基于粒子群算法的岩体微震源分层定位方法
摘要
针对微震经典定位法速度模型给不准和联合定位法震源位置、发震时间和微震传播速度相互关联,解不唯一的问题,提出一种微震源定位分层处理方法,先对微震信号进行消噪、分波、到时修正和劣质信号剔除等一系列处理,初步获取正确的有效信号;然后以相邻两传感器监测到时之差与计算到时之差的残差平方和最小为目标,利用粒子群算法,识别微震源位置和速度模型;接着,根据识别到的微震源位置和速度模型,以传感器监测到时和计算到时的残差平方和最小为目标,直接求解微震源发震时间的解析解;最后,再次结合矿山实际开采现状反分析有效微震信号选取的正确性和微震源定位的准确性,必要时再次对微震信号进行处理和定位,较好地解决经典法速度模型给不准和联合法解不唯一的问题。与经典法相比提高了微震定位精度,与联合法相比提高收敛速度和解的稳定性;关联性分析表明某些震源坐标在使用分层法定位时和速度具有一定的关联性,并给出震源坐标和速度相互关联的必要条件和相互关联的几个特殊位置;算法性能分析表明为了进一步提高算法的收敛速度、定位精度和解的稳定性,传感器布置要尽量:(1)使重点关注的区域位于传感器阵列之内,且距离传感器尽量近,(2)避免可能发生微震的震源处于能使震源坐标和速度相互关联的位置上。现场爆破试验进一步验证微震源分层定位方法的可行性;最后讨论几种速度模型的选取,分析几种速度模型的优劣及工程应用的可能性。
According to the fact that it is difficult for typical microseism location(TMSL) algorithm to select a proper velocity structure and that it is not easy for joint microseism location(JMSL) algorithm that velocity structure,microseismic source coordinates and time of microseismic occurrence are associated with each other to identify unique solution,the intelligence microseism source location algorithm with hierarchical strategy(IMSHL) is suggested. Firstly,effective microseism signals are acquired by removing background noise,dividing P-wave, S-wave,correcting monitored arrival time,deleting poor microseism signals and so on;secondly,velocity structure and microseismic source coordinates are identified using particle swarm optimization(PSO) based on minimizing residual sum of squares of calculated and monitored difference of arrival time between two neighboring sensors;thirdly,analytic solution of microseism occurrence time can be gained by minimizing residual sum of squares of calculated and monitored arrival time based on the identified velocity structure and microseismic source;finally, the correction of selecting microseism signals and the accuracy of microseism source location are verified by back analysis based on the in-situ state of practical exploitation,and if necessary,source location and identification of microseism signals should be carried out for the second time. A simple example shows that convergence precision of the algorithm is improved compared with TMSL,and convergence velocity and the stability of the algorithm are also improved compared with JMSL. The correlation of coordinates of certain seismic sources and velocity for IMSHL is shown by correlation analysis,and its necessary condition and locations of several special seismic source are also provided. The analysis of the performance of IMSHL shows that,in order to further improve convergence velocity,precision and stability of solution,microseismic source should be in ranges of sensor array and close to the sensors as much as possible by proper disposal of the sensors;and the sensors should not be in the location that makes coordinates of seismic sources and velocity associated with each other. Lastly,the feasibility and advantages of IMSHL are verified by an in-situ blasting test and the applicability of several velocity structures is discussed and analyzed.
According to the fact that it is difficult for typical microseism location(TMSL) algorithm to select a proper velocity structure and that it is not easy for joint microseism location(JMSL) algorithm that velocity structure,microseismic source coordinates and time of microseismic occurrence are associated with each other to identify unique solution,the intelligence microseism source location algorithm with hierarchical strategy(IMSHL) is suggested. Firstly,effective microseism signals are acquired by removing background noise,dividing P-wave, S-wave,correcting monitored arrival time,deleting poor microseism signals and so on;secondly,velocity structure and microseismic source coordinates are identified using particle swarm optimization(PSO) based on minimizing residual sum of squares of calculated and monitored difference of arrival time between two neighboring sensors;thirdly,analytic solution of microseism occurrence time can be gained by minimizing residual sum of squares of calculated and monitored arrival time based on the identified velocity structure and microseismic source;finally, the correction of selecting microseism signals and the accuracy of microseism source location are verified by back analysis based on the in-situ state of practical exploitation,and if necessary,source location and identification of microseism signals should be carried out for the second time. A simple example shows that convergence precision of the algorithm is improved compared with TMSL,and convergence velocity and the stability of the algorithm are also improved compared with JMSL. The correlation of coordinates of certain seismic sources and velocity for IMSHL is shown by correlation analysis,and its necessary condition and locations of several special seismic source are also provided. The analysis of the performance of IMSHL shows that,in order to further improve convergence velocity,precision and stability of solution,microseismic source should be in ranges of sensor array and close to the sensors as much as possible by proper disposal of the sensors;and the sensors should not be in the location that makes coordinates of seismic sources and velocity associated with each other. Lastly,the feasibility and advantages of IMSHL are verified by an in-situ blasting test and the applicability of several velocity structures is discussed and analyzed.
