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High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements. • We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. • The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. • The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33. [ABSTRACT FROM AUTHOR]

Historically, the city of Guadalajara has been affected not only by great regional earthquakes (M > 7.0) associated with the subduction process and regional crustal structures but also by local seismic sequences, that caused moderate to severe structural damage to buildings, whose source is not clear. Between December 2015 and May 2016, two seismic sequences occurred, affecting the city of Guadalajara. Both seismic sequences were recorded by the Jalisco Seismic Accelerometric Telemetric Network. The preliminary locations for May 2016 sequence estimated by the Antelope automatic system show alignment with an NNE-SSW trend, west of the city of Guadalajara. The subsequent relocations of theses earthquakes show two N–S alignments at the west of the city of Guadalajara, which agree with December 2015 hypocenters. The focal mechanisms analysis of the earthquakes shows that most of them correspond to normal fault mechanisms that are parallel to the hypocentral alignments suggesting the existence of two active faults responsible for the seismic sequences. Furthermore, these structures might constitute a graben, which we refer to as Zapopan Graben. Additionally, we calculated that these faults are 21 and 28 km length, respectively, which indicates that could have the potential to generate shallow earthquakes that reach magnitudes of 6.2 and 6.5, and could cause significant damages in the Guadalajara Metropolitan Zone. • We analyze the first seismic data set of a seismic sequence in Guadalajara (GMZ), registered by close seismic stations. • Two active normal faults are identified using the seismicity data suggesting the existence of a local graben at GMZ. • The results of this study indicate that the seismic hazard in the GMZ should be reevaluated. • A review was carried out, as complete as possible, of the historical seismicity in the Jalisco Block region. [ABSTRACT FROM AUTHOR]