J. Reyes

ORCID: 0000-0002-2831-1148
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About
Contact & Profiles
Research Areas
  • earthquake and tectonic studies
  • Earthquake Detection and Analysis
  • Seismology and Earthquake Studies
  • Geological and Geophysical Studies Worldwide
  • Seismic Imaging and Inversion Techniques
  • Drilling and Well Engineering
  • Geotechnical Engineering and Soil Stabilization
  • Geological and Tectonic Studies in Latin America
  • Time Series Analysis and Forecasting
  • Geological and Geochemical Analysis
  • Geotechnical Engineering and Analysis
  • Dam Engineering and Safety
  • Seismic Waves and Analysis

Universidad de Santiago de Chile
2016

Universidad de Los Andes, Chile
2008-2010

A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas spread and low activity. Traditional zoning methods are based on the available catalog geological structures. It admitted that thermal resistant parameters crust provide better criteria zoning. Nonetheless, working out rheological profiles causes great uncertainty. This has generated inconsistencies, as different have been proposed same area. new method by means triclustering in...

10.3390/e17075000 article EN Entropy 2015-07-16

10.1007/s00521-010-0373-9 article EN Neural Computing and Applications 2010-05-10

Abstract Increasing attention has been paid to the prediction of earthquakes with data mining techniques during last decade. Several works have already proposed use certain features serving as inputs for supervised classifiers. However, they successfully used without any further transformation so far. In this work, principal component analysis (PCA) reduce dimensionality and generate new datasets is proposed. particular, step inserted in a methodology predict earthquakes. Tokyo, one cities...

10.1093/jigpal/jzx049 article EN Logic Journal of IGPL 2017-09-29

A study of seismic regionalization for central Chile based on a neural network is presented. scenario with six regions obtained, independently the size neighborhood or reach correlation between cells grid. The high spatial distribution zones and geographical data confirm our election training vectors network.

10.48550/arxiv.0812.1198 preprint EN other-oa arXiv (Cornell University) 2008-01-01
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