CN106094021B - A kind of microseism focus method for rapidly positioning based on arrival time difference database - Google Patents
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Abstract
本发明提供一种基于到时差数据库的微地震震源快速定位方法,属于震源定位技术领域。该方法对监测区域建立地质数值模型,并进行网格划分,每个网格点可看作特征震源点;结合特征震源点、传感器位置坐标及震源波波速可建立特征震源点到时差数据库;利用波形信息采集系统,对传感器接收到的波形信号提取震源波到达各个传感器的到时时间;计算任意两传感器的到时差,建立震源的到时差矩阵,并与特征震源点到时差数据库进行匹配搜索,可对震源进行实时快速定位。在实际微震监测工程中,事先建立特征震源点到时差数据库,无需对震源进行函数优化迭代求解,因此极大缩减了震源定位所需时间,可有效缩减岩体工程动力灾害预警时间。
The invention provides a microseismic source rapid positioning method based on a time difference database, and belongs to the technical field of seismic source positioning. This method establishes a geological numerical model for the monitoring area and divides it into grids. Each grid point can be regarded as a characteristic source point; combined with the characteristic source point, sensor position coordinates and source wave velocity, a characteristic source point-to-time difference database can be established; using The waveform information acquisition system extracts the arrival time of the source wave to each sensor from the waveform signal received by the sensor; calculates the arrival time difference of any two sensors, establishes the arrival time difference matrix of the source, and performs a matching search with the characteristic source point arrival time difference database, The source of the earthquake can be quickly located in real time. In the actual microseismic monitoring project, the time difference database of the characteristic source points is established in advance, and there is no need to perform function optimization and iterative solutions for the source, so the time required for source location is greatly reduced, and the early warning time for dynamic disasters in rock mass engineering can be effectively reduced.
Description
技术领域technical field
本发明涉及一种微地震震源定位方法,尤其指一种基于到时差数据库的微地震震源快速定位方法。The invention relates to a microseismic source positioning method, in particular to a microseismic source rapid positioning method based on an arrival time difference database.
背景技术Background technique
采矿、隧道掘进等岩体工程中,采掘活动会引起岩体内局部区域的变形或破裂,同时伴随着应变能迅速释放而产生瞬态弹性波,这种现象被称为微地震。由于微地震是岩体变形、裂纹开裂及扩展过程的伴生现象,它与围岩结构的力学行为有着密切的相关性,因此,微地震震源信号中包含了大量的关于围岩受力破坏以及地质缺陷活化过程的有用信息,可据此推断岩石材料的力学行为,预测围岩结构是否发生破坏。近年来对围岩远场致灾动载源进行实时监测预警的微震监测技术已广泛应用于岩体工程领域,其是对岩爆、冲击地压以及采空区垮塌等灾害预测预报的最有效的监测方法之一。微震震源定位是微震监测技术的核心,能否进行快速、精确的定位是微震监测系统能够发挥作用的关键。In rock mass engineering such as mining and tunneling, mining activities will cause deformation or rupture in local areas of the rock mass, and at the same time, transient elastic waves will be generated with the rapid release of strain energy. This phenomenon is called microseismic. Since microseismic is an accompanying phenomenon of rock mass deformation, crack cracking and expansion process, it is closely related to the mechanical behavior of surrounding rock structure. The useful information of the defect activation process can be used to infer the mechanical behavior of rock materials and predict whether the surrounding rock structure will be damaged. In recent years, the microseismic monitoring technology for real-time monitoring and early warning of the far-field disaster-causing source of surrounding rock has been widely used in the field of rock mass engineering. one of the monitoring methods. Microseismic source location is the core of microseismic monitoring technology, and whether it can be quickly and accurately located is the key to the function of the microseismic monitoring system.
在采矿、隧道掘进等岩体工程领域,因冲击地压、岩爆等动力灾害具有突发性,在灾害发生前虽有一定的征兆,但往往由于缺乏高效的微震定位方法,导致微震监测系统的灾害预警滞后,作业人员及设备来不及撤离工作面,给人员生命财产造成重大损失。In the field of rock mass engineering such as mining and tunneling, due to the suddenness of dynamic disasters such as rock bursts and rockbursts, although there are certain signs before the disaster occurs, the lack of efficient microseismic positioning methods often leads to microseismic monitoring systems. The disaster warning lags behind, and the operators and equipment have no time to evacuate the working face, causing heavy losses to the lives and property of the personnel.
