Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake

The earthquake-alarm model developed by Chen and Chen (Nat. Hazards 2016) is investigated to validate its forecasting performance for the 2016/2/6, ML 6.6 Meinong, Taiwan earthquake. This alarm model is based on geoelectric field skewness and kurtosis anomalies. The model parameters, such as the det...

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Main Authors: Hong-Jia Chen, Chien-Chih Chen, Guy Ouillon, Didier Sornette
Format: Article
Language:English
Published: Chinese Geoscience Union 2017-01-01
Series:Terrestrial, Atmospheric and Oceanic Sciences
Online Access: http://tao.cgu.org.tw/media/k2/attachments/v285p745.pdf
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spelling doaj-5cc4d136845f4e3d9853e717752bb40f2020-11-24T21:26:09ZengChinese Geoscience UnionTerrestrial, Atmospheric and Oceanic Sciences1017-08392311-76802017-01-0128574576110.3319/TAO.2016.11.01.01Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan EarthquakeHong-Jia ChenChien-Chih ChenGuy OuillonDidier SornetteThe earthquake-alarm model developed by Chen and Chen (Nat. Hazards 2016) is investigated to validate its forecasting performance for the 2016/2/6, ML 6.6 Meinong, Taiwan earthquake. This alarm model is based on geoelectric field skewness and kurtosis anomalies. The model parameters, such as the detection range and predicted time window, allow us to estimate the empirical relationships between geoelectric anomalies and large earthquakes. As a result, the skewness and kurtosis anomalies are shown to appear before the Meinong earthquake on the four neighboring stations (LIOQ, WANL, KAOH, and CHCH). According to the model analysis a time lag exists between anomaly clusters and earthquakes, depending on local geological features, as well as the durations over which anomalies are continuously observed, which might also display time dependence. In conclusion, this alarm model is able to correlate earthquakes and geoelectrical anomalies, with promising usefulness in forecasting large earthquakes. http://tao.cgu.org.tw/media/k2/attachments/v285p745.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Hong-Jia Chen
Chien-Chih Chen
Guy Ouillon
Didier Sornette
spellingShingle Hong-Jia Chen
Chien-Chih Chen
Guy Ouillon
Didier Sornette
Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
Terrestrial, Atmospheric and Oceanic Sciences
author_facet Hong-Jia Chen
Chien-Chih Chen
Guy Ouillon
Didier Sornette
author_sort Hong-Jia Chen
title Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
title_short Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
title_full Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
title_fullStr Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
title_full_unstemmed Using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ML 6.6 Meinong, Taiwan Earthquake
title_sort using geoelectric field skewness and kurtosis to forecast the 2016/2/6, ml 6.6 meinong, taiwan earthquake
publisher Chinese Geoscience Union
series Terrestrial, Atmospheric and Oceanic Sciences
issn 1017-0839
2311-7680
publishDate 2017-01-01
description The earthquake-alarm model developed by Chen and Chen (Nat. Hazards 2016) is investigated to validate its forecasting performance for the 2016/2/6, ML 6.6 Meinong, Taiwan earthquake. This alarm model is based on geoelectric field skewness and kurtosis anomalies. The model parameters, such as the detection range and predicted time window, allow us to estimate the empirical relationships between geoelectric anomalies and large earthquakes. As a result, the skewness and kurtosis anomalies are shown to appear before the Meinong earthquake on the four neighboring stations (LIOQ, WANL, KAOH, and CHCH). According to the model analysis a time lag exists between anomaly clusters and earthquakes, depending on local geological features, as well as the durations over which anomalies are continuously observed, which might also display time dependence. In conclusion, this alarm model is able to correlate earthquakes and geoelectrical anomalies, with promising usefulness in forecasting large earthquakes.
url http://tao.cgu.org.tw/media/k2/attachments/v285p745.pdf
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