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

Prediction of the date, magnitude and affected area of impending strong earthquakes using integration of multi precursors earthquake parameters

Usually a precursor alone might not be useful as an accurate, precise, and stand-alone criteria for the earthquake parameters prediction. Therefore it is more appropriate to exploit parameters extracted from a variety of individual precursors so that their simultaneous integration would reduce the parameters's uncertainty. &lt;br&gt;&lt;br&gt; In our previous studies, five strong earthquakes which happened in the Samoa Islands, Sichuan (China), L'Aquila (Italy), Borujerd (Iran) and Zarand (Iran) have been analyzed to locate unusual variations in the time series of the different earthquake precursors. In this study, we have attempted to estimate earthquake parameters using the detected anomalies in the mentioned case studies. &lt;br&gt;&lt;br&gt; Using remote sensing observations, this study examines variations of electron and ion density, electron temperature, total electron content (TEC), electric and magnetic fields and land surface temperature (LST) several days before the studied earthquakes. Regarding the ionospheric precursors, the geomagnetic indices &lt;i&gt;D&lt;/i&gt;&lt;sub&gt;st&lt;/sub&gt; and &lt;i&gt;K&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; were used to distinguish pre-earthquake disturbed states from the other anomalies related to the geomagnetic activities. &lt;br&gt;&lt;br&gt; The inter-quartile range of data was utilized to construct their upper and lower bound to detect disturbed states outsides the bounds which might be associated with impending earthquakes. &lt;br&gt;&lt;br&gt; When the disturbed state associated with an impending earthquake is detected, based on the type of precursor, the number of days relative to the earthquake day is estimated. Then regarding the deviation value of the precursor from the undisturbed state the magnitude of the impending earthquake is estimated. The radius of the affected area is calculated using the estimated magnitude and Dobrovolsky formula. &lt;br&gt;&lt;br&gt; In order to assess final earthquake parameters (i.e. date, magnitude and radius of the affected area) for each case study, the earthquake parameters obtained from different earthquake precursors were integrated. In other words, for each case study using the median and inter-quartile range of earthquake parameters, the bounds of the final earthquake parameters were defined. For each studied case, a close agreement was found between the estimated and registered earthquake parameters

FUSION OF MULTI PRECURSORS EARTHQUAKE PARAMETERS TO ESTIMATE THE DATE, MAGNITUDE AND AFFECTED AREA OF THE FORTHCOMING POWERFUL EARTHQUAKES

Since not any individual precursor can be used as an accurate stand alone means for the earthquake prediction, it is necessary to integrate different kinds of precursors. The precursors selected for analysis in this study include electron and ion density, electron temperature, total electron content (TEC), electric and magnetic fields and land surface temperature (LST) several days before three strong earthquakes which happened in Samoa Islands, Sichuan (China) and Borujerd (Iran). The precursor's variations were monitored using data obtained from experiments onboard DEMETER (IAP, ISL, ICE and IMSC) and Aqua-MODIS satellites. Regarding the ionospheric precursors, the geomagnetic indices Dst and Kp were used to distinguish pre-earthquake disturbed states from the other anomalies related to the geomagnetic activities. The inter-quartile range of data was utilized to construct their upper and lower bound to detect disturbed states outsides the bounds which might be associated with impending earthquakes. When the disturbed state associated with impending earthquake is detected, based on the type of precursor, the number of days relative to earthquake day is estimated. Then regarding the deviation value of the precursor from the undisturbed state the magnitude of impending earthquake is estimated. The radius of the affected area is calculated using the estimated magnitude and Dobrovolsky formula. In order to assess final earthquake parameters (which are date, magnitude and radius of the affected area) for each case study, using the median and inter-quartile range of earthquake parameters obtained from different precursors, the approximate bounds of final earthquake parameters are defined. For each studied case, a good agreement was found between the estimated and registered earthquake parameters

