-
摘要: 针对波速分层的区域岩体，在异向波速模型的基础上，对垂向上的应力波按岩体波速值大小作分段区别，推导震源应力波走时关系式，建立分层速度定位目标函数，基于此提出一种由参数准备、层速度反演、微震定位三个模块组成的分层速度定位模型SV，并采用遗传算法进行优化求解.然后，对分层速度定位模型在已构建微震监测系统的白鹤滩水电站左岸岩质边坡进行验证.微震事件重定位结果表明，分层速度定位模型定位微震事件的最大、最小和平均偏离层内错动带程度指标较单一速度模型分别降低了57.17%、36.51%和57.35%，证明了定位模型在波速分层的区域岩体微震定位应用中比单一速度定位模型更加合理可靠.Abstract: As a kind of three-dimensional detection of seismic events, microseismic (MS) monitoring techniques have been widely used in the world for many years to assess the stability of surrounding rock. The location of MS events is foundation of MS monitoring. Resulting from anisotropy and inhomogeneity of regional rock masses, the velocity of stress waves may be different in different directions and areas. So, it is difficult to figure out a reasonably accurate location. Aimed at the regional rock masses with stratified velocities, a simplified and stable stratified velocity location model (SV) is proposed.The SV model is proposed based on the anisotropic velocity model, which simplifies the propagation path of stress waves into a straight line, which consists of horizontal and vertical velocities, that is, one for underground sensors and the other for surface sensors. Taking into account that the vertical stress waves pass through some rock formations at different velocities, the model is constructed part by part according to the values of each interval velocity. The first thing is to derive the nonlinear equation of stress wave travel time by spatial analytic geometry's operation and analysis. The target function of SV is established by using L2 norm of the time residual between the measured value and calculated value of the travel time difference of each two sensors. Secondly, based on the established target function, the SV is proposed, which consists of parameters-preparation module, interval velocities inversion module and seismic location module. In the parameters-preparation module, the regional rock mass is divided into some wave-velocity layers according to engineering geological data. And, using spatial analytic geometry's operation and analysis, the analytical expressions for layer interfaces are derived. In the interval velocities inversion module, the target function to solve velocities is figured out according to the form of the target function of SV. Then we collect some blast events or rockburst with known coordinate values. Using the genetic algorithm (GA), we can get optimization solutions of velocities. In the seismic location module, using the determined velocities, the target functions to locate MS events are figured out according to the form of the target function of SV as well. It should be emphasized that during the optimal solving, for every wave-velocity layer, the concrete forms of target function and searching regions are different from each other. Using GA, the optimization solutions of a MS event for each wave-velocity can be worked out, and the minimum of optimization solutions is considered to be the location of one MS event.In order to verify the effectiveness and reliability of SV, we applied it to a slope engineering on the left bank of the Baihetan hydropower station, where a MS monitoring system (ESG, from Canada) has been installed. Firstly, the slope was divided into 11 wave-velocity layers, which included 4 kinds of velocities, according to the sonic wave testing given by engineering geological investigation data. And, the analytical expressions for layer interfaces were worked out. Secondly, we collected 4 blast events that occurred at 610 m-elevation irrigation and drainage galleries. Using GA, we got optimization solutions of velocities, which are 4500 m·s-1, 4900 m·s-1, 3750 m·s-1, 5000 m·s-1, respectively, through minimizing the target function. And then, 11 target functions to locate MS events for each wave-velocity were figured out by using the determined velocities and parameters of layer interfaces. The MS events were collected from 1 to 18, April, 2015. The locations of these MS events were determined by using the simplified single-velocity model, of which the velocity is 4600 m·s-1 (approximate mean value of 4 velocities above). Finally, those MS events, which were scattered around two dislocation bands (LS331, LS337), were relocated by using the SV. The relocation results show that the maximum, minimum and average deviations from dislocation bands decrease by 57.17%, 36.51% and 57.35%, respectively. It is clear that SV is more convenient and reliable than the single velocity location model aimed at those regional rock masses with stratified velocities.The improved location model (SV), which is based on the anisotropic velocity model, takes into account the impact from the different velocities of rock formations with different lithological characters. Compared with the single velocity model, the accuracy of MS events location aimed at the regional rock masses with stratified velocities is improved obviously. However, like all MS events location models, it requires an accurate velocity-distribution of regional rock masses. Further study on improving the accuracy of MS events location can be achieved through considering velocity partitions inside each rock formation and finding a stable location model, which is more suited to the calculation of the actual stress wave traveltimes.
