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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 163 records · Page 9Linked to original sources
USGS2020

Documentation of Surface Fault Rupture and Ground‐Deformation Features Produced by the 4 and 5 July 2019 Mw 6.4 and Mw 7.1 Ridgecrest Earthquake Sequence

The M w "> M w Mw 6.4 and M w "> M w Mw 7.1 Ridgecrest earthquake sequence occurred on 4 and 5 July 2019 within the eastern California shear zone of southern California. Both events produced extensive surface faulting and ground deformation within Indian Wells Valley and Searles Valley. In the weeks following the earthquakes, more than six dozen scientists from government, academia, and the private sector carefully documented the surface faulting and ground‐deformation features. As of December 2019, we have compiled a total of more than 6000 ground observations; approximately 1500 of these simply note the presence or absence of fault rupture or ground failure, but the remainder include detailed descriptions and other documentation, including tens of thousands of photographs. More than 1100 of these observations also include quantitative field measurements of displacement sense and magnitude. These field observations were supplemented by mapping of fault rupture and ground‐deformation features directly in the field as well as by interpreting the location and extent of surface faulting and ground deformation from optical imagery and geodetic image products. We identified greater than 68 km of fault rupture produced by both earthquakes as well as numerous sites of ground deformation resulting from liquefaction or slope failure. These observations comprise a dataset that is fundamental to understanding the processes that controlled this earthquake sequence and for improving earthquake hazard estimates in the region. This article documents the types of data collected during postearthquake field investigations, the compilation effort, and the digital data products resulting from these efforts.

California↗
USGS2020

Airborne lidar and electro-optical imagery along surface ruptures of the 2019 Ridgecrest earthquake sequence, Southern California

Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the M 6.4 foreshock, occurred on July 4 on a ~17 km long, northeast-southwest oriented, left-lateral zone of faulting. Following the M 7.1 mainshock on July 5 (local time), extensive northwest-southeast-oriented, right-lateral faulting was then also mapped along a ~50 km long zone of faults, including sub-parallel splays in several areas. The largest slip was observed in the epicentral area, and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution lidar and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter, or ppsm) and high resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping (NCALM) acquired the airborne imagery with a Titan multispectral lidar system and DiMAC aerial digital camera, and USGS acquired GPS ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake Ranges.

Calilfornia↗
USGS2019

Preface to the focus section on the 30 November 2018 Mw 7.1 Anchorage, Alaska earthquake

On 30 November 2018, a magnitude ( ⁠ M w "> M w ⁠ ) 7.1 intraslab earthquake occurred under the most densely populated area of Alaska, ∼ 12    km "> ∼ 12 km north of downtown Anchorage. It generated the strongest ground shaking in the greater Anchorage area since the 1964 M w "> M w 9.2 Great Alaska earthquake. Although the 30 November 2018 earthquake did not cause deaths or serious injuries, its economical and psychological impacts on the local population were profound. It caused widespread power outages, structural and nonstructural damage to public and private buildings, and damage to roadways and railways, and resulted in closures of several schools and multiple businesses.

Alaska↗
USGS2020

Forecasting for a fractured land: A case study of the communication and use of aftershock forecasts from the Mw 7.8 2016 Kaikōura earthquake in Aotearoa New Zealand

Operational earthquake forecasts (OEFs) are represented as time‐dependent probabilities of future earthquake hazard and risk. These probabilities can be presented in a variety of formats, including tables, maps, and text‐based scenarios. In countries such as Aotearoa New Zealand, the U.S., and Japan, OEFs have been released by scientific organizations to agencies and the public, with the intent of providing information about future earthquake hazard and risk, so that people can use this information to inform their decisions and activities. Despite questions being raised about the utility of OEF for decision‐making, past earthquake events have shown that agencies and the public have indeed made use of such forecasts. Responses have included making decisions about safe access into buildings, cordoning, demolition safety, timing of infrastructure repair and rebuild, insurance, postearthquake building standards, postevent land‐use planning, and public communication about aftershocks. To add to this body of knowledge, we undertook a survey to investigate how agencies and GNS Science staff used OEFs that were communicated following the M w 7.8 2016 Kaikōura earthquake in Aotearoa New Zealand. We found that agencies utilized OEFs in many of the ways listed previously, and we document individual employee’s actions taken in their home‐life context. Challenges remain, however, regarding the interpretation of probabilistic information and applying this to practical decision‐making. We suggest that science agencies cannot expect nontechnical users to understand and utilize forecasts without additional support. This might include developing a diversity of audience‐relevant OEF information for communication purposes, alongside advice on how such information could be utilized.

