Prophet时序预测模型在电离层TEC异常探测中的应用

提出一种基于Facebook 开源的Prophet预测模型进行电离层TEC异常识别的新方法。 首先, 对比分析了该方法与传统时间序列预测方法(ARIMA模型等)预测电离层TEC建模背景值的精度, 以及与经典电离层TEC异常识别方法(滑动四分位法)提取前面对应一致的电离层TEC背景值的精度。 结果表明, Prophet预测模型预测建模背景值的精度要明显优于其他方法, 且预测的建模精度比ARIMA模型等方法高2.55倍左右, 比滑动四分位法高10.74倍左右。 同时, 在最佳预测建模区间时, 其精度值大小比较依次为RMSE_IQR=10.5841>RMSE_ARIMA=3.2780>RMSE_Prophet=0.8469, 说明传统探测法预测建模背景值时具有较大的不足。 随后, 以2017年8月8日九寨沟7.0级地震为例, 利用该方法分析了电离层TEC异常扰动情况, 并对比验证了该方法的有效性和准确性。 实验结果表明: 在震前第10 d和第2 d电离层TEC发生较为明显的负异常, 第7 d电离层TEC发生较为明显的正异常。 对比实验表明, Prophet预测模型的有效性和准确性明显优于滑动四分位法。     

辽宁省数字化地震前兆数据库的建立

地震前兆数据一直是地震分析预报必不可少的重要数据资源。随着辽宁省地震前兆台站数字化技术改造的开展，地震前兆模拟记录将逐步被数字化记录所取代。我们结合数字化技术改造的实际情况，采用先进的网络关系数据库模型构造了辽宁省数字地震前兆数据库。该数据库直接面向整个地震系统，为地震预报提供详实的数据服务和先进的研究手段。本文对数据库系统、数据库总体设计及数据共享服务也进行了深入研究和讨论。     

Prompt derivation of TEC from GEONET data for space weather monitoring in Japan

Continuous monitoring of ionospheric conditions is essential to monitoring and forecasting space weather. The worldwide use of global navigation satellite systems like the Gobal Positioning System (GPS) makes it possible to continuously monitor the total electron content (TEC) of the ionosphere and plasmasphere up to a height of about 20,000 km. We have developed a system for deriving the TEC from GEONET data rapidly and we use the TEC distribution over Japan in the daily operations of the Space Weather Forecast Center at NICT (RWC Tokyo of ISES). Using instrumental biases from a few days before enables us to drastically shorten the processing time for deriving TEC. The latest TEC values (with a delay of about 1 h) are obtained every 3 h, and most of the values are within 2 TEC units of the actual TEC. We have found our system for deriving TEC rapidly to be useful for continuously monitoring the progress of ionospheric storms under any ionospheric conditions, even those under which the usual ionosonde observations are unable to obtain F-region profiles.     

120°E磁赤道上空电离层电子浓度总含量的混沌特性分析及非线性预测初探

本文利用120°E磁赤道上空电离层TEC1996～2004年的观测数据讨论电离层TEC参量的混沌特性,并探讨利用混沌理论对电离层参量进行预报的可能性. 实际计算表明,在指定经纬度的TEC时序数据中存在混沌特性,其关联维数为36092,李雅普诺夫指数为03369. 采用混沌“加权一阶局域法”进行预测较为成功. 对预测误差分析表明,预测点在1～144之间误差相对较小,标准差约为76438 TECU,相关系数约为09172.     

2016年缅甸M7.2地震前中国区域电离层TEC扰动的时空分布

基于GPS TEC数据, 以2016年4月13日发生在缅甸的M7.2地震为例, 通过限定上、 下阈值的扰动提取方法分析了地震电离层效应在中国区域的时空分布。 结果显示, 该地震前2~4天, 中国区域的TEC扰动为地震电离层前兆; 其中4月11日(震前2天)电离层扰动受地震的影响最大, 扰动范围可达2816 km; 同时还发现4月11日异常最明显的区域在震中偏北方向(中国南部), 单独提取该区域经度链上的JPL TEC数据, 发现异常区域内北驼峰北移的现象, 并用静电场理论对异常区域向北偏移的物理机制做了分析解释。     

Lessons and Questions from Thirty Years of Testing the Precursory Swarm Hypothesis

