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

2011 ◽  
Vol 11 (4) ◽  
pp. 1099-1108 ◽  
Author(s):  
M. R. Saradjian ◽  
M. Akhoondzadeh
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Wavelet Transform ◽  
Anomaly Detection ◽  
Land Surface ◽  
Seasonal Effects ◽  
Filter Method ◽  
Strong Earthquakes ◽  
Thermal Anomalies ◽  
Filter Methods

Abstract. 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.

scholarly journals Fault distance-based approach in thermal anomaly detection before strong Earthquakes

2020 ◽  
Author(s):  
Arash Karimi Zarchi ◽  
Mohammad Reza Saradjian Maralan
Keyword(s):  
Anomaly Detection ◽  
Land Surface ◽  
Input Data ◽  
Active Faults ◽  
Thermal Anomaly ◽  
Detection Methods ◽  
False Alarms ◽  
Earthquake Intensity ◽  
Strong Earthquakes ◽  
Thermal Anomalies

Abstract. The recent scientific studies in the context of earthquake precursors reveal some processes connected to seismic activity including thermal anomaly before earthquakes which is a great help for making a better decision regarding this disastrous phenomenon and reducing its casualty to a minimum. This paper represents a method for grouping the proper input data for different thermal anomaly detection methods using the land surface temperature (LST) mean in multiple distances from the corresponding fault during the 40 days (i.e. 30 days before and 10 days after impending earthquake) of investigation. Six strong earthquakes with Ms > 6 that have occurred in Iran have been investigated in this study. We used two different approaches for detecting thermal anomalies. They are mean-standard deviation method also known as standard method and interquartile method which is similar to the first method but uses different parameters as input. Most of the studies have considered thermal anomalies around the known epicentre locations where the investigation can only be performed after the earthquake. This study is using fault distance-based approach in predicting the earthquake regarding the location of the faults as the potential area. This could be considered as an important step towards actual prediction of earthquake’s time and intensity. Results show that the proposed input data produces less false alarms in each of the thermal anomaly detection methods compared to the ordinary input data making this method much more accurate and stable considering the easy accessibility of thermal data and their less complicated algorithms for processing. In the final step, the detected anomalies are used for estimating earthquake intensity using Artificial Neural Network (ANN). The results show that estimated intensities of most earthquakes are very close to the actual intensities. Since the location of the active faults are known a priori, using fault distance-based approach may be regarded as a superior method in predicting the impending earthquakes for vulnerable faults. In spite of the previous investigations that the studies were only possible aftermath, the fault distance-based approach can be used as a tool for future unknown earthquakes prediction. However, it is recommended to use thermal anomaly detection as an initial process to be jointly used with other precursors to reduce the number of investigations that require more complicated algorithms and data processing.

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

scholarly journals Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 753 ◽  
Author(s):  
Guodong Zhang ◽  
Hongmin Zhou ◽  
Changjing Wang ◽  
Huazhu Xue ◽  
Jindi Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
High Resolution ◽  
Spatial Resolution ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Surface Albedo ◽  
Data Sets ◽  
Broadleaf Forest ◽  
Kalman Filter Algorithm

Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo“ approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model . The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.

scholarly journals Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

2021 ◽  
Vol 2123 (1) ◽  
pp. 012044
Author(s):  
Sukarna ◽  
Elma Yulia Putri Ananda ◽  
Maya Sari Wahyuni
Keyword(s):  
Kalman Filter ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Data ◽  
Percentage Error ◽  
Filter Method ◽  
Rainfall Forecasting ◽  
Forecasting Methods ◽  
Filter Methods ◽  
Kalman Filter Method

Abstract Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

scholarly journals Surface Temperature Multiscale Monitoring by Thermal Infrared Satellite and Ground Images at Campi Flegrei Volcanic Area (Italy)

2019 ◽  
Vol 11 (9) ◽  
pp. 1007 ◽  
Author(s):  
Teresa Caputo ◽  
Eliana Bellucci Sessa ◽  
Malvina Silvestri ◽  
Maria Fabrizia Buongiorno ◽  
Massimo Musacchio ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Environmental Research ◽  
Campi Flegrei ◽  
Landsat 8 ◽  
Volcanic Area ◽  
Thermal Anomalies ◽  
Surface Temperatures