传统的微震定位方法大多涉及迭代求解函数的最优值问题,但在数据量庞大的情况下,迭代过程会浪费大量宝贵的动力灾害预测预警时间,因此对震源的快速、精确求解是微震定位算法的努力方向。基于此,本发明提出一种基于到时差数据库的微地震震源快速定位方法,且随网格划分密度越大,定位结果越精确。Most of the traditional microseismic positioning methods involve the optimal value of the iterative solution function. However, in the case of a large amount of data, the iterative process will waste a lot of precious time for dynamic disaster prediction and early warning. direction of efforts. Based on this, the present invention proposes a fast positioning method for microseismic sources based on the arrival time difference database, and the greater the grid division density, the more accurate the positioning result.
发明内容Contents of the invention
为解决上述问题,本发明目的是提供一种基于到时差数据库的微地震震源快速定位方法,通过预先对监测区域进行数值建模及网格划分,建立特征震源点到时差数据库,利用数据库的快速匹配特点对实际震源进行定位,同时通过提高网格划分密度,可达到快速、精确定位的目的。In order to solve the above problems, the object of the present invention is to provide a microseismic source rapid positioning method based on the arrival time difference database. By performing numerical modeling and grid division on the monitoring area in advance, a characteristic source point arrival time difference database is established. Matching features are used to locate the actual source, and at the same time, the purpose of fast and accurate positioning can be achieved by increasing the grid division density.
本发明方法的具体步骤为:The concrete steps of the inventive method are:
a.在被监测区域布设传感器,Ti(i=1,2,...,n)表示第i个传感器,其位置坐标可表示为(xi,yi,zi);a. Arrange sensors in the monitored area, T i (i=1,2,...,n) represents the i-th sensor, and its position coordinates can be expressed as ( xi , y, zi ) ;
b.对被监测区域进行数值建模,并进行网格划分,将每个网格节点作为代表该位置的特征震源点Pi(i=1,2,...,n),其位置坐标为(xoi,yoi,zoi);b. Carry out numerical modeling for the monitored area, and perform grid division, each grid node is used as a characteristic source point P i (i=1,2,...,n) representing the position, and its position coordinates is (x oi , y oi , z oi );
c.计算每个特征震源点Pi的到时差矩阵Nkij,建立到时差数据库;c. Calculate the arrival time difference matrix N kij of each characteristic source point P i , and establish the arrival time difference database;
①均质条件下,单一特征震源点Pi到时差矩阵求解原理如下:①Under homogeneous conditions, the principle of solving the time difference matrix from a single characteristic source point P i is as follows:
假设震源波传播速度为v,Li(i=1,2,...,n)为传感器Ti至特征震源点Pi的距离;ti(i=1,2,...,n)为震动波到达传感器Ti的时刻,t0为特征震源点Pi震源产生的时刻,则:Assuming that the source wave propagation velocity is v, L i (i=1,2,...,n) is the distance from sensor T i to characteristic source point P i ; t i (i=1,2,...,n ) is the moment when the shock wave reaches the sensor T i , and t 0 is the moment when the source of the characteristic source point P i is generated, then:
则震源波到达两任意不同传感器Ti和Tj的到时差矩阵元素Δtij可表示为:Then the arrival time difference matrix element Δt ij of the source wave arriving at two arbitrary different sensors T i and T j can be expressed as:
②各向异性非均质条件下,单一特征震源点Pi到时差矩阵的求解基于射线追踪算法,原理如下:②Under the condition of anisotropy and heterogeneity, the solution of the single characteristic source point P i to the time difference matrix is based on the ray tracing algorithm, and the principle is as follows:
将特征震源点Pi发出的震源波离散成若干段的射线,对各段射线的轨迹和走时累加,得出各向异性非均质介质中射线分布和到达各个传感器的时间信息。已知特征震源点Pi、传感器Ti位置坐标及区域速度结构模型的情况下,震源的射线路径可由射线参数p唯一确定:Discretize the source wave emitted by the characteristic source point P i into several segments of rays, and accumulate the trajectory and travel time of each segment of rays to obtain the distribution of rays in anisotropic heterogeneous medium and the time information of reaching each sensor. When the characteristic source point P i , the position coordinates of the sensor T i and the regional velocity structure model are known, the ray path of the source can be uniquely determined by the ray parameter p:
式中:Δ是震中距(特征震源点与传感器两点之间的水平距离),p=sinθk/vk是射线参数;vk,θk,hk,分别表示第k层的速度、入射角、真实厚度和等效厚度;l,s,zs分别表示模型总层数、特征震源点所在层数及其深度。In the formula: Δ is the epicentral distance (horizontal distance between the characteristic source point and two points of the sensor), p=sinθ k /v k is the ray parameter; v k , θ k , h k , Respectively represent the velocity, incident angle, real thickness and equivalent thickness of the kth layer; l, s, z s respectively represent the total number of layers of the model, the number of layers where the characteristic source point is located and its depth.
