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Results for “Seismological Research Letters”

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USGS2019

The LArge-n Seismic Survey in Oklahoma (LASSO) experiment

In 2016, the U.S. Geological Survey deployed more than 1,800 vertical-component nodal seismometers in Grant County, Oklahoma to study induced seismic activity associated with production of the Mississippi Limestone Play. The LArge-n Seismic Survey in Oklahoma (LASSO) array operated for approximately one month, covering a 25-km-by-32-km region with a nominal station spacing of ~400 m. Primary goals of the deployment were to detect microseismic events not captured by the sparser regional network stations and to provide nearly unaliased records of the seismic wavefield. A more complete record of earthquakes allows us to map the spatiotemporal evolution of induced event sequences and illuminates the structures on which the events occur. Dense records of the seismic wavefield also provide improved measurements of the earthquake source, including focal mechanisms and stress drops. Taken together, we can use these findings to glean insights into the processes that induce earthquakes. Here, we describe the array layout, features of the nodal sensors, data recording configurations, and the field deployment. We also provide examples of earthquake waveforms recorded by the array to illustrate data quality and initial observations. LASSO array data provide a significant resource for understanding the occurrence of earthquakes induced by wastewater disposal.

Kansas, Oklahoma↗

USGS2019

Regional Global Navigation Satellite System networks for crustal deformation monitoring

Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high‐precision, three‐component velocity fields covering broad geographic regions as well as position time series that track time‐varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real‐time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real‐time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real‐time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real‐time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real‐time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System‐acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.

Seismological Research Letters↗

USGS2019

Quaternary displacement on the Joiner Ridge Fault, eastern Arkansas

The New Madrid seismic zone of the central United States is an intraplate seismic zone with blind structures that are not seismically active but may pose seismic hazards. The Joiner Ridge fault is the 35 km long east-bounding fault of the Joiner Ridge blind horst located in eastern Arkansas approximately 50 km northwest of Memphis, Tennessee. Shallow S-wave (SH-mode) seismic reflection profiles, continuous cores, and radiometric dating of Quaternary alluvium across the Joiner Ridge fault reveal down-to-the-east reverse faulting and folding within of the top of the Eocene strata and overlying Quaternary Mississippi River alluvium. The base of the Quaternary alluvium has an age of 20.3 ka and is vertically displaced 12 m, resulting in an average slip rate of 0.6 + 0.1 mm/yr over the past 20.3 ka. The overlying late Wisconsinan and Holocene alluvial facies are also displaced by the Joiner Ridge fault. These facies increase in thickness across the Joiner Ridge fault and were used to calculate late Wisconsinan and Holocene slip rates. The JRF slipped 7 m between 20.3 ka and 17.5 ka (2.8 ka), reflecting a slip rate of 2.5 + 0.3 mm/yr. From 12.3 ka to 11.5 ka (0.8 ka) the JRF slipped 3 m at an average slip rate of 3.8 + 0.9 mm/yr. There were 2 m of slip on the JRF between 11.5 ka and 8.9 ka (2.6 ka), reflecting a slip rate of 0.8 + 0.3 mm/yr. No apparent slip has occurred on the JRF within the last 8.90 ka. This research illustrates that slip rates on the JRF have varied through the late Wisconsinan and early Holocene, but the Joiner Ridge fault has been inactive since the middle Holocene.

Arkansas↗

USGS2019

The 30 November 2018 Mw7.1 Anchorage Earthquake

The M w "> M w 7.1 47 km deep earthquake that occurred on 30 November 2018 had deep societal impacts across southcentral Alaska and exhibited phenomena of broad scientific interest. We document observations that point to future directions of research and hazard mitigation. The rupture mechanism, aftershocks, and deformation of the mainshock are consistent with extension inside the Pacific plate near the down‐dip limit of flat‐slab subduction. Peak ground motions > 25 % g "> g >25%g were observed across more than 8000    km 2 "> 8000 km 2 ⁠ , though the most violent near‐fault shaking was avoided because the hypocenter was nearly 50 km below the surface. The ground motions show substantial variation, highlighting the influence of regional geology and near‐surface soil conditions. Aftershock activity was vigorous with roughly 300 felt events in the first six months, including two dozen aftershocks exceeding M 4.5. Broad subsidence of up to 5 cm across the region is consistent with the rupture mechanism. The passage of seismic waves and possibly the coseismic subsidence mobilized ground waters, resulting in temporary increases in stream flow. Although there were many failures of natural slopes and soils, the shaking was insufficient to reactivate many of the failures observed during the 1964 M 9.2 earthquake. This is explained by the much shorter duration of shaking as well as the lower amplitude long‐period motions in 2018. The majority of observed soil failures were in anthropogenically placed fill soils. Structural damage is attributed to both the failure of these emplaced soils as well as to the ground motion, which shows some spatial correlation to damage. However, the paucity of instrumental ground‐motion recordings outside of downtown Anchorage makes these comparisons challenging. The earthquake demonstrated the challenge of issuing tsunami warnings in complex coastal geographies and highlights the need for a targeted tsunami hazard evaluation of the region. The event also demonstrates the challenge of estimating the probabilistic hazard posed by intraslab earthquakes.

