A method and system for locating microseismic sources in deep-buried tunnels

Abstract

This invention provides a method and system for locating microseismic sources in deeply buried tunnels, relating to the field of microseismic source location technology. The method includes: acquiring first, second, and third information; initializing a particle swarm based on the first, second, and third information to obtain an initial particle swarm parameter set; identifying diffraction paths based on the particle swarm parameter set to obtain a determination result of diffraction occurrence conditions; calculating the minimum diffraction path based on the diffraction identification result to obtain a set of shortest path lengths; performing wave velocity inversion based on the set of shortest path lengths and the second information to obtain the optimal wave velocity value and the spatial location of the seismic source; and calculating the seismic origin time based on the optimal wave velocity value and the spatial location of the seismic source to obtain seismic origin time data. This invention achieves accurate modeling and path compensation for elastic wave diffraction effects, thereby effectively eliminating propagation path deviations caused by the existence of voids and significantly improving the accuracy and reliability of microseismic source location in deeply buried tunnels.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of microseismic source positioning, in particular to a deep-buried tunnel microseismic source positioning method and system. BACKGROUND

[0002] In the field of tunnel engineering safety monitoring, microseismic source positioning technology, as a key means for early warning of rock mass rupture risk, has been widely used in the construction and operation stages of deep-buried tunnels. The core of the technology is to analyze the elastic wave signals received by the sensors to invert the spatio-temporal parameters of the seismic source. However, in a complex tunnel network, especially when the microseismic source and the sensors are distributed on both sides of the excavated empty area, the elastic wave propagation path will be significantly diffracted due to the existence of the empty area, resulting in a large deviation of the traditional positioning method based on the assumption of ideal straight-line path. The existing technology usually uses simplified geometric models or basic optimization algorithms to handle the path problem, but this method does not fully consider the diffraction effect of the wave at the edge of the empty area, and only relies on linear propagation assumptions or local path corrections, which makes the path length calculation disconnected from the actual physical process, and further introduces positioning errors that cannot be ignored. Especially in the scenario where the tunnel cross-sectional size is comparable to the wavelength, the error is further amplified, which restricts the improvement of positioning accuracy.

[0003] Based on the above-mentioned shortcomings of the prior art, there is an urgent need for a deep-buried tunnel microseismic source positioning method and system. SUMMARY

[0004] The purpose of the present application is to provide a deep-buried tunnel microseismic source positioning method to improve the above-mentioned problems. In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a deep-buried tunnel microseismic source positioning method, comprising:

[0006] obtaining first information, second information and third information, the first information comprising spatial coordinate information of a microseismic sensor array, the second information comprising a microseismic signal arrival time sequence, and the third information comprising three-dimensional geometric parameters of a tunnel empty area;

[0007] performing particle swarm initialization according to the first information, the second information and the third information to obtain an initial particle swarm parameter set;

[0008] performing diffraction path identification according to the particle swarm parameter set, reducing the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional cross-section by projection method, and checking whether the straight-line path passes through the tunnel empty area projection region point by point along the tunnel axis direction to obtain a judgment result of the diffraction occurrence condition;

[0009] performing minimum diffraction path calculation according to the diffraction identification result to obtain a shortest path length set of actual propagation;

[0010] According to the shortest path length set and the second information, wave velocity inversion is performed to obtain an optimal wave velocity value and a spatial position of a seismic source;

[0011] According to the optimal wave velocity value and the spatial position of the seismic source, a time of origin is calculated, the theoretical arrival time of each sensor is back calculated, and the measured residual time difference is minimized to obtain time of origin data.

