Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data

Abstract

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.

A martingale-based temporal analysis of pre-earthquake anomalies at Jiuzhaigou, China, in the period of 2009-2018

“…In [14], [24], Ho and Wechsler proposed a Martingale framework for detecting changes and found the feasibility of the martingale method for detecting changes in unlabelled data streams. In our previous work, we applied the martingale theory algorithm proposed by Ho et al to analysed the OLR dataset, and it was effective to obtain the abnormal information from OLR data [5], [7], [27]. In probability theory, a martingale is a sequence of random variables (i.e., a stochastic process) for which, at a particular time, the conditional expectation of the next value in the sequence, given all prior values, is equal to the present value.…”

A tool for Swarm satellite data analysis and anomaly detection

“…TEC data were also analyzed with an Artificial Bee Colony Optimization algorithm in [4]. More recently in [11] the authors propose a statistical approach, the Geometric Moving Average for change detection in Outgoing Longwave Radiation with promising results. Variations in the very low and ultra low frequency bands have been the focus in [12], [13].…”

A Fuzzy Shape-Based Anomaly Detection and Its Application to Electromagnetic Data

“…All the above methods improved the change detection in terms of sensitivity of the algorithm but it is known that it is difficult to detect frequency changes. A more complex probabilistic approach for AD was also proposed in [2] where the authors combine martingales with a Geometric Moving Average to detect anomalies. This method , although it does not make use of a user selected window length still needs parameter tuning.…”

“…1. https://physionet.org/cgi-bin/atm/ATM Its amplitude has to be above or close to a momentum Magnitude (M w ) of 6 as defined in 2 . This choice is in-line with the literature [13].…”