Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan (doi:10.21979/N9/JSUTCD)

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Part 2: Study Description
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Document Description

Citation

Title:

Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan

Identification Number:

doi:10.21979/N9/JSUTCD

Distributor:

DR-NTU (Data)

Date of Distribution:

2021-06-01

Version:

1

Bibliographic Citation:

Cheong, Siew Ann, 2021, "Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan", https://doi.org/10.21979/N9/JSUTCD, DR-NTU (Data), V1

Study Description

Citation

Title:

Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan

Identification Number:

doi:10.21979/N9/JSUTCD

Authoring Entity:

Cheong, Siew Ann (Nanyang Technological University)

Software used in Production:

Python

Grant Number:

MOST 110-2634-F-008-008

Grant Number:

MOST 110-2634-F-008-008

Distributor:

DR-NTU (Data)

Access Authority:

Cheong, Siew Ann

Depositor:

Cheong, Siew Ann

Date of Deposit:

2021-06-01

Holdings Information:

https://doi.org/10.21979/N9/JSUTCD

Study Scope

Keywords:

Earth and Environmental Sciences, Earth and Environmental Sciences, GEMS, Geoelectric field, HMM

Abstract:

(C, V, S, K) index time series data generated using 0.5-Hz GEMS time series data from Taiwan. In this data set, C is a modified autocorrelation function, V is the variance, S the skewness, and K the kurtosis of the GEMS geo-electric field time series.

Kind of Data:

Pickled Python files

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Identification Number:

10.5194/nhess-2021-378

Bibliographic Citation:

Wen, H., Chen, H. J., Chen, C. C., Ciamarra, M. P., & Cheong, S. A. (2022). Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan. Natural Hazards and Earth System Sciences Discussions, 2022, 1-35.

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