First posted March 18, 2024 For additional information, contact: Earthquake Science CenterU.S. Geological Survey350 N. Akron Rd.Moffett Field, CA 94035 This report is an update to the presentation by Schulz (1989) introducing potential users to the creepmeter data collected between the publication of Schulz's report and mid-2020. The creepmeter network monitors aseismic, surface slip at various locations on the Hayward, Calaveras, and San Andreas Faults in northern and central California. There are different designs of creepmeters and these are briefly described. For a majority of the creepmeters, these data are automatically sent to the U.S. Geological Survey (USGS) offices where they are stored and processed. In addition, for most of the creepmeters, occasional manual measurements are made and these are compared with digitally recorded data. For some sites, the comparisons indicated degradation of the electronic sensor and consequently corrections are made to the digital data. The largest transient deformation is that which followed the 2004, M6, Parkfield earthquake. Various functions found in the literature that have been used to model postseismic slip were tested with the observed postseismic behavior seen on the creepmeters in the vicinity of Parkfield, California. No single function adequately fit all the data from these Parkfield instruments. This report is a discussion and analysis of data from creepmeters deployed by the USGS. The discussion primarily focuses on instruments that are currently operating in 2020 or have operated quite recently but are no longer in service.

ABSTRACTDynamic strains have never played a role in determining local earthquake magnitudes, which are routinely set by displacement waveforms from seismic instrumentation (e.g., ML). We present a magnitude scale for local earthquakes based on broadband dynamic strain waveforms. This scale is derived from the peak root-mean-squared strains (A) in 4589 records of dynamic strain associated with 365 crustal earthquakes and 77 borehole strainmeters along the Pacific-North American plate boundary on the west coast of the United States and Canada. In this data set, catalog moment magnitudes range from 3.5≤Mw≤7.2, and hypocentral distances range from 6≤R≤500 km. The 1D representation of geometrical spreading and attenuation of A common to all strain data is logA0(R)=−0.00072R−1.45log(R). After correcting for instrument gain, site terms, and event terms, the magnitude scale, MDS=logA−logA0(R)−log(3×10−9), scales as ≈0.92Mw with a residual standard deviation of 0.19. This close association with Mw holds for events east of the −124° meridian; west of this boundary, however, a constant correction of 0.41 is needed to adjust for additional along-path attenuation effects. As a check on the accuracy of this magnitude scale, we apply it to dynamic strain records from three strainmeters located in the near field of the 2019 M 6.4 and 7.1 Ridgecrest earthquakes. Results from these six records are in agreement to within 0.5 magnitude units, and five out of six records are in agreement to within 0.34 units.

ABSTRACT Peak ground velocity (PGV) is a commonly used parameter in earthquake ground-motion models (GMMs) and hazard analyses, because it is closely related to structural damage and felt ground shaking, and is typically measured on broadband seismometers. Here, we demonstrate that strainmeters, which directly measure in situ strain in the bulk rock, can easily be related to ground velocity by a factor of bulk shear-wave velocity and, thus, can be used to measure strain-estimated PGV. We demonstrate the parity of velocity to strain utilizing data from borehole strainmeters deployed along the plate boundaries of the west coast of the United States for nine recent M 4.4–7.1 earthquakes in California, including the largest two events of the July 2019 Ridgecrest earthquake sequence. PGVs derived from maximum horizontal shear strains fall within the range of seismic-estimated values recorded at the same distances. We compare the strain-estimated data with GMMs based on seismic PGVs and find consistency in residual polarity (positive vs. negative; the sign of the difference between observed and modeled data) for certain earthquake–station paths, where some paths indicate an overestimation and others indicate an underestimation of strain-derived PGVs, as compared with the GMMs. We surmise that this may be indicative of over or underestimation of shear-wave velocity along those paths, as compared with the average velocity used to derive PGV from strain measurements, or indicative of repeatable site and path effects that are not accounted for in our analyses. This direct comparison of strain with velocity can highlight physical path effects, as well as improve the density and capability of ground-motion recordings.