Alaska↗

USGS2019

Ground-motion amplification in Cook Inlet region, Alaska from intermediate-depth earthquakes, including the 2018 MW=7.1 Anchorage earthquake

We measure pseudospectral and peak ground motions from 44 intermediate‐depth M w ≥ 4.9 "> M w ≥ 4.9 earthquakes in the Cook Inlet region of southern Alaska, including those from the 2018 M w "> M w 7.1 earthquake near Anchorage, to identify regional amplification features ( ⁠ 0.1 – 5    s "> 0.1 – 5 s period). Ground‐motion residuals are computed with respect to an empirical ground‐motion model for intraslab subduction earthquakes, and we compute bias, between‐, and within‐event terms through a linear mixed‐effects regression. Between‐event residuals are analyzed to assess the relative source characteristics of the Cook Inlet earthquakes and suggest a difference in the scaling of the source with depth, relative to global observations. The within‐event residuals are analyzed to investigate regional amplification, and various spatial patterns manifest, including correlations of amplification with depth of the Cook Inlet basin and varying amplifications east and west of the center of the basin. Three earthquake clusters are analyzed separately and indicate spatial amplification patterns that depend on source location and exhibit variations in the depth scaling of long‐period basin amplification. The observations inform future seismic hazard modeling efforts in the Cook Inlet region. More broadly, they suggest a greater complexity of basin and regional amplification than is currently used in seismic hazard analyses.

Alaska↗

USGS2019

Using component ratios to detect metadata and instrument problems of seismic stations: Examples from 18 years of GEOSCOPE data

Replacement or deterioration of seismic instruments and the evolution of the installation conditions and sites can alter the seismic signal in very subtle ways, so it is notoriously difficult to monitor the signal quality of permanent seismic stations. We present a simple tool, energy ratios between each pair of the three recorded components, aimed at characterizing and monitoring signal quality, as a complement to existing methods. To calculate stable daily energy ratios over a large frequency range (0.01 Hz – 5 Hz), we use the daily median energy ratio over all 5-minute windows within the day. The method is applied to all GEOSCOPE stations, for continuous BH channel data collected since 2001. We show applications to identify past gain problems (stations ROCAM and CRZF), to provide feedback after field interventions at remote sites (Antarctic station DRV), and to shed light on complex instrument problems (stations ECH and KIP). Our results show that component energy ratios have excellent time resolution and that they are visually simple for identification of problems. They can be used both for ongoing continuous monitoring of the signal quality, or as a tool to identify past problems. The associated python codes are available on GitHub.

Seismological Research Letters↗

USGS2019

Ground failure from the Anchorage, Alaska, earthquake of 30 November 2018

Investigation of ground failure triggered by the 2018 M w "> M w Mw 7.1 Anchorage earthquake showed that landslides, liquefaction, and ground cracking all occurred and caused significant damage. Shallow rock falls and rock slides were the most abundant types of landslides, but they occurred in smaller numbers than global models that are based on earthquake magnitude predict; this might result from the 2018 earthquake being an intraslab event. Liquefaction was common in alluvial and intertidal areas; ground deformation probably related to liquefaction damaged numerous houses and port facilities in Anchorage. Ground cracking was pervasive near the edges of slopes in hilly areas and caused perhaps the most significant property damage of all types of ground failure. A complex of slump–earth flows was triggered along coastal bluffs in southern Anchorage where slides also occurred in 1964; the 2018 slides involved both mobilization of new landside material and reactivation of parts of the 1964 landslide deposits. Large translational slides that formed during the 1964 Alaska earthquake showed evidence of deformation along pre‐existing failure surfaces but did not reactivate with new net downslope displacement. Modeling suggests that ground motion in 2018 was of insufficient duration and too high frequency to trigger reactivation of the deep landslides.