求得参数p后,可确定特征震源点Pi与传感器Ti两点间的传播轨迹,则震源波从特征震源点Pi到达传感器Ti的到时为:After the parameter p is obtained, the propagation trajectory between the characteristic source point P i and the sensor T i can be determined, then the arrival time of the source wave from the characteristic source point P i to the sensor T i is:
则震源波到达两任意不同传感器Ti和Tj的到时差矩阵元素Δtij可表示为:Then the arrival time difference matrix element Δt ij of the source wave arriving at two arbitrary different sensors T i and T j can be expressed as:
Δtij=ti-tj;Δt ij =t i -t j ;
③到时差数据库的建立原理如下:③ The establishment principle of the arrival time difference database is as follows:
已知传感器Ti坐标(xi,yi,zi)和特征震源点Pi坐标(xoi,yoi,zoi),求得每个特征震源点到两任意传感器的到时差Δtij,组成到时差矩阵NKij。在有n个传感器的情况下,每个特征震源点共可得到个到时差,并组成一个包含个数据的到时差矩阵NKij。则将每个特征震源点信息及其对应的到时差矩阵录入数据库,可建立到时差数据库。Given the coordinates of the sensor T i ( xi , y i , zi ) and the coordinates of the characteristic source point P i (x oi , y oi , z oi ), the arrival time difference Δt ij from each characteristic source point to two arbitrary sensors can be obtained , composed to the time difference matrix N Kij . In the case of n sensors, each characteristic source point can be obtained to the time difference, and form a containing Arrival time difference matrix N Kij of data. Then, the information of each characteristic source point and its corresponding arrival time difference matrix are entered into the database, and the time difference database can be established.
d.利用波形信息采集系统,对传感器接收到的波形信号提取震源波到达各个传感器的到时时间;d. Use the waveform information acquisition system to extract the arrival time of the source wave to each sensor from the waveform signal received by the sensor;
e.计算任意两传感器的到时差,建立震源(xo,yo,zo)的到时差矩阵,并与特征震源点到时差数据库进行匹配搜索,对震源进行实时快速定位。e. Calculate the arrival time difference of any two sensors, establish the arrival time difference matrix of the seismic source (x o , y o , z o ), and perform a matching search with the characteristic source point arrival time difference database to quickly locate the seismic source in real time.
其中,步骤b中数值建模基于被监测区域地质勘探资料及区域速度结构模型,网格划分密度与震源定位精度成正比,划分密度越大,定位精度越高。Among them, the numerical modeling in step b is based on the geological exploration data of the monitored area and the regional velocity structure model, and the grid division density is proportional to the seismic source positioning accuracy. The greater the division density, the higher the positioning accuracy.
步骤b中所建数值模型为各向异性非均质体模型(包括各向同性均质体模型),被监测区域地质勘探资料及区域速度结构模型越详细,则模型越精确,震源定位精度越高。The numerical model built in step b is an anisotropic heterogeneous body model (including an isotropic homogeneous body model). The more detailed the geological exploration data and regional velocity structure model of the monitored area, the more accurate the model and the higher the seismic source positioning accuracy. high.