-
-
[1] BaiC Y, Greenhalgh S. 2006. 3D local earthquake hypocenter determination with an irregular shortest-path method. Bulletin of the Seismological Society of America, 96(6):2257-2268.
[2] Bai C Y, Zhao R, Greenhalgh S. 2010. Rapid 3-D earthquake location using a hybrid global-local inversion approach. Pure and Applied Geophysics, 167(11):1377-1387.Chen B R, Feng X T, Li S L, et al. 2009. Microseism source location with hierarchical strategy based on particle swarm optimization. Chinese Journal of Rock Mechanics and Engineering (in Chinese), 28(4):740-749.
[3] Collins D S, Pinnock I, Toya Y, et al. 2014. Seismic event location and source mechanism accounting for complex block geology and voids.//48th US Rock Mechanics/Geomechanics Symposium. Minneapolis, Minnesota:American Rock Mechanics Association.
[4] Dong L J, Li X B, Tang L Z, et al. 2011. Mathematical functions and parameters for microseismic source location without pre-measuring speed. Chinese Journal of Rock Mechanics and Engineering (in Chinese), 30(10):2057-2067.
[5] Feng G L, Feng X T, Chen B R, et al. 2015. Sectional velocity model for microseismic source location in tunnels. Tunnelling and Underground Space Technology, 45:73-83.
[6] Gao Y T, Wu Q L, Wu S C, et al. 2015. Source parameters inversion based on minimum error principle. Journal of Central South University (Science and Technology) (in Chinese), 46(8):3054-3060.
[7] Gong S Y, Dou L M, Ma X P, et al. 2012. Study on the construction and solution technique of anisotropic velocity model in the location of coal mine tremor. Chinese Journal of Geophysics (in Chinese), 55(5):1757-1763, doi:10.6038/j.issn.0001-5733.2012.05.033.
[8] Hosseini Z, Collins D, Shumila V, et al. 2015. Induced Microseismic Monitoring in Salt Caverns.//49th US Rock Mechanics/Geomechanics Symposium. San Francisco, California:American Rock Mechanics Association.
[9] Inglada V. 1928. The calculation of the stove coordinates of a Nahbebens. Gerlands Beitrage Zur Geophysik, (19):73-98.
[10] Jiang F X, Yang S H, Cheng Y H, et al. 2006. A study on microseismic monitoring of rock burst in coal mine. Chinese Journal of Geophysics (in Chinese), 49(5):1511-1516.
[11] Kaiser J. 1950. Studies on the occurrence of noise in the tensile test[Ph. D. thesis]. Munich:Technical University of Munich.
[12] Leighton F, Blake W. 1970. Rock noise source location techniques. Washington:Bureau of Mines.
[13] Li J, Gao Y T, Xie Y L, et al. 2014. Improvement of microseism locating based on simplex method without velocity measuring. Chinese Journal of Rock Mechanics and Engineering (in Chinese), 33(7):1336-1345.
[14] Liu F Y. 1984. Simultaneous inversion of earthquake hypocenters and velocity structure (Ⅰ)——theory and method. Chinese J. Geophys. (Acta Geophysica Sinica) (in Chinese), 27(2):167-175.
[15] Liu J, Zheng S H, Wong Y L. 2003. The inversion of non-elasticity coefficient, source parameters, site response using genetic algorithms. Acta Seismologica Sinica (in Chinese), 25(2):211-218.
[16] Lian C, Li S L, Dong M, et al. 2006. Method of spherical surface shearing in earthquake location. Journal of Geodesy and Geodynamics (in Chinese), 26(2):99-103.
[17] Lu C P, Dou L M, Wang Y F, et al. 2010. Microseismic effect of coal materials rockburst failure induced by hard roof. Chinese J. Geophys. (in Chinese), 53(2):450-456, doi:10.3969/j.issn.0001-5733.2010.02.024.
[18] Obert L. 1977. The microseismic method:discovery and early history.//Proceedings of the First Conference on Acoustic Emission/Microseismic Activity in Geologic Structures and Materials. Clausthal-Zellerfeld:Trans. Tech. Publications, 11-12.
[19] Pavlis G L, Booker J R. 1980. The mixed discrete-continuous inverse problem:Application to the simultaneous determination ofearthquake hypocenters and velocity structure. Journal of Geophysical Research:Solid Earth, 85(B9):4801-4810.