In 1976 Frank Evison identified the first examples of earthquake swarms as long-term precursors of main-shock events, and thereby discovered the predictive scaling relations of long-term seismogenesis. From this time on, forecasting became the main focus of his research. After learning from an early attempt to communicate forecasts confidentially to government, he recognised the importance of hypothesis testing, and the precursory swarm hypothesis was cast in a form similar to a regional likelihood model. Tests of its performance relative to a stationary Poisson model at M ≥ 5.8 in New Zealand were begun in 1977. The initial hypothesis was that of a 1–1 relation between swarms and main-shock events. Following a study of the Japan catalogue, the generalised swarm hypothesis, in which multiple swarms were precursory to multiple main-shock events, was formulated. Tests of this form of the hypothesis at M ≥ 6.8 were initiated in a region of surveillance east of Japan in 1983. Eventually the generalised hypothesis was adopted in New Zealand also. In 1999, tests were begun in a region of Greece. In 1994–1995, several main-shock events favourable to the swarm hypothesis occurred, however four main-shock events near Arthur’s Pass, New Zealand, occurred without precursory swarms. Subsequent analysis showed that events called “quarms”, which were similar to swarms but more protracted in time, had preceded these events. This led to the proposal of a qualitative physical process to account for swarms, quarms and the predictive relations: A three-stage faulting process, in which a major crack induces aftercracks in its neighbourhood, just as a main shock induces aftershocks. An inference from this process was that the most general long-term precursor should be an increase of seismicity at similar magnitudes to the eventual aftershocks. It turned out that such a precursory scale increase nearly always occurs before major earthquakes and conforms to the predictive scaling relations. Setting aside the problem of identifying the scale increase before the major earthquake, the EEPAS (Every Earthquake a Precursor According to Scale) forecasting model was formulated. The success of this relatively weak model in forecasting major events in New Zealand, California, Japan and Greece shows that the predictive scaling relations are ubiquitous in earthquake catalogues. Although none of the formal tests of the swarm hypothesis were successful in their own terms, they were beneficial in identifying shortcomings in its formulation, thereby leading to improved understanding of long-term seismogenesis and a better forecasting model. Some puzzling aspects of the scaling relations are whether they vary regionally, and why the precursor area and aftershock area scale differently with magnitude. A more practical question is whether the EEPAS model can be strengthened, by making use of the clustering of some precursors in swarms and quarms, to bring us nearer to the original goal of forecasting individual major earthquakes.     

LEO GPS接收机仪器偏差估计

LEO GPS观测已成为空间电离层研究重要手段之一,通过GPS双频观测值获取的TEC则是电离层探测的一个重要参量,为获取高精度TEC需估计和消除GPS接收机仪器偏差（DCB）.本文旨在探索一种全新的LEO GPS接收机仪器偏差的估计方法：基于电离层球对称的假设,利用CHAMP和COSMIC原始GPS观测数据,采用几何映射函数,通过最小二乘解算出GPS接收机仪器偏差.结果表明：(1)2008年1月份期间,通过上述方法解算的仪器偏差都较稳定,相比COSMIC网上发布结果,标准偏差都在0.6 ns以内;（2）COSMIC（轨道高度大约800 km）仪器偏差估计结果优于CHAMP（轨道高度大约400 km）的结果,原因为：对于不同轨道高度LEO GPS仪器偏差估计,其较高轨道高度的电离层球对称假设影响较小.     

Data replenishment of five moderate earthquake sequences in Japan,with semi-automatic cluster selection

Missing early aftershocks following relatively large or moderate earthquakes can cause significant bias in the analysis of seismic catalogs. In this paper, we systematically address the aftershock missing problem for five earthquake sequences associated with moderate-size events that occurred inland Japan, by using a stochastic replenishing method. The method is based on the notion that if a point process (e.g., earthquake sequence) with time-independent marks (e.g., magnitudes) is completely observed, it can be transformed into a homogeneous Poisson process by a bi-scale empirical transformation. We use the Japan Meteorological Agency (JMA) earthquake catalog to select the aftershock data and replenish the missing early events using the later complete part of each aftershock sequence. The time windows for each sequence span from 6 months before the mainshock to three months after. The semi-automatic spatial selection uses a clustering method for the epicentral selection of earthquakes. The results obtained for the original JMA catalog and replenished datasets are compared to get insight into the biases that the missing early aftershocks may cause on the Omori-Utsu law parameters’ estimation, characterizing the aftershock decay with time from the mainshock. We have also compared the Omori-Utsu law parameter estimates for two datasets following the same mainshock; the first dataset is the replenished sequence, while the second dataset has been obtained by waveform-based analysis to detect early aftershocks that are not recorded in the JMA catalog. Our results demonstrate that the Omori-Utsu law parameters estimated for the replenished datasets are robust with respect to the threshold magnitude used for the analyzed datasets. Even when using aftershock time windows as short as three days, the replenished datasets provide stable Omori-Utsu law parameter estimations. The p-values for all the analyzed sequences are about 1.1 and c-values are significantly smaller compared to those of original datasets. Our findings prove that the replenishment method is a fast, reliable approach to address the missing aftershock problem.     