其中,震源到时差矩阵与特征震源点到时差数据库的匹配采用相似度匹配搜索法,相似度高的特征震源点可认为该震源的实际位置。Among them, the matching between the source-to-time difference matrix and the characteristic source points to the time-difference database adopts the similarity matching search method, and the characteristic source points with high similarity can be regarded as the actual location of the source.
本发明的上述技术方案的有益效果如下:The beneficial effects of above-mentioned technical scheme of the present invention are as follows:
本发明基于数据库快速匹配搜索的特点,在实际微震监测工程中,事先建立特征震源点到时差数据库,无需对震源进行函数优化迭代求解,因此极大缩减了震源定位所需时间,同时,随网格划分密度越大,定位精度越高,故可有效缩减岩体工程动力灾害预警时间,给工作人员及设备提供一定有效安全保障。The present invention is based on the characteristics of fast matching search of the database. In the actual microseismic monitoring project, the time difference database of the characteristic source point is established in advance, and there is no need to perform function optimization and iterative solution to the source, thus greatly reducing the time required for source positioning. At the same time, with the network The greater the grid division density, the higher the positioning accuracy, so it can effectively reduce the early warning time of rock mass engineering dynamic disasters, and provide certain effective safety guarantees for staff and equipment.
附图说明Description of drawings
图1为本发明基于到时差数据库的微地震震源快速定位方法实施例模型示意图;Fig. 1 is the schematic diagram of the embodiment model of the microseismic source rapid location method based on the arrival time difference database of the present invention;
图2为网格密度为8m×8m×8m的立方体模型;Figure 2 is a cube model with a grid density of 8m x 8m x 8m;
图3为某实测震源波形信号图;Fig. 3 is a certain measured seismic source waveform signal diagram;
图4为不同网格密度下定位误差对比图;Figure 4 is a comparison diagram of positioning errors under different grid densities;
图5为不同网格密度下传统网格搜索算法与本发明方法计算耗时对比图。Fig. 5 is a comparison chart of calculation time between the traditional grid search algorithm and the method of the present invention under different grid densities.
具体实施方式detailed description
为使本发明的技术方案和优点更加清楚,下面将结合附图及具体实施例进行详细说明。In order to make the technical solutions and advantages of the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.
(1)为验证本发明的有效性,以均质条件下为例进行说明,其中非均质条件的求解原理与之相同。设计验证模型如图1所示,为一均质模型,假定有8个传感器A,B,C,D,E,F,G,H分别安装于立方体八个顶点位置,模拟实测震源I,J,K,L,M,N,O,P各点位置坐标如表1和表2所示。(1) In order to verify the effectiveness of the present invention, the homogeneous condition is taken as an example, and the solution principle of the heterogeneous condition is the same. The design verification model is shown in Figure 1. It is a homogeneous model. It is assumed that there are 8 sensors A, B, C, D, E, F, G, and H respectively installed at the eight vertices of the cube, and the simulated seismic sources I, J , K, L, M, N, O, P position coordinates are shown in Table 1 and Table 2.
表1Table 1
表2Table 2
(2)对微震监测区域进行数值建模,并进行网格划分。(2) Numerical modeling of the microseismic monitoring area and grid division.
如图2所示,模型划分网格密度为8m×8m×8m,共512个特征震源点,可计算每个特征震源点的位置坐标及特征震源点与传感器之间的距离。基于以上信息及波速v可求得特征震源点的震源波到达每个传感器的时间。As shown in Figure 2, the grid density of the model is 8m×8m×8m, and there are 512 characteristic source points in total. The position coordinates of each characteristic source point and the distance between the characteristic source point and the sensor can be calculated. Based on the above information and the wave velocity v, the time for the source wave of the characteristic source point to reach each sensor can be obtained.
(3)计算每个特征震源点的到时差矩阵,建立到时差数据库。(3) Calculate the arrival time difference matrix of each characteristic source point, and establish the arrival time difference database.