[0012] In a second aspect, the application further provides a deep-buried tunnel microseismic source positioning system, comprising:

[0013] An acquisition module is configured to acquire first information, second information and third information, wherein the first information comprises spatial coordinate information of a microseismic sensor array, the second information comprises a microseismic signal arrival time sequence, and the third information comprises three-dimensional geometric parameters of a tunnel empty area;

[0014] A construction module is configured to perform particle swarm initialization according to the first information, the second information and the third information to obtain an initial particle swarm parameter set;

[0015] A discrimination module is configured to perform diffraction path discrimination according to the particle swarm parameter set, to reduce the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional cross section by projection method, to verify whether the straight path passes through the tunnel empty area projection area along the tunnel axis direction point by point, and to obtain a judgment result of diffraction occurrence condition;

[0016] A calculation module is configured to perform minimum diffraction path calculation according to the diffraction discrimination result to obtain a shortest path length set of actual propagation;

[0017] An inversion module is configured to perform wave velocity inversion according to the shortest path length set and the second information to obtain an optimal wave velocity value and a spatial position of a seismic source;

[0018] A calculation module is configured to perform minimum diffraction path calculation according to the diffraction discrimination result to obtain a shortest path length set of actual propagation;

[0019] The application has the following beneficial effects:

[0020] The application realizes accurate modeling and path compensation of elastic wave diffraction effect, thereby effectively eliminating the propagation path deviation caused by the existence of the empty area, and significantly improving the accuracy and reliability of deep-buried tunnel microseismic source positioning. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0022] Figure 1 A flow chart of a deep-buried tunnel microseismic source positioning method according to an embodiment of the present application;

[0023] Figure 2 A structural schematic diagram of a deep-buried tunnel microseismic source positioning system according to an embodiment of the present application;

[0024] Figure 3 A schematic diagram for judging whether a straight line segment crosses a tunnel empty area according to an embodiment of the present application;

[0025] Figure 4 A diffraction path of an elastic wave across a tunnel excavation empty area according to an embodiment of the present application;

[0026] Figure 5 A tunnel cross-section shape schematic diagram according to an embodiment of the present application;

[0027] Figure 6 A top diffraction path schematic diagram according to an embodiment of the present application;

[0028] Figure 7 A bottom diffraction path schematic diagram according to an embodiment of the present application;

[0029] Figure 8 A spatial plane distribution diagram of a diffraction path according to an embodiment of the present application.

[0030] In the figure, 901 is an acquisition module, 902 is a construction module, 903 is a discrimination module, 904 is a calculation module, 905 is an inversion module, 906 is a solution module. DETAILED DESCRIPTION

[0031] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0032] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second" and the like are only used to distinguish description, and cannot be understood as indicating or implying relative importance.

[0033] Embodiment 1

[0034] The embodiment provides a deep-buried tunnel microseismic source positioning method.

[0035] Referring to Figure 1 , the method includes steps S100 to S600.

[0036] In step S100, first information, second information and third information are acquired, the first information includes spatial coordinate information of a microseismic sensor array, the second information includes a microseismic signal arrival time sequence, and the third information includes three-dimensional geometric parameters of a tunnel empty area.

[0037] Step S100 collects three types of basic data through the system, including spatial coordinate information of the microseismic sensor array, microseismic signal arrival time sequence and three-dimensional geometric parameters of the tunnel empty area. The sensor coordinate information establishes the spatial reference framework of the microseismic monitoring network, the signal arrival time sequence records the time characteristics of elastic wave propagation, and the three-dimensional geometric parameters of the tunnel empty area accurately describe the spatial form and size characteristics of the straight wall and arch-shaped section. When the microseismic source and the sensor are distributed on both sides of the tunnel, the straight path of elastic wave propagation will pass through the excavation empty area, at this time, the wave propagation will follow the Huygens-Fresnel principle and diffract, resulting in the actual propagation path deviating from the ideal straight path. Research shows that when the elastic wave wavelength (about 25 m) is greater than the tunnel section size (2-14 m), the diffraction phenomenon will significantly affect the wave propagation path, which makes it a prerequisite to accurately obtain the three-dimensional geometric parameters of the tunnel empty area to correct the propagation path error. This step establishes the association system of spatial coordinates, time sequence and geometric constraints, providing indispensable data support for subsequent diffraction path discrimination and wave velocity inversion, effectively solving the problem of poor positioning accuracy caused by ignoring the diffraction effect of the empty area in traditional methods.

