Conference Agenda

This study is aimed to studying electromagnetic anomalies before main shock and aftershocks of Jiuzhaigou earthquake (M=7.0, August 8, 2017) and comparing the phenomana observed by the ground-based CSELF network and by the SWARM satellites. The Jiuzhaigou earthquake (M=7.0, 13:19:46, August 8, 2017, UTC) occurred in the Sichuan province. The CSELF network consists of 30 stations across two main seismic belts in China, in which 15 stations are located in Sichuan and Yunnan provinces. Each station records five alternate EM filed components (Ex, Ey, Hx, Hy, Hz) in a frequency band of 0.001-1000Hz. The data have been recording for about 3 years using the network. In the study on the EM anomalies before earthquakes, the following steps are involved. The first step is to choose the quality data from huge amount of the observed data. Secondly Top-Down Level analysis is carried out for identifying and catching anomalies in the data based on the different time and different frequencies either for Network data or for SWARM data. The final step is to investigate the relationship of anomalies with earthquake events.

Through analysis on the huge amount of Network data, the time series from August 6^th to 12^th is good meaning on obvious disturbance noise existing in the data. But some anomalous phenomena appeared before main shock and successive 18 mid-strong aftershocks. Except for three aftershocks the anomalies are featured as (1) anomalous pulsating clustering of EM fields appeared simultaneously at several stations, e.g., at the station of JianGe in Sichuan, at the LiJiang and JingGu stations in Yunnan with 205km, 770km and 1110km distances to the epicenter, respectively. The smooth variation of EM fields appeared between adjacent clusterings. (2) The pulsating clustering started at about 12-13 minutes before the earthquake and lasted for about 10 minutes and recovered at about 3 minutes before the shock. (3) Individual pulse in the clustering has a period of about 60-80s. (4) The amplitude of maximum pulse in the clustering is about 70% higher than the background value of corresponding EM component. The anomalous pulses seem to be decreased with the distance to epicenter. The clustering form is similar to those of the Pc3-Pc5 pulse clustering, but the observed anomalies by SWARM appeared in the different time section. The clustering is also not caused by co-seismic waves (P and S waves). It is postulated that the anomalies before each shocks may be caused by the shocks during the process of earthquake generation.

Acknowledgement: Tang J, Chen X, Zhan Y, Xiao Q, etc. from IGCEA joined the CSELF observation. The study is supported by NDICC (15212Z0000001) and NSFC (41374077）.

Yaxin Bi¹, Vyron Christodoulou¹, George Wilkie¹, Zhao Guoze², Ming Huang¹ and Han Bing²

1) Faculty of Computing, Engineering and the Built Environment, University of Ulster, Co Antrim, United Kingdom

2) Institute of Geology, China Earthquake Administration, Beijing, China

Email: y.bi@ulster.ac.uk

Electromagnetic (EM) field is sensitive to the stress of plate tectonics, and changes in the duration of earthquake preparation, the changes would cause electromagnetic emission to transmit into ionosphere, which could be observed by satellites. There were a number of studies conducted on DEMETER satellite data, the results shown that precursory phenomena were captured before earthquakes. Piša D, et al. (2013) carried out a rigorous statistic analysis on the 8400 earthquakes that have a magnitude of 5 or greater than 5 and electromagnetic perturbations within 440 kilometers of the earthquake epicenters, the results revealed that the probability of electromagnetic attenuation was very high before 0-4 hours of the events. Le et al. (2015) conducted a survey on studies of ionospheric abnormal behaviors before some great earthquakes and reported ionospheric disturbance to different extent.

This study reports the progress of development of anomaly detection algorithms and their application to analysing the SWARM satellites data and discovering precursory phenomena before large earthquakes. The study selects three earthquakes, i.e. the Ludian earthquake with a magnitude 6.2 occurred on 3 August 2014 in Yunnan, China, the Peloponnese earthquake with a 5.7 magnitude occurred in southern Greece on 29 August 2014 and the Eketahuna earthquake with a 6.8 magnitude occurred in Peru on 20 January 2014. For each earthquake, a 1000kmx1000km study area is defined and divided into 9 grids. For each grid a time series data is generated, as a result each area has 9 sets of time series data. The duration of the selected data is from 25th March 2014 to 24 January 2015, which were recorded by the Vector Field Magnetometer (VFM).

