Dr. Benavente-Bravo, Roberto

Estimating locking degree at faults is important for determining the spatial distribution of slip deficit at seismic gaps. Inverse methods of varying complexity are commonly used to estimate fault locking. Here we present an innovative approach to infer the degree of locking from surface GNSS velocities by means of supervised learning (SL) algorithms. We implemented six different SL regression methods and apply them in the Central Chile subduction. These methods were first trained on synthetic distributions of locking and then used to infer the locking from GNSS observations. We tested the performance of each algorithm and compared our results with a least squares inversion method. Our best results were obtained using the Ridge regression, which gives a root mean square error (RMSE) of 1.94 mm/yr compared to GNSS observations. The ML-based locking degree distribution is consistent with results from the EPIC Tikhonov regularized least squares inversion and previously published locking maps. Our study demonstrates the effectiveness of machine learning methods in estimating fault locking and slip, and provides flexible options for incorporating prior information to avoid slip instabilities based on the characteristics of the training set. Exploring uncertainties in the physical model during training could improve the robustness of locking estimates in future research efforts.

Geodetically constrained interseismic interplate coupling has been widely used to assess seismic potential in subduction zones. Modeling interseismic deformation is challenging, as it involves interplate coupling and often ignores continental internal deformation processes. We present a novel methodology to jointly estimate interplate coupling along with upper plate rigid motion and surface strain, constrained by GNSS‐derived velocities. We use a least squares inversion with a spatially variable Equal Posterior Information Condition Tikhonov regularization, accounting for observational and elastic structure uncertainties. Our modeling reveals three megathrust regions with high tsunamigenic earthquake potential located within the Atacama Seismic Gap (Chile). This study indicates the presence of a downdip segmentation located just above the 1995 (Mw8.0) Antofagasta earthquake rupture, raising concerns for the potential of tsunamigenic earthquake occurrence at shallower depths. Additionally, we show that surface motion is dominated by strain, with rather negligible rigid motion, dismissing the rigid Andean microplate model typically assumed in previous studies.

Since the 1985 M 8.0 central Chile earthquake, national strong‐motion seismic networks have recorded ten megathrust earthquakes with magnitudes greater than M 7.5 at the convergent margin, defined by the contact between the Nazca and South American plates. The analysis of these earthquake records have led to improved hazard analyses and design codes for conventional and seismically protected structures. Although strong‐motion baseline correction is required for a meaningful interpretation of these records, correction methods have not been applied consistently in time. The inconsistencies between correction methods have been neglected in the practical use of these records in practice. Consequently, this work aims to provide a new strong‐motion database for researchers and engineers, which has been processed by traceable and consistent data processing techniques. The record database comes from three uncorrected strong motion Chilean databases. All the records are corrected using a four‐step novel methodology, which detects the P‐wave arrival and introduces a baseline correction based on the reversible‐jump Markov chain Monte Carlo method. The resulting strong motion database has more than 2000 events from 1985 to the date, and it is available to download at the Simulation Based Earthquake Risk and Resilience of Interdependent Systems and Networks (SIBER‐RISK) project website.

Here, the ionospheric response to earthquakes, earthquakes inducing tsunamis, and volcanic eruptions are presented as a contribution to the so‐called ionospheric seismology with the eventual development of real‐time warning systems in mind. A thorough analysis of Traveling Ionospheric Disturbances (TIDs) observed after these lithospheric events in South America is presented. It is based on a decade of total electron content (TEC) anomaly maps constructed explicitly for this purpose, likely the most extensive survey ever for South America. Three disturbance types are identified: TIDs generated by shock‐acoustic waves, by gravity waves, possibly induced by tsunami waves, and by Rayleigh surface waves. TIDs are observed after earthquakes with epicenters on the Pacific Ocean east coast, except one in the middle of the ocean. TIDs‐generating earthquake thresholds are found to be Mw ≥ 7.0 and depth ≤40, and TID amplitudes and ranges are proportional to earthquake magnitude. Fault mechanism and satellite‐receiver pair geometry are also considered. TIDs after volcanic eruptions confirm that atmospheric resonances are already reported. TIDs propagation direction depends strongly on the geomagnetic field direction, propagation toward the geomagnetic equator being more efficient. It was only possible to add some kind of vertical disturbance‐propagation evidence to TEC TIDs identification in some cases using ionograms from nearby ionosondes. A denser ionosonde network with greater sounding frequency would be necessary for further study.

Inversions of geodetic data are regularly used to estimate interseismic locking in subduction zones. However, the ill‐posed nature of these problems motivates us to include prior information, physically consistent with processes of the subduction seismic cycle. To deal with model instabilities, we present an inversion method to estimate both plate‐locking and model uncertainties by inverting Global Navigation Satellite System derived velocities based on a Bayesian model selection scheme. Our method allows us to impose positivity constraints via a multivariate folded‐normal distribution, with a specified covariance matrix. Model spatial correlations are explored and ranked to find models that best explain the observed data and for a better understanding of locking models. This approach searches for hyperparameters of the prior joint multivariate probability density function (PDF) of model parameters that minimize the Akaike Bayesian Information Criterion (ABIC). To validate our approach, we invert synthetic displacements from analytic models, yielding satisfactory results. We then apply the method to estimate the plate‐locking in Central Chile (28°–39°S) and its relation to the coseismic slip distribution of earthquakes with magnitudes Mw > 8.0, on the subduction zone since 2010. We also search among different prior PDFs for a single ductile‐fragile limit depth. Our results confirms a spatial correlation between locked asperities and the 2010 Mw 8.8 Maule and 2015 Mw 8.3 Illapel earthquake rupture zones. The robustness of our locking model shows potential to improve future seismic and tsunami hazard estimations.

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.

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.