引文
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[20]李爱兵,周爱民,尹彦波,等.柿竹园多金属矿床群空区条件下的崩落特性研究[J].岩石力学与工程学报,2008,27[11]:2234–2243.[LI Aibing,ZHOU Aimin,YIN Yanbo,et al.Research on caving characteristics of Shizhuyuan polymetallic mine under multi-mined-out-area conditions[J].Chinese Journal of Rock Mechanics and Engineering,2008,27[11]:2234–2243.[in Chinese]]
[2]GEIGER L.Probability method for the determination of earthquake epicenters from arrival time only[J].Bull.St.Louis.Univ.,1912,8:60–71.
[3]LIENERT B R,BERG E,FRAZER L N.Hypocenter:an earthquake location method using centered,scaled,and adaptively damped least squares[J].Bulletin of the Seismological Society of America,1986,76[3]:771–783.
[4]NELSON G D,VIDALE J.E.Earthquake locations by3D finite difference travel times[J].Bulletin of the Seismological Society of America,1990,80[2]:395–410.
[5]赵仲和.多重模型地震定位程序及其在北京台网的应用[J].地震学报,1983,5[2]:242–254.[ZHAO Zhonghe.An earthquake location program with multiple velocity model and its application to the Beijing seismic network[J].Acta Seismologica Sinica,1983,5[2]:242–254.[in Chinese]]
[6]PRUGGER A F,GENDZWILL D J.Microearthquake location:a nonlinear approach that makes use of a simplex stepping procedure[J].Bulletin of the Seismological Society of America,1988,78[2]:799–815.
[7]CROSSON R S.Crustal structure modeling of earthquake data1,simultaneous least squares estimation of hypocenter and velocity parameters[J].Journal of Geophysical Research,1976,81[17]:3036–3046.
[8]AKI K,LEE W H K.Determination of three-dimensional velocity anomalies under a seismic array using first P arrival times from local earthquakes,part1:a homogeneous initial model[J].Journal of Geophysical Research,1976,81[23]:4381–4399.
[9]PAVLIS G,BOOKER J R.The mixed discrete-continuous inverse problem:application of the simultaneous determination of earthquake hypocenters and velocity structure[J].Journal of Geophysical Research,1980,85[9]:4801–4810.
[10]赵仲和.北京地区地震参数与速度结构的联合测定[J].地球物理学报,1983,26[2]:131–139.[ZHAO Zhonghe.The joint determination of hypocenter parameters and velocity structure in the Beijing area of China[J].Chinese Journal of Geophysics,1983,26[2]:131–139.[in Chinese]]
[11]刘福田.震源位置和速度结构的联合反演[I]——理论和方法[J].地球物理学报,1984,27[2]:167–175.[LIU Futian.Simultaneous inversion of earthquake hypocenters and velocity structure[I]:theory and method[J].Chinese Journal of Geophysics,1984,27[2]:167–175.[in Chinese]]
[12]郭贵安,冯锐.新丰江水库三维速度结构和震源参数的联合反演[J].地球物理学报,1992,35[3]:331–341.[GUO Fu′an,FENG Rui.The joint inversion of3D velocity structure and source parameters in Xinfengjiang reservoir[J].Chinese Journal of Geophysics,1992,35[3]:331–341.[in Chinese]]
[13]马宏生,张国民,周龙泉,等.川滇地区中小震重新定位与速度结构的联合反演研究[J].地震,2008,28[2]:29–38.[MA Hongsheng,ZHANG Guomin,ZHOU Longquan,et al.Simultaneous inversion of small earthquake relocation and velocity structure in Sichuan-Yunnan area[J].Earthquake,2008,28[2]:29–38.[in Chinese]]
[14]LEE W H K,STEWART S W.Principles and applications of microearthquake networks[M].New York:Academic Press,1981.
[15]KENNEDY J,EBERHART R C.Particle swarm optimization[C]//International Conference on Neural Networks.New Jersey:IEEE Service Center,1995:1942–1948.
[16]YUHUI S,EBERHART R C.A modified particle swarm optimizer[C]//Proceedings of IEEE International Conference on Evolutionary Computation.Anchorage:[s.n.],1998:69–73.
[17]陈炳瑞,冯夏庭.压缩搜索空间与速度范围粒子群优化算法[J].东北大学学报[自然版],2005,26[5]:488–491.[CHEN Bingrui,FENG Xiating.Particle swarm optimization with constriction ranges of search space and velocity[J].Journal of Northeastern University[Natural Science],2005,26[5]:488–491.[in Chinese]]
[18]袁节平.柿竹园矿的采矿地压及其防治[J].矿业研究与开发,1997,17[4]:26–29.[YUAN Jieping.Mining induced ground pressure and its prevention in Shizhuyuan mine[J].Ming Research and Development,1997,17[4]:26–29.[in Chinese]]
[19]刘小林.预测模型在柿竹园矿地压灾害预测预报中的应用研究[J].矿业研究与开发,2008,28[3]:56–58.[LIU Xiaolin.Application of a forecast model to forecast of ground pressure disasters in Shizhuyuan[J].Ming Research and Development,2008,28[3]:56–58.[in Chinese]]
[20]李爱兵,周爱民,尹彦波,等.柿竹园多金属矿床群空区条件下的崩落特性研究[J].岩石力学与工程学报,2008,27[11]:2234–2243.[LI Aibing,ZHOU Aimin,YIN Yanbo,et al.Research on caving characteristics of Shizhuyuan polymetallic mine under multi-mined-out-area conditions[J].Chinese Journal of Rock Mechanics and Engineering,2008,27[11]:2234–2243.[in Chinese]]
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