Four different methods are used to generate time series data, i.e. first day, middle, predefined and average points in order to investigate artificial anomalies introduced when generating time series data. The three detection algorithms of CUSUM-EWMA, Fuzzy-inspired and Hot-SAX are specifically selected to address the unknown nature of the EM signals with respect to their duration, their amplitude and frequency changes, they are applied to analyse 27 sets of time series data in order to detect anomalous phenomena before these three earthquakes. The detected results show various phenomena, and no specific patterns can be discovered, which are closely related to the times of occurrence of these earthquakes. From this studying results, the interesting points are observed as follows:

• the algorithms are capable of detecting anomalies, the CUSUM-EWMA provides good anomaly detection, but it struggles in different anomaly cases.
• the satellites observe the whole earth, their revisit time and orbit reveal a serious constraint in generating sufficient and high quality time series data for earthquakes.
• difficulties appear in selecting the fuzzy membership functions (MF) that depend a lot on the form of the input signals

References:

1. Piša D, Němec F, Santolik O, et al. 2013. Additional attenuation of natural VLF electromagnetic waves observed by the DEMETER spacecraft resulting from preseismic activity. J Geophys Res, 118: 5286–5295.
2. Huijun Le, Jing Liu, Biqiang Zhao, Libo Liu. Recent progress in ionospheric earthquake precursor study in China: A brief review, Journal of Asian Earth Sciences. Volume 114, Part 2, Pages 420-430.

In this work we report the development of a system pipeline for the analysis of the Swam satellite electromagnetic data. Our objective is to provide a streamlined functional tool for analyzing electromagnetic data over regions and investigate the relationship of precursory electromagnetic signals to seismic events. The process of the system pipeline consists of three stages of data extraction, data pre-processing and anomaly detection. The first stage provides an interactive interface, allowing users to define study regions and periods of seismic events, and then extract data from the Swarm CDF data archive. The second stage consists of four different pre-processing methods, including the first arrival sampling within regions, middle points and average value, which address the data sparsity problem and the cause of artificial anomalies in a defined region. The last stage offers a range anomaly detection functions underpinned with a variant of the basic CUSUM-EWMA statistical algorithm, fuzzy-logics inspired method, and HOT-SAX method, etc. To demonstrate the potentials of the tool in applying different kinds of algorithms under an anomaly detection scope of electromagnetic sequential time series data, we select a seismic event under scrutiny is in Ludian, China and occurred on 03/08/2014, and present the usefulness of our approach and pinpoint some critical problems regarding satellite data that were identified.

With the support the Wireless Electro-Magnetic Method (WEM) project, we built the first Control Source Extremely Low Frequency (CSELF) continuous observation network which include 30 electromagnetic stations in Beijing Capital Area (BCA) and Southern Section of North-South Seismic Belt in China for the artificial and nature source singles recording. The instruments collect the data 16 seconds every ten minutes with sample rate of 256Hz and then the whole day’s data was analyzed with the method of Flourier transformation and the FFT length was set as 4096. After that we can get the spectrum with the frequency range from 3Hz to 48Hz and the Schumann resonance and six harmonic frequencies can be observed clearly, however, the peak frequency of Schumann resonance are slightly different due to the stations’ location and other factors.

By comparing the long-term observation data of the same station, we can see that 1.The annual variation of the spectrum in Schumann resonance frequency is basically the same as that of other frequency bands. the intensity of the magnetic field is strong in summer, low in winter and the law of long term change conforms to the half cycle sine wave form. From January to July, the power spectral density is increasing, while from July to December, the spectral density of the vibration amplitude decreases.2. The power spectrum of Schumann resonance frequency is smaller than that of surrounding frequency, that is, its variation is more concentrated. 3.For one station the peak frequency of Schumann resonance shift during time. Take Lijiang as an example, and the peak frequency of the first Schumann resonance frequency of the north to south magnetic field component in one year is between 7.5Hz and 7.9Hz, and tends to low frequency in winter and summer, and to high frequency in spring and autumn.