Browsing by Author "Salazar, Pablo"

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    Coseismic slip and afterslip of the 2015 Mw 8.3 Illapel (Chile) earthquake determined from continuous GPS data
    (John Wiley & Sons Ltd., 2016) Shrivastava, Mahesh N.; González, Gabriel; Moreno, Marcos; Chlieh, Mohamed; Salazar, Pablo; Reddy, C.D.; Báez, Juan Carlos; Yanez Carrizo Gonzalo Alejandro; González, Juan; De La Llera Martin Juan Carlos; Pontificia Universidad Católica de Chile. National Research Center for Integrated Natural Disaster Management
    We analyzed the coseismic and early postseismic deformation of the 2015, M-w 8.3 Illapel earthquake by inverting 13 continuous GPS time series. The seismic rupture concentrated in a shallow (<20km depth) and 100km long asperity, which slipped up to 8m, releasing a seismic moment of 3.6x10(21)Nm (M-w=8.3). After 43days, postseismic afterslip encompassed the coseismic rupture. Afterslip concentrated in two main patches of 0.50m between 20 and 40km depth along the northern and southern ends of the rupture, partially overlapping the coseismic slip. Afterslip and aftershocks confined to region of positive Coulomb stress change, promoted by the coseismic slip. The early postseismic afterslip was accommodated similar to 53% aseismically and similar to 47% seismically by aftershocks. The Illapel earthquake rupture is confined by two low interseismic coupling zones, which coincide with two major features of the subducting Nazca Plate, the Challenger Fault Zone and Juan Fernandez Ridge.
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    Multi-station automatic classification of seismic signatures from the Lascar volcano database
    (2023) Salazar, Pablo; Yupanqui, Franz; Meneses, Claudio; Layana, Susana; Yanez, Gonzalo
    This study was aimed to build a multi-station automatic classification system for volcanic seismic signatures such as hybrid, long period, tremor, tectonic, and volcano-tectonic events. This system was based on a probabilistic model made using transfer learning, which has, as the main tool, a pre-trained convolutional network named AlexNet. We designed five experiments using different datasets with data that were real, synthetic, two different combinations of these (combined 1 and combined 2), and a balanced subset without synthetic data. The experiment presented the highest scores when a process of data augmentation was introduced into processing sequence. Thus, the lack of real data in some classes (imbalance) dramatically affected the quality of the results, because the learning step (training) was overfitted to the more numerous classes. To test the model stability with variable inputs, we implemented a k-fold cross-validation procedure. Under this approach, the results reached high predictive performance, considering that only the percentage of recognition of the tectonic events (TC) class was partially affected. The results obtained showed the performance of the probabilistic model, reaching high scores over different test datasets. The most valuable benefit of using this technique was that the use of volcano seismic signals from multiple stations provided a more generalizable model which, in the near future, can be extended to multi-volcano database systems. The impact of this work is significant in the evaluation of hazard and risk by monitoring the dynamic evolution of volcanic centers, which is crucial for understanding the stages in a volcano's eruptive cycle.
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    Upper plate reverse fault reactivation and the unclamping of the megathrust during the 2014 northern Chile earthquake sequence
    (2015) González, Gabriel; Salazar, Pablo; Loveless, John P.; Allmendinger, Richard W.; Aron Melo, Felipe Alejandro; Shrivastava, Mahesh