[0038] Step S200 initializes the particle swarm according to the first information, the second information and the third information to obtain an initial particle swarm parameter set;

[0039] Step S200 determines the particle swarm search space boundary based on the sensor coordinates, the tunnel empty area range and the microseismic waveform collected, and initializes the candidate source location and the wave velocity parameter to establish an initial solution set for swarm intelligence optimization. This processing not only retains the global search capability of the algorithm, but also avoids the exploration of meaningless areas through physical space constraints.

[0040] Step S300 discriminates the diffraction path according to the particle swarm parameter set, reduces the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional section through the projection method, and verifies whether the straight path passes through the tunnel empty area projection area along the tunnel axis direction point by point to obtain the judgment result of the diffraction occurrence condition;

[0041] Step S400 calculates the minimum diffraction path according to the diffraction discrimination result to obtain a set of shortest path lengths of actual propagation;

[0042] Step S400 performs minimization calculation on the path where diffraction occurs, distinguishes the two diffraction modes of the arch top and the bottom plate, and analyzes the geometric path length of each mode respectively, and finally selects the minimum value as the actual propagation path. This process ensures that the path calculation conforms to the minimum time principle of wave propagation.

[0043] Step S500 performs wave velocity inversion according to the set of shortest path lengths and the second information to obtain the optimal wave velocity value and the spatial position of the source;

[0044] The step S500 combines the shortest path length with the measured time sequence to construct a fitness function, and synchronously optimizes the wave velocity and the source coordinate parameters through a particle swarm algorithm, effectively solving the problem of mutual coupling of path assumption error and wave velocity estimation in traditional methods.

[0045] The step S600 calculates the time of origin according to the optimal wave velocity value and the spatial position of the source, and obtains the time of origin data by backstepping the theoretical arrival time of each sensor and minimizing the measured residual error.

[0046] The step S600 backsteps the theoretical time of origin based on the optimized parameters, and completes the full parameter calibration of the microseismic source space-time positioning by minimizing the residual error between the theoretical arrival time and the measured value, which ensures the accuracy of the positioning result in the time dimension.

[0047] Further, the step S200 includes the step S210 to the step S230.

[0048] The step S210 defines the search space boundary according to the first information, the second information and the third information, determines the three-dimensional space boundary parameters of the possible existence of the microseismic source by analyzing the microseismic sensor coordinate distribution and the tunnel empty area geometric range, and picks up the layered positioning of the PS wave, to obtain a search space boundary set;

[0049] The step S220 initializes the particle position according to the search space boundary set, and obtains an initial particle position set by randomly generating a group of candidate microseismic source coordinate points in the three-dimensional space boundary.

[0050] The step S230 initializes the wave velocity parameter according to the initial particle position set, and obtains an initial particle swarm parameter set containing the position and the wave velocity by setting the wave velocity value range and assigning values.

[0051] Specifically, step S210 first performs a search space boundary definition process according to the first information (spatial coordinate information of the microseismic sensor array) and the third information (three-dimensional geometric parameters of the tunnel empty area), determines the three-dimensional space boundary parameters where the microseismic source is likely to exist by analyzing the spatial distribution of the sensor coordinates, the geometric range of the tunnel empty area, and the microseismic signal arrival time sequence, and obtains a search space boundary set. This process takes into account the actual constraint that the microseismic source is usually located in the surrounding rock area rather than inside the empty area in the tunnel environment, thereby obtaining a search space boundary set. Then, step S220 performs a particle position initialization process based on the boundary set, generates a set of candidate microseismic source coordinate points within the defined three-dimensional space boundary, which ensures global coverage of the search while avoiding exploration of invalid positions (such as inside the empty area), and obtains an initial particle position set. Then, step S230 performs a wave speed parameter initialization process according to the initial particle position set, sets a reasonable wave speed value range and assigns values, combines the wave speed parameter with the position parameter, and finally obtains an initial particle swarm parameter set containing position and wave speed. These processing steps are sequentially progressive, with boundary definition providing a physical constraint for position initialization, and position initialization setting an associated framework for wave speed parameter, which overall ensures that the particle swarm optimization algorithm starts searching from a starting point that meets the wave propagation characteristics in the deep-buried tunnel scenario, effectively improving the efficiency and accuracy of subsequent diffraction path calculation and wave speed inversion.

