Seismic and GNSS strain-based probabilistic seismic hazard evaluation for northern Chile using DAS Magnitude Scale

Abstract Background Probabilistic Seismic Hazard Assessment (PSHA) is a leading methodology for determining key ground motion parameters such as Peak Ground Acceleration (PGA) and spectral acceleration (SA), essential for structural design. This approach uses extensive earthquake data, typically spanning over a century, leveraging frequency and magnitude statistics. However, long-term ground shaking probabilities may not always be accurately captured by traditional data-driven methods. To address these limitations, this study develops a PSHA map for Northern Chile using both seismic and GNSS (Global Navigation Satellite System) data. A curated homogeneous earthquake catalog, based on the advanced seismic moment magnitude scale M wg (Das Magnitude Scale) , replaces the traditional M w scale to ensure superior accuracy, particularly for intermediate and smaller earthquakes. Results Using the earthquake catalog, seismicity parameters ‘a’ and ‘b’ from the Gutenberg-Richter relationship were derived. Seismogenic modeling and Ground Motion Models (GMMs) were applied to estimate ground motion probabilities for a 475-year return period. Additionally, a PSHA map was constructed using GNSS strain rates, translating velocity-derived strain rates into seismic moment rates and ground shaking probabilities for seismic source zones. Comparative analyses revealed higher PGA values from GNSS strain data compared to seismic catalog data. GNSS strain data proved invaluable for refining seismic segmentation in Northern Chile, enhancing the precision of PSHA calculations. Conclusions A PSHA map for Northern Chile, synthesizing seismic catalog data and GNSS strain rates using a Logic Tree-based algorithm, has been developed for a 475-year return period. This map provides a critical tool for generating seismic hazard assessments aligned with building codes and emergency planning protocols. By integrating GNSS strain rates and seismic data, this study advances the reliability and accuracy of long-term ground shaking predictions.