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At least 235 records · Page 13Linked to original sources

USGS2025

Remote single-station seismic monitoring of the July–October 2022 earthquake swarm at Tau volcano, American Samoa

From July to October 2022, a non-eruptive volcanic earthquake swarm occurred within ~15 km of Taʻū Island, located in eastern American Samoa. Felt reports from local residents were the only available information about the swarm when it started, as American Samoa lacked a seismic monitoring network. We developed a consistent single-station catalog for the entire swarm, using seismic data from the nearest station IU.AFI, ~250 km away. We applied the EQTransformer deep-learning model (Mousavi et al., 2020), automatically picking Pn and Sn arrivals on IU.AFI continuous data. We retained only events with Sn-Pn times of 22.5–25 seconds, consistent with the expected locations based on felt reports, then detected smaller swarm events with subsequent template-matching. This single-station catalog characterized the swarm’s onset and escalation to peak activity before a multi-agency field response team installed a local seismic network in mid-August 2022. This permanent seismic network captured the swarm’s decline. EQTransformer identified short S-P times on the first two locally deployed seismometers, both Raspberry Shake sensors, to constrain the swarm’s distance from Taʻū Island. Modern seismological processing methods, combined with basic observations such as felt reports, can quickly contribute useful information during an earthquake response in a poorly monitored region.

American Somoa↗

USGS1981

Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 imperial valley, California, earthquake

We have taken advantage of the recent increase in strong-motion data at close distances to derive new attenuation relations for peak horizontal acceleration and velocity. This new analysis uses a magnitude-independent shape, based on geometrical spreading and anelastic attenuation, for the attenuation curve. An innovation in technique is introduced that decouples the determination of the distance dependence of the data from the magnitude dependence.

California↗

USGS2015

The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.

The Quake‐Catcher Network (QCN) is an expanding seismic array made possible by thousands of participants who volunteered time and resources from their computers to record seismic data using low‐cost accelerometers (http://qcn.stanford.edu/; last accessed December 2014). Sensors based on Micro‐Electromechanical Systems (MEMS) technology have rapidly improved over the last few years due to the demand of the private sector (e.g., automobiles, cell phones, and laptops). For strong‐motion applications, low‐cost MEMS accelerometers have promising features due to an increasing resolution and near‐linear phase and amplitude response ( Cochran, Lawrence, Christensen, and Jakka, 2009 ; Clayton et al. , 2011 ; Evans et al. , 2014 ). Each volunteer computer monitors ground motion and communicates using the Berkeley Open Infrastructure for Network Computing (BOINC, Anderson, 2004 ). Using a standard short‐term average, long‐term average (STLA) algorithm ( Earle and Shearer, 1994 ; Cochran, Lawrence, Christensen, Chung, 2009 ; Cochran, Lawrence, Christensen, and Jakka, 2009 ), volunteer computer and sensor systems detect abrupt changes in the acceleration recordings. Each time a possible trigger signal is declared, a small package of information containing sensor and ground‐motion information is streamed to one of the QCN servers ( Chung et al. , 2011 ). Trigger signals, correlated in space and time, are then processed by the QCN server to look for potential earthquakes.

Seismological Research Letters↗

USGS1995

Near real-time monitoring of seismic events and status of portable digital recorders using satellite telemetry

Near real-time monitoring of seismic events and status of portable 16-bit digital recorders has been established for arrays near Parkfield, Mammoth Lakes, and San Francisco, California. This monitoring system provides near real-time seismic event identification (rough location and magnitude) and a cost-effective means to maintain arrays at near 100% operational level. Principal objectives in the design of this system have been portability and low-cost telemetry. The system has been developed to use portable digital seismic recorders ( GEOS —General Earthquake Observation System) and portable data collection platforms ( DCP 's) for the Geostationary Operational Environmental Satellite (GEOS) telemetry system. Data are transferred asynchronously from the GEOS seismic system through a microprocessor-controlled interface every 10 min. The interface stores, determines priority, converts, and synchronously transfers these data to a Sutron Corp. model 8004 DCP for transmission through the GEOS satellite telemetry system. Event parameters include trigger time, peak amplitude, time of peak amplitude, and event duration. Instrument configuration parameters, transmitted at system start-up time and every 24 hr, include recording parameters, trigger parameters, GEOS software version, clock reference, and location parameter. Instrument status includes battery voltage, number of events, and percentage of tape usage. These data are transmitted as appropriate to the U.S. Geological Survey satellite downlink and computers located in Menlo Park, California, where they are processed and displayed.