Alaska↗

USGS2019

Evaluation of ground‐motion models for U.S. Geological Survey seismic hazard models: 2018 Anchorage, Alaska, Mw 7.1 subduction zone earthquake sequence

Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska ( ⁠ M w "> M w 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ "> σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

Alaska↗

USGS2020

Revision of Boore (2018) Ground‐motion predictions for Central and Eastern North America: Path and offset adjustments and extension to 200 m/s <= Vs30 <= 3000 m/s

The three sets of ground‐motion predictions (GMPs) of Boore (2018 ; hereafter, B18) are compared with a much larger dataset than was used in deriving the predictions. The B18 GMPs work well for response spectra at periods between ∼ 0.15 "> ∼ 0.15 ∼0.15 and 4.0 s after an adjustment accounting for a path bias at distances beyond 200 km—this was the maximum distance used to derive the stress parameters on which the simulations in B18 are based. An additional offset adjustment is needed in the B18 predictions for short and long periods. The adjustment at short periods may be because the κ 0 "> κ 0 κ0 of 0.006 s stipulated by the Next Generation Attenuation‐East (NGA‐East) project to be used in deriving the GMPs is inconsistent with the observations on rock sites. The explanation for the offset adjustment at long periods is not clear, but it could be a combination of limitations of the point‐source stochastic model for longer period motions, as well as a decreasing number of observations at longer periods available to constrain the simulations on which the predictions are based. The predictions of B18, developed for very‐hard‐rock sites ( ⁠ V S 30 "> V S 30 VS30 of 2000 and 3000    m / s "> 3000 m / s 3000 m/s ⁠ ), have here been extended down to V S 30 "> V S 30 VS30 values as low as 200    m / s "> 200 m / s 200 m/s ⁠ . I find, as have others, that for a given V S 30 "> V S 30 VS30 ⁠ , there is generally less site amplification for central and eastern North America (CENA) than for the active crustal region dataset used for the Boore, Stewart, et al. (2014 ; hereafter, BSSA14) GMP equations. This might have an impact on conclusions of several previous studies of CENA GMPs that used the site amplifications in BSSA14 in comparing data and predictions. An additional finding is that the κ 0 "> κ 0 κ0 implied by recordings on a subset of stations in the Charlevoix region located on rock (data from these stations were not used in the analysis described earlier) is more consistent with a value near 0.014 s than the 0.006 s value used in B18 and the NGA‐East project.

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 < 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 ∼ 5    s "> ∼5 s ⁠ .

New Mexico↗

USGS2019

The sedimentary record of the 2018 Anchorage Earthquake in Eklutna Lake, Alaska: Calibrating the lacustrine seismograph

The 30 November 2018 M w "> M M w "> w 7.1 Anchorage earthquake caused modified Mercalli intensities of V¼ to V½ at Eklutna Lake (south central Alaska). A few hours after the earthquake, a “dirt streak” was observed on the lake surface, followed by a peak in sediment turbidity values ( ⁠ ∼ 80 "> ∼ 80 times normal) at a drinking water facility, which receives water from the lake through a pipe. These observations hint toward turbidity currents triggered by the earthquake in Eklutna Lake. Here, we study 32 short sediment cores retrieved from across Eklutna Lake and observe a millimeter‐to‐centimeter scale turbidite that can be confidently attributed to the 2018 earthquake in all coring locations. X‐ray computed tomography, grain‐size, and color‐spectral analyses of the turbidite show that it shares physical characteristics with the turbidite generated by the 1964 M w "> M w 9.2 Great Alaska earthquake, while it is considerably different from turbidites caused by historical floods. The 2018 turbidite reaches its largest thickness in the inflow‐proximal basin, but when compared to the 1964 turbidite and thereby canceling out local site effects, it is relatively thick in the inflow‐distal sub‐basin. The latter was exposed to stronger shaking during the 2018 earthquake, and this relative thickness trend may therefore be attributed to shaking intensity and gives an indication of the location of the earthquake epicenter relative to the basin axis. Furthermore, in contrast to the 1964 turbidite, which was sourced from both deltas and hemipelagic slopes, the 2018 turbidite was sourced from deltas only, as evidenced by its distribution. These results confirm that while it is generally accepted that shaking intensities of ≥ VI "> ≥ VI are needed to trigger turbidity currents from hemipelagic slopes, intensities as low as V¼ can be sufficient to trigger turbidity currents from deltaic slopes. Our results show that proglacial lakes can sensitively record differences in shaking intensity and that investigating deposits from recent earthquakes is crucial to calibrate the lacustrine seismograph.