在有8个传感器的情况下,每个特征震源点共可得到个到时差,并组成一个包含56个数据的到时差矩阵NKij,用于表示该特征震源点的到时信息。以特征震源点(1m,2m,3m)为例,其到时差矩阵如表3所示。In the case of 8 sensors, a total of each characteristic source point can be obtained arrival time difference, and form a arrival time difference matrix N Kij containing 56 data, which is used to represent the arrival time information of the characteristic source point. Taking the characteristic source points (1m, 2m, 3m) as an example, the arrival time difference matrix is shown in Table 3.
表3table 3
基于特征震源点(1m,2m,3m)的构建方法,计算模型中512个特征震源点的到时差矩阵并将其保存在数据库中,从而可以建立一个特征震源点到时差数据库。Based on the construction method of characteristic source points (1m, 2m, 3m), the arrival time difference matrix of 512 characteristic source points in the model is calculated and stored in the database, so that a characteristic source point arrival time difference database can be established.
(4)利用波形信息采集系统,对传感器接收到的波形信号提取震源波到达各个传感器的到时时间。(4) Use the waveform information acquisition system to extract the arrival time of the source wave to each sensor from the waveform signal received by the sensor.
如图3所示为某实测震源波形信号图,基于此图利用到时拾取算法如长短时均值比法、AIC法等可自动提取震源波传至每个传感器的到时时间。Figure 3 is a waveform signal diagram of a measured seismic source. Based on this diagram, the arrival time of the seismic source wave to each sensor can be automatically extracted by using time-to-time picking algorithms such as the long-short time-average ratio method and the AIC method.
(5)计算任意两传感器的到时差,建立实测震源的到时差矩阵,并与特征震源点到时差数据库进行匹配搜索,对实测震源进行实时快速定位。(5) Calculate the arrival time difference of any two sensors, establish the arrival time difference matrix of the measured source, and perform a matching search with the characteristic source point arrival time difference database, and quickly locate the measured source in real time.
同步骤(3),可求得各实测震源点到时差矩阵,并利用相似度匹配算法将其与特征震源点到时差数据库进行匹配搜索,可快速求其位置坐标。Similar to step (3), the time difference matrix from each measured source point can be obtained, and the similarity matching algorithm is used to match and search it with the characteristic source point to time difference database, and its position coordinates can be quickly obtained.
不同网格密度下,本发明方法定位误差如图4所示,随网格密度增加,定位精度显著提升。不同网格密度下,传统网格搜索算法与本发明方法计算耗时对比如图5所示,由图5可知,本发明方法计算效率高,且随着网格密度增大,可极大缩减震源定位所用时间。Under different grid densities, the positioning error of the method of the present invention is shown in Figure 4, and the positioning accuracy is significantly improved as the grid density increases. Under different grid densities, the time-consuming comparison between the traditional grid search algorithm and the method of the present invention is shown in Figure 5. From Figure 5, it can be seen that the calculation efficiency of the method of the present invention is high, and as the grid density increases, it can be greatly reduced The time taken to locate the source.
综上所述,在实际工程应用中,为保证定位精确性的要求,必然会增加网格搜索密度,此时本发明方法可大幅度提高微震震源定位效率,从而为岩体工程致灾动载源的及时预测预报提供技术支持。To sum up, in actual engineering applications, in order to ensure the accuracy of positioning, the grid search density will inevitably be increased. At this time, the method of the present invention can greatly improve the efficiency of microseismic source positioning, thereby providing disaster-induced dynamic load for rock mass engineering. Provide technical support for timely forecasting and forecasting of sources.
以上所述是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明技术原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。The above is a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also It should be regarded as the protection scope of the present invention.
