB) PSD database from 1679 stations that contains no smoothing or binning of the spectral estimates. Including station outages, our database has a mean of 98.9% data completeness, and we present maps showing the spatial and temporal variability of seismic noise in six bands of interest between 0.2- and 75-s period. At 0.2 s period, seismic noise across the eastern U.S. is predominantly anthropogenically generated and may be subsequently amplified more than 20 decibels in the sandy and water-saturated sediments of the southeastern U.S. Coastal Plain and Mississippi Embayment. In these sediments, 1 s noise shows similar amplification and is generated through a variety of mechanisms including cultural activity throughout Kentucky and the southeastern Appalachian Mountains, lake waves around the Great Lakes, and ocean waves throughout New England, the Pacific Northwest, and Florida. Both 0.2 and 1 s noise levels are the lowest in the Intermountain West portion of the U.S. We attribute this to a combination of installations on crystalline rocks and reduced population density. Finally, we find that sensors emplaced in sandy, water-saturated sediments observe median, diurnal variations in vertical component power at 18 to 75 s period, which we infer arise through local deformation driven by pressure variations. Ultimately, our results underscore that for shallow (<5 m depth) sensor installation, bedrock provides superior broadband noise performance compared to unconsolidated sediments.
The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.
As seismologists continue to place more stringent demands on data quality, accurately described metadata are becoming increasingly important. In order to better constrain the orientation and sensitivities of seismometers deployed in U.S. Geological Survey networks, the Albuquerque Seismological Laboratory (ASL) has recently begun identifying true north with a fiber optic gyroscope (FOG) and has developed methodologies to constrain mid-band, vertical component sensitivity levels to less than 1% in a controlled environment. However, questions remain regarding the accuracy of this new alignment technique as well as if instrument sensitivities and background noise levels are stable when the seismometers are installed in different environmental settings. In this study, we examine the stability and repeatability of these parameters by reinstalling two high-quality broadband seismometers (Streckeisen STS-2.5 and Nanometrics T-360 Global Seismographic Network (GSN) version) at different locations around the ASL and comparing them to each other and a reference STS-6 seismometer that stayed stationary for the duration of the experiment. We find that even in different environmental conditions, the sensitivities of the two broadband seismometers stayed stable to within 0.1% and that orientations attained using the FOG are generally accurate to within a degree. However, one install was off by 5° due to a mistake made by the installation team. These results indicate that while technology and methodologies are now in place to calibrate and orient a seismometer to within 1°, human error both during the installation and while producing the metadata is often a limiting factor. Finally, we find that background noise levels at short periods (0.1–1 s) become noisier when the sensors are emplaced in unconsolidated materials, whereas the noise levels at long periods (30–100 s) are not sensitive to local geological structure on the vertical components.
Clock accuracy is a basic parameter of any seismic station and has become increasingly important for seismology as the community seeks to refine structures and dynamic processes of the Earth. In this study, we measure the arrival time differences of moderate repeating earthquakes with magnitude 5.0–5.9 in the time range of 1991–2017 at the same seismic stations by cross‐correlating their highly similar waveforms and thereby identify potential timing errors from the outliers of the measurements. The method has very high precision of about 10 ms and shows great potential to be used for routine inspection of the timing accuracy of historical and future digital seismic data. Here, we report 5131 probable cases of timing errors from 451 global and regional stations available from the Incorporated Research Institutions for Seismology Data Management Center, ranging from several tens of milliseconds to over 10 s. Clock accuracy seems to be a prevailing problem in permanent stations with long‐running histories. Although most of the timing errors have already been tagged with low timing quality, there are quite a few exceptions, which call for greater attention from network operators and the seismological community. In addition, seismic studies, especially those on temporal changes of the Earth’s media from absolute arrival times, should be careful to avoid misinterpreting timing errors as temporal changes, which is indeed a problem in some previous studies of the Earth’s inner core boundary.
Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predicted amplitude with the average station background noise level. This approach has significant uncertainty because of unknown regional attenuation and complications in computing small event power spectra, and it fails to account for the specific capabilities of the automatic seismic phase picker used in monitoring. We develop a data‐driven approach to determine network detection thresholds using a multiband phase picking algorithm that is currently in use at the U.S. Geological Survey National Earthquake Information Center. We apply this picking algorithm to cataloged earthquakes to determine an empirical relationship of the observability of earthquakes as a function of magnitude and distance. Using this relationship, we produce maps of detection threshold using station spatial configuration and station noise levels. We show that quiet, well‐sited stations significantly increase the detection capabilities of a network compared with a network composed of many noisy stations. Because our method is data driven, it has two distinct advantages: (1) it is less dependent on theoretical assumptions of source spectra and models of regional attenuation, and (2) it can easily be applied to any seismic network. This tool allows for an objective approach to the management of stations in regional seismic networks.
Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600 s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.