Time series of position estimates from Global Navigational Satellite System (GNSS) are used to measure the velocities of points on the surface of the Earth. Along with the velocity estimates, a measure of the precision is needed to assess the quality of the velocity measurement. Here, I evaluate rate uncertainties provided by four different methods that have been applied to geodetic time series. The most rigorous approach uses a data covariance that incorporates a variety of noise processes relevant to geodetic time series but is computationally demanding. Two other approaches are efficient algorithms and are used widely, but both can provide less rigorous estimates of the rate uncertainty. I propose and evaluate a fourth method, which provides estimates of rate uncertainty closer to the rigorous approach but is significantly less computationally demanding. I have evaluated all three methods against the more rigorous method using both simulations and time series from 190 GNSS sites. For data best characterized as having a flicker type noise process, one of the widely used methods overestimates the uncertainty by up to a factor of 2, while the other widely used method underestimates the uncertainty by less than a factor of 2. For a random-walk process, both methods underestimate the rate uncertainty by a factor of 3 to 5.

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 similar to 50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within similar to 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 realtime estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.

Estimates of background noise of Global Positioning System‐derived time series of positions for 740 sites in the western United States are examined. These data consist of daily epochs of three components of displacements that are at least 9.75 years long within the interval between 2000 and 2018. We find that these time series have significant temporal correlations that could be represented as a combination of white, flicker, random‐walk, and band‐pass filtered noise. From this noise model, two other metrics are computed: the root‐mean‐square of seasonal noise, that is, the integrated power spectrum between 0.5 and 2 cycles per year, and the standard error in position rate for a 10‐year‐long time series. These two metrics are used to evaluate potential correlations with different geographic regions and with different methods of construction of monuments used to attach the Global Positioning System antenna to the Earth's surface. The sites with the lowest noise, both in terms of rate error and seasonal root‐mean‐square, are located in semiarid regions east of the rain shadow provided by the Cascade and Sierra Nevada mountain ranges. In addition, according to statistical rank tests, monuments known as drilled‐braced monuments perform 30% to 50% better than other monument types (buildings, boreholes, piers, etc.) in terms of having smaller rate errors and lower seasonal noise.

