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.

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.

Autonomous Detection of Thermal Anomalies in Data Centers

2009 ◽  
Author(s):  
Manish Marwah ◽  
Ratnesh K. Sharma ◽  
Wilfredo Lugo
Keyword(s):  
Anomaly Detection ◽  
Data Center ◽  
Data Centers ◽  
Principal Component ◽  
Thermal Anomaly ◽  
Real Data ◽  
Detection Methods ◽  
Large Temperature ◽  
Efficient Operation ◽  
Thermal Anomalies

In recent years, there has been a significant growth in number, size and power densities of data centers. A significant part of data center power consumption is attributed to the cooling infrastructure, consisting of computer air conditioning units (CRACs), chillers and cooling towers. For energy efficient operation and management of the cooling resources, data centers are beginning to be extensively instrumented with temperature sensors. While this allows cooling actuators, such as CRAC set point temperature, to be dynamically controlled and data centers operated at higher temperatures to save energy, it also increases chances of thermal anomalies. Furthermore, considering that large data centers can contain thousands to tens of thousands of such sensors, it is virtually impossible to manually inspect and analyze the large volumes of dynamic data generated by these sensors, thus necessitating autonomous mechanisms for thermal anomaly detection. Also, in addition to threshold-based detection methods, other mechanisms of anomaly detection are also necessary. In this paper, we describe the commonly occurring thermal anomalies in a data center. Furthermore, we describe — with examples from a production data center — techniques to autonomously detect these anomalies. In particular, we show the usefulness of a principal component analysis (PCA) based methodology to a large temperature sensor network. Specifically, we examine thermal anomalies such as those related to misconfiguration of equipment, blocked vent tiles, faulty sensor and CRAC related anomalies. Furthermore, several of these anomalies normally go undetected since no temperature thresholds are violated. We present examples of the thermal anomalies and their detection from a real data center.

scholarly journals Pre-seismic Thermal Anomalies from Satellite Observations: A Review

2017 ◽  
Author(s):  
Zhong-Hu Jiao ◽  
Jing Zhao ◽  
Xinjian Shan
Keyword(s):  
Anomaly Detection ◽  
Thermal Anomaly ◽  
Global Scale ◽  
Satellite Observations ◽  
Detection Methods ◽  
Thermal Anomalies ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Earth Observation Satellites ◽  
Future Work

Abstract. Detecting thermal anomalies prior to strong earthquakes is a key in understanding and forecasting earthquake activities because of its recognition of thermal radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper dedicatedly reviews the progress and development of pre-seismic thermal anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth’s surface, are discussed. Second, different anomaly detection methods, which are used to extract thermal anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring and improve short- and medium-term forecasting, which should play a large and growing role in pre-seismic thermal anomaly research.

Thermal anomaly detection in datacenters

2011 ◽  
Vol 226 (8) ◽  
pp. 2104-2117 ◽  
Author(s):  
Yang Yuan ◽  
Eun Kyung Lee ◽  
Dario Pompili ◽  
Junbi Liao
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Large Scale ◽  
Thermal Anomaly ◽  
Detection Methods ◽  
Support Vector ◽  
Self Organizing Map ◽  
Air Conditioner ◽  
Thermal Anomalies ◽  
The Relationship

The high density of servers in datacenters generates a large amount of heat, resulting in the high possibility of thermally anomalous events, i.e. computer room air conditioner fan failure, server fan failure, and workload misconfiguration. As such anomalous events increase the cost of maintaining computing and cooling components, they need to be detected, localized, and classified for taking appropriate remedial actions. In this article, a hierarchical neural network framework is proposed to detect small- (server level) and large-scale (datacenter level) thermal anomalies. This novel framework, which is organized into two tiers, analyzes the data sensed by heterogeneous sensors such as sensors built in the servers and external sensors (Telosb). The proposed solution employs a neural network to learn about (a) the relationship among sensing values (i.e. internal, external, and fan speed) and (b) the relationship between the sensing values and workload information. Then, the bottom tier of our framework detects thermal anomalies, whereas the top tier localizes and classifies them. Our solution outperforms other anomaly-detection methods based on regression model, support vector machine, and self-organizing map, as shown by the experimental results.

Contextual Anomaly Detection Methods for Addressing Intrusion Detection

2021 ◽  
pp. 151-181
Author(s):  
Florian Gottwalt ◽  
Elizabeth J. Chang ◽  
Tharam S. Dillon
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Promising Method ◽  
Detection Methods ◽  
False Alarms ◽  
Cyber Crime ◽  
Number Of False Alarms ◽  
Network Anomaly Detection

One promising method to detect cyber-crime is anomaly detection, which enables one to detect new, unseen attacks. Despite this ability, anomaly detection methods only have limited utilization in practice, due to the high number of false alarms generated. Recent research has shown that the number of false alarms can be reduced drastically by considering the context in which these alarms occur. However, important questions include, What does context mean in the realm of anomaly detection? and How can it be incorporated to identify potential cyber-crime? To address these questions, this chapter provides novel definitions of context and contextual anomaly detection methods. Based on these, a new taxonomy is proposed for contextual anomaly detection methods, which organizes the methods by the specific problems they address. Further, the chapter highlights the potential of contextual anomaly detection for the reduction of false alarms, particularly for network anomaly detection and provides an introduction and holistic overview of the field for professionals and researchers.

