仪器地震烈度实时预测方法研究

发布时间：2018-04-30 11:57

本文选题：地震预警 + 烈度预测　；参考：《中国地震局工程力学研究所》2017年硕士论文

【摘要】：地震预警是近二十年来新发展起来的一种减小地震灾害的有效手段,其利用从地震发生到破坏性地震波到来之前的这段时间快速获取并向公众发布地震信息,从而达到减小人员伤亡和财产损失的目的。越来越多的研究者通过利用地震实时波形快速计算的地震仪器烈度来快速评估地震破坏程度。为了能够在地震预警的过程中利用P波初始信息快速获得地震仪器烈度的估计值,本文提出两种实时持续预测仪器烈度的办法,并利用研究成果对四次不同台网记录到的地震进行测试和分析。本文所做主要工作如下:(1)搜集并选取了1999-2016年日本KiK-net强震台网记录到的629次地震,以及基于各自台网记录到的2008年汶川地震、1999年集集地震、2014年鲁甸地震的强震记录,对这些强震记录进行了预处理和筛选,并通过STA/LTA法和AIC法对震相进行了自动捡拾。对所有选择台站的震源参数信息以及触发后1-20秒的7个地震动参数进行了提取,并计算每个台站的最终仪器烈度。(2)提出一种持续预测地震仪器烈度的办法,利用P波触发后实时的PGA和PGV对最终仪器烈度进行持续预测。地震发生后,仪器烈度的变化呈现一定的规律性,该方法通过函数来描述这种烈度的增长形式,并找出函数中的参数与震源距的关系。对上述方法的预测效果进行检验,并对结果进行改进,将每秒的烈度值分开利用,对台站触发20秒内的烈度值和最终烈度值的关系进行了统计,得到了三种模型。最后,本文对模型进行了检验,并得到了相对稳定可靠的结果。(3)提出了一种基于人工神经元网络的地震仪器烈度办法,同样利用P波触发后的信息,根据地震预警中信息获取的实际情况建立了三种模型,分别对P波触发后1-20秒的数据进行统计分析,并选择合适的样本对网格进行训练。三种模型有着不同的适用范围,预测结果也略有不同,随着P波触发后时间的推进,模型的预测结果也在逐步改善,到达20秒时,可得到相对较好的预测结果。(4)使用上述两种仪器烈度预测方法对4次不同台网记录到的地震事件进行了烈度预测,并对具有代表性的11个台站的预测结果进行了分析和对比。在此基础上,确定了模型的适用范围,并总结了一套仪器地震烈度连续预测的方法。
[Abstract]:Earthquake warning is an effective means to reduce earthquake disaster, which is developed in the past two decades. It uses the period from earthquake occurrence to the arrival of destructive seismic wave to quickly obtain and release earthquake information to the public. In order to reduce casualties and property losses. More and more researchers estimate the degree of earthquake damage by using seismic real-time waveform to calculate the intensity of seismic instruments quickly. In order to obtain the estimating value of seismic instrument intensity quickly by using the initial information of P wave in the process of earthquake early warning, this paper puts forward two methods to predict the intensity of the instrument in real time and continuously. Four earthquakes recorded by different network are tested and analyzed by using the research results. The main work of this paper is as follows: (1) collected and selected 629 earthquakes recorded by Japan's KiK-net strong earthquake network from 1999 to 2016, as well as strong earthquake records of 2008 Wenchuan earthquake, 1999 Jiji earthquake and 2014 Ludian earthquake recorded by the Japanese strong earthquake network. These strong seismic records were pretreated and screened, and the seismic phases were automatically picked up by STA/LTA and AIC methods. The source parameter information of all selected stations and 7 ground motion parameters of 1-20 seconds after trigger are extracted, and the final instrument intensity of each station is calculated. Real time PGA and PGV after P wave trigger are used to predict the final instrument intensity continuously. After the earthquake occurs, the variation of the instrument intensity presents a certain regularity. This method describes the increasing form of the intensity through the function, and finds out the relationship between the parameters of the function and the focal distance. The prediction results of the above methods are tested, and the results are improved. The intensity values per second are used separately, and the relationship between the intensity values and the final intensity values within 20 seconds triggered by the station is statistically analyzed, and three models are obtained. Finally, the model is tested, and a relatively stable and reliable result is obtained. A method of seismic instrument intensity based on artificial neural network is proposed, which also uses the information of P wave trigger. According to the actual situation of information acquisition in earthquake early warning, three kinds of models are established, the data of 1-20 seconds after P wave trigger are statistically analyzed, and the appropriate samples are selected to train the grid. The three models have different range of application, and the prediction results are slightly different. With the advance of the time after P-wave triggering, the prediction results of the three models are gradually improved, reaching 20 seconds. A relatively good prediction result can be obtained. (4) using the above two kinds of instrument intensity prediction methods, the intensity prediction of 4 seismic events recorded by different network is carried out, and the prediction results of 11 representative stations are analyzed and compared. On this basis, the applicable range of the model is determined, and a set of methods for continuous prediction of the seismic intensity of the instrument are summarized.
【学位授予单位】：中国地震局工程力学研究所
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P315.7

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