Land Surface Temperature (LST) from satellite data is a key component in many aspects of environmental research. In volcanic areas, LST is used to detect ground thermal anomalies providing a supplementary tool to monitor the activity status of a particular volcano. In this work, we describe a procedure aimed at identifying spatial thermal anomalies in thermal infrared (TIR) satellite frames which are corrected for the seasonal influence by using TIR images from ground stations. The procedure was applied to the volcanic area of Campi Flegrei (Italy) using TIR ASTER and Landsat 8 satellite imagery and TIR ground images acquired from the Thermal Infrared volcanic surveillance Network (TIRNet) (INGV, Osservatorio Vesuviano). The continuous TIRNet time-series images were processed to evaluate the seasonal component which was used to correct the surface temperatures estimated by the satellite’s discrete data. The results showed a good correspondence between de-seasoned time series of surface ground temperatures and satellite temperatures. The seasonal correction of satellite surface temperatures allows monitoring of the surface thermal field to be extended to all the satellite frames, covering a wide portion of Campi Flegrei volcanic area.

scholarly journals Satellite thermal infrared anomalies associated with strong earthquakes in the Vrancea area of Romania

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Maria A. Zoran ◽  
Roxana S. Savastru ◽  
Dan M. Savastru
Keyword(s):  
Land Surface ◽  
Thermal Infrared ◽  
Wave Radiation ◽  
Spatiotemporal Variations ◽  
Outgoing Long Wave Radiation ◽  
Strong Earthquakes ◽  
Thermal Anomalies ◽  
Noaa Avhrr ◽  
Long Wave ◽  
Before And After

AbstractThermal anomalies which are known to be significant precursors of strong earthquakes can be evidenced by spectral thermal infrared (TIR) bands recorded by sensors on board of NOAA-AVHRR and Terra/Aqua- MODIS satellite. In order to locate relevant thermal anomalous variations prior to some strong even moderate earthquakes recorded in Vrancea tectonic active zone in Romania, satellite derived geophysical parameters have been used: land surface temperature (LST), outgoing long-wave radiation (OLR) and mean air temperature (AT). Spatiotemporal variations of LST, OLR, and AT before and after three strong earthquakes in Vrancea area (M

Reliable home error identification of a 2-DOF parallel robot based on regularization methods

2018 ◽  
Vol 233 (7) ◽  
pp. 2502-2515
Author(s):  
Jiangping Mei ◽  
Jiawei Zang ◽  
Yabin Ding
Keyword(s):  
Kalman Filter ◽  
Ridge Regression ◽  
Parallel Robot ◽  
Initial Position ◽  
Regression Method ◽  
Filter Method ◽  
Filter Methods ◽  
Curve Method ◽  
Kalman Filter Method ◽  
Active Link

This paper deals with the home error (the initial position error of active link) identification of a 2-DOF parallel robot. The identification model containing the home errors of the robot and the assembly errors of a new measuring instrument is developed using distance measurement. After that, an adaptive ridge regression method and a regularized Kalman filter method are proposed to improve the identification reliability and accuracy. Particularly, a modified L-curve method is proposed to provide suitable regularization parameters for the regularized Kalman filter. Based on the selected optimal measurement positions, experiments are carried out, in which the two regularized identification methods are compared with the ordinary ridge regression and the Kalman filter methods. Results show that the reliability and accuracy of the two methods are much better than the ordinary ridge regression method, and the divergence problem of the Kalman filter can be well resolved by the regularized Kalman filter.

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

2013 ◽  
Vol 13 (4) ◽  
pp. 1077-1083 ◽  
Author(s):  
M. Akhoondzadeh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Land Surface ◽  
Linear Time ◽  
Parametric Method ◽  
Support Vector ◽  
Night Time ◽  
Data Set ◽  
Thermal Anomalies ◽  
Intelligent Methods

Abstract. 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 (Mw = 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.

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