Bulletin of the Seismological Society of America↗

USGS2008

Remote-controlled pan, tilt, zoom cameras at Kilauea and Mauna Loa Volcanoes, Hawai'i

Lists of important volcano-monitoring disciplines usually include seismology, geodesy, and gas geochemistry. Visual monitoring - the essence of volcanology - is usually not mentioned. Yet, observations of the outward appearance of a volcano provide data that is equally as important as that provided by the other disciplines. The eye was almost certainly the first volcano monitoring-tool used by early man. Early volcanology was mostly descriptive and was based on careful visual observations of volcanoes. There is still no substitute for the eye of an experienced volcanologist. Today, scientific instruments replace or augment our senses as monitoring tools because instruments are faster and more sensitive, work tirelessly day and night, keep better records, operate in hazardous environments, do not generate lawsuits when damaged or destroyed, and in most cases are cheaper. Furthermore, instruments are capable of detecting phenomena that are outside the reach of our senses. The human eye is now augmented by the camera. Sequences of timed images provide a record of visual phenomena that occur on and above the surface of volcanoes. Photographic monitoring is a fundamental monitoring tool; image sequences can often provide the basis for interpreting other data streams. Monitoring data are most useful when they are generated and are available for analysis in real-time or near real-time. This report describes the current (as of 2006) system for real-time photograph acquisition and transmission from remote sites on Kilauea and Mauna Loa volcanoes to the U.S. Geological Survey Hawaiian Volcano Observatory (HVO). It also describes how the photographs are archived and analyzed. In addition to providing system documentation for HVO, we hope that the report will prove useful as a practical guide to the construction of a high-bandwidth network for the telemetry of real-time data from remote locations.

Hawai'i↗

USGS2006

Rupture propagation of the 2004 Parkfield, California, earthquake from observations at the UPSAR

Using a short-baseline seismic array (U.S. Geological Survey Parkfield Dense Seismograph Array [UPSAR]) about 12 km west of the rupture initiation of the 28 September 2004 M 6.0 Parkfield, California, earthquake, we have observed the movement of the rupture front of this earthquake on the San Andreas fault. The sources of high-frequency arrivals at UPSAR, which we use to identify the rupture front, are mapped onto the San Andreas fault using their apparent velocity and back azimuth. Measurements of apparent velocity and back azimuth are calibrated using aftershocks, which have a compact source and known location. Aftershock back azimuths show considerable lateral refraction, consistent with a high-velocity ridge on the southwest side of the fault. We infer that the initial mainshock rupture velocity was approximately the Rayleigh speed (with respect to slower side of the fault), and the rupture then slowed to about 0.66?? near the town of Parkfield after 2 sec. The last well-correlated pulse, 4 sec after S, is the largest at UPSAR, and its source is near the region of large accelerations recorded by strong-motion accelerographs and close to northern extent of continuous surface fractures on the southwest fracture zone. Coincidence of sources with preshock and aftershock distributions suggests fault material properties control rupture behavior. High-frequency sources approximately correlate with the edges of asperities identified as regions of high slip derived from inversion of strong-motion waveforms.

Bulletin of the Seismological Society of America↗

USGS2020

Magnetic field variations in Alaska: Recording space weather events on seismic stations in Alaska

Seismometers are highly sensitive instruments to not only ground motion but also many other nonseismic noise sources (e.g., temperature, pressure, and magnetic field variations). We show that the Alaska component of the Transportable Array is particularly susceptible to recording magnetic storms and other space weather events because the sensors used in this network are unshielded and magnetic flux variations are stronger at higher latitudes. We also show that vertical‐component seismic records across Alaska are directly recording magnetic field variations between 40 and 800 s period as opposed to actual ground motion during geomagnetic events with sensitivities ranging from 0.004 to 0.48    ( m / s 2 ) / T "> 0.48 ( m / s 2 ) / T 0.48 (m/s2)/T ⁠ . These sensitivities were found on a day where the root mean square variation in the magnetic field was 225 nT. Using a method developed by Forbriger (2007 , his section 3.1), we show that improving vertical seismic resolution of an unshielded sensor by as much as 10 dB in the 100–400 s period band using magnetic data from a collocated three‐component magnetometer is possible. However, due to large spatial variations in Earth’s magnetic field, this methodology becomes increasingly ineffective as the distance between the seismometer and magnetometer increases (no more than 200 km separation). A potential solution to this issue may be to incorporate relatively low‐cost magnetometers as an additional environmental data stream at high‐latitude seismic stations. We demonstrate that the Bartington Mag‐690 sensors currently deployed at Global Seismographic Network sites are not only acceptable for performing corrections to seismic data, but are also capable of recording many magnetic field signals with similar signal‐to‐noise ratios, in the 20–1000 s period band, as the observatory grade magnetometers operated by the U.S. Geological Survey Geomagnetism Program. This approach would densify magnetic field observations and could also contribute to space weather monitoring by supplementing highly calibrated magnetometers with additional sensors.

