On the relation of moderate-short term anomaly of earth resistivity to earthquake

1139 moderate-short term anomalies of earth resistivity before 196 earthquakes with magnitude M_s=3.2-7.9 (the Ms≥4.0 event accounting for 94%) are studied in this paper, the results are concluded as following: ①There is a nonlinear function between anomaly time and magnitude of earthquake. For earthquakes Ms≤5.0 or so anomaly time linearly increases quickly with magnitude increasing; for earthquakes 5.0<M_s<6.5 the increasing rate of the time with magnitude increasing gradually become small; for earthquakes M_≥6.5 the rate is quite small.②There is a nonlinear exponential function between anomaly amplitude and magnitude. For earthquakes Ms≤5.0 or so the amplitude increases slowly with the increasing of magnitude, for earthquakes 5.0<M_s<6.5 the increasing of the amplitude is gradually accelerated with magnitude increasing; for earthquakes M_s≥6.5 the increasing is accelerated quickly with magnitude increasing. The two non-linear functions mentioned above are interpreted qualitatively, and the mechanism of this phenomenon is discussed based on the model of rheomorphic medium.

Short-term and imminent geomagnetic anomalies of the Wenchuan M_S8.0 earthquake and exploration on earthquake forecast

The diurnal variation of the geomagnetic vertical component is exhibited mainly by changes of phase and amplitude before strong earthquakes. Based on data recorded by the network of geomagnetic observatories in China for many years, the anomalous features of the appearance time of the minima of diurnal variations （i.e, low-point time） of the geo- magnetic vertical components and the variation of their spatial distribution （i.e, phenomena of low-point displacement） have been studied before the Wenchuan Ms8.0 earthquake. The strong aftershocks after two months＇ quiescence of M6 aftershocks of the Ms8.0 event were forecasted based on these studies. There are good correlativities between these geomagnetic anoma- lies and occurrences of earthquakes. It has been found that most earthquakes occur near the boundary line of sudden changes of the low-point time and generally within four days before or after the 27th or 41st day counting from the day of the appearance of the anomaly. In addition, the imminent anomalies in diurnal-variation amplitudes near the epicentral areas have also been studied before the Wenchuan earthquake.

POSSIBLE CONTRIBUTION OF A TROPICAL CYCLONE TO SHORT-TERM CLIMATE ANOMALIES IN EAST ASIA VIA SNOW COVER ON THE TIBETAN PLATEAU

Snow cover on the Tibetan Plateau(TP) has been shown to be essential for the East Asian summer monsoon.In this paper, we demonstrate that tropical cyclone(TC) 04B(1999) in the northern Indian Ocean, which made landfall during the autumn of 1999, may have contributed to climate anomalies over East Asia during the following spring and summer by increasing snow cover on the TP. Observations indicate that snow cover on the TP increased markedly after TC 04B(1999) made landfall in October of 1999. Sensitivity experiments, in which the TC was removed from a numerical model simulation of the initial field, verified that TC 04B(1999) affected the distribution as well as increased the amount of snow on the TP. In addition, the short-term numerical modeling of the climate over the region showed that the positive snow cover anomaly induced negative surface temperature, negative sensible heat flux, positive latent heat flux, and positive soil temperature anomalies over the central and southern TP during the following spring and summer. These climate anomalies over the TP were associated with positive(negative) summer precipitation anomalies over the Yangtze River valley(along the southeastern coast of China).

Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network

The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety.However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories.Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data.To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network(DMDN)to detect flights with unusual data patterns and evaluate flight trajectory safety.The technique consists of two components:Utilization of the deep long short-term memory(LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM).Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories.The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods.The proposed model can be used as an assistant decision-making tool for air traffic controllers.

An Efficient Correlation-Aware Anomaly Detection Framework in Cellular Network

Nowadays,the fifth-generation(5G)mobile communication system has obtained prosperous development and deployment,reshaping our daily lives.However,anomalies of cell outages and congestion in 5G critically influence the quality of experience and significantly increase operational expenditures.Although several big data and artificial intelligencebased anomaly detection methods have been proposed for wireless cellular systems,they change distributions of the data and ignore the relevance among user activities,causing anomaly detection ineffective for some cells.In this paper,we propose a highly effective and accurate anomaly detection framework by utilizing generative adversarial networks(GAN)and long short-term memory(LSTM)neural networks.The framework expands the original dataset while simultaneously keeping the distribution of data unchanged,and explores the relevance among user activities to further improve the system performance.The results demonstrate that our framework can achieve 97.16%accuracy and 2.30%false positive rate by utilizing the correlation of user activities and data expansion.

Anomalies of Precursory Group and Grouped Strong Earthquakes in the Sichuan-Yunnan Region

Three methods of extracting the information of anomalies of a precursory group are put forward, i.e., the mathematical analyses of the synthetic information of earthquake precursors (S), the inhomogeneous degree of precursory groups (ID) and the values of short-term and impending anomaly in near-source area (NS). Using these methods, we calculate the observational data of deformation, underground fluid and hydrochemical constituents obtained from different seismic stations in the Sichuan-Yunnan region and conclude that the synthetic precursory anomalies of a single strong earthquake with M S6.0 differ greatly from those of the grouped strong earthquakes, for the anomalous information of precursory groups are more abundant. The three methods of extracting the synthetic precursory anomaly and the related numerical results can be applied into the practice of prediction to the grouped strong earthquakes in the Sichuan-Yunnan region. Inhomogeneous degree (ID) of synthetic precursory anomaly can be identified automatically because it takes the threshold of distributive characteristics of the anomalies of precursory group as its criterion for anomaly.

The Anomaly Field of Subsurface Fluids and Its Formation and Evolution Mechanism Before Three Strong Earthquakes in the Northern North China Area

The data of pre-seismic subsurface fluid anomalies of such earthquakes as Datong-YanggaoM_s6.1 event on Oct.19,1989,western Baotou M_s6.4 event on May 3,1996 and Zhangbei-Shangyi M_s6.2 event on Jan.10,1998 are systematically collected and arranged.Then thefeatures of patterns,spatial distribution,time variation and time-spatial evolution of theseanomalies are compared and comprehensively analyzed.Then the formation and evolutionmechanism of medium-and short-term anomaly field of subsurface fluids in the northernNorth China area is proposed.The results show that the medium-term anomaly field is causedby regional tectonic activities,which further strengthen the local tectonic activities andpromote the formation and evolution of the seismic source body.The enhancement of loealtectonic activities causes the formation of anomaly field of short-term subsurface fluids,andthe evolution of source body engenders the source-precursor anomalies of subsurface fluids inthe epicenters at imminent stage.

Study on the Method of Short-Term Synthetic Earthquake Prediction in the North China Region

Based on the extraction and calculation of the short-term seismic precursory information magnitude from the 114 major precursory observations in the North China region, and together with consideration of factors such as geological structure, seismicity, crustal thickness, and in particular, the current geodynamics of the region, the authors studied the time-space evolution characteristics of the short-term earthquake precursory information magnitude and its relationship with earthquakes and proposed the index and method for the short-term synthetic prediction of earthquakes with M S≥5.0 in the North China region. The inspection through R-value shows that the method is effective to a certain extent for earthquake prediction.

地电阻率中短期异常与地震的关系

研究了 1 96次 MS3.2～ 7.9地震 ( 94%以上为 MS≥ 4.0地震 )前 1 1 39次 (道次 )地电阻率中短期异常 ,得到以下结果 :1异常时间与震级之间为非线性增长关系 ,大致分为 3个阶段 :MS≤ 5.0地震 ,异常时间随震级增大基本上是快速的线性增长 ;5.0 <MS<6.5地震 ,增长速度逐步变小 ;MS≥ 6.5地震 ,增长速度很小 .2异常幅度与震级之间为非线性的指数式增长关系 ,也大致分为 3个阶段 :MS≤ 5.0地震 ,随震级增大异常幅度增长很小 ;5.0 <MS<6.5地震 ,逐步趋于加速增长 ;MS≥ 6.5地震 ,加速增长 .定性解释、并利用流变介质模型探讨了异常时间。

云南地区地震宏观异常特征研究

经历史震例及近几年地震震前短临跟踪预报实践 ,对宏观异常进行深入剖析 ,认为 :宏观异常内容和数量多少与震级有关 ;宏观异常不只是短临和临震异常信息 ,也有不少是中短期、短期异常信息 ;宏观异常分布在震中及附近地区 ;最早出现的宏观异常地点对未来地震震中有一定指示意义。

2007年5～6月滇西南地区M≥5成组地震活动中、短临异常特征及预测

给出了2007年滇西南地区中等地震丛集活动的跟踪预测过程,以及云南地区地震活动异常和前兆异常的动态演化图像。这组中等地震中期阶段的异常特征主要是:云南地区长达3年多和滇西南地区长达12年的6级地震平静,小震调制比、高水位和部分前兆的趋势异常显示滇西南从2006年以来处于应力水平相对较高状态;中短期阶段在滇西南出现3级地震活动增强,并形成空区,小的单元体破裂后把能量集中到更大单元体,发生更大地震;短临阶段小震活动不断向震中迁移,空区被打破。中等地震丛集活动过程中,每次新的M≥5地震发生前,滇西南均出现3级震群活动,前兆均有新的异常过程出现。

北京及邻近地区中等以上地震地下流体异常识别与特征研究

系统分析了北京及其邻区多年积累的震例资料,提出该区的地下流体异常分为趋势上升型中期异常、转折型中短期异常、突发型短期异常和短临异常等类型。运用“同一化”思路、从属函数、剩余曲线及原始曲线均方差等异常判定方法,提取了中等以上地震,特别是5级以上地震的中、短、临异常实例。通过对流体异常特征的统计分析,给出了北京及邻近地区的中期、中短期、短期和短临异常的时间分布特征,并对地震前各阶段异常的超前时间进行了总结,该时间可作为发震时间的预测指标。在实际应用中可为该区的震情判定提供依据。

江淮地区布格重力异常与中强地震发生的构造环境分析

通过对布格重力异常等值线的离散化、梯度计算和粗化处理 ,生成江淮地区布格重力异常梯度分级图。布格重力异常高梯度区与中强地震的空间分布有很好的一致性 ,与低梯度分布区的弱地震活动性形成鲜明的对比。同时 ,布格重力异常高梯度区的分布具有线性展布特点 ,可以划分出两组布格重力异常梯级带 ,一组走向北东 ,另一组走向北西西。同方向的条带具有相互平行、间距近似相等的特点 ,不同方向的构造带相互交切 ,形成一幅网络状图象。沿着这些梯级带存在断裂构造 ,它们在晚第三纪至第四纪早期有过构造运动 ,但晚更新世以来活动不明显。在江淮地区 ,布格重力异常梯级带、晚第三纪至早第四纪活动断裂和中强地震在空间有比较明显的对应关系。因此 ,在现今构造活动不太强烈的地区 ,布格重力异常梯级带可以认为是划分中强地震潜在震源区的一个重要标志。

中国中强地震与天气异常探讨

从我国６．０级以上中、强地震的时空分布状态入手，选择台湾附近（北纬２０°─２５°,东经１２０°─１２３°）的中、强地震作为样本，对地震前后的５００ｈＰａ环流特征和５００ｈＰａ波谱等进行了分析，发现地震发生时产生的热能能够激发大气波谱的活跃和增幅，从而引导了地震前后冷空气的南下，进而讨论了地震爆发往往伴随强风现象的物理机制．

华北北部几次中强地震前的地磁前兆特征

阐述并分析了华北北部几个中强地震前地磁前兆异常的时空特征，即以在震前几天到几个月出现的中短期、短临前兆异常为主，短临异常尤为突出，空间分布范围较大。异常展布有一定规律性，并与地震有较好的对应关系。

流动地磁测量中地震异常信息的探索

S方法将同一测期一条流动地磁测线各测点的变化叠加,可抑制单个测点的干扰,使可能存在的震磁异常信息凸现。本文研究了安徽省流动地磁测线S异常与邻近地区小震震群活动频次和中强地震的关系;探讨了"震情窗"地区S异常与"震情窗"相关地区中强地震的相关性;讨论了同一测期流动地磁测线各测点δf"同向变化"现象。

中等山区航空重力测量的精度分析

介绍利用CHAGS系统对某中等山区进行航空重力测量的情况,并对测量数据进行处理和分析。结果表明:在中等湍流条件下,交叉点重力异常不符值的标准差为5.0×10-5m s-2,5′×5′格网平均重力异常的外部精度为5.5×10-5m s-2。

安徽巢湖--铜陵地区中强地震发生的构造标志

安徽巢湖—铜陵地区是中国大陆内部1个典型的中强地震活动区,曾发生的1585年巢县南5级、1654年庐江东南5级等4次地震呈NNE向带状展布,构成了1条醒目的中强地震活动带。野外地表地质调查、浅层物探、钻探资料、年代学样品的采集测试和断裂活动性综合分析等表明该地区矾山断裂、夏家岭断裂和朗村断裂是前第四纪断裂。铜陵断裂是1条中更新世活动的隐伏断裂,可发生中强地震,控制了近地表的3个雁列状构造的演化和发展。4次地震强度呈现了向S递减的特点,而这与晚新生代无为盆地的凹陷幅度明显大于南边的贵池盆地的特点相一致。在深部构造上,铜陵断裂空间分布特征对应着1条NNE向布格重力异常梯级带。巢湖—铜陵地区中更新世活动的铜陵断裂、雁列状分布的构造、新构造的差异运动以及布格重力异常梯级带与该地区中强地震活动带在空间上的对应性,显示了它们应是中强地震孕育和发生的构造标志。