Alaska↗

USGS2020

Using a dense seismic array to determine structure and site effects of the Two Towers earthflow in northern California

We deployed a network of 68 three-component geophones on the slow moving Two Towers earthflow in northern California. We compute horizontal-to-vertical spectral ratios (HVSRs) from the ambient seismic field. The HVSRs have two prominent peaks, one near 1.23 Hz and another between 4 and 8 Hz at most stations. The 1.23 Hz resonance is a property of the background noise field and may be due to a velocity contrast at a few hundred meters depth. We interpret the higher frequency peaks as being related to slide deposits and invert the spectral ratios for shallow velocity structure using in situ thickness measurements as a priori constraints on the inversion. The thickness of the shallowest, low-velocity layer is systematically larger than landslide thicknesses inferred from inclinometer data acquired since 2013. Given constraints from field observations and boreholes, the inversion may reflect the thickness of deposits of an older slide that is larger in spatial extent and depth than the currently active slide. Because the HVSR peaks measured at Two Towers are caused by shallow slide deposits and represent frequencies that will experience amplification during earthquakes, the depth of the actively sliding mass may be less relevant for assessing potential slide volume and associated hazard than the thicknesses determined by our inversions. More generally, our results underscore the utility of combining both geotechnical measurements and subsurface imaging for landslide characterization and hazard assessment.

California↗

USGS2019

USGS near-real-time products-and their use-for the 2018 Anchorage earthquake

In the minutes to hours after a major earthquake, such as the recent 2018 M w "> M w 7.1 Anchorage event, the U.S. Geological Survey (USGS) produces a suite of interconnected earthquake products that provides diverse information ranging from basic earthquake source parameters to loss estimates. The 2018 Anchorage earthquake is the first major domestic earthquake to occur since several new USGS products have been developed, thus providing an opportunity to discuss the newly expanded USGS earthquake product suite, its timeliness, performance, and reception. Overall, the products were relatively timely, accurate, well received, and widely used, including by the media, who used information and visualizations from many products to frame their early reporting. One downside of the codependence of multiple products is that reasonable updates to upstream products (e.g., magnitude and source characterization) can result in significant changes to downstream products; this was the case for the Anchorage earthquake. However, the coverage of strong‐motion stations and felt reports was so dense that the ShakeMap and downstream products were relatively insensitive to changes in magnitude or fault‐plane orientation once the ground‐motion data were available. Shaking and loss indicators initially fluctuated in the first hour or two after the earthquake, but they stabilized quickly. To understand how the products are being used and how effectively they are being communicated, we analyze the media coverage of USGS earthquake products. Most references to USGS products occurred within the first 48 hr after the event. The lack of coverage after 48 hr could indicate that longer‐term products addressing what actions the USGS is taking or what early reconnaissance has revealed might be useful for those people wanting additional information about the earthquake.

Alaska↗

USGS2020

How processing methodologies can distort and bias power spectral density estimates of seismic background noise

Power spectral density (PSD) estimates are widely used in seismological studies to characterize background noise conditions, assess instrument performance, and study quasi‐stationary signals that are difficult to observe in the time domain. However, these studies often utilize different processing techniques, each of which can inherently bias the resulting PSD estimates. The level of smoothing, the size of the data window, and the method used for actually estimating the spectral content can all have strong influences on PSD estimates and background noise statistics. We show that although smoothing reduces the variance of the PSD estimate, the corresponding decrease in frequency resolution can eliminate or distort features of interest. For instance, popular software packages such as Incorporated Research Institutions for Seismology Modular Utility for STAatistical kNowledge Gathering (MUSTANG) and earlier versions of Portable Array Seismic Studies of the Continental Lithosphere Quick Look eXtended (PQLX), which were designed for data quality control and are effective in that regard, are less suitable for scientific studies that require accurate resolution of spectral peaks, even for peaks as broad as the primary ( ⁠ ∼ 14    s "> ∼ 14 s period) and secondary ( ⁠ ∼ 7    s "> ∼ 7 s period) microseisms. We also demonstrate how the 1 and 3 hr data windows used in MUSTANG and PQLX can be strongly influenced by energy generated from moderate‐size ( ⁠ M > ∼ 4.8 "> M > ∼ 4.8 ⁠ ) teleseismic earthquakes. The ubiquity of these events is likely skewing median ambient‐noise estimates by as much as 5 dB upward, for periods of 10–50 s at high‐quality broadband stations. Finally, we illustrate that many of the discrepancies between global low‐noise models are attributable to processing methodologies rather than fundamental differences in the underlying seismic data.

Seismological Research Letters↗

USGS2020

A brief introduction to seismic instrumentation: Where does my data come from?

