2014
DOI: 10.1007/s11069-014-1519-3
|View full text |Cite
|
Sign up to set email alerts
|

Seismo-ionospheric precursory anomalies detection from DEMETER satellite data based on data mining

Abstract: Widespread researches and studies on earthquake prediction show that seismoionospheric disturbances can be observed over seismic regions before an earthquake. However, it is still hard to detect accurate pre-seismic ionosphere anomaly and use them to predict earthquake. To solve the problem, we propose a method that can extract the feature of pre-seismic ionospheric anomalies based on data mining. The main theme of this method can be described as follows: First, we mine frequent itemsets from pre-seismic ionos… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
7
1
1

Relationship

0
9

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 3 publications
0
5
0
Order By: Relevance
“…Wang et al [37] applied heavy data mining to large data sets of DEMETER from 1 January 2008 to 30 June 2008, in order to test the accuracy of this method as a possible earthquake prediction tool. The main result is that the prediction accuracy reaches 0.7 when both the electron density and temperature from DEMETER are considered as featuring the pre-seismic ionosphere anomalies.…”
Section: Introductionmentioning
confidence: 99%
“…Wang et al [37] applied heavy data mining to large data sets of DEMETER from 1 January 2008 to 30 June 2008, in order to test the accuracy of this method as a possible earthquake prediction tool. The main result is that the prediction accuracy reaches 0.7 when both the electron density and temperature from DEMETER are considered as featuring the pre-seismic ionosphere anomalies.…”
Section: Introductionmentioning
confidence: 99%
“…Second, differing from the traditional statistics and data mining methods, a more advanced machine learning architecture is used for pre-earthquake perturbation analysis. Last, most of the existing methods are validated by a small number of samples, such as Wang et al [76], which may have a strong bias toward the values in a larger area or a longer time interval. However, LightGBM is applied to different datasets, whereas global features are learned during the training process.…”
Section: Discussionmentioning
confidence: 99%
“…In [43], the "Lithosphere-atmosphere-ionosphere coupling" system was proposed to find convincing evidence of the ionosphere effect. At present, there are three main pathways of Lithosphere-Atmosphere-Ionosphere Coupling [44][45][46][47][48][49][50]: chemical, electromagnetic, and acoustic. The chemical pathway mainly refers to the influence of changes in geochemical parameters (gas or radon diffusion, change of water level, etc.,) on the ionosphere.…”
Section: Ionospheric Structure Anomaly Before Earthquakementioning
confidence: 99%