The GAGE Data and Field Response to the 2019 Ridgecrest Earthquake Sequence

Abstract

The July 2019 Ridgecrest sequence was observed in exquisite detail by the National Science Foundation’s (NSF) Geodetic Facility for the Advancement of Geoscience (GAGE) Network of the Americas (NOTA), which has a dense array of continuously observing Global Navigation Satellite System (GNSS) stations and subarrays of strain and seismic borehole networks in southern California. Two hundred and eighteen GNSS and 10 borehole NOTA stations within 250 km of the epicentral area recorded the sequence. Special downloads of high-rate data from sites within a specified radius of each earthquake were initiated by the GAGE Facility for the time period of 1.5 days before and 1.5 days after each event to ensure transient deformation was captured at a high-temporal resolution. Rapid field deployments of temporary GNSS stations were carried out by UNAVCO in support of NSF-funded investigators and U.S. Geological Survey activities. The data recorded by the permanent network are available from the GAGE Facility’s Data Center at UNAVCO, data recorded at the temporary campaign sites will also be made available on completion of data collection. The OpenTopography project, of which UNAVCO is a partner, released a preliminary pre-event digital surface model of the area covering the Ridgecrest earthquake sequence to support the ongoing imaging efforts to measure the deformation from these events. In this article, we document the significant amount of detailed, open-access geodetic data available from GAGE to study this sequence and advance our understanding of earthquake processes, the geodynamics of the California eastern shear zone, and our capacity to respond to damaging earthquakes for research.

Highly Heterogeneous Pore Fluid Pressure Enabled Rupture of Orthogonal Faults During the 2019 Ridgecrest Mw7.0 Earthquake

“…The earthquake sequence produced very complex surfaced ruptures (Brandenberg et al, 2019) and numerous aftershocks on tens of fault segments (Ross et al, 2019;Shelly, 2020). Surface deformation was very well recorded by geodetic observations including GPS and satellite images (Fielding et al, 2020;Floyd et al, 2020;Mattioli et al, 2020;Melgar et al, 2019;Wang & Bürgmann, 2020;Xu et al, 2020). All these observations have clearly shown that the sequence ruptured conjugate faults.…”

FinDerS(+): Real-Time Earthquake Slip Profiles and Magnitudes Estimated from Backprojected Displacement with Consideration of Fault Source Maturity Gradient

“…For the Ridgecrest earthquake, we use strong-motion data from the Southern California Seismic Network (SCSN), as well as preprocessed 1-Hz displacement time series from 10 GNSS stations from the Geodetic Facility for the Advancement of Geoscience (GAGE) Network of the Americas (NOTA) that were obtained through UNAVCO (Mattioli et al, 2020). For the Kumamoto earthquake, we use strong-motion records from both KiK-net and K-NET stations, as well as preprocessed GEONET Global Positioning System time series downloaded from Ruhl et al (2019).…”

“…Color of station markers shows their respective distance range relative to the FinDer line-source. and in displacement (Ruhl et al, 2019;Mattioli et al, 2020), so we do not apply any filters. We simply detrend the data and, like the seismic data, we remove the pre-event median of the data by calculating the median amplitude between the start time of the waveform and the arrival of the P-wave, assuming vp 6.1 km/s, and subtracting this value from the entire waveform.…”

Real‐Time Seismogeodetic Earthquake Magnitude Estimates for Local Tsunami Warnings

“…Empirical scaling relationships using peak‐ground‐displacements (PGDs) measured from high‐rate GNSS displacements (Crowell et al., 2013; Melgar et al., 2015; Ruhl et al., 2019) have been successfully used to rapidly estimate magnitude in earthquake early warning contexts (Hodgkinson et al., 2020; Mattioli et al., 2020; Melbourne et al., 2021). Retrospective studies of several tsunamigenic events in Japan and Chile have also demonstrated the utility of scaling relationships as an element of tsunami warning systems and to derive models of coastal inundation (Blewitt et al., 2006; Melgar et al., 2016; Ohta et al., 2012).…”