Claims (5)
- A kind of 1. microseism focus method for rapidly positioning based on arrival time difference database, it is characterised in that:Comprise the following steps:A. sensor, T are laid in area to be monitoredi(i=1,2 ..., n) represents i-th of sensor, and its position coordinates can represent For (xi,yi,zi);B. numerical modeling is carried out to area to be monitored, and carries out mesh generation, using each grid node as representing the position Feature focal point Pi(i=1,2 ..., n), its position coordinates is (xoi,yoi,zoi);C. each feature focal point P is calculatediArrival time difference matrix NKij, establish arrival time difference database;D. shape information acquisition system is utilized, the waveform signal extraction focus ripple received to sensor reaches each sensor The then time;E. the arrival time difference of any two sensorses is calculated, establishes focus (xo,yo,zo) arrival time difference matrix, and with feature focal point Pi Arrival time difference database carries out matching search, and focus is positioned real-time;In step c, under processing condition, single features focal point PiArrival time difference Matrix Solving principle is as follows:Assuming that focus velocity of wave propagation is v, Li(i=1,2 ..., n) it is sensor TiTo feature focal point PiDistance;ti(i= 1,2 ..., n) it is that shock wave reaches sensor TiAt the time of, t0It is characterized focal point PiAt the time of focus produces, then:Focus ripple reaches two any different sensors TiAnd TjArrival time difference matrix element Δ tijIt is expressed as:In step c, under anisotropy heterogeneous conditions, single features focal point PiThe solution of arrival time difference matrix is chased after based on ray Track algorithm, principle are as follows:By feature focal point PiThe focus ripple sent is separated into more than one section of ray, and the track of each section of ray is added up with when walking, Draw radiation profile and the temporal information of each sensor of arrival in anisotropy nonisotropic medium;Known features focal point Pi、 Sensor TiIn the case of position coordinates and zone velocity structural model, the ray path of focus can be uniquely true by ray parameter p It is fixed:In formula:Δ is epicentral distance, i.e. horizontal range between 2 points of feature focal point and sensor;P=sin θk/vkIt is ray ginseng Number;vk, θk, hk,Speed, incidence angle, actual thickness and the equivalent thickness of kth layer are represented respectively;L, s, zsModel is represented respectively The number of plies and its depth where total number of plies, feature focal point;After trying to achieve parameter p, it may be determined that feature focal point PiWith sensor TiThe propagation trajectories of point-to-point transmission, then focus ripple from feature shake Source point PiReach sensor TiBe then:<mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>l</mi> </munderover> <mfrac> <mover> <msub> <mi>h</mi> <mi>k</mi> </msub> <mo>~</mo> </mover> <mrow> <msub> <mi>v</mi> <mi>k</mi> </msub> <msqrt> <mrow> <mn>1</mn> <mo>-</mo> <msup> <mi>p</mi> <mn>2</mn> </msup> <msup> <msub> <mi>v</mi> <mi>k</mi> </msub> <mn>2</mn> </msup> </mrow> </msqrt> </mrow> </mfrac> <mo>;</mo> </mrow>Then focus ripple reaches two any different sensors TiAnd TjArrival time difference matrix element Δ tijIt is represented by:Δtij=ti- tj。
- 2. the microseism focus method for rapidly positioning based on arrival time difference database as claimed in claim 1, it is characterised in that:Step The density of mesh generation is directly proportional to seismic source location precision in rapid b, and division density is bigger, and positioning precision is higher.
- 3. the microseism focus method for rapidly positioning based on arrival time difference database as claimed in claim 1, it is characterised in that:Step The numerical model of numerical modeling is anisotropy heterogeneous body model and isotropism homogeneous body Model in rapid b.
- 4. the microseism focus method for rapidly positioning based on arrival time difference database as claimed in claim 1, it is characterised in that:Step In rapid c, arrival time difference database to establish principle as follows:Known sensor TiCoordinate (xi,yi,zi) and feature focal point PiCoordinate (xoi,yoi,zoi), try to achieve each feature focal point To the arrival time difference Δ t of two any sensorsij, form arrival time difference matrix NKij;In the case where there is n sensor, each feature shake Source point can obtain altogetherIndividual arrival time difference, and form one and includeThe arrival time difference matrix N of individual dataKij;Then by each feature focus Point information and its corresponding arrival time difference matrix input database, establish arrival time difference database.
- 5. the microseism focus method for rapidly positioning based on arrival time difference database as claimed in claim 1, it is characterised in that:Step The matching of focus arrival time difference matrix and feature focal point arrival time difference database uses similarity mode search method, similarity in rapid e High feature focal point confirms as the physical location of the focus.
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