[20] Song W Q, Xu B B, Yu Z C, et al. 2015. Reconstruction of the micro-seismic source vector field and fissure interpretation based on the anisotropy analysis. Chinese J. Geophys. (in Chinese), 58(2):656-663, doi:10.6038/cjg20150226.
[21] Spencer C, Gubbins D. 1980. Travel-time inversion for simultaneous earthquake location and velocity structure determination in laterally varying media. Geophysical Journal International, 63(1):95-116.
[22] Spence W. 1980. Relative epicenter determination using P-wave arrival-time differences. Bulletin of the Seismological Society of America, 70(1):171-183.
[23] Thurber C H. 1985. Nonlinear earthquake location:theory and examples. Bulletin of the Seismological Society of America, 75(3):779-790.
[24] Wang H T, Chen Y, Zhuang C T. 2006. A local earthquake locating method based on genetic algorithm. Earthquake (in Chinese), 26(2):12-18.
[25] Xu N W, Tang C A, Li L C, et al. 2011. Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in southwestern China. International Journal of Rock Mechanics and Mining Sciences, 48(6):950-963.
[26] Zhang B H, Deng J H, Zhou Z H, et al. 2012. Analysis of monitoring microseism in areas controlled by faults near powerhouse in Dagangshan hydropower station. Rock and Soil Mechanics (in Chinese), 33(Suppl. 2):213-218.
[27] Zhang H, Zhang Z L, Yao H, et al. 2014. The study on precise positioning of earthquake in Dachang mining area based on genetic algorithm. Chinese Journal of Engineering Geophysics (in Chinese), 11(2):260-265.
[28] Zhao Z, Zeng R S. 1987. A method for improving the determination of earthquake hypocenters. Chinese J. Geophys. (in Chinese), 30(4):379-388.[HS2*2]
[29] 陈炳瑞, 冯夏庭, 李庶林等. 2009. 基于粒子群算法的岩体微震源分层定位方法. 岩石力学与工程学报, 28(4):740-749.
[30] 董陇军, 李夕兵, 唐礼忠等. 2011. 无需预先测速的微震震源定位的数学形式及震源参数确定. 岩石力学与工程学报, 30(10):2057-2067.
[31] 高永涛, 吴庆良, 吴顺川等. 2015. 基于误差最小原理的微震震源参数反演. 中南大学学报, 46(8):3054-3060.
[32] 巩思园, 窦林名, 马小平等. 2012. 煤矿矿震定位中异向波速模型的构建与求解. 地球物理学报, 55(5):1757-1763, doi:10.6038/j.issn.0001-5733.2012.05.033.
[33] 姜福兴, 杨淑华, 成云海等. 2006. 煤矿冲击地压的微地震监测研究. 地球物理学报, 49(5):1511-1516.
[34] 李健, 高永涛, 谢玉玲等. 2014. 基于无需测速的单纯形法微地震定位改进研究. 岩石力学与工程学报, 33(7):1336-1345.
[35] 刘福田. 1984. 震源位置和速度结构的联合反演(Ⅰ)——理论和方法. 地球物理学报, 27(2):167-175.
[36] 刘杰, 郑斯华, 黄玉龙. 2003. 利用遗传算法反演非弹性衰减系数、震源参数和场地响应. 地震学报, 25(2):211-218.
[37] 廉超, 李胜乐, 董曼等. 2006. 球面交切法地震定位. 大地测量与地球动力学, 26(2):99-103.
[38] 陆菜平, 窦林名, 王耀峰等. 2010. 坚硬顶板诱发煤体冲击破坏的微震效应. 地球物理学报, 53(2):450-456, doi:10.3969/j.issn.0001-5733.2010.02.024.
[39] 宋维琪, 徐奔奔, 喻志超等. 2015. 基于各向异性分析的微地震震源矢量场重建和裂缝解释. 地球物理学报, 58(2):656-663, doi:10.6038/cjg20150226.
[40] 王洪体, 陈阳, 庄灿涛. 2006. 基于浮点遗传算法的近震定位方法. 地震, 26(2):12-18.
[41] 张伯虎, 邓建辉, 周志辉等. 2012. 大岗山水电站厂房断层控制区域微震监测分析. 岩土力学, 33(增2):213-218.
[42] 张华, 张忠利, 姚宏等. 2014. 基于遗传算法的大厂矿区地震定位研究. 工程地球物理学报, 11(2):260-265.
[43] 赵珠, 曾融生. 1987. 一种修定震源参数的方法. 地球物理学报, 30(4):379-388.
-
计量
- 文章访问数: 2094
- PDF下载数: 1741
- 施引文献: 0
下载: