基于爆炸現(xiàn)場痕跡反演爆源參數(shù)方法及應用
發(fā)布時間：2018-08-31 17:43
【摘要】：爆源參數(shù)反演在爆炸現(xiàn)場痕跡分析中是重點關(guān)注的問題。為了從爆炸現(xiàn)場痕跡數(shù)據(jù)中獲得爆源參數(shù)信息（爆源藥量、爆源埋深/懸空高度），本文以爆炸產(chǎn)生的痕跡數(shù)據(jù)為基礎(chǔ)，采用數(shù)據(jù)驅(qū)動的前沿技術(shù)（廣義神經(jīng)網(wǎng)絡(luò)GRNN、粒子群PSO、支持向量機SVM、以及非線性非穩(wěn)態(tài)信號分析算法HHT/EMD）建立了基于爆炸現(xiàn)場痕跡反演爆源參數(shù)的方法。最終將這些方法應用于爆源參數(shù)反演系統(tǒng)軟件中，并得到初步應用。本文主要研究內(nèi)容和主要成果如下： 一、基于非線性回歸的數(shù)學理論，將廣義神經(jīng)網(wǎng)絡(luò)引入到對土介質(zhì)炸坑痕跡反演爆源參數(shù)的模型構(gòu)建中：以炸坑直徑與深度、土壤類型作為GRNN的輸入層，爆源藥量與埋深作為GRNN的輸出層，進行模型訓練與構(gòu)建，并進行了實驗驗證。與傳統(tǒng)經(jīng)驗公式反演出的結(jié)果進行了比較，粘土、沙土、沙粘混合土壤介質(zhì)中的炸坑痕跡反演爆源藥量和爆源埋深的精度有明顯提高：基于GRNN的炸坑痕跡反演得到的藥量及埋深相對誤差在30%以內(nèi)；建立的算法對藥量及埋深的反演誤差平均值分別為：15.41%，，16.93%，與傳統(tǒng)經(jīng)驗公式相比精度均有明顯提升。 二、建立了分維數(shù)值與爆炸地震波幅值衰減指數(shù)的關(guān)系，并針對近年來對爆炸地震波振動信號進行能量和頻譜特征分析常用的數(shù)據(jù)處理方法EMD/HHT進行了改進。通過PSO-SVM組合模型的應用與優(yōu)化，有效地解決端點效應，提高信號分解的準確度和可信度；并利用改進的EMD/HHT方法找到了爆炸地震波信號的局部時間尺度本征信息，反演了微差爆破中的微差延期時間。同時，基于GRNN建立了根據(jù)爆炸震動參數(shù)反演爆源參數(shù)的方法，確定了爆源藥量與震動峰值速度、爆心距的隱式非線性關(guān)系，并將反演結(jié)果與實測結(jié)果進行對比分析，反演藥量的誤差平均值由薩道夫斯基經(jīng)驗公式的51.57%提高到了13.32%。 三、構(gòu)建了基于爆炸現(xiàn)場的玻璃痕跡反演爆源參數(shù)的方法。根據(jù)建筑常用的平板玻璃的幾何特征量（玻璃長、寬、厚度）和爆心距，利用神經(jīng)網(wǎng)絡(luò)建立爆源藥量反演算法，并進行了實驗驗證，結(jié)果表明爆源藥量反演誤差平均值從薩道夫斯基經(jīng)驗公式的51.15%提高到了19.3%。同時，對反演中玻璃破壞的臨界壓力數(shù)據(jù)庫表，利用PSO-SVM的數(shù)據(jù)延拓方法對小厚度尺寸玻璃所能承受臨界超壓值進行了補充，為后期玻璃破壞臨界超壓數(shù)據(jù)庫的使用提供了數(shù)據(jù)保障。 四、基于單類痕跡反演，開展了基于爆炸現(xiàn)場多類痕跡系統(tǒng)地反演爆源參數(shù)的探索研究，初步建立了綜合爆炸現(xiàn)場三類主要痕跡因素（炸坑痕跡、玻璃痕跡、爆炸震動記錄）反演爆源參數(shù)的方法，包括：炸坑—玻璃綜合反演爆源參數(shù)、炸坑—爆炸震動綜合反演爆源參數(shù)和炸坑—玻璃—爆炸震動綜合反演爆源參數(shù)的方法。分別將三因素反演效果與各自單因素反演結(jié)果進行對比，結(jié)果表明，綜合三因素反演精度高于單因素反演。 五、本文將上述三種單因素反演方法以及三種多因素反演方法應用到軟件“爆源參數(shù)反演系統(tǒng)”的核心模塊“爆源特征反演分析”中。該軟件是當前和未來爆炸案件偵破的重要手段，為調(diào)查人員及相關(guān)專家等提供了爆炸現(xiàn)場分析支持工具。 本文研究工作的主要學術(shù)貢獻在于將數(shù)據(jù)驅(qū)動理念與技術(shù)引入到由爆炸現(xiàn)場的各類痕跡數(shù)據(jù)獲取爆源參數(shù)信息的過程中，提出了多種爆源參數(shù)反演方法，并經(jīng)檢驗與驗證比傳統(tǒng)方法具有更高的精度，對于爆炸現(xiàn)場分析具有實用意義，將為爆炸案件的分析與偵破工作提供技術(shù)支持。
[Abstract]:Inversion of explosive source parameters is an important problem in the analysis of explosive traces. In order to obtain the information of explosive source parameters (explosive charge, explosive source buried depth / suspended height) from the explosive traces data, this paper uses the data-driven frontier technology (generalized neural network GRNN, particle swarm optimization, support direction) based on the traces data generated by explosion. SVM and non-linear unsteady-state signal analysis algorithm HHT/EMD are used to retrieve the parameters of explosive source based on the traces of explosion site.
