Browsing by Author "Donoso, Felipe"

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    A machine learning approach for slow slip event detection using GNSS time-series
    (2023) Donoso, Felipe; Yanez, Vicente; Ortega-Culaciati, Francisco; Moreno, Marcos
    Extracting tectonic transient displacements on the Earth's surface from Global Navigation Satellite System (GNSS) time series remains a challenge, because GNSS station displacements depend on multiple processes occurring simultaneously, along with noise that obscures low-magnitude transient signals. We present a novel method for automatic detection of slow slip events (SSEs) in time series of a GNSS network by training a supervised machine learning (ML) model for classification. The proposed methodology detects both temporally and spatially the signatures of SSEs or regional transients within a GNSS network. The time series of a GNSS network were transformed into grayscale images, from which descriptors, including Bag of Visual Words (BoW) and Extended Local Binary Patterns (ELBP), were extracted. These descriptors served as input features for two distinct ML models: Support Vector Machines (SVM) and Artificial Neural Networks (NN). To train and test the ML classification model, two 3-year synthetic datasets were generated, one with GNSS networks featuring slow slip events (SSEs) of varying location, duration, onset time, and magnitude, and the other without SSEs, resulting in positive and negative sets, respectively. For each GNSS network, an image was created by combining the east and north components of the time series, which have been previously detrended and common mode error filtered. Each image is further divided into sub-images corresponding to 60 days time windows, in order to temporarily detect the existence of a transient. For training and testing, the datasets were separated into 75% for training and 25% for testing, each with 50% positive and 50% negative cases. In the final step, we analyze the positively classified images, representing the time windows in which the classifier detected transients. Within each of these windows, we identify the network's time series with the highest velocity, indicating the stations and geographic area where the detected transients occurred. The test results demonstrate that both ML models achieved high performance using both ELBP and BoW descriptors as features. Finally, our ML models were validated on a real dataset with a transient signal recorded before the 2014 Iquique earthquake in Chile, and they effectively detected this anomalous signal. The proposed method can effectively detect transient signals related to SSEs with high accuracy, sensitivity, and specificity in both the test and instrumentally recorded datasets.
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    Distributed Predictive Secondary Control for Imbalance Sharing in AC Microgrids
    (2022) Navas-Fonseca, Alex; Burgos-Mellado, Claudio; Gomez, Juan S.; Donoso, Felipe; Tarisciotti, Luca; Saez, Doris; Cardenas, Roberto; Sumner, Mark
    This paper proposes a distributed predictive secondary control strategy to share imbalance in three-phase, three-wire isolated AC Microgrids. The control is based on a novel approach where the imbalance sharing among distributed generators is controlled through the control of single-phase reactive power. The main characteristic of the proposed methodology is the inclusion of an objective function and dynamic models as constraints in the formulation. The controller relies on local measurements and information from neighboring distributed generators, and it performs the desired control action based on a constrained cost function minimization. The proposed distributed model predictive control scheme has several advantages over solutions based on virtual impedance loops or based on the inclusion of extra power converters for managing single-phase reactive power among distributed generators. In fact, with the proposed technique the sharing of imbalance is performed directly in terms of single-phase reactive power and without the need for adding extra power converters into the microgrid. Contrary to almost all reported works in this area, the proposed approach enables the control of various microgrid parameters within predefined bands, providing a more flexible control system. Extensive simulation and Hardware in the Loop studies verify the performance of the proposed control scheme. Moreover, the controller's scalability and a comparison study, in terms of performance, with the virtual impedance approach were carried out.
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    Mosaicking Andean morphostructure and seismic cycle crustal deformation patterns using GNSS velocities and machine learning
    (2023) Yanez-Cuadra, Vicente; Moreno, Marcos; Ortega-Culaciati, Francisco; Donoso, Felipe; Baez, Juan Carlos; Tassara, Andres
    We use unsupervised machine learning techniques to analyze continental-scale crustal motions in areas affected by the seismic cycle of large subduction earthquakes along the Chilean Trench. Specifically, we use the agglomerative clustering algorithm as an exploratory tool to investigate spatial patterns in GNSS regional velocities without the complexity of modeling a physical source. We present a continental-scale velocity field including all available GNSS data for two-time windows (pre-2014, 2018-2021) that represents two periods with different deformation patterns of the seismic cycle. We test two different pre-processing methodologies for the design of machine learning features from the GNSS-derived velocities. The first method uses the direction and magnitude of the secular rates as input features to the clustering algorithm. These results show a clustering spatially related to seismic cycle deformation, separating latitudinal segments with different velocities in the fore-arc and back-arc, as well as regions affected by postseismic relaxation. Thus, highlighting the effectiveness of this method for mapping first-order patterns of active deformation in a subduction zone, that are particularly related to variations on interplate coupling and postseismic transient deformation. In a more sophisticated approach, we use surface strain and rotational rates from GNSS velocities as features in the second methodology. Here, we develop a novel methodology to estimate strain and rotation rates accounting for the spatial heterogeneity of the GNSS-network. We determine the spatial scale at which these features are estimated by least squares inversions, by using a Bayesian model class selection method. The distribution of stations allows to identify heterogeneities in strain and rotation rates at spatial scales larger than 50 km, being particularly notorious the main features of regional deformation at scales > 100 km. Interestingly, the results show a spatial correlation between seismic segmentation in the fore-arc and geologic and structural domains in the arc and back-arc. Our results demonstrate the ability of the combination of inverse and machine learning methods to efficiently identify active deformation patterns and their relationship to the subduction seismic cycle and regional-scale geological structures. Furthermore, our analysis suggests that Andean geological structures influence the observed deformation field.