northern part of South Island↗
USGS2020

When source and path components trade off in ground-motion prediction equations

Current research on ground‐motion models (also known as ground‐motion prediction equations [GMPEs]) and their uncertainties focus on the separate contributions of source, path, and site to both median values and their variability. Implicit here is the assumption that the event term, path term, and site term reflect only properties of the source, path, and site, respectively. Events with larger stress drop generate more high‐frequency energy, and thus more ground motion. Therefore, the correlation of high‐frequency (i.e., peak ground acceleration [PGA] or peak ground velocity [PGV]) event terms in GMPEs with stress drop is taken to be genuine. However, PGA and PGV ground‐motion observations of the 2014 M "> M 6.0 South Napa, California, earthquake clearly violate these assumptions. For this earthquake, high‐frequency ground‐motion residuals of recorded ground motion with respect to Next Generation Attenuation‐West2 Project (NGA‐West2) ground‐motion models show a dependence on distance, biasing the calculation of the event term by incorrectly mapping a regional attenuation effect into it. We examine the trade‐off between source and path effects for the South Napa earthquake and a well‐recorded California subset of the NGA‐West2 data. We fit near‐source (i.e., within 20 or 50 km) event terms and remaining differential geometrical spreading and anelastic attenuation terms in comparison to a simultaneous inversion for the source and path terms. This South Napa instance highlights one situation for which the high‐frequency event term can be interpreted as relative stress drop only when the distance dependence of the ground motions does not bias the residuals.

California↗
USGS2020

The potential of using dynamic strains in earthquake early warning applications

We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain‐derived magnitudes for the largest two Ridgecrest events to the real‐time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real‐time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.

British Columbia, California, Oregon, Washington↗
USGS2020

Rapid geodetic observations of spatiotemporally varying postseismic deformation following the Ridgecrest earthquake sequence: The U.S. Geological Survey response

The U.S. Geological Survey’s geodetic response to the 4–5 July 2019 (Pacific time) Ridgecrest earthquake sequence comprised primarily the installation and/or reoccupation of Global Navigation Satellite System (GNSS) monumentation. Our response focused primarily on the United States’ Navy’s China Lake Naval Air Weapons Station base (NAWSCL). This focus was because much of the surface rupture occurred on the NAWSCL and because of NAWSCL access restrictions only permitting Federal and State of California personnel. In total, we measured or are still measuring at 24 sites, 14 of which were on the NAWSCL and, as of this writing, operational. The majority of sites were set up as continuous stations logging at either 1 sample per second or 1 sample per 15 s. Two stations were recording a 200 m cross‐rupture aperture starting ∼10 hr after the M 6.4 event, and they recorded the coseismic displacements of the M 7.1. Approximately, 1 hr after the M 7.1 event, two new stations were recording a ∼200 m cross‐rupture aperture of the surface rupture. In the days following, we established the rest of the stations ranging to a distance of ∼15 km from the M 7.1 principal rupture trace. The lack of differential displacement across the M 6.4 rupture during the M 7.1 event suggests that it did not reactivate the M 6.4 plane. The lack of differential cross‐fault displacement for both events suggests that rapid shallow afterslip did not occur at those two locations. The postseismic time series from these stations shows centimeters of horizontal displacement over periods of a few months. They record a mixture of fault‐parallel and fault‐normal displacements that, in conjunction with analysis of more spatially complete Interferometric Synthetic Aperture Radar displacement fields, suggest that both poroelastic and afterslip phenomena occur along the M 6.4 and 7.1 rupture planes. Using preliminary data from these and other regional stations, we also explore the Ridgecrest sequence’s effect on regional GNSS time series and the differentiation of long‐term postseismic motions and secular deformation rates. We find that redefining a common‐mode noise filter using different GNSS stations that are assumed to be unaffected by the earthquakes results in small but systematic differences in the regional velocity field estimate.

