مقاله انگلیسی رایگان در مورد پیش بینی زلزله بر اساس تقسیم بندی جامعه – الزویر ۲۰۱۸

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله پیش بینی زلزله بر اساس تقسیم بندی جامعه
عنوان انگلیسی مقاله Earthquake prediction based on community division
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۹ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه الزویر
نوع نگارش مقاله مقاله پژوهشی (Research article)
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی عمران
گرایش های مرتبط زلزله
مجله فیزیک A: مکانیک آماری و کاربرد آن – Physica A: Statistical Mechanics and its Applications
دانشگاه Software College – Northeastern University – Shenyang – PR China
کلمات کلیدی شبکه زلزله، شبکه وزنی جهت دار، پیش بینی، تقسیم جامعه
کلمات کلیدی انگلیسی Earthquake network, directed weighted network, prediction, community division
شناسه دیجیتال – doi
https://doi.org/10.1016/j.physa.2018.05.035
کد محصول E9270
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I. INTRODUCTION

arthquake is a worldwide problem since it has destructive power. Even if in a small earthquake belt, earthquake can happen thousands of times per year. Earthquake is one of the most important natural phenomena that hazards our life and property. Fortunately, a number of researchers devote to revealing the regularities of the phenomena and make fruitful achievements in a long research history. In these achievements, the most famous one is the Gutenburg-Richter law[1] that reveals the relationship between earthquake magnitude and frequency, which is often used to study earthquake in geophysics[2-4], and the other one is Omori law[5] that describes the relationship between the frequency of the aftershocks and the time interval. For modeling the earthquakes, Baiesi and Paczuski[6,7] defined different tremor events as nodes of a network, where a pair of node is linked if the correlation between them exceeds a certain threshold. Abe and Suzuki[8-15] considered that each pair of successive earthquakes events are associated. While, He Xuan[16] proposed a different approach to build the network edge based on the space-time influence domain. In order to build an earthquake network, Abe S and Suzuki N[8] proposed a research method of modeling a network of earthquake regions. Firstly, the earthquake region is divided into a number of cells one by one. If an earthquake happens in a cell, the cell is defined as a node in the earthquake network. Further, if two nodes are both affected by an identical earthquake, a link between the nodes is added into the network, and in an earthquake event, if two tremors occur in a node, self-loop is applied to the node. After studying the earthquake data of Southern California and Japan, it is found that earthquake networks of the two regions have scale-free characteristic by statistic on degree distribution of network node. Well, based on the study of Gardner J K[17] , He Xuan[16] proposed a construction method of earthquake network by the space-time influence domain. Lin et al[18] established a earthquake recurrence network based on the magnitude time series of California. The constructed networks are unweighted. However, in actual networks, the importance of different edge is different. Therefore, it is necessary to introduce the “weight” associated with the edge attributes, and then build a weighted earthquake network. In current earthquake prediction studies, Kong Q[19] designs an intelligent earthquake data analysis software, Myshake, which can be used to collect data for early prediction of earthquakes and enhance EEW’s ability of prediction(EEW, earthquake early-warning).Howell S et al[20] use statistical methods to alleviate earthquake hazards by pattern selection. Zhang Y et al[21] use an image segmentation method to establish a fast search engine for detecting the location of the source rapidly after an earthquake. Recently, the research of earthquake prediction based on complex network is increasing. He X et al[16,22] select the nodes in the California earthquake network by the K kernel theory, and predicate the earthquakes by the Bayesian network. Men K P et al[23,24] predicate M ≥ ۷ Earthquakes in Xinjiang Region and M ≥ ۸ Earthquakes in mainland of China by designing an ordered network. However, these studies only select a very small number of seismic data or a few nodes for research. In fact, when making prediction, we should study all regions and most of the seismic data. In this paper, the seismic data of South California(114.0W-122.0W,۳۲٫۰N -37.0N) is taken as the research object(1992-2014 seismic data is used to construct the network, and 2015 data is used to make earthquake prediction , data is from http://service.scedc.caltech.edu/ftp/catalogs/SCEC_DC/).

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