Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011

On 11 March 2011 at 14:46:23 LT, the 4th largest earthquake ever recorded with a magnitude of 9.0 occurred near the northeast coast of Honshu in Japan (38.322<sup>°</sup> N, 142.369<sup>°</sup> E, Focal depth 29.0 km). In order to acknowledge the capabilities of Total Electron Content (TEC) ionospheric precursor, in this study four methods including mean, median, wavelet transform, and Kalman filter have been applied to detect anomalous TEC variations concerning the Tohoku earthquake. The duration of the TEC time series dataset is 49 days at a time resolution of 2 h. All four methods detected a considerable number of anomalous occurrences during 1 to 10 days prior to the earthquake in a period of high geomagnetic activities. In this study, geomagnetic indices (i.e. <i>D</i><sub>st</sub>, <i>K</i><sub><i>p</i></sub>, <i>A</i><sub><i>p</i></sub> and <i>F</i>10.7) were used to distinguish pre-earthquake anomalies from the other anomalies related to the geomagnetic and solar activities. A good agreement in results was found between the different applied anomaly detection methods on TEC data

Thermal anomalies detection before strong earthquakes (<i>M</i> > 6.0) using interquartile, wavelet and Kalman filter methods

Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT) data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected, indicating that the anomalous behaviors can be related to impending earthquakes. The proposed method receives its credibility from the overall capabilities of the three integrated methods

FUSION OF LANDSAT- 8 THERMAL INFRARED AND VISIBLE BANDS WITH MULTI-RESOLUTION ANALYSIS CONTOURLET METHODS

Land surface temperature image is an important product in many lithosphere and atmosphere applications. This image is retrieved from the thermal infrared bands. These bands have lower spatial resolution than the visible and near infrared data. Therefore, the details of temperature variation can't be clearly identified in land surface temperature images. The aim of this study is to enhance spatial information in thermal infrared bands. Image fusion is one of the efficient methods that are employed to enhance spatial resolution of the thermal bands by fusing these data with high spatial resolution visible bands. Multi-resolution analysis is an effective pixel level image fusion approach. In this paper, we use contourlet, non-subsampled contourlet and sharp frequency localization contourlet transform in fusion due to their advantages, high directionality and anisotropy. The absolute average difference and RMSE values show that with small distortion in the thermal content, the spatial information of the thermal infrared and the land surface temperature images is enhanced

FUSION OF NON-THERMAL AND THERMAL SATELLITE IMAGES BY BOOSTED SVM CLASSIFIERS FOR CLOUD DETECTION

The goal of ensemble learning methods like Bagging and Boosting is to improve the classification results of some weak classifiers gradually. Usually, Boosting algorithms show better results than Bagging. In this article, we have examined the possibility of fusion of non-thermal and thermal bands of Landsat 8 satellite images for cloud detection by using the boosting method. We used SVM as a base learner and the performance of two kinds of Boosting methods including AdaBoost.M1 and σ Boost was compared on remote sensing images of Landsat 8 satellite. We first extracted the co-occurrence matrix features of non-thermal and thermal bands separately and then used PCA method for feature selection. In the next step AdaBoost.M1 and σ Boost algorithms were applied on non-thermal and thermal bands and finally, the classifiers were fused using majority voting. Also, we showed that by changing the regularization parameter (C) the result of σ Boost algorithm can significantly change and achieve overall accuracy and cloud producer accuracy of 74%, and 0.53 kappa coefficient that shows better results in comparison to AdaBoost.M1

A QUICK SEASONAL DETECTION AND ASSESSMENT OF INTERNATIONAL SHADEGAN WETLAND WATER BODY EXTENT USING GOOGLE EARTH ENGINE CLOUD PLATFORM

Understanding the variation of Water Extent (WE) can provide insights into Wetland conservation and management. In this study, and-inter inner-annual variations of WE were analyzed during 2019–2021 to understand the spatiotemporal changes of the International Shadegan Wetland, Iran. We utilized a thresholding process on Modified Normalized Difference Water Index (MNDWI) to extract the WE quickly and accurately using the Google Earth Engine (GEE) platform. The water surface analysis showed that: (1) WE had a downward trend from 2019 to 2021, with the overall average WE being 1405.23&thinsp;km2; (2) the water area reached its peak due to the water supply to International Shadegan Wetland through the Jarahi River and upstream reservoirs at the end of 2019 and the beginning of 2020, and the largest water body appeared in Winter 2019, reaching 1953.31&thinsp;km2. In contrast, the smallest water body appeared in Autumn 2021, reaching 563.56&thinsp;km2; (3) The WE of the wetland showed predictable seasonal characteristics. The water area in Winter was the largest, with an average value of 1829.1&thinsp;km2, while it was the smallest in Summer, with an average value of 1100.3&thinsp;km2; (4) The average water area in 2019 was 1490.5&thinsp;km2 whereas in 2020 and 2021 decreased by 9% and 25%, respectively, and reached 968.6&thinsp;km2 and 811.9&thinsp;km2. Finally, to evaluate the proposed model, its results were compared with the Random Forest (RF) classification results. Accordingly, Histogram Analysis (HA) classification achieved 94.6% of the average overall accuracy and the average Kappa coefficient of 0.93, but the RF method obtained 95.38% of the average overall accuracy and an average Kappa coefficient of 0.94

A combined estimator using TEC and b-value for large earthquake prediction

[EN] Ionospheric anomalies have been shown to occur a few days before several large earthquakes. The published works normally address examples limited in time (a single event or few of them) or space (a particular geographic area), so that a clear method based on these anomalies which consistently yields the place and magnitude of the forthcoming earthquake, anytime and anywhere on earth, has not been presented so far. The current research is aimed at prediction of large earthquakes, that is with magnitude M-w 7 or higher. It uses as data bank all significant earthquakes occurred worldwide in the period from January 1, 2011 to December 31, 2018. The first purpose of the research is to improve the use of ionospheric anomalies in the form of TEC grids for earthquake prediction. A space-time TEC variation estimator especially designed for earthquake prediction will show the advantages with respect to the use of simple TEC values. 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LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (M_w&thinsp;=&thinsp;7.5) OF 25 APRIL 2015

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth&thinsp;=&thinsp;15.0 km) a powerful earthquake of Mw&thinsp;=&thinsp;7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection

Support vector machines for TEC seismo-ionospheric anomalies detection

Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs) are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC) time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011), Haiti (12 January 2010) and Samoa (29 September 2009). The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU) at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012) obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method to detect the novelty changes of a nonlinear time series such as variations of earthquake precursors

Novelty detection in time series of ULF magnetic and electric components obtained from DEMETER satellite experiments above Samoa (29 September 2009) earthquake region

Using ULF (ultra low frequency) measurements of magnetometer and ICE (Instrument Champ Electrique) experiments on board the DEMETER satellite, possible irregularities in ULF magnetic and electric components have been surveyed in the vicinity of Samoa (29 September 2009) earthquake region. The data used in this paper cover the period from 1 August 2009 to 11 October 2009. The anomalous variations in magnetic components (&lt;i&gt;B&lt;/i&gt;&lt;sub&gt;x&lt;/sub&gt;, &lt;i&gt;B&lt;/i&gt;&lt;sub&gt;y&lt;/sub&gt; and &lt;i&gt;B&lt;/i&gt;&lt;sub&gt;z&lt;/sub&gt;) were clearly observed on 1 and 3 days before the event. It is seen that the periodic patterns of the magnetic components obviously changed prior to the earthquake. These unusual variations have been also observed in the variations of polarization index obtained from the magnetic components during the whole period at ~10:30 and ~22:30 LT. It is concluded that the polarization exhibits an apparent increase on 1 and 3 days preceding the earthquake. These observed unusual disturbances in ULF magnetic components were acknowledged using the detected perturbations in ULF electric components (&lt;i&gt;E&lt;/i&gt;&lt;sub&gt;x&lt;/sub&gt;, &lt;i&gt;E&lt;/i&gt;&lt;sub&gt;y&lt;/sub&gt; and &lt;i&gt;E&lt;/i&gt;&lt;sub&gt;z&lt;/sub&gt;) in the geomagnetic coordinate system. Finally, the results reported in this paper were compared with previous results for this Samoa earthquake. Hence, the detected anomalies resulting from the magnetometer and ICE ULF waveforms in quiet geomagnetic conditions could be regarded as seismo-ionospheric precursors

A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (<i>M</i>_w = 6.4)

In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (<i>M</i>_w = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods