Thermal anomalies detection before Saravan earthquake (April 16th, 2013, MW = 7.8) using time series method, satellite, and meteorological data

Khalili, Mehdi; Eskandar, Seyed Sabereh Abdollahi; Panah, Seyed Kazem Alavi

doi:10.1007/s12040-019-1286-3

Cited by 9 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Temperature anomaly was well identified before and after the earthquake in Saravan, Iran by using MODIS-Aqua satellite imagery (Khalili et al, 2020). The research compared the surface temperature before and after earthquake and explained the increased temperature on 3-7 days after earthquake (Khalili et al, 2020). That increasing was only triggered by the earthquake, not by seasonal weather pattern (Khalili et al, 2020).…”

Section: Introductinmentioning

confidence: 95%

“…between SST before and SST during the earthquake. Temperature anomaly was well identified before and after the earthquake in Saravan, Iran by using MODIS-Aqua satellite imagery (Khalili et al, 2020). The research compared the surface temperature before and after earthquake and explained the increased temperature on 3-7 days after earthquake (Khalili et al, 2020).…”

Section: Introductinmentioning

confidence: 96%

“…The research compared the surface temperature before and after earthquake and explained the increased temperature on 3-7 days after earthquake (Khalili et al, 2020). That increasing was only triggered by the earthquake, not by seasonal weather pattern (Khalili et al, 2020).…”

Section: Introductinmentioning

confidence: 99%

See 2 more Smart Citations

Identification of Sea Surface Temperature Anomaly during Earthquake in Southern Java Island using Google Earth Engine Datasets

Rusydi

Masitoh

2023

IJG

View full text Add to dashboard Cite

The certainty of Sea Surface Temperature (SST) anomaly during earthquakes in Java Island was still not clear identified. This research tried to identify any possibilities of SST anomaly during earthquake in Southern Java Island. It used Google Earth Engine datasets including daily NOAA Climate Data Record (CDR) - Woods Hole Oceanographic Institution (WHOI): Sea Surface Temperature Version 2 which has resolution of 0.25 arc degrees for 20 years and processed them to depict SST trends in pre-earthquake and post-earthquake situation. The statistic test of t-Test Paired Two Sample was also applied to certain the significant difference of SST in both situations. SST fluctuation during the earthquake was still inconsistent and unclear to consider as an anomaly. There were other complicated factors excluding earthquake to influence SST fluctuation. The earthquake did not trigger directly to fluctuate SST, even not to make it being an anomaly.

show abstract

Section: Introductinmentioning

confidence: 95%

Section: Introductinmentioning

confidence: 96%

See 1 more Smart Citation

Identification of Sea Surface Temperature Anomaly during Earthquake in Southern Java Island using Google Earth Engine Datasets

Rusydi

Masitoh

2023

IJG

View full text Add to dashboard Cite

show abstract

“…The software system for foreshadow observation data monitoring and automatic anomaly identification was developed in Visual Basic 6.0 language, which is flexible, efficient and practical [16]. The system implements the download and format conversion functions for analog precursor data files.…”

Section: Abnormal Automatic Identification System Designmentioning

confidence: 99%

Application of Intelligent Optimization Algorithms to the Design of Automatic Generation of Software Tests for Data Anomaly Identification

Zhou

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper firstly constructs a system for monitoring precursor observation data and automatic identification of anomalies, designs the functions of downloading and format conversion of precursor data files and downloading and decompression of compressed files, and adopts the interpolation method to pre-process the precursor observation data. Secondly, a method based on the SURF intelligent optimization algorithm is adopted to automatically recognize and classify anomalous data from seismic big data. Finally, experiments were carried out to predict short-acuity earthquakes, and the experimental results were analyzed and contrasted. The results show that the accuracy of 2-day prediction and 4-day prediction gradually stabilizes at 0.728~0.785 when the training reaches 100 steps and gradually decreases and reaches about 0.65 with the increase of the prediction time period, which verifies the feasibility and practicality of the system. The research in this paper provides a new idea and method for earthquake precursor data anomaly identification, which has certain research and application value.

show abstract

“…Analysis of the thermal anomaly phenomenon using satellite techniques for monitoring seismically active regions has been carried out since the 1980s (Gorny et al, 1988). Many studies have reported that thermal anomalous space-time transients were detected in the OLR or thermal infrared radiation (TIR) before and/or during the earthquake preparation stage (Tronin, 1996(Tronin, , 2000Tramutoli et al, 2001Tramutoli et al, , 2005Tramutoli et al, , 2009Tramutoli et al, , 2013Di Bello et al, 2004;Ouzounov and Freund, 2004;Ouzounov et al, 2006Ouzounov et al, , 2007Pulinets et al, 2006;Genzano et al, 2007Genzano et al, , 2009Genzano et al, , 2010Genzano et al, , 2015Pulinets and Dunajeck, 2007;Aliano et al, 2008a,b;Pergola et al, 2010;Xiong et al, 2010Xiong et al, , 2013Xiong et al, , 2015Pulinets and Ouzounov, 2011;Jing et al, 2012;Lisi et al, 2015;Xiong and Shen, 2017;Khalili et al, 2019Khalili et al, , 2020. The main mechanisms proposed for the temperature increase are (1) the emission of diffuse gases (e.g., radon and CO 2 ) leading to a local greenhouse effect or near-ground air ionization to release heat (Tronin, 2002;Pulinets et al, 2006;Tramutoli et al, 2009Tramutoli et al, , 2013Koll and Cronin, 2018) and (2) positive hole charge or the excitation of an electric field from the stressed rock resulting in infrared emission and energy flow (Ouzounov and Freund, 2004;Freund, 2007a,b;Freun...…”

Section: Introductionmentioning

confidence: 99%

Earth’s Outgoing Longwave Radiation Variability Prior to M ≥6.0 Earthquakes in the Taiwan Area During 2009–2019

Lee

Ouzounov

et al. 2020

Front. Earth Sci.

View full text Add to dashboard Cite

This paper proposes an analysis method, using the National Oceanic and Atmospheric Administration satellite data, to trace variations in outgoing longwave radiation (OLR) for finding the precursors of earthquakes. The significance of these observations is investigated using data sets of recent M ≥6.0 earthquakes around the Taiwan area from 2009 to 2019. We suggest that the precursory signal could be an E Index anomaly (EA) in the form of substantial thermal releases distributed near the epicenter. The consecutive appearances of OLR EAs are observed as precursors 2-15 days before significant earthquakes, and we refer to this as a pre-earthquake OLR E Index anomaly (POEA). We interpret these thermal sources as possibly originating from electromagnetics together with gas emissions associated with pre-seismic processes. This study highlights the potential of OLR anomalous changes in earthquake precursor studies, at least in the Taiwan region.

show abstract

Thermal anomalies detection before Saravan earthquake (April 16th, 2013, MW = 7.8) using time series method, satellite, and meteorological data

Cited by 9 publications

References 36 publications

Identification of Sea Surface Temperature Anomaly during Earthquake in Southern Java Island using Google Earth Engine Datasets

Identification of Sea Surface Temperature Anomaly during Earthquake in Southern Java Island using Google Earth Engine Datasets

Application of Intelligent Optimization Algorithms to the Design of Automatic Generation of Software Tests for Data Anomaly Identification

Earth’s Outgoing Longwave Radiation Variability Prior to M ≥6.0 Earthquakes in the Taiwan Area During 2009–2019

Contact Info

Product

Resources

About