Firstly, based on the mathematical theory of nonlinear regression, the generalized neural network is introduced into the model construction of inversion of blasting source parameters for the blasting pit traces in soil medium: the diameter and depth of the blasting pit, the soil type as the input layer of GRNN, the explosive charge and the buried depth as the output layer of GRNN, the model training and construction are carried out, and the experimental verification is carried out. Comparing the inversion results of the empirical formulas, the precision of inversion of explosive charge and depth of explosive source in clay, sandy soil and sand-clay mixed soil has been improved obviously: the relative error of explosive charge and depth obtained by inversion of explosive crater trace based on GRNN is less than 30%; the inversion error of explosive quantity and depth is equal to that of the established algorithm. The mean value is 15.41%, 16.93%, respectively, and the accuracy is obviously improved compared with the traditional empirical formula.
Secondly, the relationship between the fractal dimension and the amplitude attenuation index of explosive seismic wave is established, and the EMD/HHT data processing method, which is commonly used to analyze the energy and spectrum characteristics of explosive seismic wave vibration signals in recent years, is improved. The end effect is effectively solved and the accuracy of signal decomposition is improved by the application and optimization of PSO-SVM combined model. The local time scale intrinsic information of explosive seismic wave signal is found by using the improved EMD/HHT method, and the millisecond delay time in millisecond blasting is inverted. The inversion results are compared with the measured ones. The average error of the inversion is increased from 51.57% to 13.32%.
Thirdly, a method of inversion of explosive source parameters based on glass traces in explosion site is constructed. According to the geometric characteristics (length, width and thickness of glass) and the distance between explosive centers, the inversion algorithm of explosive source charge is established by using neural network. The experimental results show that the average inversion error of explosive source charge is from Sadovsky. At the same time, the data continuation method of PSO-SVM is used to supplement the critical overpressure value of glass with small thickness, which provides a data guarantee for the use of the critical overpressure database of glass failure in later period.
Fourthly, based on the inversion of single trace, the exploration and study of inversion of blasting source parameters are carried out systematically based on multi-trace in the explosion site. The inversion methods of blasting source parameters for three main trace factors (crater trace, glass trace, blasting vibration record) in the comprehensive explosion site are preliminarily established, including: crater-glass comprehensive inversion of blasting source parameters, blasting crater. The three-factor inversion results are compared with the single-factor inversion results. The results show that the inversion accuracy of the three-factor inversion is higher than that of the single-factor inversion.
