WHEN THE GROUND STARTS TO MOVE: RAPID SEISMIC MAGNITUDE ESTIMATION IN CHILE USING MULTI-STATION PEAK DISPLACEMENTS AND ENSEMBLE MODELS
DOI:
https://doi.org/10.23854/07199562.2026621.penaKeywords:
rapid magnitude estimation, peak displacement, reproducible pipeline, miniSEED, tree-based ensemblesAbstract
Rapid magnitude estimation is essential for early earthquake response and automated modules that require a consistent event size shortly after detection. Recent work shows that early ground-motion amplitudes combined with source–station geometry can approximate magnitude, and that ensemble models capture nonlinear relations with strong robustness. Yet many studies provide limited reproducible detail on the full chain that converts miniSEED and StationXML into comparable physical variables under explicit quality control. We present a reproducible FDSN-based workflow for Chile. Our hypothesis is that a physically consistent pipeline using scalar sensitivity calibration, filtering, numerical integration (single or double depending on units) and drift correction can produce early displacement features that generalize over time. The workflow automates station selection within 500 km, construction of consistent N/E/Z trios, integrity checks for downloads and metadata, and extraction of per-component peak amplitudes, horizontal/3D metrics, and source–station distances. The final dataset includes 6641 event–station pairs from 316 events (2020–2025) with Mww as target. We compare tree-based ensembles against baselines using event-grouped cross-validation and a 2025 temporal hold-out. At event level, median aggregation yields typical 2025 performance MAE 0.15, RMSE 0.23 and R2 0.7. The main contribution is a traceable module for rapid/operational magnitude estimation conditioned on external detection/location, not an end-to-end seconds-latency EEW validation.
Downloads
References
Aedo, D., Melnick, D., Cisternas, M., & Brill, D. (2024). Tectonic control on great earthquake periodicity in south-central Chile. Communications Earth & Environment, 5(1), 703. https://doi.org/10.1038/s43247-024-01834-5
Amerthen, S., Aron Nambikai Fernando, J., & Praba, M. (2025). Seismopredict: Advanced Machine Learning for Earthquake Damage Assessment. 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), (pp. 1-7). Bidar, India. https://doi.org/10.1109/ICISCN64258.2025.10934698
Arrais, S., Urquiza-Aguiar, L., & Tripp-Barba, C. (2022). Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review. Sensors, 22(14), 5186. https://doi.org/10.3390/s22145186
Chandrakumar, C., Prasanna, R., Stephens, M., & Tan, M. L. (2022). Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review. Frontiers in Sensors, 3, 1. https://doi.org/10.3389/fsens.2022.1020202
Cremen, G., Galasso, C., & Zuccolo, E. (2022). Investigating the potential effectiveness of earthquake early warning across Europe. Nature Communications, 13(1), 639. https://doi.org/10.1038/s41467-021-27807-2
da Silva, L. d., Montecino Castro, H., Aguayo Arias, M. I., González-Rodríguez, L., Rodríguez-López, L., & Cotias Simões, L. M. (2022). Signals of Surface Deformation Areas in Central Chile, Related to Seismic Activity—Using the Persistent Scatterer Method and GIS. Applied Sciences, 12(5), 2575. https://doi.org/10.3390/app12052575
Devi D, R., Govindarajan, P., & N, V. (2024). Towards real-time earthquake forecasting in Chile: Integrating intelligent technologies and machine learning. Computers and Electrical Engineering, 117, 109285. https://doi.org/10.1016/j.compeleceng.2024.109285
Dybing, S., Yeck, W., Cole, H., & Melgar, D. (2024). Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale. Bulletin of the Seismological Society of America, 114(3), 1523--1538. https://doi.org/10.1785/0120230171
Esposito, M., Marzorati, S., Belli, A., Ladina, C., Palma, L., Calamita, C., Pantaleo, D., & Pierleoni, P. (2024). Low-cost MEMS accelerometers for earthquake early warning systems: A dataset collected during seismic events in central Italy. Data in Brief, 53, 110174. https://doi.org/10.1016/j.dib.2024.110174
Geiß, C., Priesmeier, P., Aravena Pelizari, P., Soto Calderon, A. R., Schoepfer, E., Riedlinger, T., Villar Vega, M., Santa María, H., Gómez Zapata, J. C., Pittore, M., So, E., & Fekete, A. (2023). Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile. Natural Hazards, 119(2), 779-804. https://doi.org/10.1007/s11069-022-05672-6
GEOFON Program (GFZ German Research Centre for Geosciences). (2006). Plate Boundary Observatory Network Northern Chile (IPOC Seismic Network) (Network code CX). https://doi.org/10.14470/PK615318