Modern seismology has been able to take advantage of several technological advances. These include feedback loops in the seismometer, specialized digitizers with absolute timing, and compression formats for storing data. While all of these advances have helped to improve the field, they can also leave newcomers a bit confused. Our goal here is to give a brief overview of how recordings of seismic ground motion originate. We discuss the chain of events that are required to obtain digital data plus how these steps can be reversed to recover units of ground motion such as acceleration, velocity, or displacement. Finally, we show a few examples of data that has become compromised because of various non-ground motion signals. We hope to give a quick practical introduction to allow the reader to become familiar with the various jargon used in observational seismology.

Seismological Research Letters↗

USGS2020

Goals and development of the Alaska Volcano Observatory Seismic Network and application to forecasting and detecting volcanic eruptions

The Alaska Volcano Observatory (AVO) seismic network has been in operation since 1988 and during this time has grown from 29 to 217 seismic stations providing real-time monitoring of 32 active volcanoes in Alaska, as well as useful data for regional earthquake monitoring. Since 1988, AVO has detected 59 volcanic eruptions at Aleutian arc volcanoes, and 31 of these have been captured by local seismic instrumentation. As part of this monitoring effort, AVO has cataloged more than 120,000 earthquake hypocenters and magnitudes associated with volcanic processes throughout the arc. This high rate of volcanic activity provides an excellent opportunity to study seismicity associated with magmatic and eruptive processes and develop and refine analytical techniques to track volcanic seismicity and warn of hazardous eruptions. The network is currently undergoing an extensive upgrade, replacing aging short-period analog seismometers with digital broadband instruments. These are expected to improve AVO’s seismic capability and further facilitate other geophysical instrumentation such as continuous Global Positioning System receivers, infrasound sensors, and web cams.

Seismological Research Letters↗

USGS2019

Highlights of a cursory study of behavior of three instrumented buildings during the Mw7.1 Anchorage, Alaska, earthquake of November 30, 2018

This is a cursory study of the recorded responses of three buildings instrumented by the U.S. Geological Survey (USGS) in Anchorage, Alaska, during the M w "> M w Mw 7.1 earthquake of 30 November 2018. The earthquake caused the strongest shaking in Anchorage since the well‐known 1964 M w "> M w Mw 9.2 Great Alaska earthquake. Since the 1964 event, several structures (buildings and bridges) in Anchorage have been instrumented by the USGS, and their responses have been recorded during multiple events. For each of the three buildings (the 14‐story Frontier Building, the 20‐story Atwood Building, and the 22‐story Hilton Hotel) studied herein, essential dynamic characteristics and significant behavioral aspects such as beating and torsional motions are identified. Recorded peak accelerations and displacements are provided, and average drift ratios are computed using the peak displacements at the roof levels with respect to the ground level. These average drift ratios imply that the motions are at levels expected not to cause damage to the buildings. Visualization videos of both the 14‐story Frontier and the 20‐story Atwood Buildings have been developed to display overall shaking of the buildings during the earthquake.

Alaska↗

USGS2020

Earthquake early warning ShakeAlert 2.0: Public rollout

The ShakeAlert Earthquake Early Warning System is designed to automatically identify and characterize the initiation and rupture evolution of large earthquakes, estimate the intensity of ground shaking that will result, and deliver alerts to people and systems that may experience shaking, prior to the occurrence of shaking at their location. It is configured to issue alerts to locations within the West Coast of the U.S. In 2018, ShakeAlert 2.0 went live in a regional public test in the first phase of a general public rollout. The ShakeAlert system is now providing alerts to over sixty institutional partners in the three states of the Western U.S. where most of the nation’s earthquake risk is concentrated: California, Oregon, and Washington. The ShakeAlert 2.0 product for public alerting is a message containing a polygon enclosing a region predicted to experience Modified Mercalli Intensity ≥ IV for an earthquake of M5.0 or larger, corresponding to moderate-to-strong ground shaking. A polygon format alert is the easiest description for selective re-broadcasting mechanisms (e.g. cell towers) and is a requirement for some mass notification systems such as the Federal Emergency Management Agency’s Integrated Public Alert and Warning System. ShakeAlert 2.0 is tested using historic waveform data consisting of 60 M3.5+ and 25 M5.0+ earthquakes, in addition to other anomalous waveforms. For the historic event test, the average M5+ false alert rate/missed event rate for ShakeAlert 2.0 is 8%/16%, and the M3.5+ false alert rate/missed event rate is 10%/36.7%. Real-time performance metrics are also presented to assess how the system behaves in regions that are well-instrumented, sparsely instrumented, and offshore.

British Columbia, California, Oregon, Washinton↗