Fifthly, the above three single factor inversion methods and three multi-factor inversion methods are applied to the core module of the software "Explosion Source Parameter Inversion System", "Explosion Source Characteristic Inversion Analysis". The software is an important means for the detection of current and future explosion cases, and provides an explosion site analysis branch for investigators and related experts. Hold tools.
The main academic contribution of this research work lies in introducing the concept and technology of data driving into the process of obtaining the information of explosive source parameters from various trace data of explosion site, and putting forward several inversion methods of explosive source parameters, which are proved to be more accurate than the traditional methods and have practical significance for explosion site analysis. It will provide technical support for the analysis and detection of explosion cases.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：X82
本文編號：2215704
[Abstract]:Inversion of explosive source parameters is an important problem in the analysis of explosive traces. In order to obtain the information of explosive source parameters (explosive charge, explosive source buried depth / suspended height) from the explosive traces data, this paper uses the data-driven frontier technology (generalized neural network GRNN, particle swarm optimization, support direction) based on the traces data generated by explosion. SVM and non-linear unsteady-state signal analysis algorithm HHT/EMD are used to retrieve the parameters of explosive source based on the traces of explosion site.
Firstly, based on the mathematical theory of nonlinear regression, the generalized neural network is introduced into the model construction of inversion of blasting source parameters for the blasting pit traces in soil medium: the diameter and depth of the blasting pit, the soil type as the input layer of GRNN, the explosive charge and the buried depth as the output layer of GRNN, the model training and construction are carried out, and the experimental verification is carried out. Comparing the inversion results of the empirical formulas, the precision of inversion of explosive charge and depth of explosive source in clay, sandy soil and sand-clay mixed soil has been improved obviously: the relative error of explosive charge and depth obtained by inversion of explosive crater trace based on GRNN is less than 30%; the inversion error of explosive quantity and depth is equal to that of the established algorithm. The mean value is 15.41%, 16.93%, respectively, and the accuracy is obviously improved compared with the traditional empirical formula.
Secondly, the relationship between the fractal dimension and the amplitude attenuation index of explosive seismic wave is established, and the EMD/HHT data processing method, which is commonly used to analyze the energy and spectrum characteristics of explosive seismic wave vibration signals in recent years, is improved. The end effect is effectively solved and the accuracy of signal decomposition is improved by the application and optimization of PSO-SVM combined model. The local time scale intrinsic information of explosive seismic wave signal is found by using the improved EMD/HHT method, and the millisecond delay time in millisecond blasting is inverted. The inversion results are compared with the measured ones. The average error of the inversion is increased from 51.57% to 13.32%.
Thirdly, a method of inversion of explosive source parameters based on glass traces in explosion site is constructed. According to the geometric characteristics (length, width and thickness of glass) and the distance between explosive centers, the inversion algorithm of explosive source charge is established by using neural network. The experimental results show that the average inversion error of explosive source charge is from Sadovsky. At the same time, the data continuation method of PSO-SVM is used to supplement the critical overpressure value of glass with small thickness, which provides a data guarantee for the use of the critical overpressure database of glass failure in later period.
Fourthly, based on the inversion of single trace, the exploration and study of inversion of blasting source parameters are carried out systematically based on multi-trace in the explosion site. The inversion methods of blasting source parameters for three main trace factors (crater trace, glass trace, blasting vibration record) in the comprehensive explosion site are preliminarily established, including: crater-glass comprehensive inversion of blasting source parameters, blasting crater. The three-factor inversion results are compared with the single-factor inversion results. The results show that the inversion accuracy of the three-factor inversion is higher than that of the single-factor inversion.
Fifthly, the above three single factor inversion methods and three multi-factor inversion methods are applied to the core module of the software "Explosion Source Parameter Inversion System", "Explosion Source Characteristic Inversion Analysis". The software is an important means for the detection of current and future explosion cases, and provides an explosion site analysis branch for investigators and related experts. Hold tools.
The main academic contribution of this research work lies in introducing the concept and technology of data driving into the process of obtaining the information of explosive source parameters from various trace data of explosion site, and putting forward several inversion methods of explosive source parameters, which are proved to be more accurate than the traditional methods and have practical significance for explosion site analysis. It will provide technical support for the analysis and detection of explosion cases.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：X82
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