Type of publication
Publication in Conference Proceedings/Workshop
Year of publication
Springer - 6th International Conference on Machine Learning, Optimization, and Data Science

David Llácer, Beatriz Otero, Rubén Tous, Marisol Monterrubio-Velasco, José Carlos Carrasco-Jiménez and Otilio Rojas


Llácer, D. [et al.]. Random forest parameterization for earthquake catalog generation. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020: Siena, Italy, July 19-23, 2020: revised selected papers, part I". Berlín: Springer, 2020, p. 233-243. ISBN 978-3-030-64583-0. DOI 10.1007/978-3-030-64583-0_22. 

Short summary
An earthquake is the vibration pattern of the Earth’s crust induced by the sliding of geological faults. They are usually recorded for later studies. However, strong earthquakes are rare, small-magnitude events may pass unnoticed and monitoring networks are limited in number and efficiency. Thus, earthquake catalog are incomplete and scarce, and researchers have developed simulators of such catalogs. In this work, we start from synthetic catalogs generated with the TREMOL-3D software. TREMOL-3D is a stochastic-based method to produce earthquake catalogs with different statistical patterns, depending on certain input parameters that mimics physical parameters. When an appropriate set of parameters are used, TREMOL-3D could generate synthetic catalogs with similar statistical properties observed in real catalogs. However, because of the size of the parameter space, a manual searching becomes unbearable. Therefore, aiming at increasing the efficiency of the parameter search, we here implement a Machine Learning approach based on Random Forest classification, for an automatic parameter screening. It has been implemented using the machine learning Python’s library SciKit Learn.