Continuous Monitoring of Ground-Motion Parameters

We applied a time-domain method to continuously monitor groundmotion parameters to facilitate rapid determination of the geographical distribution of ground motion and earthquake parameters. This approach minimizes the impact of the sudden increase in the worload on data acquisition facilities and streamlines the operation of a seismic network, especially during a complex earthquakes sequence. The incoming continuous time series is processed with the use of various time-domain recursive filters to compute ground-motion velocity, acceleration, energy, Wood-Anderson seismograms, and narrow-band responses at 0.3, 1.0, and 3.0 sec, which are used for computation of response spectral amplitudes. The method is implemented in the Southern California Digital Seismic Network. The method can be implemented at every field station, which would make it possible to process the time series locally and continuously telemeter desired amplitude parameters with sufficient accuracy from a field station to multiple users through a relatively low data-rate communication line (e.g., regular telephone line or limited bandwidth satellite link)

pyCSEP:A Python Package For Earthquake Forecast Developers

For government officials and the public to act on real-time forecasts of earthquakes, the seismological community needs to develop confidence in the underlying scientific hypotheses of the forecast generating models by assessing their predictive skill. For this purpose, the Collaboratory for the Study of Earthquake Predictability (CSEP) provides cyberinfrastructure and computational tools to evaluate earthquake forecasts. Here, we introduce pyCSEP, a Python package to help earthquake forecast developers embed model evaluation into the model development process. The package contains the following modules: (1) earthquake catalog access and processing, (2) data models for earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines. pyCSEP can evaluate earthquake forecasts expressed as expected rates in space-magnitude bins, and simulation-based forecasts that produce thousands of synthetic seismicity catalogs. Most importantly, pyCSEP contains community-endorsed implementations of statistical tests to evaluate earthquake forecasts, and provides well defined file formats and standards to facilitate model comparisons. The toolkit will facilitate integrating new forecasting models into testing centers, which evaluate forecast models and prediction algorithms in an automated, prospective and independent manner, forming a critical step towards reliable operational earthquake forecasting.This research was supported by the Southern California Earthquake Center (Contribution No. 11030). SCEC is funded by NSF Cooperative Agreement EAR-1600087 & USGS Cooperative Agreement G17AC00047. Maximilian J. Werner and Danijel Schorlemmer received funding from the European Union's Horizon 2020 research and innovation program (Number 821115, RISE: Real-Time Earthquake Risk Reduction for a Resilient Europe)

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M>6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (415000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept', being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20per cent) of CyberShake simulations, but can explain MMI values of all>400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate', ‘strong' or ‘very strong' shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5-7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong' to ‘severe' shaking in the LA basin; however, warning times for these events could exceed 30

The Collaboratory for the Study of Earthquake Predictability:Achievements and Priorities

The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global cyberinfrastructure for prospective evaluations of earthquake forecast models and prediction algorithms. CSEP’s goals are to improve our understanding of earthquake predictability, advance forecasting model development, test key scientific hypotheses and their predictive power, and improve seismic hazard assessments. Since its inception in California in 2007, the global CSEP collaboration has been conducting forecast experiments in a variety of tectonic settings and at a global scale and now operates four testing centers on four continents to automatically and objectively evaluate models against prospective data. These experiments have provided a multitude of results that are informing operational earthquake forecasting systems and seismic hazard models, and they have provided new and, sometimes, surprising insights into the predictability of earthquakes and spurned model improvements. CSEP has also conducted pilot studies to evaluate ground-motion and hazard models. Here, we report on selected achievements from a decade of CSEP, and we present our priorities for future activities.Published1305-13136T. Studi di pericolosità sismica e da maremotoJCR Journa

Unified Structural Representation of the southern California crust and upper mantle

We present a new, 3D description of crust and upper mantle velocity structure in southern California implemented as a Unified Structural Representation (USR). The USR is comprised of detailed basin velocity descriptions that are based on tens of thousands of direct velocity (Vp, Vs) measurements and incorporates the locations and displacement of major fault zones that influence basin structure. These basin descriptions were used to developed tomographic models of crust and upper mantle velocity and density structure, which were subsequently iterated and improved using 3D waveform adjoint tomography. A geotechnical layer (GTL) based on Vs30 measurements and consistent with the underlying velocity descriptions was also developed as an optional model component. The resulting model provides a detailed description of the structure of the southern California crust and upper mantle that reflects the complex tectonic history of the region. The crust thickens eastward as Moho depth varies from 10 to 40 km reflecting the transition from oceanic to continental crust. Deep sedimentary basins and underlying areas of thin crust reflect Neogene extensional tectonics overprinted by transpressional deformation and rapid sediment deposition since the late Pliocene. To illustrate the impact of this complex structure on strong ground motion forecasting, we simulate rupture of a proposed M 7.9 earthquake source in the Western Transverse Ranges. The results show distinct basin amplification and focusing of energy that reflects crustal structure described by the USR that is not captured by simpler velocity descriptions. We anticipate that the USR will be useful for a broad range of simulation and modeling efforts, including strong ground motion forecasting, dynamic rupture simulations, and fault system modeling. The USR is available through the Southern California Earthquake Center (SCEC) website (http://www.scec.org)