First posted April 23, 2018 For additional information, contact: Contact Information, Menlo Park, Calif. Office—Earthquake Science Center U.S. Geological Survey 345 Middlefield Road, MS 977 Menlo Park, CA 94025 https://earthquake.usgs.gov/ Seismic hazard assessments that are based on a variety of data and the best available science, coupled with rapid synthesis of real-time information from continuous monitoring networks to guide post-earthquake response, form a solid foundation for effective earthquake loss reduction. With this in mind, the Earthquake Hazards Program (EHP) of the U.S. Geological Survey (USGS) Natural Hazards Mission Area (NHMA) engages in a variety of undertakings, both established and emergent, in order to provide high quality products that enable stakeholders to take action in advance of and in response to earthquakes. Examples include the National Seismic Hazard Model (NSHM), development of tools for improved situational awareness such as earthquake early warning (EEW) and operational earthquake forecasting (OEF), research about induced seismicity, and new efforts to advance comprehensive subduction zone science and monitoring. Geodetic observations provide unique and complementary information directly relevant to advancing many aspects of these efforts (fig. 1). EHP scientists have long leveraged geodetic data for a range of influential studies, and they continue to develop innovative observation and analysis methods that push the boundaries of the field of geodesy as applied to natural hazards research. Given the ongoing, rapid improvement in availability, variety, and precision of geodetic measurements, considering ways to fully utilize this observational resource for earthquake loss reduction is timely and essential. This report presents strategies, and the underlying scientific rationale, by which the EHP could achieve the following outcomes: The EHP is an authoritative source for the interpretation of geodetic data and its use for earthquake loss reduction throughout the United States and its territories.The USGS consistently provides timely, high quality geodetic data to stakeholders.Significant earthquakes are better characterized by incorporating geodetic data into USGS event response products and by expanded use of geodetic imaging data to assess fault rupture and source parameters.Uncertainties in the NSHM, and in regional earthquake models, are reduced by fully incorporating geodetic data into earthquake probability calculations.Geodetic networks and data are integrated into the operations and earthquake information products of the Advanced National Seismic System (ANSS).Earthquake early warnings are improved by more rapidly assessing ground displacement and the dynamic faulting process for the largest earthquakes using real-time geodetic data.Methodology for probabilistic earthquake forecasting is refined by including geodetic data when calculating evolving moment release during aftershock sequences and by better understanding the implications of transient deformation for earthquake likelihood.A geodesy program that encompasses a balanced mix of activities to sustain missioncritical capabilities, grows new competencies through the continuum of fundamental to applied research, and ensures sufficient resources for these endeavors provides a foundation by which the EHP can be a leader in the application of geodesy to earthquake science. With this in mind the following objectives provide a framework to guide EHP efforts:Fully utilize geodetic information to improve key products, such as the NSHM and EEW, and to address new ventures like the USGS Subduction Zone Science Plan.Expand the variety, accuracy, and timeliness of post-earthquake information products, such as PAGER (Prompt Assessment of Global Earthquakes for Response), through incorporation of geodetic observations.Determine if geodetic measurements of transient deformation can significantly improve estimates of earthquake probability.Maintain an observational strategy aligned with the target outcomes of this document that includes continuous monitoring, recording of ephemeral observations, focused data collection for use in research, and application-driven data processing and analysis systems.Collaborate on research, development, and operation of affordable, high-precision seafloor geodetic methods that improve earthquake forecasting and event response.Advance computational techniques and instrumentation to enable use of strategies like repeat-pass imagery and low-cost geodetic sensors for earthquake response, monitoring, and research.Engage stakeholders and collaborate with partner institutions to foster operational and research objectives and to safeguard the continued health of geodetic infrastructure upon which we mutually depend.Maintaining a vibrant internal research program provides the foundation by which the EHP can remain an effective and trusted source for earthquake science. Exploiting abundant new data sources, evaluating and assimilating the latest science, and pursuing novel avenues of investigation are means to fulfilling the EHP’s core responsibilities and realizing the important scientific advances envisioned by its scientists. Central to the success of such a research program is engaging personnel with a breadth of competencies and a willingness and ability to adapt these to the program’s evolving priorities, enabling current staff to expand their skills and responsibilities, and planning holistically to meet shared workforce needs. In parallel, collaboration with external partners to support scientific investigations that complement ongoing internal research enables the EHP to strengthen earthquake information products by incorporating alternative perspectives and approaches and to study topics and geographic regions that cannot be adequately covered internally.With commensurate support from technical staff who possess diverse skills, including engineering, information technology, and proficiency in quantitative analysis combined with basic geophysical knowledge, the EHP can achieve the geodetic outcomes identified in this document.

An earthquake early warning (EEW) system, ShakeAlert, is under development for the West Coast of the United States. This system currently uses the first few seconds of waveforms recorded by seismic instrumentation to rapidly characterize earthquake magnitude, location, and origin time; ShakeAlert recently added a seismic line source algorithm. For large to great earthquakes, magnitudes estimated from the earliest seismic data alone generally saturate. Real-time Global Navigation Satellite System (GNSS) data can directly measure large displacements, enabling accurate magnitude estimates for M(w)7+events, possibly before rupture termination. GNSS-measured displacements also track evolving slip and, alone or in combination with seismic data, constrain finite-fault models. Particularly for large-magnitude, long-rupture events, GNSS-based magnitude and rupture extent estimates can improve updates to predicted shaking and thus alert accuracy. GNSS data processing centers at ShakeAlert partner institutions provide real-time streams to the EEW system, and three geodetic EEW algorithms have been developed through the ShakeAlert collaboration. These algorithms will undergo initial testing within ShakeAlert's computational architecture using a suite of input data that includes simulated real-time displacements from synthetic earthquakes and GNSS recordings from recent earthquakes worldwide. Performance will be evaluated using metrics and standards consistent with those adopted for ShakeAlert overall. This initial assessment will guide method refinement and synthesis of the most successful features into a candidate geodetic algorithm for the ShakeAlert production system. In parallel, improvements to geodetic networks and streamlining approaches to data processing and exchange will ensure robust geodetic data availability in the event of an earthquake.