Alaska↗

USGS1999

Calculation of broadband time histories of ground motion: Comparison of methods and validation using strong-ground motion from the 1994 Northridge earthquake

This article compares techniques for calculating broadband time histories of ground motion in the near field of a finite fault by comparing synthetics with the strong-motion data set for the 1994 Northridge earthquake. Based on this comparison, a preferred methodology is presented. Ground-motion-simulation techniques are divided into two general methods: kinematic- and composite-fault models. Green's functions of three types are evaluated: stochastic, empirical, and theoretical. A hybrid scheme is found to give the best fit to the Northridge data. Low frequencies (< 1 Hz) are calculated using a kinematic-fault model and a 3D finite-difference code to propagate energy through a realistic 3D velocity structure. High frequencies (> 1 Hz) are calculated using a composite-fault model with a fractal subevent size distribution and stochastic, bandlimited, white-noise Green's functions. At frequencies below 1 Hz, theoretical elastic-wave-propagation synthetics introduce proper seismic-phase arrivals of body waves and surface waves. The 3D velocity structure more accurately reproduces record durations for the deep sedimentary basin structures found in the Los Angeles region. At frequencies above 1 Hz, scattering effects become important and wave propagation is more accurately represented by stochastic Green's functions. A fractal subevent size distribution for the composite fault model ensures an ω −2 spectral shape over the entire frequency band considered (0.1-20 Hz).

California↗

USGS2016

An automatic P‐Phase arrival‐time picker

Presented is a new approach for picking P ‐phase arrival time in single‐component acceleration or broadband velocity records without requiring detection interval or threshold settings. The algorithm P PHASE P ICKER transforms the signal into a response domain of a single‐degree‐of‐freedom (SDOF) oscillator with viscous damping and then tracks the rate of change of dissipated damping energy to pick P ‐wave phases. The SDOF oscillator has a short natural period and a correspondingly high resonant frequency, which is higher than most frequencies in a seismic wave. It also has a high damping ratio (60% of critical). At this damping level, the frequency response approaches the Butterworth maximally flat magnitude filter, and phase angles are preserved. The relative input energy imparted to the oscillator by the input signal is converted to elastic strain energy and then dissipated by the damping element as damping energy. The damping energy yields a smooth envelope over time; it is zero in the beginning of the signal, zero or near zero before the P ‐phase arrival, and builds up rapidly with the P wave. Because the damping energy function changes considerably at the onset of the P wave, it is used as a metric to track and pick the P ‐phase arrival time. The P PHASE P ICKER detects P ‐phase onset using the histogram method. Its performance is compared with picking techniques using short‐term‐average to long‐term‐average ratio, and a picking method that finds the first P ‐phase arrival time using the Akaike information criterion. A large set of records with various intensities and signal‐to‐noise ratios is used for testing the P PHASE P ICKER , and it is demonstrated that P PHASE P ICKER is able to more accurately pick the onset of genuine signals against the background noise and to correctly distinguish between whether the first arrival is a P wave (emergent or impulsive) or whether the signal is from a faulty sensor.

Bulletin of the Seismological Society of America↗

USGS2020

The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics

Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and waveform metrics such as the time‐averaged shear‐wave velocity to 30 m depth ( ⁠ V S 30 "> V S 30 ⁠ ), finite‐rupture distances, and spectral accelerations. This data set includes 22,708 records from 133 events from 4 July 2019 (UTC) to 18 October 2019 with a magnitude range from 3.6 to 7.1. We expect that the rapid collection and dissemination of this information will facilitate detailed studies of these ground motions. In this article, we describe the data selection, processing steps, and how to access the data.

California↗

USGS2014

The evolution of seismic monitoring systems at the Hawaiian Volcano Observatory

In the century since the Hawaiian Volcano Observatory (HVO) put its first seismographs into operation at the edge of Kīlauea Volcano’s summit caldera, seismic monitoring at HVO (now administered by the U.S. Geological Survey [USGS]) has evolved considerably. The HVO seismic network extends across the entire Island of Hawai‘i and is complemented by stations installed and operated by monitoring partners in both the USGS and the National Oceanic and Atmospheric Administration. The seismic data stream that is available to HVO for its monitoring of volcanic and seismic activity in Hawai‘i, therefore, is built from hundreds of data channels from a diverse collection of instruments that can accurately record the ground motions of earthquakes ranging in magnitude from <1 to ≥8. In this chapter we describe the growth of HVO’s seismic monitoring systems throughout its first hundred years of operation. Although other references provide specific details of the changes in instrumentation and data handling over time, we recount here, in more general terms, the evolution of HVO’s seismic network. We focus not only on equipment but also on interpretative products and results that were enabled by the new instrumentation and by improvements in HVO’s seismic monitoring, analytical, and interpretative capabilities implemented during the past century. As HVO enters its next hundred years of seismological studies, it is well situated to further improve upon insights into seismic and volcanic processes by using contemporary seismological tools.

Hawaii↗

USGS2021

Field response and surface rupture characteristics of the 2020 M6.5 Monte Cristo Range earthquake, central Walker Lane, Nevada

The M 6.5 Monte Cristo Range earthquake that occurred in the central Walker Lane on 15 May 2020 was the largest earthquake in Nevada in 66 yr and resulted in a multidisciplinary scientific field response. The earthquake was the result of left‐lateral slip along largely unmapped parts of the Candelaria fault, one of a series of east–northeast‐striking faults that comprise the Mina deflection, a major right step in the north–northwest structural grain of the central Walker Lane. We describe the characteristics of the surface rupture and document distinct differences in the style and orientation of fractures produced along the 28 km long rupture zone. Along the western part of the rupture, left‐lateral and extensional displacements occurred along northeasterly and north‐striking planes that splay off the eastern termination of the mapped Candelaria fault. To the east, extensional and right‐lateral displacements occurred along predominantly north‐striking planes that project toward well‐defined Quaternary and bedrock faults. Although, the largest left‐lateral displacement observed was ∼ 20 cm ⁠ , the majority of displacements were < 5 cm and were distributed across broad zones up to 800 m wide, which are not likely to be preserved in the geologic record. The complex pattern of surface rupture is consistent with a network of faults defined in the shallow subsurface by aftershock seismicity and suggests that slip partitioning between east‐striking left‐lateral faults and north to northwest‐striking right‐lateral faults plays an important role in accommodating northwest‐directed transtension in the central Walker Lane.

Nevada↗

USGS2012

Listening to the 2011 magnitude 9.0 Tohoku-Oki, Japan, earthquake

The magnitude 9.0 Tohoku-Oki, Japan, earthquake on 11 March 2011 is the largest earthquake to date in Japan’s modern history and is ranked as the fourth largest earthquake in the world since 1900. This earthquake occurred within the northeast Japan subduction zone (Figure 1 ), where the Pacific plate is subducting beneath the Okhotsk plate at rate of ∼8–9 cm/yr (DeMets et al. 2010 ). This type of extremely large earthquake within a subduction zone is generally termed a “megathrust” earthquake. Strong shaking from this magnitude 9 earthquake engulfed the entire Japanese Islands, reaching a maximum acceleration ∼3 times that of gravity (3 g). Two days prior to the main event, a foreshock sequence occurred, including one earthquake of magnitude 7.2. Following the main event, numerous aftershocks occurred around the main slip region; the largest of these was magnitude 7.9. The entire foreshocks-mainshock-aftershocks sequence was well recorded by thousands of sensitive seismometers and geodetic instruments across Japan, resulting in the best-recorded megathrust earthquake in history. This devastating earthquake resulted in significant damage and high death tolls caused primarily by the associated large tsunami. This tsunami reached heights of more than 30 m, and inundation propagated inland more than 5 km from the Pacific coast, which also caused a nuclear crisis that is still affecting people’s lives in certain regions of Japan.

Seismological Research Letters↗

USGS1988

Aftershock patterns and main shock faulting

We have compared aftershock patterns following several moderate to large earthquakes with the corresponding distributions of coseismic slip obtained from previous analyses of the recorded strong ground motion and teleseismic waveforms. Well-located aftershock hypocenters are projected onto the main shock fault plane, and their positions are examined relative to the zones of coseismic displacement indicated by the estimated distributions of main shock slip. We also examine the aftershock focal mechanisms, when these data are available, in an attempt to identify possible patterns of secondary faulting within the aftershock zone.

