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John Langbein

Publications and source records attributed to John Langbein.

8 recordsLinked to original sources
USGS2024

Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California

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.

California↗
USGS2020

Strain-estimated ground motions associated with recent earthquakes in California

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 "> M 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.

California↗
USGS2020

Methods for rapidly estimating velocity precision from GNSS time series in the presence of temporal correlation: A new method and comparison of existing methods

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.

Journal of Geophysical Research↗
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↗
USGS2019

Evaluation of temporally correlated noise in global navigation satellite system time series: Geodetic monument performance

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.

Journal of Geophysical Research B: Solid Earth↗
USGS2005

Preliminary report on the 28 September 2004, M 6.0 Parkfield, California earthquake

The M w 6.0 Parkfield earthquake struck central California at 17:15:14 UTC on 28 September 2004. The epicenter was located 11 km southeast of the rural town of Parkfield, adjacent to Gold Hill and on the San Andreas Fault ( Figure 1 ). The California Integrated Seismic Network (CISN) reported that the hypocenter was located at 35.819°N, 120.364°W at a depth of 8.8 km. From the distribution of aftershocks and from models of seismograms, strain changes, and geodetic displacements from the earthquake, it appears that the rupture propagated to the northwest along the San Andreas Fault from its hypocenter beneath Gold Hill to Middle Mountain. Because of the earthquake's moderate size and the low population density, only minimal damage was reported, but strong ground motions of approximately 1 g were recorded at a few isolated points (Shakal et al. , 2005, this issue).

California↗
USGS1997

The Loma Prieta, California, earthquake of October 17, 1989: Aftershocks and postseismic effects

While the damaging effects of the earthquake represent a significant social setback and economic loss, the geophysical effects have produced a wealth of data that have provided important insights into the structure and mechanics of the San Andreas Fault system. Generally, the period after a large earthquake is vitally important to monitor. During this part of the seismic cycle, the primary fault and the surrounding faults, rock bodies, and crustal fluids rapidly readjust in response to the earthquake's sudden movement. Geophysical measurements made at this time can provide unique information about fundamental properties of the fault zone, including its state of stress and the geometry and frictional/rheological properties of the faults within it. Because postseismic readjustments are rapid compared with corresponding changes occurring in the preseismic period, the amount and rate of information that is available during the postseismic period is relatively high. From a geophysical viewpoint, the occurrence of the Loma Prieta earthquake in a section of the San Andreas fault zone that is surrounded by multiple and extensive geophysical monitoring networks has produced nothing less than a scientific bonanza. The reports assembled in this chapter collectively examine available geophysical observations made before and after the earthquake and model the earthquake's principal postseismic effects. The chapter covers four broad categories of postseismic effect: (1) aftershocks; (2) postseismic fault movements; (3) postseismic surface deformation; and (4) changes in electrical conductivity and crustal fluids.

California↗