Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data

In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and postoccurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data.

[1]  Harry Wechsler,et al.  A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Shen-Shyang Ho,et al.  A martingale framework for concept change detection in time-varying data streams , 2005, ICML.

[3]  Francesca Bovolo,et al.  A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Emrah Dogan,et al.  An unsupervised learning algorithm: application to the discrimination of seismic events and quarry blasts in the vicinity of Istanbul , 2011 .

[5]  W. A. Shewhart,et al.  The Application of Statistics as an Aid in Maintaining Quality of a Manufactured Product , 1925 .

[6]  Yaxin Bi,et al.  Study of Outgoing Longwave Radiation Anomalies Associated with Two Earthquakes in China Using Wavelet Maxima , 2009, HAIS.

[7]  Taposh Banerjee,et al.  Data-Efficient Quickest Change Detection in Minimax Settings , 2013, IEEE Transactions on Information Theory.

[8]  Yaxin Bi,et al.  A Geometric Moving Average Martingale method for detecting changes in data streams , 2012, SGAI Conf..

[9]  Lixin Wu,et al.  GEOSS-Based Thermal Parameters Analysis for Earthquake Anomaly Recognition , 2012, Proceedings of the IEEE.

[10]  Viktor Wesztergom,et al.  Ultra Low Frequency (ULF) European multi station magnetic field analysis before and during the 2009 earthquake at L'Aquila regarding regional geotechnical information , 2011 .

[11]  Silvia Scarpetta,et al.  Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano , 2009, WIRN.

[12]  Menas Kafatos,et al.  Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes , 2004 .

[13]  Ondrej Santolik,et al.  Spacecraft observations of electromagnetic perturbations connected with seismic activity , 2008 .

[14]  Fanliang Kong,et al.  A dynamic method of air temperature forecast , 2004 .

[15]  Joel Susskind,et al.  Contributions to climate research using the AIRS Science Team version-5 products , 2011, Optical Engineering + Applications.

[16]  Matthias Holschneider,et al.  From Alarm‐Based to Rate‐Based Earthquake Forecast Models , 2012 .

[17]  Nemanja Ilic,et al.  Distributed Change Detection Based on a Consensus Algorithm , 2011, IEEE Transactions on Signal Processing.

[18]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Kenji Yamanishi,et al.  A unifying framework for detecting outliers and change points from non-stationary time series data , 2002, KDD.

[20]  João Gama,et al.  Change Detection with Kalman Filter and CUSUM , 2006, Discovery Science.

[21]  Lorenzo Bruzzone,et al.  Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images , 2013, IEEE Transactions on Image Processing.

[22]  Manuel Davy,et al.  An online kernel change detection algorithm , 2005, IEEE Transactions on Signal Processing.

[23]  Francesca Bovolo,et al.  Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Menas Kafatos,et al.  An early warning system for coastal earthquakes , 2006 .

[25]  A. R. Crathorne,et al.  Economic Control of Quality of Manufactured Product. , 1933 .

[26]  Ondrej Santolik,et al.  Decrease of intensity of ELF/VLF waves observed in the upper ionosphere close to earthquakes: A statistical study , 2009 .

[27]  J. Shaw,et al.  Uplift of the Longmen Shan and Tibetan plateau, and the 2008 Wenchuan (M = 7.9) earthquake , 2009, Nature.

[28]  Ludmila I. Kuncheva,et al.  Change Detection in Streaming Multivariate Data Using Likelihood Detectors , 2013, IEEE Transactions on Knowledge and Data Engineering.

[29]  Sanjay Ranka,et al.  Statistical change detection for multi-dimensional data , 2007, KDD '07.

[30]  Eamonn J. Keogh,et al.  HOT SAX: efficiently finding the most unusual time series subsequence , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[31]  Jun Geng,et al.  Non-Bayesian Quickest Change Detection With Stochastic Sample Right Constraints , 2013, IEEE Transactions on Signal Processing.

[32]  Yoshimori Honkura,et al.  Rapid changes in the electrical state of the 1999 Izmit earthquake rupture zone , 2013, Nature Communications.

[33]  XuanLong Nguyen,et al.  Sequential Detection of Multiple Change Points in Networks: A Graphical Model Approach , 2012, IEEE Transactions on Information Theory.

[34]  Menas Kafatos,et al.  Outgoing long wave radiation variability from IR satellite data prior to major earthquakes , 2007 .

[35]  Harry Wechsler,et al.  Detecting Changes in Unlabeled Data Streams Using Martingale , 2007, IJCAI.

[36]  Alexander Gammerman,et al.  Testing Exchangeability On-Line , 2003, ICML.

[37]  Marcus A. Maloof,et al.  Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.

[38]  S. Muthukrishnan,et al.  Sequential Change Detection on Data Streams , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[39]  Martin R. Varley,et al.  Detection of Weak Seismo-Electric Signals Upon the Recordings of the Electrotelluric Field by Means of Neuro-Fuzzy Technology , 2007, IEEE Geoscience and Remote Sensing Letters.

[40]  J. Douglas Zechar,et al.  Bayesian Forecast Evaluation and Ensemble Earthquake Forecasting , 2012 .