California↗

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↗

USGS2002

Evidence from the AD 2000 Izu islands earthquake swarm that stressing rate governs seismicity

Magma intrusions and eruptions commonly produce abrupt changes in seismicity far from magma conduits 1,2,3,4 that cannot be associated with the diffusion of pore fluids or heat 5 . Such ‘swarm’ seismicity also migrates with time, and often exhibits a ‘dog-bone’-shaped distribution 3,4,6,7,8,9 . The largest earthquakes in swarms produce aftershocks that obey an Omori-type (exponential) temporal decay 10,11,12 , but the duration of the aftershock sequences is drastically reduced, relative to normal earthquake activity 7,13 . Here we use one of the most energetic swarms ever recorded to study the dependence of these properties on the stress imparted by a magma intrusion 8,11,14,15 . A 1,000-fold increase in seismicity rate and a 1,000-fold decrease in aftershock duration occurred during the two-month-long dyke intrusion. We find that the seismicity rate is proportional to the calculated stressing rate, and that the duration of aftershock sequences is inversely proportional to the stressing rate. This behaviour is in accord with a laboratory-based rate/state constitutive law 16 , suggesting an explanation for the occurrence of earthquake swarms. Any sustained increase in stressing rate—whether due to an intrusion, extrusion or creep event—should produce such seismological behaviour.

Izu Islands↗

USGS2010

Historical seismograms for unravelling a mysterious earthquake: The 1907 Sumatra Earthquake

History of instrumental seismology is short. Seismograms are available only for a little more than 100 years; high-quality seismograms are available only for the last 50 years and the seismological database is very limited in time. To extend the database, seismograms of old events are of vital importance. Many unusual earthquakes are known to have occurred, but their seismological characteristics are poorly known. The 1907 Sumatra earthquake is one of them (1907 January 4, M = 7.6). Gutenberg and Richter located this event in the outer-rise area of the Sunda arc. This earthquake is known to be anomalous because of its extensive tsunami, which is disproportionate of its magnitude. The tsunami affected the coastal areas over 950 km along the Sumatran coast. We investigated this earthquake using the historical seismograms we could collect from several seismological observatories. We examined the P -wave arrival times listed in the Strassburg Bulletin (1912) and other station bulletins. The scatter of the Observed−Computed traveltime residuals ranges from –30 to 30 s, too large to locate the event accurately. The uncertainty of the epicentre estimated from an S-P grid-search relocation study is at least 1° (∼110 km). We interpreted the Omori seismograms from Osaka, Mizusawa and Tokyo, and the Wiechert seismograms from Göttingen and Uppsala by comparing them with the seismograms simulated from modern broad-band seismograms of the 2002, 2008 and two 2010 Sumatra earthquakes which occurred near the 1907 earthquake. From the amplitude of Rayleigh waves recorded on the Omori seismograms we conclude that the magnitude of the 1907 earthquake at about 30 to 40 s is about 7.8 (i.e. 7.5 to 8.0). The SH waveforms recorded on the Göttingen and Uppsala seismograms suggest that the 1907 earthquake is a thrust earthquake at a shallow depth around 30 km. The most likely scenario is that the 1907 earthquake initiated on the subduction interface, and slowly ruptured up-dip into the shallow sediments and caused the extensive tsunami. Although their quantity and quality are limited, historical seismograms provide key quantitative information about old events that cannot be obtained otherwise. This underscores the importance of preserving historical seismograms.

Geophysical Journal International↗

USGS2021

A review of timing accuracy across the Global Seismographic Network

The accuracy of timing across a seismic network is important for locating earthquakes as well as studies that use phase‐arrival information (e.g., tomography). The Global Seismographic Network (GSN) was designed with the goal of having reported timing be better than 10 ms. In this work, we provide a brief overview of how timing is kept across the GSN and discuss how clock‐quality metrics are embedded in Standard for Exchange of Earthquake Data records. Specifically, blockette 1001 contains the timing‐quality field, which can be used to identify time periods when poor clock quality could compromise timing accuracy. To verify the timing across the GSN, we compare cross‐correlation lags between collocated sensors from 1 January 2000 to 1 January 2020. We find that the mean error is less than 10 ms, with much of the difference likely coming from the method or uncertainty in the phase response of the instruments. This indicates that timing across the GSN is potentially better than 10 ms. We conclude that unless clock quality is compromised (as indicated in blockette 1001), GSN data’s timing accuracy should be suitable for most current seismological applications that require 10 ms accuracy. To assist users, the GSN network operators have implemented a “gsn_timing” metric available via the Incorporated Research Institutions for Seismology Data Management Center that helps users identify data with substandard timing accuracy (the 10 ms design goal of the GSN).

Seismological Research Letters↗