Most time series of geophysical phenomena have temporally correlated errors. From these measurements, various parameters are estimated. For instance, from geodetic measurements of positions, the rates and changes in rates are often estimated and are used to model tectonic processes. Along with the estimates of the size of the parameters, the error in these parameters needs to be assessed. If temporal correlations are not taken into account, or each observation is assumed to be independent, it is likely that any estimate of the error of these parameters will be too low and the estimated value of the parameter will be biased. Inclusion of better estimates of uncertainties is limited by several factors, including selection of the correct model for the background noise and the computational requirements to estimate the parameters of the selected noise model for cases where there are numerous observations. Here, I address the second problem of computational efficiency using maximum likelihood estimates (MLE). Most geophysical time series have background noise processes that can be represented as a combination of white and power-law noise, \(1/f^{\alpha }\) with frequency, f. With missing data, standard spectral techniques involving FFTs are not appropriate. Instead, time domain techniques involving construction and inversion of large data covariance matrices are employed. Bos et al. (J Geod, 2013. doi: 10.1007/s00190-012-0605-0) demonstrate one technique that substantially increases the efficiency of the MLE methods, yet is only an approximate solution for power-law indices >1.0 since they require the data covariance matrix to be Toeplitz. That restriction can be removed by simply forming a data filter that adds noise processes rather than combining them in quadrature. Consequently, the inversion of the data covariance matrix is simplified yet provides robust results for a wider range of power-law indices.

The 24 August 2014 M w 6.0 South Napa, California earthquake produced significant offsets on 12 borehole strainmeters in the San Francisco Bay area. These strainmeters are located between 24 and 80 km from the source, and the observed offsets ranged up to 400 parts per billion (ppb), which exceeds their nominal precision by a factor of 100. However, the observed offsets of tidally calibrated strains differ by up to 130 ppb from predictions based on a moment tensor derived from seismic data. The large misfit can be attributed to a combination of poor instrument calibration and better modeling of the strain field from the earthquake. Borehole strainmeters require in situ calibration, which historically has been accomplished by comparing their measurements of Earth tides with the strain tides predicted by a model. Although the borehole strainmeter accurately measures the deformation within the borehole, the long‐wavelength strain signals from tides or other tectonic processes recorded in the borehole are modified by the presence of the borehole and the elastic properties of the grout and the instrument. Previous analyses of surface‐mounted, strainmeter data and their relationship with the predicted tides suggest that tidal models could be in error by 30%. The poor fit of the borehole strainmeter data from this earthquake can be improved by simultaneously varying the components of the model tides up to 30% and making small adjustments to the point source model of the earthquake, which reduces the RMS misfit from 130 ppb to 18 ppb. This suggests that relying on tidal models to calibrate borehole strainmeters significantly reduces their accuracy.

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Slow inflation began at Long Valley Caldera in late 2011, coinciding with renewed swarm seismicity. Ongoing deformation is concentrated within the caldera. We analyze this deformation using a combination of GPS and InSAR (TerraSAR-X) data processed with a persistent scatterer technique. The extension rate of the dome-crossing baseline during this episode (CA99 to KRAC) is 1cm/yr, similar to past inflation episodes (1990-1995 and 2002-2003), and about a tenth of the peak rate observed during the 1997 unrest. The current deformation is well modeled by the inflation of a prolate spheroidal magma reservoir approximate to 7km beneath the resurgent dome, with a volume change of approximate to 6x10(6)m(3)/yr from 2011.7 through the end of 2014. The current data cannot resolve a second source, which was required to model the 1997 episode. This source appears to be in the same region as previous inflation episodes, suggesting a persistent reservoir.