Search USGS⌕ Search
Home/Archive/USGS

USGS · 70029339

Slicing up the San Francisco Bay Area: Block kinematics and fault slip rates from GPS-derived surface velocities

M. A. d'Alessio·I.A. Johanson·R. Burgmann·D.A. Schmidt·M.H. Murray
View original source ↗Export metadata ↓

Abstract

Observations of surface deformation allow us to determine the kinematics of faults in the San Francisco Bay Area. We present the Bay Area velocity unification (BA??VU??, "bay view"), a compilation of over 200 horizontal surface velocities computed from campaign-style and continuous Global Positioning System (GPS) observations from 1993 to 2003. We interpret this interseismic velocity field using a three-dimensional block model to determine the relative contributions of block motion, elastic strain accumulation, and shallow aseismic creep. The total relative motion between the Pacific plate and the rigid Sierra Nevada/Great Valley (SNGV) microplate is 37.9 ?? 0.6 mm yr-1 directed toward N30.4??W ?? 0.8?? at San Francisco (??2??). Fault slip rates from our preferred model are typically within the error bounds of geologic estimates but provide a better fit to geodetic data (notable right-lateral slip rates in mm yr-1: San Gregorio fault, 2.4 ?? 1.0; West Napa fault, 4.0 ?? 3.0; zone of faulting along the eastern margin of the Coast Range, 5.4 ?? 1.0; and Mount Diablo thrust, 3.9 ?? 1.0 of reverse slip and 4.0 ?? 0.2 of right-lateral strike slip). Slip on the northern Calaveras is partitioned between both the West Napa and Concord/ Green Valley fault systems. The total convergence across the Bay Area is negligible. Poles of rotation for Bay Area blocks progress systematically from the North America-Pacific to North America-SNGV poles. The resulting present-day relative motion cannot explain the strike of most Bay Area faults, but fault strike does loosely correlate with inferred plate motions at the time each fault initiated. Copyright 2005 by the American Geophysical Union.

Explore related subjects

Journal of Geophysical Research B: Solid EarthArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M. A. d'Alessio, I.A. Johanson, R. Burgmann, D.A. Schmidt, M.H. Murray. 2005-06-16. Slicing up the San Francisco Bay Area: Block kinematics and fault slip rates from GPS-derived surface velocities. https://doi.org/10.1029/2004jb003496

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2023

High-resolution InSAR reveals localized pre-eruptive deformation inside the crater of Agung Volcano, Indonesia

During a volcanic crisis, high-rate, localized deformation can indicate magma close to the surface, with important implications for eruption forecasting. However, only a few such examples have been reported, because frequent, dense monitoring is needed. High-resolution Synthetic Aperture Radar (SAR) is capable of achieving <1 m spatial resolution and sub-weekly revisit times, but is under-used. Here we use high-resolution satellite SAR imagery from COSMO-SkyMed, TerraSAR-X, and Sentinel-1 to detect intra-crater uplift preceding the November 2017 onset of eruptive activity at Agung, Indonesia. Processing the SAR imagery with an up-to-date, accurate, high-resolution digital elevation model was crucial for preventing aliasing of the deformation signal and for accurate georeferencing. We show that >15 cm of line-of-sight shortening occurred over a 400-by-400 m area on the crater floor in September-October 2017, accompanying a deep seismic swarm and flank dyke intrusion. We attribute the deformation to the pressurization of a shallow (<200 m deep) hydrothermal system by the injection of magmatic gases and fluids. We also observe a second pulse of intra-crater deformation of 3–5 cm within 4 days to 11 hr prior to the first phreatomagmatic eruption, which is consistent with interaction between the hydrothermal system and the ascending magma. This phreatomagmatic eruption created the central pathway used during the final stages of magma ascent. Our observations have important implications for understanding unrest and eruption forecasting, and demonstrate the potential of monitoring with high-resolution SAR.

Agung Volcano, Bali↗
USGS2023

Identification of seismo-volcanic regimes at Whakaari/White Island (New Zealand) via systematic tuning of an unsupervised classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.

Whakaari, White Island↗
USGS2022

Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation

Results from nine microgravity campaigns from Kı̄lauea, Hawaiʻi, spanning most of the volcano's 2008–2018 summit eruption, indicate persistent mass accumulation at shallow levels. A weighted least squares approach is used to recover microgravity results from a network of benchmarks around Kı̄lauea's summit, eliminate instrumental drift, and restore suspected data tares. A total mass of 1.9 × 10 11 kg was determined from these microgravity campaigns to have accumulated below Kı̄lauea Caldera during 2009–2015 at an estimated depth of 1.3 km below sea level. Only a fraction of this mass is reflected in surface deformation, and this is consistent with previously reported discrepancies between subsurface mass accumulation and observed surface deformation. The discrepancy, amongst other independent evidence from gas emissions, seismicity, and continuous gravimetry, indicate densification of magma in the reservoirs below the volcano summit. This densification may have been driven by degassing through the summit vent. It is hypothesized that during the final years of the summit eruption, magma densification resulted in a buildup of pressure in the reservoirs that may have contributed to the lower East Rift Zone outbreak of 2018. The observed mass accumulation beneath Kı̄lauea could not have been detected through other techniques and illustrates the importance of microgravity measurements in volcano monitoring.

Hawai'i↗