GPS资料反映的日本东北MW9.0地震的孕震特征

在对海洋板块俯冲型地震孕震模式的分阶段变形特征进行分析的基础上,结合同震位错反演结果分析了M_W9.0级地震的同震变形特征,分析过程中通过精度检验讨论了该反演结果的可靠性.通过对震前和同震GPS结果的对比分析,讨论了二者的差异性.GPS应变(率)结果表明,震前日本岛应变积累主要反映了太平洋板块和菲律宾板块的俯冲作用,同震结果表现为指向震源方向拉张应变的释放,对日本岛的主要影响区域介于35°N和43°N之间.震前和同震GPS剖面结果反映的变形特征具有互补性,但量值相差上百倍.震前的GPS速度、应变率剖面和时间序列结果表明靠近日本岛东海岸一侧可能存在变形趋于极限现象.通过对此次地震可能前兆的分析表明,中长期预测方面、震前GPS时间序列的趋势性偏离、前震活动、震源区b值降低等现象在一定程度上反映了此次地震的孕震特征.     

GNSS在中国大陆的地震预测应用研究进展与展望

GNSS技术的快速发展为地震预测研究提供了前所未有的大尺度、高精度的观测结果,为强震变形模型的发展提供了可靠的观测约束。本文针对GNSS技术在中国大陆地震预测中的应用,系统梳理了断裂带滑动特征描述、变形场动态演化解析、应变集中过程识别、潜在震源危险程度判断等方面的研究进展。通过典型震例总结了GNSS资料在长、中、短临不同的地震预测阶段的应用。针对地震中长期预测,基于构造动力过程给出了强震危险性时空逼近的科学思路,即“板块边界动力作用—大-中尺度动态形变场—应力应变增强/集中区—孕震危险段中短期危险性的时空逼近”的过程。在此基础上,针对GNSS监测能力提升、地震孕育过程相关的多尺度地壳形变动态信息获取、GNSS多参量动力学模型构建及产出等问题进行了讨论和发展展望。总体而言,GNSS技术的应用显著增强了我国地震预测的地壳形变观测基础支撑,丰富了对大陆地震孕育发生物理过程的科学认识,推动了大陆地震预测科学思路和预测方法的发展,并促进了地震预测由经验预测向物理预测的拓展。     

Anomalous Variations of Ionosphere Associated with the Strong Earthquake at Pakistan-Iran Border at a Low Latitude Station Agra,India

In this paper, we analyze the TEC data for April 2013 observed at Agra station, India (geogr. lat. 27.2° N, long. 78° E) to examine the effect of earthquake of magnitude M = 7.8 which occurred on 16 April 2013 at Pakistan–Iran border region. We process the TEC data using the s statistical criterion to find out anomalous variation in TEC data. We also study the VLF propagation signal from NPM, Hawaii (21.42° N, 158° W), which is monitored at the same station (Agra station) in the light of this earthquake as well as solar flares. The nighttime fluctuation method is used to analyze the VLF data for the period of ±5 days from the day of earthquake (11–21 April 2013). The anomalous enhancements and depletions are found in TEC data on 1–9 days before the occurrence of event.     

Long-range Earthquake Forecasting with Every Earthquake a Precursor According to Scale

Scaling relations previously derived from examples of the precursory scale increase before major earthquakes show that the precursor is a long-term predictor of the time, magnitude, and location of the major earthquake. These relations are here taken as the basis of a stochastic forecasting model in which every earthquake is regarded as a precursor. The problem of identifying those earthquakes that are actually precursory is thus set aside, at the cost of limiting the strength of the resulting forecast. The contribution of an individual earthquake to the future distribution of hazard in time, magnitude and location is on a scale determined, through the scaling relations, by its magnitude. Provision is made for a contribution to be affected by other earthquakes close in time and location, e.g., an aftershock may be given low weight. Using the New Zealand catalogue, the model has been fitted to the forecasting of shallow earthquakes exceeding magnitude 5.75 over the period 1965–2000. It fits the data much better than a baseline Poisson model with a location distribution based on proximity to the epicenters of past earthquakes. Further, the model has been applied, with unchanged parameters, to the California region over the period 1975–2001. There also, it performs much better than the baseline model fitted to the same region over the period 1951–1974; the likelihood ratio is 10¹⁵ in favor of the present model. These results lend credence to the precursory scale increase phenomenon, and show that the scaling relations are pervasive in earthquake catalogues. The forecasting model provides a new baseline model against which future refinements, and other proposed models, can be tested. It may also prove to be useful in practice. Its applicability to other regions has still to be established.     

Application of fault diagnosis method in earthquake prediction

Thedevelopmentandthevariationoftheearthquakeprocessisaverycomplicatedoneandaffectedandlimitedbymanyfactors.Thekeyoftheaccuratepredictionistosetupadynamiccomprehensivepredictionmodelbasedonthecomprehensionoftheprecursorcharacteristicandtheattentionofthevariationwithtimeofthesefactors.Inthispaper,wetrytoapplythefaultdiagnosistectonictoeanhquakeprediction.Thefaultdiagnosistectonicisanapplicableboundaryscience.Thebasicideaistojudgeifthefaultoccurinthesystemandtodeterminewhereandwhenthefaultoccuran…     

丰满台水管仪记录到的8级大地震前兆波特征浅析

总结了丰满台水管仪两个测项在4次≥8级大震前记录的前兆波,对其特征进行初步分析,认为可能是断层预滑或断裂预扩展产生的,可能是大地震孕育过程中的一种反应,对大震的预报有一定的参考意义。通过对前兆波特征的深入研究可以为地震预测提供有力的依据。     

On the GPS-based ionospheric perturbation after the Tohoku earthquake of March 11, 2011

Based on the data from the GPS receiving networks in Japan and America which have a high time resolution (2 min), two-dimensional (2D) distributions of the variations in the ionospheric total electron content (TEC) are constructed both close to and far from of the epicenter of the submarine earthquake of March 11, 2011 in Japan. Above the epicenter, a diverging multi-period disturbance appears after the main shock due to the acoustic gravity waves. Far from the epicenter, the wave trains associated with the tsunamigenic atmospheric internal gravity waves are revealed. These atmospheric waves significantly advance the arrival of the tsunami signal initially on the Hawaiian islands and then on the western coast of North America. The presence of the tsunami precursor in the form of atmospheric gravity waves is supported by the numerical calculations and by the analysis of the dispersion relation for the waves in the atmosphere. The detected ionospheric responses close and far from the epicenter can be used in the early tsunami warning systems.     

人工神经网络在地震前兆混沌时间序列预测和处理中的应用

人工神经网络是用来模拟人脑智能特点和结构的一种模型,具有很强的非线性映射功能．把它引用到地震前兆观测数据的分析处理中,可为前兆观测更好地服务于地震分析预报开辟出一条新路,也是对人工神经网络方法应用的推广．本文分析了时间序列的可预测性,给出了用人工神经网络预测地震前兆混沌时间序列的方法,并以江宁台和徐州台SQ 型地倾斜仪观测及溧阳台体应变观测的时间序列为例,对其作了预测和处理．结果表明:用该方法处理达到的精度能满足实际工作的需要,因而该方法在今后的实际地震分析预报工作中具有重要应用价值．      

Pattern Informatics and its Application for Optimal Forecasting of Large Earthquakes in Japan