Remote Sensed Land Surface Temperature Anomalies for Earthquake Prediction

2017 ◽  
Vol 31 ◽  
pp. 120-134 ◽  
Author(s):  
Mebrouk Bellaoui ◽  
Abdelatif Hassini ◽  
Kada Bouchouicha
Keyword(s):  
Surface Temperature ◽  
Ambient Temperature ◽  
Temperature Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Thermal Anomaly ◽  
Thermal Anomalies ◽  
The Past ◽  
National Office

Thermal anomaly Detection prior to earthquake events has been widely confirmed by researchers over the past decade. In this paper, we use robust satellite technique approach (RST) on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdès Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of robust satellite technique as an approach highly effective for monitoring the earthquakes.

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

scholarly journals UAV-BASED THERMAL ANOMALY DETECTION FOR DISTRIBUTED HEATING NETWORKS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 499-505
Author(s):  
A. Sledz ◽  
J. Unger ◽  
C. Heipke
Keyword(s):  
Anomaly Detection ◽  
Negative Impact ◽  
Low Cost ◽  
District Heating ◽  
Thermal Anomaly ◽  
Hot Water ◽  
False Alarms ◽  
Real World Data ◽  
Promising Alternative ◽  
Area Of Interest

Abstract. District Heating Systems (DHS) distribute heat in terms of hot water or steam. Loss of media (water or steam), and thus energy, is expensive and has a negative impact on the environment. It is therefore of great interest to develop techniques to detect and localize potential leakages fast and cost-effectively. To avoid interference with the operating process of a DHS, airborne thermography comes into place. The use of Unmanned Aerial Vehicles (UAV) as a flexible and low-cost platform equipped with a Thermal InfraRed (TIR) camera is a promising alternative to a conventional manned flight.This paper describes a method for automatic thermal anomaly detection of a DHS. Thermal data acquisition using a UAV is followed by photogrammetric processing of the TIR images. In this way, a thermal orthophoto is produced. The next step is an identification of anomalies by means of image analysis. We apply the Laplacian of Gaussian (LoG) blob detector to find high temperature regions in areas of interest of a thermal orthomosaic. This area of interest is defined around the DHS position in the images as defined in a Geographic Information System. Finally, segmentation and classification are employed to reduce false alarms and localize thermal anomalies. An experimental evaluation using real-world data is presented, showing that the developed method deliverers promising results.

scholarly journals High Stability Anomaly Detection in Random Environments

2021 ◽  
Vol 34 ◽  
Author(s):  
Masaru Ide
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Anomaly Detection ◽  
Autonomous Vehicles ◽  
Input Data ◽  
Learning Systems ◽  
Detection Methods ◽  
Random Environments ◽  
Pairwise Correlation

We propose anomaly detection to refine input data for predictive machine learning systems. When training, if there are outliers such as spike noises mixed in the input data, the quality of the trained model is deteriorated. The removing such outliers would be expected the service quality of machine learning systems improves such as autonomous vehicles and ship navigation. Conventionally, anomaly detection methods generally require the support of domain experts, and they could not treat with unstable random environments well. We propose a new anomaly detection method, which is highly stable and is capable of treating with random environments without experts. The proposed methods focus on a pairwise correlation between two input time-series, change rates of them are calculated and summarized on a quadrant chart for further analysis. The experiment using an open time-series dataset shows that the proposed methods successfully detect anomalies, and the detected data points are easily illustrated in a human-interpretable way. 

scholarly journals Quasi-synchronous ionospheric and surface latent heat flux anomalies before the 2007 Pu'er earthquake in China

2013 ◽  
Vol 1 (3) ◽  
pp. 2439-2454 ◽  
Author(s):  
K. Qin ◽  
L. X. Wu ◽  
X. Y. Ouyang ◽  
X. H. Shen ◽  
S. Zheng
Keyword(s):  
Active Faults ◽  
Thermal Anomaly ◽  
Alpha Particles ◽  
Physical Models ◽  
Observation System ◽  
Thermal Anomalies ◽  
Multiple Parameters ◽  
Earth Observation System ◽  
Spatio Temporal ◽  
Pu’Er Earthquake

Abstract. Pre-earthquake ionospheric and thermal anomalies are two widely-reported short-term earthquake precursors. This paper attempts to examine the possible relationship between the ionospheric anomaly and the thermal anomaly related to the 2007 Ms =6.4 Pu'er earthquake. The spatio-temporal statistical analyses of multi-years SLHF data from USA NCEP/NCAR Reanalysis Project reveal that local SLHF enhancements appeared 11, 10 and 7 days before the Pu'er earthquake, respectively. As contrasted to the formerly reported local ionospheric Ne enhancement 9 days before the shocking observed by DEMETER satellite, it is discovered that the SLHF and Ne anomalies are quasi-synchronous and have nice spatial correspondence with the epicentre and the local active faults. Based on current physical models presented, we suggest that the air ionization in Pu'er seismogenic process, possible resulted from radon leaking out and subsequent alpha particles emitting, and/or positive holes activating and recombining, is the common cause of the observed ionospheric and SLHF anomalies before the Pu'er earthquake. This is valuable for understanding the seismogenic coupling processes and for recognizing earthquake anomaly with multiple parameters from integrated Earth observation system.

Sign in / Sign up

Export Citation Format

Share Document