Bi, Yaxin

Christodoulou, Vyron

Zhao, Gouge

English

It has been hypothesized that electromagnetic (EM) anomalies act as precursors to seismic ac- tivities. More recently, there have been a lot of studies regarding seismic events and their possi- ble link with EM sequential anomalies from dif- ferent sources. A lot of work has been done such as in [1], where statistical methods have been used to prove this connection. Machine learning (ML) methods were used in [2] . Here, to ana- lyze the data we use simple and computationally e cient methods. The two proposed methods, a novel variant of Cumulative Sum (CUSUM) with Exponentially Weighted Moving Average (EWMA) and a Fuzzy Inspired Approach are evaluated under new EM observations by the SWARM satellites. Speci cally we are investi- gating two seismic events occurred on the 6th of December at 02:43 and 18:20 respectively and their possible causal links with EM anomalies

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SeismicAnomalyDetection inTimeSeriesElectromagneticDatabytheSWARMSatellitesVyron Christodoulou1, Yaxin Bi1, Guoze Zhao2christodoulou-v@email.ulster.ac.uk, y.bi@ulster.ac.uk, zhaogz@ies.ac.cn1 School of Computing and Mathematics, Ulster University, Newtownabbey, Co. Antrim, BT37 0QB, U.K2 State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, ChinaAbstractIt has been hypothesized that electromagnetic(EM) anomalies act as precursors to seismic ac-tivities. More recently, there have been a lot ofstudies regarding seismic events and their possi-ble link with EM sequential anomalies from dif-ferent sources. A lot of work has been done suchas in [1], where statistical methods have beenused to prove this connection. Machine learning(ML) methods were used in [2] . Here, to ana-lyze the data we use simple and computationallyeﬃcient methods. The two proposed methods,a novel variant of Cumulative Sum (CUSUM)with Exponentially Weighted Moving Average(EWMA) and a Fuzzy Inspired Approach areevaluated under new EM observations by theSWARM satellites. Speciﬁcally we are investi-gating two seismic events occurred on the 6th ofDecember at 02:43 and 18:20 respectively andtheir possible causal links with EM anomalies.Objectives• Study and analyze characteristics ofanomalies that are thought to act as pre-cursors to seismic events.• Develop, evaluate and compare new meth-ods from diﬀerent ﬁelds in detectinganomalies in time series EM sequentialdata.• Identify any possible correlations betweenEM anomalies and seismic events.• Investigate possible models for predict-ing seismic events by the analysis of theanomalies in time series data.MethodsThe CUSUM-EWMA variant:C+n = max[0, x+n − (zn +K) + C+n−1] (1)• x+n , value in iteration n• EWMA's statisticzn = λxn + (1− λ)zn−1 (2)• K, slack value allowed by the CUSUMFuzzy Inspired Approach:Diﬀerent serialized components work towardsthe anomaly detection.Method I: CUSUM-EWMAMethod II: Fuzzy InspiredstartInput (RawData, Labels,Order, Data Points)Apply S-G smoothingxcorr(or)>=xcorr(i) FISInput(Peaks,Gradient) Encode Crisp OutputEvaluate similaritywith comparisonCompute FFT of original datasetCompute FFT i:i+n for each PeakFind iterationLogicaloutputLogical OutputEndIntensityShapeTFDataThe SWARM constellation of satellites,launched in 2013 to help us understand theearth's interior and its eﬀects, consists ofthree identical satellites. SWARM A andSWARM B that orbit at an altitude of 450kmand SWARM C at an altitude of 530km [3].The earth's magnetic ﬁeld is measured bythe satellites' Vector Field Magnetometer(VFM) at a continuous per second rate. Afterthe preprocessing the data from a total of86400x72 were transformed to a 720x1 vector.21st Nov 30th Nov 10th Dec 20th Dec 30st Dec 9th Jan 19th Jan 31st JanIntensity (nT)×1043.43.53.63.73.83.944.1Swarm AData (Cont'd)21st Nov 30th Nov 10th Dec 20th Dec 30st Dec 9th Jan 19th Jan 31st Jan×1043.33.43.53.63.73.83.94Swarm BResultsThe Cusum-EWMA:21st NovIntensity (nT)30th Nov 10th Dec 20th Dec 30th Dec 9th Jan 19th Jan 31st Jan00.10.20.30.40.50.60.70.80.91Swarm B21st Nov 30th Nov 10th Dec 20th Dec 30st Dec 9th Jan 19th Jan 31st JanIntensity (nT)00.10.20.30.40.50.60.70.80.91Swarm BThe Fuzzy Inspired:100 110 120 130 140 150 160 170 180 190 200×1043.623.643.663.683.73.723.743.76Data points100 110 120 130 140 150 160 170 180 190 200Intensity (mV)×1043.543.563.583.63.623.643.66Swarm BSummaryThe methods can detect anomalous sequencesin the dates prior and post seismic event thatoccur less frequently afterwards. Anomalies ap-pear even a month after the seismic events. Al-though the results are promising, further workneeds to be carried out in order to produce moreaccurate and conﬁdent results. These methodscall for a deep understanding of the nature ofthe anomalies and investigation of any seasonal-ity or patterns in the data. To achieve a higherorder of understanding, knowledge from a rangeof ﬁelds has to be drawn such as Statistics, Ge-ology, ML and Physics.References[1] Bi, Y. et al. A comparative analysis for detect-ing seismic anomalies in data sequences of outgo-ing longwave radiation, Springer, Berlin Heidelberg,(2009)[2] Zhao, Guoze, et al. Advances in alternating electro-magnetic ﬁeld data processing for earthquake mon-itoring in China, Science, China Earth Sciences,(2015)[3] Friis-Christensen, E. et al. Swarm: A constellationto study the Earth's magnetic ﬁeld, Earth, planetsand space, (2006)

Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites

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Seismic Anomaly Detection in Time Series Electromagnetic Data by the SWARM Satellites

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