| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560336 |
| ISSN: | 1405-5546 |
| Autors: | Rojas, Otilio1 Otero, Beatriz3 Alvarado, Leonardo2 Mus, Sergi3 Tous, Rubén3 |
| Institucions: | 1Barcelona Supercomputing Center, Barcelona. España 2Universidad Central de Venezuela, Caracas. Venezuela 3Universitat Politécnica de Catalunya, Barcelona. España |
| Any: | 2019 |
| Període: | Abr-Jun |
| Volum: | 23 |
| Número: | 2 |
| Paginació: | 335-350 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | P and S seismic waves, Earthquake hypocenters, Supervised, Unsupervised and semisupervised, Deep and convolutional neural networks, Training and testing data sets, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
