CN106094021B - A kind of microseism focus method for rapidly positioning based on arrival time difference database - Google Patents

Abstract

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

A fast microseismic source location method based on time-of-arrival time difference database

technical field

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.

Background technique

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.

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.

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.

Contents of the invention

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.

The concrete steps of the inventive method are:

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. 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. Calculate the arrival time difference matrix N _kij of each characteristic source point P _i , and establish the arrival time difference database;

①Under homogeneous conditions, the principle of solving the time difference matrix from a single characteristic source point P _i is as follows:

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:

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:

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:

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:

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.

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:

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:

Δt _ij =t _i -t _j ;

③ The establishment principle of the arrival time difference database is as follows:

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. 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. 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.

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.

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.

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.

The beneficial effects of above-mentioned technical scheme of the present invention are as follows:

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.

Description of drawings

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;

Figure 2 is a cube model with a grid density of 8m x 8m x 8m;

Fig. 3 is a certain measured seismic source waveform signal diagram;

Figure 4 is a comparison diagram of positioning errors under different grid densities;

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.

detailed description

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) 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.

Table 1

sensor X/m Y/m Z/m A 0 0 0 B 0 8 0 C 8 8 0 D. 8 0 0 E. 0 0 8 f 0 8 8 G 8 8 8 h 8 0 8

Table 2

Measured source X/m Y/m Z/m I 1.1231 2.3546 3.2458 J 4.3256 2.3654 7.1254 K 7.2158 6.3478 5.1864 L 2.2541 4.1267 7.2541 m 6.3214 4.2589 7.1568 N 5.3698 7.1256 1.5894

(2) Numerical modeling of the microseismic monitoring area and grid division.

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) Calculate the arrival time difference matrix of each characteristic source point, and establish the arrival time difference database.

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.

table 3

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) 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.

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) 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.

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.

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.

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.

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)

1. 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 monitored_i(i=1,2 ..., n) represents i-th of sensor, and its position coordinates can represent For (x_i,y_i,z_i)；

   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 P_i(i=1,2 ..., n), its position coordinates is (x_oi,y_oi,z_oi)；

   C. each feature focal point P is calculated_iArrival time difference matrix N_Kij, 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 (x_o,y_o,z_o) arrival time difference matrix, and with feature focal point P_i Arrival time difference database carries out matching search, and focus is positioned real-time；

   In step c, under processing condition, single features focal point P_iArrival time difference Matrix Solving principle is as follows：

   Assuming that focus velocity of wave propagation is v, L_i(i=1,2 ..., n) it is sensor T_iTo feature focal point P_iDistance；t_i(i= 1,2 ..., n) it is that shock wave reaches sensor T_iAt the time of, t₀It is characterized focal point P_iAt the time of focus produces, then：

   Focus ripple reaches two any different sensors T_iAnd T_jArrival time difference matrix element Δ t_ijIt is expressed as：

   In step c, under anisotropy heterogeneous conditions, single features focal point P_iThe solution of arrival time difference matrix is chased after based on ray Track algorithm, principle are as follows：

   By feature focal point P_iThe 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 P_i、 Sensor T_iIn 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 θ_k/v_kIt is ray ginseng Number；v_k, θ_k, h_k,Speed, incidence angle, actual thickness and the equivalent thickness of kth layer are represented respectively；L, s, z_sModel 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 P_iWith sensor T_iThe propagation trajectories of point-to-point transmission, then focus ripple from feature shake Source point P_iReach sensor T_iBe then：

   <mrow> <msub> <mi>t</mi> <mi>i</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;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 T_iAnd T_jArrival time difference matrix element Δ t_ijIt is represented by：Δt_ij=t_i- t_j。
2. 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. 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. 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 T_iCoordinate (x_i,y_i,z_i) and feature focal point P_iCoordinate (x_oi,y_oi,z_oi), try to achieve each feature focal point To the arrival time difference Δ t of two any sensors_ij, form arrival time difference matrix N_Kij；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 data_Kij；Then by each feature focus Point information and its corresponding arrival time difference matrix input database, establish arrival time difference database.
5. 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.