Search for: All records

Abstract The Calaveras Fault (CF) branches from the San Andreas Fault (SAF) near San Benito, extending sub‐parallel to the SAF for about 50 km with only 2–6 km separation and diverging northeastward. Both the SAF and CF are partially coupled, exhibit spatially variable aseismic creep and have hosted moderate to large earthquakes in recent decades. Understanding how slip partitions among the main fault strands of the SAF system and establishing their degree of coupling is crucial for seismic hazard evaluation. We perform a timeseries analysis using more than 5 years of Sentinel‐1 data covering the Bay Area (May 2015–October 2020), specifically targeting the spatiotemporal variations of creep rates around the SAF‐CF junction. We derive the surface creep rates from cross‐fault InSAR timeseries differences along the SAF and CF including adjacent Sargent and Quien Sabe Faults. We show that the variable creep rates (0–20 mm/yr) at the SAF‐CF junction are to first order controlled by the angle between the fault strike and the background stress orientation. We further examine the spatiotemporal variation of creep rates along the SAF and CF and find a multi‐annual coupling increase during 2016–2018 the subparallel sections of both faults, with the CF coupling change lagging behind the SAF by 3–6 months. Similar temporal variations are also observed in bothb‐values inferred from declustered seismicity and aseismic slip rates inferred from characteristic repeating earthquakes. 

ABSTRACT The 2019 Ridgecrest earthquake sequence culminated in the largest seismic event in California since the 1999 Mw 7.1 Hector Mine earthquake. Here, we combine geodetic and seismic data to study the rupture process of both the 4 July Mw 6.4 foreshock and the 6 July Mw 7.1 mainshock. The results show that the Mw 6.4 foreshock rupture started on a northwest-striking right-lateral fault, and then continued on a southwest-striking fault with mainly left-lateral slip. Although most moment release during the Mw 6.4 foreshock was along the southwest-striking fault, slip on the northwest-striking fault seems to have played a more important role in triggering the Mw 7.1 mainshock that happened ∼34  hr later. Rupture of the Mw 7.1 mainshock was characterized by dominantly right-lateral slip on a series of overall northwest-striking fault strands, including the one that had already been activated during the nucleation of the Mw 6.4 foreshock. The maximum slip of the 2019 Ridgecrest earthquake was ∼5  m, located at a depth range of 3–8 km near the Mw 7.1 epicenter, corresponding to a shallow slip deficit of ∼20%–30%. Both the foreshock and mainshock had a relatively low-rupture velocity of ∼2  km/s, which is possibly related to the geometric complexity and immaturity of the eastern California shear zone faults. The 2019 Ridgecrest earthquake produced significant stress perturbations on nearby fault networks, especially along the Garlock fault segment immediately southwest of the 2019 Ridgecrest rupture, in which the coulomb stress increase was up to ∼0.5  MPa. Despite the good coverage of both geodetic and seismic observations, published coseismic slip models of the 2019 Ridgecrest earthquake sequence show large variations, which highlight the uncertainty of routinely performed earthquake rupture inversions and their interpretation for underlying rupture processes. 

Abstract Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017),https://doi.org/10.1002/2017jb014047as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017),https://doi.org/10.1002/2017jb014047catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat. 

Abstract In Southern Cascadia, precise Global Navigation Satellite System (GNSS) measurements spanning about 15 years reveal steady deformation due to locking on the Cascadia megathrust punctuated by transient deformation from large earthquakes and episodic tremor and slip events. Near the Mendocino Triple Junction, however, we recognize several abrupt GNSS velocity changes that reflect a different process. After correcting for earthquakes and seasonal loading, we find that several dozen GNSS time series show spatially coherent east‐west velocity changes of ~2 mm/yr and that these changes coincide in time with regionalM> 6.5 earthquakes. We consider several hypotheses and propose that dynamically triggered changes in megathrust coupling best explain the data. Our inversions locate the coupling changes slightly updip of the tremor‐producing zone. We speculate that fluid exchange surrounding the tremor region may be important. Such observations of transient coupling changes are rare and challenging to explain mechanistically but have important implications for earthquake processes on faults.