Seismological Research Letters↗
USGS2020

Capturing, preserving and digitizing legacy seismic data from the Montserrat Volcano Observatory analog seismic network, July 1995 – December 2004

An eruption of the Soufrière Hills Volcano (SHV) on the eastern Caribbean island of Montserrat began on 18 July 1995 and continued until February 2010. Within nine days of the eruption onset, an existing four‐station analog seismic network (ASN) was expanded to 10 sites. Telemetered data from this network were recorded, processed, and archived locally using a system developed by scientists from the U.S. Geological Survey (USGS) Volcano Disaster Assistance Program (VDAP). In October 1996, a digital seismic network (DSN) was deployed with the ability to capture larger amplitude signals across a broader frequency range. These two networks operated in parallel until December 2004, with separate telemetry and acquisition systems (analysis systems were merged in March 2001). Although the DSN provided better quality data for research, the ASN featured superior real‐time monitoring tools and captured valuable data including the only seismic data from the first 15 months of the eruption. These successes of the ASN have been rather overlooked. This article documents the evolution of the ASN, the VDAP system, the original data captured, and the recovery and conversion of more than 230,000 seismic events from legacy SUDS, Hypo71, and Seislog formats into Seisan database with waveform data in miniSEED format. No digital catalog existed for these events, but students at the University of South Florida have classified two-thirds of the 40,000 events that were captured between July 1995 and October 1996. Locations and magnitudes were recovered for ~10,000 of these events. Real-time seismic amplitude measurement, seismic spectral amplitude measurement, and tiltmeter data were also captured. The result is that the ASN seismic dataset is now more discoverable, accessible, and reusable, in accordance with FAIR data principles. These efforts could catalyze new research on the 1995–2010 SHV eruption. Furthermore, many observatories have data in these same legacy data formats and might benefit from procedures and codes documented here.

Soufrière Hills Volcano↗
USGS2020

California Historical Intensity Mapping Project (CHIMP): A consistently reinterpreted dataset of seismic intensities for the past 162 years and implications for seismic hazard maps

Historical seismic intensity data are useful for myriad reasons, including assessment of the performance of Probabilistic Seismic Hazard Assessment (PSHA) models and corresponding hazard maps by comparing their predictions to a dataset of historically observed intensities in the region. To assess PSHA models for California, a long and consistently interpreted intensity record is necessary. For this purpose, the California Historical Intensity Mapping Project (CHIMP) has compiled a dataset that combines and reinterprets intensity information that has been stored in disparate and sometimes hard-to-access locations. The CHIMP dataset also includes new observations of intensity from archival research and oral history collection. Version 1 of the dataset includes 46,502 intensity observations for 62 earthquakes with estimated magnitudes ranging from 4.7 to 7.9. The 162 years of shaking data show observed shaking lower than expected from seismic hazard models. This discrepancy is reduced, but persists, if historical intensity data for the largest earthquakes are smoothed to reduce the effects of spatial under-sampling. Possible reasons for this discrepancy include other limitations of the CHIMP dataset, the hazard models, and the possibility that California seismicity throughout the historical period has been lower than the long-term average. Some of these issues may also explain similar discrepancies observed for Italy and Japan.

California↗
USGS2020

Installation and performance of the Albuquerque Seismological Laboratory small-aperture posthole array

The Global Seismographic Network (GSN) has been used extensively by seismologists to characterize large earthquakes and image deep earth structure. Although the network’s original design goals have been met, the seismological community has suggested that the incorporation of small-aperture seismic arrays at select sites may improve performance of the network and enable new observations. As a pilot study for this concept, we have created a 500 m aperture, nine-element broadband seismic array around the GSN station ANMO (Albuquerque, New Mexico) at the U.S. Geological Survey Albuquerque Seismological Laboratory (ASL). The array was formed by supplementing the secondary borehole seismometer (90 m depth) at ANMO with eight additional 2.6 m posthole sites. Each station’s seismometer was oriented using a fiber optic gyroscope to within 2.0° of north. Data quality, particularly on the vertical components, is excellent with median power levels closely tracking the secondary sensor at ANMO at frequencies lower than 1 Hz. Horizontal component data are more variable at low frequencies (< 0:02 Hz), with the type of installation and local geography appearing to strongly influence the amount of tilt-induced noise. Throughout the article, we pose several fundamental questions related to the variability and precision of seismic wavefield measurements that we seek to address with data from this array. In addition, we calculate the array response and show a few examples of using the array to obtain back azimuths of a local event and a continuous narrowband noise source. The apparent velocity of the event across the array is then used to infer the local P-wave velocity at the ASL. Near real-time data collected from the array along with collocated meteorological, magnetic, and infrasound data are freely available in near-real time from the Incorporated Research Institutions for Seismology Data Management Center.