Pattern Informatics (PI) technique can be used to detect precursory seismic activation or quiescence and make an earthquake forecast. Here we apply the PI method for optimal forecasting of large earthquakes in Japan, using the data catalogue maintained by the Japan Meteorological Agency. The PI method is tested to forecast large (magnitude m ≥ 5) earthquakes spanning the time period 1995–2004 in the Kobe region. Visual inspection and statistical testing show that the optimized PI method has forecasting skill, relative to the seismic intensity data often used as a standard null hypothesis. Moreover, we find in a retrospective forecast that the 1995 Kobe earthquake (m = 7.2) falls in a seismically anomalous area. Another approach to test the forecasting algorithm is to create a future potential map for large (m ≥ 5) earthquake events. This is illustrated using the Kobe and Tokyo regions for the forecast period 2000–2009. Based on the resulting Kobe map we point out several forecasted areas: The epicentral area of the 1995 Kobe earthquake, the Wakayama area, the Mie area, and the Aichi area. The Tokyo forecast map was created prior to the occurrence of the Oct. 23, 2004 Niigata earthquake (m = 6.8) and the principal aftershocks with 5.0 ≤ m. We find that these events were close to in a forecasted area on the Tokyo map. The PI technique for regional seismicity observation substantiates an example showing considerable promise as an intermediate-term earthquake forecasting in Japan.     

THE METHOD OF CONSTRUCTING IONOSPHERIC TEC BACKGROUND FIELD BASED ON SVR MODEL

There are many factors related to the variations of TEC, and the changes of TEC caused by earthquake only occupy a small portion. Therefore, it is vital how to exclude the ionospheric interference of non-seismic factors accurately in the process of seismic ionospheric anomaly extraction. This study constructed a TEC non-seismic dynamic background field considering the influence of solar and geomagnetic activities. Firstly, the TEC components of half-year cycle and annual cycle are extracted by wavelet decomposition. Then, it establishes a regression model between TEC in which periodic factors are removed and solar activity index, geomagnetic activity index with SVR method(support vector regression)in non-seismic period. Finally, based on the constructed model, the solar activity index and geomagnetic activity index is used to reconstruct aperiodic components of TEC in earthquake's period. From the reconstructed aperiodic components of TEC plus the half-year periodic components and annual periodic components of TEC in the same period, the non-seismic dynamic background field is obtained. Comparing the residuals relative to original TEC values in non-seismic dynamic background field and traditional sliding window background, there are apparent monthly periodic change and semi-annual periodic change in the residuals of sliding window background, which can have obvious impacts on the subsequent seismic ionospheric anomaly detection. In order to test the validity of seismic TEC anomaly detection based on the background field construction method, this paper investigated the long time series TEC anomalies near Wenchuan city(30°N, 100°E)from March 1 to September 26 in 2008. It is found that under the condition of non-seismic disturbance such as solar activity and geomagnetic activity, TEC abnormal disturbance is rarely detected by non-seismic dynamic background field method, when compared with the traditional sliding time-window method. And before the earthquake, more TEC anomalies were detected based on the proposed method, also, they were more intense than those extracted by sliding window method. Therefore, the TEC background field construction method based on SVR(support vector regression)has superiorities in both system errors elimination, which are caused by solar, geomagnetism, the non-seismic ionospheric disturbance events and periodic fluctuations of TEC, and in reducing the false alarm rate of seismic TEC anomaly. Moreover, it can also improve the seismic TEC anomaly detection ability. In addition, this paper analyzed the time-spatial distribution of TEC anomaly before three earthquakes on May 12, August 21 and August 30, 2008. They were mainly negative abnormal perturbations and often distributed on the equatorial side of epicenter.     

汶川大地震前后电离层电子含量的扰动分析

为了分析地震前后电离层电子含量的变化,本文选取四川汶川发生8.0级地震前后10天电离层中的电子含量作为研究对象,使用IGS网站提供的VTEC数据,采取统计学分析方法,结合太阳和地磁活动信息,对此次地震前后电离层VTEC资料进行了详细的统计分析和讨论,数据分析结果表明大地震前电离层电子含量会出现明显扰动,电离层中电子含量的异常变化有可能是地震发生的前兆信息.     

试论大地电磁测深法在地震前兆探索中的进一步应用

从大地电磁测深理论分析和实际资料数字试验出发,我们探讨了有关提高大地电磁测深法监测深部电性变化前兆效能的某些问题.研究结果表明,在所定义的几种大地电磁视电阻率中,尚未加以利用的阻抗实部视电阻率具有某些独特的优点,可能成为监测深部电性变化的一个较好的参数指标;应根据各记录道误差的分配,计算过程误差的传播和累加对不同大地电磁响应函数产生不同的影响,选择相对精度较高的参数作为主要监测参量;而识别深部电性变化的多参数综合判别方法应是基本的分析方法.实际观测到的反映地震前兆的某些大地电磁资料证实了上述论证.