[0052] Further, step S300 includes steps S310 to S330.

[0053] Step S310, according to the particle swarm parameter set, performs a projection cross-section setting process, selects a vertical cross-section along the tunnel axis direction at a predetermined interval, generates a series of two-dimensional projection planes perpendicular to the tunnel axis, and obtains a cross-section sequence.

[0054] Step S320, according to the cross-section sequence, performs a three-dimensional path projection process, substitutes the straight line equation between the microseismic source and the sensor into each cross-section plane equation to solve the intersection coordinates, maps the three-dimensional space point to the two-dimensional projection plane, and obtains a two-dimensional projection point set.

[0055] Step S330, according to the two-dimensional projection point set, performs a region inclusion discrimination process, checks whether each projection point falls within the arched region or straight wall region of the tunnel cross-section, determines whether the straight line path passes through the tunnel empty area, and obtains a determination result of the diffraction occurrence condition.

[0056] It can be understood that the process first simplifies the model structure in the tunnel excavation process into a three-dimensional entity formed by scanning a plurality of cross sections along the tunnel axis direction, and simplifies the propagation path between the microseismic source and the monitoring particle into an infinitely long three-dimensional line segment; then dimension reduction processing is performed, a plane perpendicular to the tunnel axis direction is selected as the projection plane, and the three-dimensional tunnel body and the straight line are projected onto the two-dimensional plane; on the projection plane, the complex tunnel space is simplified into a straight wall and arch-shaped plane, and the straight line is simplified into a point, so that whether the three-dimensional straight line passes through the tunnel is converted into the geometric problem of whether the two-dimensional point falls in the straight wall and arch-shaped region; finally, iteration identification is performed, since the tunnel axis may be curved and the cross section may vary, a cross section is taken every certain distance along the tunnel axis for identification, and as long as the condition is met at any cross section, the blasting disturbance wave is identified to pass through the adjacent tunnel excavation empty area and cause diffraction of the path due to the existence of the empty area. Specifically, the preferred identification method is:

[0057] Firstly, since the tunnel cross section is straight wall and arch-shaped, i.e. the vault is arch-shaped and the bottom is the side wall. Therefore, the tunnel cross section is divided into two parts, the vault part is represented by an arch function (circular arc or parabola). The straight wall part can be represented by a rectangle or a rectangle.

[0058] The straight line passes through a point and its direction vector . The straight line equation can be expressed as:

[0059] ;

[0060] In the formula, is the parameter of the given curve; is the three-dimensional space coordinates of any point on the straight line .

[0061] Next, the projection cross section is selected along the tunnel axis direction, a series of cross sections are selected at a certain interval , and each cross section is a plane perpendicular to the tunnel axis. For each cross section , a straight wall and arch-shaped combined region is formed after projection. Assuming that the upper boundary of the vault is a circular arc vault, the vault is set as a circular arc with a radius , and the center coordinates , then the arch equation is:

[0062] ;

[0063] If it is a parabola, it can be represented by a parabola equation:

[0064] ;

[0065] wherein, is the opening of the parabola; is the coordinate of the lowest point of the vault;

[0066] If the straight wall part is vertical, its height is , ranging from , represents the position range of the wall in the horizontal direction. If the straight wall is bevelled, it can be represented by a straight line equation:

[0067] ;

[0068] wherein, is the slope; is the intercept.

[0069] Further, the process of determining whether the straight line segment passes through the tunnel empty area is shown in Figure 3 . By determining whether the projection point is above (or below) the circular arc / parabola, it can be determined whether the intersection point falls within the arched area.

[0070] For the straight wall part, it is determined whether the projection point is within the rectangular area. If the straight wall is a vertical wall, it is determined whether the coordinate of the projection point is less than the height of the straight wall . At the same time, it is checked whether the coordinate of is within the coordinate interval of the straight wall. If the straight wall is a bevelled wall, it is determined whether the projection point is below the straight wall line using the straight line equation.

