Dr. Benavente-Bravo, Roberto

We compiled a novel microseismicity catalog for the Central Chile megathrust (29°–35°S), comprising 8,750 earthquakes between April 2014 and December 2018. These events describe a pattern of three trenchward open half-ellipses, consisting of a continuous, coast-parallel seismicity band at 30–45 km depth, and narrow elongated seismicity clusters that protrude to the shallow megathrust and separate largely aseismic regions along strike. To test whether these shapes could outline highly coupled regions (“asperities”) on the megathrust, we invert GPS displacement data for interplate locking. The best-fit locking model does not show good correspondence to seismicity, possibly due to lacking resolution. When we prescribe high locking inside the half-ellipses, however, we obtain models with similar data fits that are preferred according to the Bayesian Information Criterion (BIC). We thus propose that seismicity on the Central Chile megathrust may outline three adjacent highly coupled regions, two of them located between the rupture areas of the 2010 Maule and the 2015 Illapel earthquakes, a segment of the Chilean margin that may be in a late interseismic stage of the seismic cycle.

The detection of transient events related to slow earthquakes in GNSS positional time series is key to understanding seismogenic processes in subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal and spatial detection of transient signals. The PICCA is based on an optimal combination of the principal (PCA) and independent component analysis (ICA) of positional time series of a GNSS network. We assume that the transient signal is mostly contained in one of the principal or independent components. To detect the transient, we applied a method where correlations between sliding windows of each PCA/ICA component and each time series are calculated, obtaining the stations affected by the slow slip event and the onset time from the resulting correlation peaks. We first tested and calibrated the method using synthetic signals from slow earthquakes of different magnitudes and durations and modelled their effect in the network of GNSS stations in Chile. Then, we analyzed three transient events related to slow earthquakes recorded in Chile, in the areas of Iquique, Copiapó, and Valparaíso. For synthetic data, a 150 days event was detected using the PCA-based method, while a 3 days event was detected using the ICA-based method. For the real data, a long-term transient was detected by PCA, while a 16 days transient was detected by ICA. It is concluded that simultaneous use of both signal separation methods (PICCA) is more effective when searching for transient events. The PCA method is more useful for long-term events, while the ICA method is better suited to recognize events of short duration. PICCA is a promising tool to detect transients of different characteristics in GNSS time series, which will be used in a next stage to generate a catalog of SSEs in Chile

We propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the empirical orthogonal function analysis to identify the potential initial locations, followed by an optimization heuristic that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 Mw8.4 Illapel Chile earthquake and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from three tsunameters during 14 min, but with a minimum average time lag of nearly 5 min between simulated and forecasted waveforms. Additional tests show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modelled seismic scenarios.

Variability characterization of tsunami generation is quintessential for proper hazard estimation. For this purpose we isolate the variability which stems solely from earthquake spatial source complexity, by simulating tsunami inundation in the near-field with a simplified digital elevation model, using nonlinear shallow water equations. For earthquake rupture, we prescribe slip to have a log-normal probability distribution function and von Kármán correlation between each subfault pair, which we assume decreases with increasing euclidean distance between them. From the generated near-field inundation time-series, emanating from several thousand synthetic slip realizations across a magnitude 9 earthquake, we extract several tsunami intensity measures at the coast. Results show that all considered tsunami intensity measures and potential energy variability increase with increasing spatial slip correlations. Finally, we show that larger spatial slip correlations produce higher tsunami intensity measure exceedance probabilities within the near-field, which highlights the need to quantify the uncertainty of earthquake spatial slip correlation.

Central Chile is exposed to tsunami hazard, and large earthquakes and tsunamis have occurred over the last 500 years. Tsunami hazard analysis in Chile has been traditionally implemented by means of a deterministic approach, which is based on historical events and uniform slip distribution. The objective of the present study is to improve tsunami hazard analysis in central Chile (30°S to 38°S). To encompass the purpose, stochastic earthquake scenarios of magnitude Mw 8.8 to 9.2 were generated. Two different sets of stochastic tsunami scenarios were selected by means of the Stochastic Reduced Order Model (SROM), which were applied to Quintero bay to perform a Probabilistic Tsunami Hazard Analysis (PTHA). The results showed that PTHA of Quintero bay from stochastic tsunami scenarios agrees with paleotsunami records in the bay, while a deterministic tsunami scenario underestimated the hazard. Two sets (50 and 100 scenarios, respectively) give similar results when smaller return periods are analyzed. However, for larger return periods (Unknown node type: font 2000 yr) the set of 100 scenarios show better results consistent with previous paleoseismological findings. The methodology implemented here can be replicated in other seismic regions in Chile as well as in other active subduction zones, thus, both near field and far field events can be analyzed.

Obtaining slip distributions for earthquakes results in an ill-posed inverse problem. While this implies that only limited and uncertain information can be recovered from the data, inferences are typically made based only on a single regularized model. Here, we develop an inversion approach that can quantify uncertainties in a Bayesian probabilistic framework for the finite fault inversion (FFI) problem. The approach is suitably efficient for rapid source characterization and includes positivity constraints for model parameters, a common practice in FFI, via coordinate transformation to logarithmic space. The resulting inverse problem is nonlinear and the most probable solution can be obtained by iterative linearization. In addition, model uncertainties are quantified by approximating the posterior probability distribution by a Gaussian distribution in logarithmic space. This procedure is straightforward since an analytic expression for the Hessian of the objective function is obtained. In addition to positivity, we apply smoothness regularization to the model in logarithmic space. Simulations based on surface wave data show that smoothing in logarithmic space penalizes abrupt slip changes less than smoothing in linear space. Even so, the main slip features of models that are smooth in linear space are recovered well with logarithmic smoothing. Our synthetic experiments also show that, for the data set we consider, uncertainty is low at the shallow portion of the fault and increases with depth. In addition, a simulation with a large station azimuthal gap of 180° significantly increases the slip uncertainties. Further, the marginal posterior probabilities obtained from our approximate method are compared with numerical Markov Chain Monte Carlo sampling. We conclude that the Gaussian approximation is reasonable and meaningful inferences can be obtained from it. Finally, we apply the new approach to observed surface wave records from the great Illapel earthquake (Chile, 2015, Mw = 8.3). The location and amplitude of our inferred peak slip is consistent with other published solutions but the spatial slip distribution is more compact, likely because of the logarithmic regularization. We also find a minor slip patch downdip, mainly in an oblique direction, which is poorly resolved compared to the main slip patch and may be an artefact. We conclude that quantifying uncertainties of finite slip models is crucial for their meaningful interpretation, and therefore rapid uncertainty quantification can be critical if such models are to be used for emergency response.