English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

High rate Global Navigation Satellite System (HR GNSS) data can be highlyuseful for earthquake analysis as it provides continuous high-rate measurementsof ground motion. This data can be used to estimate the magnitude, to assessthe potential of an earthquake for generating tsunamis, and to analyze diverseparameters related to the seismic source. Particularly, in this work, wepresent the first results of a deep learning model based on a convolutionalneural network for earthquake magnitude estimation, using HR GNSS displacementtime series. The influence of different dataset configurations, such as stationnumbers, epicentral distances, signal duration, and earthquake size, wereanalyzed to figure out how the model can be adapted to various scenarios. Weexplored the potential of the model for global application and compared itsperformance using both synthetic and real data from different seismogenicregions. The performance of our model at this stage was satisfactory inestimating earthquake magnitude from synthetic data. Comparable results wereobserved in tests using synthetic data from a different tectonic region thanthe training data. Furthermore, the model was tested using real data fromdifferent regions and magnitudes, resulting in a good accuracy, provided thatthe data from a particular group of stations had similar epicentral distanceconstraints to those used during the model training. The robustness of the DLmodel can be improved to work independently from the window size of the timeseries and the number of stations, enabling faster estimation by the modelusing only near field data. Overall, this study provides insights for thedevelopment of future DL approaches for earthquake magnitude estimation withHR-GNSS data, emphasizing the importance of proper handling and careful dataselection for further model improvements.

Exploring a CNN Model for Earthquake Magnitude Estimation using HR-GNSS data