Dynamical system analysis and forecasting of deformation produced by an earthquake fault

We present a method of constructing low-dimensional nonlinear models describing the main dynamical features of a discrete 2D cellular fault zone, with many degrees of freedom, embedded in a 3D elastic solid. A given fault system is characterized by a set of parameters that describe the dynamics, rheology, property disorder, and fault geometry. Depending on the location in the system parameter space we show that the coarse dynamics of the fault can be confined to an attractor whose dimension is significantly smaller than the space in which the dynamics takes place. Our strategy of system reduction is to search for a few coherent structures that dominate the dynamics and to capture the interaction between these coherent structures. The identification of the basic interacting structures is obtained by applying the Proper Orthogonal Decomposition (POD) to the surface deformations fields that accompany strike-slip faulting accumulated over equal time intervals. We use a feed-forward artificial neural network (ANN) architecture for the identification of the system dynamics projected onto the subspace (model space) spanned by the most energetic coherent structures. The ANN is trained using a standard back-propagation algorithm to predict (map) the values of the observed model state at a future time given the observed model state at the present time. This ANN provides an approximate, large scale, dynamical model for the fault.Comment: 30 pages, 12 figure

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

PICES Press, Vol. 20, No. 1, Winter 2012

•2011 PICES Science: A Note from the Science Board Chairman (pp. 1-6) •2011 PICES Awards (pp. 7-9) •Beyond the Terrible Disaster of the Great East Japan Earthquake (pp. 10-12) •A New Era of PICES-ICES Scientific Cooperation (p. 13) •New PICES Jellyfish Working Group Formed (pp. 14-15) •PICES Working Group on North Pacific Climate Variability (pp. 16-18) •Final U.S. GLOBEC Symposium and Celebration (pp. 19-25) •2011 PICES Rapid Assessment Survey (pp. 26-29) •Introduction to Rapid Assessment Survey Methodologies for Detecting Non-indigenous Marine Species (pp. 30-31) •The 7th International Conference on Marine Bioinvasions (pp. 32-33) •NOWPAP/PICES/WESTPAC Training Course on Remote Sensing Data Analysis (pp. 34-36) •PICES-2011 Workshop on “Trends in Marine Contaminants and their Effects in a Changing Ocean” (pp. 37-39) •The State of the Western North Pacific in the First Half of 2011 (pp. 40-42) •Yeosu Symposium theme sessions (p. 42) •The Bering Sea: Current Status and Recent Events (pp. 43-44) •News of the Northeast Pacific Ocean (pp. 45-47) •Recent and Upcoming PICES Publications (p. 47) •New leadership for the PICES Fishery Science Committee (p. 48

Pseudo-prospective Evaluation of UCERF3-ETAS Forecasts During the 2019 Ridgecrest Sequence

The 2019 Ridgecrest sequence provides the first opportunity to evaluate Uniform California Earthquake Rupture Forecast v.3 with epidemic‐type aftershock sequences (UCERF3‐ETAS) in a pseudoprospective sense. For comparison, we include a version of the model without explicit faults more closely mimicking traditional ETAS models (UCERF3‐NoFaults). We evaluate the forecasts with new metrics developed within the Collaboratory for the Study of Earthquake Predictability (CSEP). The metrics consider synthetic catalogs simulated by the models rather than synoptic probability maps, thereby relaxing the Poisson assumption of previous CSEP tests. Our approach compares statistics from the synthetic catalogs directly against observations, providing a flexible approach that can account for dependencies and uncertainties encoded in the models. We find that, to the first order, both UCERF3‐ETAS and UCERF3‐NoFaults approximately capture the spatiotemporal evolution of the Ridgecrest sequence, adding to the growing body of evidence that ETAS models can be informative forecasting tools. However, we also find that both models mildly overpredict the seismicity rate, on average, aggregated over the evaluation period. More severe testing indicates the overpredictions occur too often for observations to be statistically indistinguishable from the model. Magnitude tests indicate that the models do not include enough variability in forecasted magnitude‐number distributions to match the data. Spatial tests highlight discrepancies between the forecasts and observations, but the greatest differences between the two models appear when aftershocks occur on modeled UCERF3‐ETAS faults. Therefore, any predictability associated with embedding earthquake triggering on the (modeled) fault network may only crystalize during the presumably rare sequences with aftershocks on these faults. Accounting for uncertainty in the model parameters could improve test results during future experiments.Maximilian J. Werner and Warner Marzocchi 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). This research was supported by the Southern California Earthquake Center (SCEC; Contribution Number 10082). SCEC is funded by National Science Foundation (NSF) Cooperative Agreement EAR‐1600087 and the U.S. Geological Survey (USGS) Cooperative Agreement G17AC00047

An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.Peer ReviewedPostprint (published version