New Mexico↗
USGS2020

Very‐long‐period (VLP) seismic artifacts during the 2018 caldera collapse at Kīlauea, Hawaii

Throughout the 2018 eruption of Kīlauea volcano (Hawai‘i), episodic collapses of a portion of the volcano’s summit caldera produced repeated M w "> M w 4.9–5.3 earthquakes. Each of these 62 events was characterized by a very‐long‐period (VLP) seismic signal ( ⁠ &gt; 40 &#x2009;&#x2009; s "> > 40 s ⁠ ). Although collapses in the later stage of the eruption produced earthquakes with significant amplitude clipping on near‐summit broadband seismometers, the first 12 were accurately recorded. For these initial collapse events, we compare average VLP seismograms at six near‐summit locations to synthetic seismograms derived from displacements at collocated Global Positioning System stations. We show that the VLP seismic signal was generated by a radially outward and upward ramp function in displacement. We propose that at local distances the period of the VLP seismic signal is solely dependent on the duration of this ramp function and the instrument transfer function, that is, the seismic VLP is an artifact of the bandlimited instrument response and not representative of real ground motion. The displacement ramp function imposes a sinc‐function velocity amplitude spectrum that cannot be fully recovered through standard seismic instrument deconvolution. Any near‐summit VLP signals in instrument‐response‐corrected velocity or displacement seismograms from these collapse events are subject to severe band limitation. Similarly, the seismic amplitude response is not flat through the low‐frequency corner, for example, instrument‐response‐uncorrected seismograms scaled by instrument sensitivity are equally prone to band limitation. This observation is crucial when attempting to clarify the different contributions to the VLP source signature. Not accounting for this effect could lead to misunderstanding of the magmatic processes involved.

Hawaii↗
USGS2020

Now trending … Earthquake information

The U.S. Geological Survey Earthquake Hazards Program has overall successfully fulfilled its mission of providing timely earthquake information via web applications and other methods. Imagine a single month of earthquake data delivery, serving 3.6 billion total data requests, including 29 million pageviews by 7.1 million users, 606 million automated data feeds, and 45 million catalog downloads. Yet, some challenges and lapses in delivery have happened at critical times, including during the Ridgecrest earthquakes in 2019. We delve into the evolving demand for real‐time information as well as the technologies put in place to support the ever‐growing number of users in an increasingly mobile world.

Seismological Research Letters↗
USGS2020

Recording the aurora borealis (northern lights) at seismometers across Alaska

We examine three continuously recording data sets related to the aurora: all‐sky camera images, three‐component magnetometer data, and vertical‐component, broadband seismic data as part of the EarthScope project (2014 to present). Across Alaska there are six all‐sky cameras, 13 magnetometers, and &gt; 200 "> > 200 >200 seismometers. The all‐sky images and magnetometers have the same objective, which is to monitor space weather and improve our understanding of auroral activity, including the influence on magnetic fields in the ground. These variations in the magnetic field are also visible on seismometers, to the extent that during an auroral event, the long‐period (40–800 s) waves recorded by a seismometer are magnetic field variations, not true ground motion. Although this is a problem—one that can be rectified with magnetic shielding at each seismometer site—it is also an opportunity because the present seismic array in Alaska is much broader than the coverage by magnetometers and all‐sky cameras. Here we focus on three aurora events and document a direct link between aurora images in the night sky and seismometer recordings on ground. Simultaneous recordings by magnetometers provide a critical link between the sky images and the seismometer recordings. We document qualitative correlations among sky, magnetic, and seismic data. The findings suggest that the signature of auroral activity is widespread across seismometers in Alaska, implying that the seismic array could be used to enhance the spatial resolution of the existing network of all‐sky cameras and magnetometers. Future efforts to improve the multisensor seismic stations in Alaska, for the purpose of monitoring seismic and auroral activity, should consider installation of all‐sky cameras, installation of magnetometers, and magnetic shielding of seismic sensors.

Alaska↗
USGS2020

Shaking is almost always a surprise: The earthquakes that produce significant ground motion

Although small earthquakes are expected to produce weak shaking, ground motion is highly variable and there are outlier earthquakes that generate more shaking than expected—sometimes significantly more. We explore datasets of M 0.5–8.3 earthquakes to determine the relative impact of frequent, smaller-magnitude earthquakes that rarely produce strong ground motion, to rare, large earthquakes that always cause strong shaking. We find that the natural variability of ground motion, combined with the Gutenberg–Richter magnitude–frequency relationship, ensures that most occurrences of any ground motion come from earthquakes of smaller magnitude than expected, often > 2 magnitude units smaller. This holds even for very strong shaking ( > 20% g ), suggesting that M < 7 earthquakes could be a significant source of damage.