Han, G., Wang, D., & Sun, L. (2025). Estimating Moment Magnitudes of Moderate-to-Large Earthquakes in Western China Based on the Direct P Waves. Bulletin of the Seismological Society of America, 1. https://doi.org/10.1785/0120250079
Joshi, A., Vishnu, C., & Krishna Mohan, C. (2022). Early detection of earthquake magnitude based on stacked ensemble model. Journal of Asian Earth Sciences: X, 8, 100122. https://doi.org/10.1016/j.jaesx.2022.100122
Kaushal, A., Gupta, A. K., & Sehgal, V. K. (2025). Hybrid CatBoost and SVR Model for Earthquake Prediction Using the LANL Earthquake Dataset. Informatica, 49(14). https://doi.org/10.31449/inf.v49i14.6524
Kolivand, P. S. (2024). A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence. Earth Sci Inform, 17, 957–984. https://doi.org/10.1007/s12145-024-01253-2
Megna, A., Cimini, G. B., Marchetti, A., Pagliuca, N. M., & Santini, S. (2022). A Waveform Dataset in Continuous Mode of the Montefeltro Seismic Network (MF) in Central-Northern Italy from 2018 to 2020. Data, 7(12), 169. https://doi.org/10.3390/data7120169
Navarro, A., Castro-Artola, O., García-Guerrero, E., Aguirre-Castro, O., Tamayo Pérez, U., López-Mercado, C., & Inzunza Gonzalez, E. (2025). Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review. Applied Sciences, 15, 1. https://doi.org/10.3390/app15073492
Noda, S., Iwata, N., & Korenaga, M. (2024). Improving the Rapidity of Magnitude Estimation for Earthquake Early Warning Systems for Railways. Sensors, 24(22), 7361. https://doi.org/10.3390/s24227361
Pasten, D., Saravia, G., Vogel, E., & Posadas, A. (2022, 12). Information theory and earthquakes: Depth propagation seismicity in northern Chile. Chaos, Solitons and Fractals, 165(2), 112874. https://doi.org/10.1016/j.chaos.2022.112874
Sarangpure, N., Dhamde, V., Roge, A., Doye, J., Patle, S., & Tamboli, S. (2023). Automating the Machine Learning Process using PyCaret and Streamlit. 2023 2nd International Conference for Innovation in Technology (INOCON), (pp. 1-5). Bangalore, India. https://doi.org/10.1109/INOCON57975.2023.10101357
Shen, X., Hou, B., Lu, J., & Li, S. (2025). Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude. Applied Sciences, 15(5), 2587. https://doi.org/10.3390/app15052587
Temneanu, M. C., Donciu, C., & Serea, E. (2025). Self-Contained Earthquake Early Warning System Based on Characteristic Period Computed in the Frequency Domain. Applied Sciences, 15(16), 9026. https://doi.org/10.3390/app15169026
Turino, T., Saputro, R., & Karyono, G. (2025). Comparative Analysis of Decision Tree, Random Forest, Svm, and Neural Network Models for Predicting Earthquake Magnitude. Jurnal Teknik Informatika (Jutif), 6, 755-774. https://doi.org/10.52436/1.jutif.2025.6.2.2378
Universidad de Chile. (2012). Red Sismológica Nacional (RSN) (Network code C1). https://doi.org/https://www.fdsn.org/networks/detail/C1/
Universidad de Chile, Dept. de Geofísica (DGF). (1991). C: Chilean National Seismic Network. https://www.fdsn.org/networks/detail/C/
USGS Earthquake Catalog. (2025). United States Geological Survey. https://earthquake.usgs.gov/earthquakes/search/
Wang, Y., Li, X., Wang, Z., & Liu, J. (2023). Deep learning for magnitude prediction in earthquake early warning,. Gondwana Research, 123, 164-173. https://doi.org/10.1016/j.gr.2022.06.009
Wibisono, A., & Naufal Rahmadika, R. (2025). Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications. Jurnal Ilmu Komputer Dan Informasi, 18(2), 251–259. https://doi.org/10.21609/jiki.v18i2.1539
Wu, Y.-M., Mittal, H., Lin, Y.-H., & Chang, Y.-H. (2023). Magnitude determination using cumulative absolute absement for earthquake early warning. Geoscience Letters, 10(1), 1. https://doi.org/10.1186/s40562-023-00314-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Revista Geográfica de Chile Terra Australis
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright notice:
Revista Geográfica de Chile Terra Australis retains the economic rights (copyright) of the works published in the Revista Geográfica de Chile Terra Australis. The reuse of the content is allowed under a license:
CC BY
Recognition
This license allows others to distribute, remix, tweak and build upon your work, even for commercial purposes, as long as you are acknowledged as the author of the original creation. This is the most helpful license offered. It is recommended for maximum dissemination and use of the materials subject to the license.