Investigation of VLF and HF waves showing seismo-ionospheric anomalies induced by the 29 September 2009 Samoa earthquake (<i>M</i>_w=8.1)

In Samoa Islands, a powerful earthquake took place at 17:48:10.99 UTC (06:48:10.99 LT) on 29 September 2009 with a magnitude Mw=8.1. Using ICE (Instrument Champ Electrique) and IMSC (Instrument Magnetic Search Coil) experiments onboard the DEMETER (Detection of Electromagnetic Emissions Transmitted from Earthquake Regions) satellite we have surveyed possible variations in electromagnetic signals transmitted by the ground-based VLF transmitter NPM in Hawaii and in HF plasma waves close to the Samoa earthquake during the seismic activity. The indices Dst and Kp were used to distinguish pre-earthquake anomalies from the other anomalies related to the geomagnetic activities. In a previous study we have shown that anomalies in IAP (plasma analyzer) and ISL (Langmuir probe) experiments onboard the DEMETER and also TEC (Total Electron Content) data appear 1 to 5 days before the Samoa earthquake. In this paper we show that the anomalies in the VLF transmitter signal and in the HF range appear with the same time scale. The lack of significant geomagnetic activities indicates that these anomalous behaviors could be regarded as seismo-ionospheric precursors. It is also shown that comparative analysis is more effective in seismo-ionospheric studies

TREND ASSESSMENT OF SPATIO-TEMPORAL CHANGE OF TEHRAN HEAT ISLAND USING SATELLITE IMAGES

Numerous investigations on Urban Heat Island (UHI) show that land cover change is the main factor of increasing Land Surface Temperature (LST) in urban areas, especially conversion of vegetation and bare soil to concrete, asphalt and other man-made structures. On the other hand, other human activities like those which cause to burning fossil fuels, that increase the amount of carbon dioxide, may raise temperature in global scale in comparison with small scales (urban areas). In this study, multiple satellite images with different spatial and temporal resolutions have been used to determine Land Surface Temperature (LST) variability in Tehran metropolitan area. High temporal resolution of AVHRR images have been used as the main data source when investigating temperature variability in the urban area. The analysis shows that UHI appears more significant at afternoon and night hours. But the urban class temperature is almost equal to its surrounding vegetation and bare soil classes at around noon. It also reveals that there is no specific difference in UHI intense during the days throughout the year. However, it can be concluded that in the process of city expansion in years, UHI has been grown both spatially and in magnitude. In order to locate land-cover types and relate them to LST, Thematic Mapper (TM) images have been exploited. The influence of elevation on the LST has also been studied, using digital elevation model derived from SRTM database

LEAST SQUARE APPROACH FOR ESTIMATING OF LAND SURFACE TEMPERATURE FROM LANDSAT-8 SATELLITE DATA USING RADIATIVE TRANSFER EQUATION

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data

Women who Sexually Offend Display Three Main Offense Styles: A Re-Examination of the Descriptive Model of Female Sexual Offending

This study examined a theory constructed to describe the offense process of women who sexually offend-the Descriptive Model of Female Sexual Offending (DMFSO). In particular, this report sets out to establish whether the original three pathways (or offending styles) identified within United Kingdom convicted female sexual offenders and described within the DMFSO (i.e., Explicit-Approach, Directed-Avoidant, Implicit-Disorganized) were applicable to a small sample (N = 36) of North American women convicted of sexual offending. Two independent raters examined the offense narratives of the sample and-using the DMFSO-coded each script according to whether it fitted one of the three original pathways. Results suggested that the three existing pathways of the DMFSO represented a reasonable description of offense pathways for a sample of North American women convicted of sexual offending. No new pathways were identified. A new "Offense Pathway Checklist" devised to aid raters' decision making is described and future research and treatment implications explored

A PROBABILITY MODEL FOR DROUGHT PREDICTION USING FUSION OF MARKOV CHAIN AND SAX METHODS

Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data