Seismological Research Letters↗
USGS2020

Leveraging deep learning in global 24/7 real-time earthquake monitoring at the National Earthquake Information Center

Machine‐learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real‐time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep‐learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short‐term average/long‐term average [STA/LTA]) are fed to trained neural network models to improve automatic seismic‐arrival (pick) timing and estimate seismic‐arrival phase type and source‐station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic‐phase arrivals that represent a globally distributed set of source‐station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic‐phase association, resulting in reduced false associations and improved location estimates.

Seismological Research Letters↗
USGS2020

Imaging the tectonic grain of the Northern Cordillera orogen using Transportable Array receiver functions

Azimuthal variations in receiver function conversions can image lithospheric structural contrasts and anisotropic fabrics that together compose tectonic grain. We apply this method to data from EarthScope Transportable Array in Alaska and additional stations across the northern Cordillera. The best‐resolved quantities are the strike and depth of dipping fabric contrasts or interfaces. We find a strong geographic gradient in such anomalies, with large amplitudes extending inboard from the present‐day subduction margin, the Aleutian arc, and an influence of flat‐slab subduction of the Yakutat microplate north of the Denali fault. An east–west band across interior Alaska shows low‐amplitude crustal anomalies. Anomaly amplitudes correlate with structural intensity (density of aligned geological elements), but are the highest in areas of strong Cenozoic deformation, raising the question of an influence of current stress state. Imaged subsurface strikes show alignment with surface structures. We see concentric strikes around arc volcanoes implying dipping magmatic structures and fabric into the middle crust. Regions with present‐day weaker deformation show lower anomaly amplitudes but structurally aligned strikes, suggesting pre‐Cenozoic fabrics may have been overprinted or otherwise modified. We observe general coherence of the signal across the brittle‐plastic transition. Imaged crustal fabrics are aligned with major faults and shear zones, whereas intrafault blocks show imaged strikes both parallel to and at high angles to major block‐bounding faults. High‐angle strikes are subparallel to neotectonic deformation, seismicity, fault lineaments, and prominent metallogenic belts, possibly due to overprinting and/or co‐evolution with fault‐parallel fabrics. We suggest that the underlying tectonic grain in the northern Cordillera is broadly distributed rather than strongly localized. Receiver functions thus reveal key information about the nature and continuity of tectonic fabrics at depth and can provide unique insights into the deformation history and distribution of regional strain in complex orogenic belts.

Alaska↗
USGS2020

The Alaska Amphibious Community Seismic Experiment

The Alaska Amphibious Community Seismic Experiment (AACSE) is a shoreline‐crossing passive‐ and active‐source seismic experiment that took place from May 2018 through August 2019 along an &#x223C; 700 &#x2009;&#x2009; km "> ∼ 700 km ∼700 km long section of the Aleutian subduction zone spanning Kodiak Island and the Alaska Peninsula. The experiment featured 105 broadband seismometers; 30 were deployed onshore, and 75 were deployed offshore in Ocean Bottom Seismometer (OBS) packages. Additional strong‐motion instruments were also deployed at six onshore seismic sites. Offshore OBS stretched from the outer rise across the trench to the shelf. OBSs in shallow water ( ⁠ &lt; 262 &#x2009;&#x2009; m "> < 262 m <262 m depth) were deployed with a trawl‐resistant shield, and deeper OBSs were unshielded. Additionally, a number of OBS‐mounted strong‐motion instruments, differential and absolute pressure gauges, hydrophones, and temperature and salinity sensors were deployed. OBSs were deployed on two cruises of the R/V Sikuliaq in May and July 2018 and retrieved on two cruises aboard the R/V Sikuliaq and R/V Langseth in August–September 2019. A complementary 398‐instrument nodal seismometer array was deployed on Kodiak Island for four weeks in May–June 2019, and an active‐source seismic survey on the R/V Langseth was arranged in June 2019 to shoot into the AACSE broadband network and the nodes. Additional underway data from cruises include seafloor bathymetry and sub‐bottom profiles, with extra data collected near the rupture zone of the 2018 M w "> M w Mw 7.9 offshore‐Kodiak earthquake. The AACSE network was deployed simultaneously with the EarthScope Transportable Array (TA) in Alaska, effectively densifying and extending the TA offshore in the region of the Alaska Peninsula. AACSE is a community experiment, and all data were made available publicly as soon as feasible in appropriate repositories.

Alaska↗