[0071] Then traverse all cross sections . For each cross section, if the projection point falls within the arched area or the straight wall area, it is determined that the straight line passes through the tunnel empty area. If the projection point on all cross sections is not within the tunnel projection area, it is determined that the straight line does not pass through the tunnel empty area.

[0072] It should be noted that the determination of the intersection point of the straight line and the cross section plane depends on the equation of the plane. Assuming that the normal vector of the cross section is , and a point on the cross section is . The equation of the plane is:

[0073] ;

[0074] wherein, is an arbitrary point on the cross section; is a known point on the cross section; is the normal vector of the plane.

[0075] Then, the parametric equation of the straight line can be expressed as:

[0076]

[0077] where, is the starting point of the straight line; is the direction vector of the straight line; is the parameter; is an arbitrary point on the straight line.

[0078] To find the intersection point of the straight line and the plane, substitute the straight line equation into the plane equation. First, substitute the straight line equation into the plane equation:

[0079]

[0080] Solve the linear equation of the parameter

[0081]

[0082] where, is the dot product of the vector from the starting point of the straight line to the center of the cross section and the normal vector of the plane; is the dot product of the normal vector of the plane and the direction vector of the straight line.

[0083] If , it means that the straight line is parallel to the plane, and there is no intersection point. If , it means that the straight line intersects the plane and there is one intersection point. Then the specific coordinates of the intersection point can be calculated by solving the parameter , and it is determined whether the intersection point is within the cross section

[0084] .

[0085] Step S410, according to the diffraction identification result, a diffraction type identification process is performed, by distinguishing the three-dimensional space geometric relationship into two propagation modes of vault diffraction path and bottom plate diffraction path, to obtain a set of diffraction path types to be calculated;

[0086] Step S420, according to the set of diffraction path types, a segmented path geometric analysis process is performed, by solving the tangent point coordinates through the simultaneous space plane equation and tunnel cylindrical equation, and respectively calculating the straight line segment length and the curve segment arc length, to obtain the geometric length data of various types of diffraction paths;

[0087] ​​​​Step S430: Perform minimum path selection processing based on geometric length data. By comparing the numerical values ​​of the arch diffraction path and the base plate diffraction path, take the minimum value as the shortest path length for actual propagation, and obtain the set of shortest path lengths.

[0088] Specifically, such as Figure 4 As shown, according to the principle of least path propagation, the elastic waves generated by the rupture will diffract along the tunnel wall when passing through the excavation voids of adjacent tunnels, and there exists... A plane intersects the tunnel at an angle. Two tangent lines exist within this plane that are tangent to the tunnel, with the points of tangency being... , And the extensions intersect at one point. Additionally, along the bottom slab of the left-line tunnel... A three-dimensional spatial coordinate system is established at the midpoint of the tunnel axis. This three-dimensional spatial coordinate system is based on the tunnel axis. The axis, the vertical direction of the tunnel is The axis, the tunnel tangential direction is axis.

[0089] Based on the three-dimensional spatial relationship between the tunnel microseismic source and the monitoring point, there are two diffraction paths for elastic waves to reach the monitoring point through the excavated tunnel void; one is that the elastic wave travels from the seismic source... The point propagates outward in the form of a spherical wave, reaching... After reaching the point, the upper arc-shaped arch of the tunnel diffracts around the tunnel wall and passes through the top area to reach the point. From point Reaching the monitoring point in a straight line This type of propagation path is defined as diffraction path 1; secondly: explosion disturbance wave point. It propagates outward in the form of a spherical wave, reaching a point at the bottom where it intersects with the tunnel sidewall. Then, it travels in a straight line around the side wall of the base plate, traversing the open area to reach a certain point. Then proceed in a straight line to the point. This type of propagation path is defined as diffraction path 2.

[0090] like Figure 5 As shown, the arc of the upper part of the tunnel is assumed to be part of a complete cylinder. Therefore, the equation of the tunnel cylindrical surface of the three-dimensional cylinder can be assumed to be:

[0091] ;

[0092] ;

[0093] in, For any value, For radius, represents the depth direction coordinate of the spatial point on the cross section perpendicular to the tunnel axis.

[0094] The intersection equation of the plane and the cylindrical surface is:

[0095] ;

[0096] wherein, , , , is a constant, is the coordinate of a point.

[0097] Then, the simultaneous equations can express the parameter form of the coordinate of a point:

[0098] ;

[0099] ;

[0100] ;

[0101] As shown in Figure 6 , in the diffraction path 1, the elastic wave is emitted from the microseismic source in the form of a spherical wave, propagates to the tunnel vault curved surface, is diffracted and forms a curve segment bounded by the tangent points and , and finally reaches the monitoring particle ; the shortest distance of the path is composed of the straight line segment , the arc segment and the straight line segment , wherein the coordinates of the tangent points and are determined by solving the simultaneous equations of the curve parameter equation and the spatial plane equation, and the length of the arc segment is calculated by segmenting the spatial distribution of the vault curved surface, using the Euclidean distance formula and the arc length formula, and finally the path conforms to the principle of the shortest path, that is, when the projection of the path in the overhead distribution coincides with a straight line, it corresponds to the shortest propagation path in the three-dimensional space.

[0102] wherein, according to the tangent and normal line geometric theorem of the plane, the given curve parameter equation in the three-dimensional space is:

[0103] ;

[0104] ;

[0105] ;

[0106] wherein, is a parameter function.

[0107] The tangent direction of a curve at a point is given by the tangent vector at that point:

[0108] ;

[0109] ;

[0110] ;

[0111] where, , , is the tangent vector at the point of tangency, is the derivative function.

[0112] The points of tangency for the diffraction of the tunnel empty region are , and and . The tangent lines passing through the points , are represented by , , respectively, with parametric forms and . According to the geometric theorem of the tangent and normal lines of a plane, the tangent vectors can be expressed as:

[0113] ;

[0114] ;

[0115] According to the known conditions of the tangent lines, the tangent line equations can be expressed as:

[0116] ;

[0117] The tangent line equations are:

[0118] ; According to

[0119] , ,

[0120] ;

[0121] According to the parametric forms of the coordinate points, they can be expressed as:

[0122] ;

[0123] Since , by analogy, we have:

[0124] ;

[0125] According to the assumed coordinates of the tangent point, further arrangement is as follows:

[0126] ;

[0127] ;

[0128] According to the axiom of Euclidean geometry, the straight line distance between two points in a two-dimensional plane is the shortest. According to the three-dimensional space diffraction path distribution (such as Figure 8 ). It can be seen that when the top view distribution of the diffraction path and the straight line distance between the two points coincides with a straight line, the corresponding space diffraction path is also the shortest path. Therefore, the following conclusion can be drawn: when the top view distribution of the space plane coincides with a straight line and the tunnel axis is parallel to , the corresponding three-dimensional space propagation path is the shortest path, which meets the minimum path theorem.

[0129] Based on the above description of the shortest route of the three-dimensional space blasting disturbance wave diffraction, the spatial three-dimensional plane equation expression can be simplified as:

[0130] ;

[0131] ;

[0132] Since the coordinates of the blasting disturbance source point and the monitoring mass point are known, they are assumed to be and , respectively, then:

[0133] ;

[0134] ;

[0135] Further, we can get:

[0136] ;

[0137] ;

[0138] The three-dimensional space plane equation can be expressed as:

[0139] ;

[0140] Further, we can get the expression of the tangent point coordinates about :

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] According to the aforementioned triangle function identity formula: and , respectively, can be obtained:

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] The shortest path curve of elastic wave through the tunnel excavation empty area to the monitoring particle and the two tangent point coordinates of the tunnel vault are expressed as:

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] It can be seen that the tangent point coordinates only exist in relation to the radius of the tunnel vault, the coordinates of the microseismic source and the coordinates of the monitoring particle.

[0160] wherein the straight line segment , The distance can be solved by using the three-dimensional Euclidean distance formula. When the three-dimensional Euclidean distance formula is directly used to solve the distance of the curve segment using the coordinates of the point, the distance of the segment cannot be truly expressed. Therefore, in order to accurately calculate the The distance between the segments, the vault curved surface is spatially distributed, as shown in Figure 8 The point is moved to , until the value of is equal to , and the remaining , coordinate values do not need to be changed to obtain the coordinate points . Through spatial plane distribution Figure 8 It can be seen that the straight line distance of is solved by the three-dimensional Euclidean distance formula, and the straight line distance of is solved by the arc length formula.

[0161] The length is calculated by the arc length formula and the cosine similarity, and the process is relatively simple, only the final formula is listed:

[0162] ;

[0163] At this time, the straight line distance of , is calculated, and the value of the curved segment is solved by calculating the Pythagorean theorem. The detailed solving process of the shortest path distance is not solved and listed one by one.

[0164] As shown in Figure 7 , in the diffraction path 2, the elastic wave propagates from the microseismic source to the tunnel bottom arch point , and then diffraction to the other side arch point in a straight line along the bottom plate hole wall, and finally straight to the monitoring point ; The shortest distance of this path is composed of straight line segments , and , wherein the coordinates of points and are derived by constructing a spatial plane equation perpendicular to the plane and combining the tunnel section parameters, and the lengths of each line segment are calculated by the three-dimensional Euclidean distance formula. The minimum distance of the elastic wave diffraction across the empty area is taken as the smaller value of the length of the two paths, so as to ensure that the path selection conforms to the minimum time principle of wave propagation.

[0165] According to the section parameter information, the following information can be derived:

[0166] ;

[0167] Among them, isThe vertical distance from the center of the semicircle to the bottom of the tunnel, The radius of the semicircle, The angle between the center of the circle and the intersection with the horizontal plane, The central angle of the circle. The three-dimensional spatial coordinates of the points can be represented as Therefore, , And The three-dimensional spatial coordinates of the points can be represented as ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; the vertical distance from the center of the semicircle to the bottom of the tunnel, the radius of the semicircle, the angle between the center of the circle and the intersection with the horizontal plane, the central angle of the circle.

[0168] Therefore, , and The three-dimensional spatial coordinates of the points can be represented as

[0169] ;

[0170] ;

[0171] ;

[0172] where, the span of the tunnel section, the vertical height of the rectangle.

[0173] According to the spatial plane passing through , and is perpendicular to the plane, the corresponding downward diffraction path is the shortest. Therefore, the spatial plane equation can be obtained:

[0174] ;

[0175] According to the above solving process, the coordinates of , points are substituted to solve the coordinates of .

[0176] According to the three-dimensional Euclidean distance formula, the lengths of the line segments , and can be solved. At this time, the length is the shortest distance from the blasting disturbance wave to the monitoring particle through the diffraction under the adjacent tunnel.

[0177] In summary, the minimum distance of the elastic wave diffraction across the empty area formed by the excavation of the adjacent tunnel is .

[0178] The specific steps of the diffraction type recognition processing according to the diffraction identification result are that the model structure in the tunnel excavation process is simplified into a three-dimensional entity formed by scanning a plurality of cross sections along the tunnel axis direction. A straight line between the blasting disturbance source and the monitoring particle is simplified into an infinitely long three-dimensional line segment. A plane perpendicular to the tunnel axis direction is selected as a projection plane, and the three-dimensional tunnel body and the straight line are projected onto the two-dimensional plane. In the projection plane, the complex tunnel space is simplified into a straight wall arched plane, and the straight line is simplified into a point. At this time, whether the three-dimensional straight line passes through the tunnel is converted into whether the two-dimensional point falls within the straight wall arched geometry. Finally, since the tunnel axis may be curved, the cross section of the tunnel may change. Therefore, an interface is taken every certain distance along the tunnel axis for identification, and as long as the condition is met on any interface, it can be identified that the blasting disturbance wave of the excavation tunnel passes through the adjacent tunnel excavation empty area, but the path is diffracted due to the existence of the empty area.

[0179] Further, the step S500 includes steps S510 to S530.

[0180] The step S510 includes a fitness function construction processing according to the shortest path length set and the signal arrival time sequence. The fitness evaluation function of the optimization problem is established by calculating the matching deviation between the path length difference of each sensor and the arrival time difference, and the fitness function model is obtained.

[0181] The step S520 includes a particle swarm iterative optimization processing according to the fitness function model. The particle speed and position are dynamically updated by tracking the individual historical optimal position and the group historical optimal position, the wave velocity parameter and the source coordinate parameter are simultaneously optimized, and the updated parameter set is obtained.

[0182] The step S530 includes a convergence judgment processing according to the updated parameter set. Whether the convergence condition is met is determined by evaluating whether the fitness function value tends to zero, and the optimal wave velocity value and the source spatial position are obtained.

[0183] Specifically, based on the time difference positioning principle, first, the fitness function of the microseismic wave velocity and the microseismic source coordinate is solved, and the fitness function is expressed as:

[0184] ;

[0185] Wherein, is the number of microseismic sensor arrangement; is the arrival time difference of the microseismic sensor number and the number ; is the arrival time difference of the microseismic sensor number and the number The difference between the distances between the sensors and the microseismic source; The equivalent velocity of the microseismic signal propagating in the medium.

[0186] The The distance between the first microseismic sensor and the microseismic source is calculated using the three-dimensional Euclidean distance. When the fitness function approaches or equals 0, the solution is obtained and the three-dimensional spatial coordinates of the microseismic source are optimal. The fitness value function of the time of the earthquake occurrence is evaluated as:

[0187] ;

[0188] Particle Swarm Optimization (PSO) is a classic swarm intelligence algorithm. The core idea is derived from the simulation of the behavior of social groups of birds, fish, and other organisms. In PSO, each potential solution is regarded as a particle in the search space. These particles fly in space, constantly adjusting their speed and direction by tracking the individual historical optimal position and the group historical optimal position, and finally the entire group gradually converges to the optimal solution of the problem. The flight speed and position of the particle are updated according to the following formula:

[0189] ;

[0190] ;

[0191] In the formula, , , , are the flight speed, position, optimal source parameters searched so far, and the optimal source parameters searched so far by the entire particle swarm of the particle ; is the inertia weight.

[0192] Further, step S600 includes steps S610 to S630.

[0193] Step S610, according to the optimal wave speed value and the source spatial position, a theoretical arrival time calculation process is performed, the theoretical arrival time of each sensor is obtained by inversely calculating the ratio of the wave speed to the shortest path length, and a theoretical arrival time sequence is obtained;

[0194] Step S620, according to the theoretical arrival time sequence and the second information, a residual calculation process is performed, the time difference residual of each sensor is calculated by comparing the difference between the theoretical arrival time and the measured arrival time sequence, and a residual data set is obtained;​

[0195] Step S630, according to the residual data set, the optimization processing of the origin time is carried out, and the origin time data is obtained by solving the origin time parameter which makes the residual minimum.

[0196] Specifically, the process is in the origin time solving stage of the microseismic source positioning algorithm. First, based on the optimal wave velocity value and the source spatial position obtained by inversion, the theoretical arrival time of each sensor is back calculated through the physical relationship between wave velocity and shortest path length, forming a theoretical arrival time sequence. Then, the sequence is compared with the measured microseismic signal arrival time sequence, and the time difference residual of each sensor is calculated, so as to obtain the residual data set, quantifying the deviation between the theoretical model and the measured data. Finally, based on the residual set, an optimization algorithm is used to minimize the sum of squares of residuals, and the origin time parameter which makes the residual globally minimum is solved, and finally the accurate origin time data is output, ensuring the integrity and reliability of the microseismic source space-time positioning result.

[0197] Embodiment 2:

[0198] As shown in Figure 2 , the embodiment provides a microseismic source positioning system for deep buried tunnel, the system comprises:

[0199] The acquisition module is configured to acquire first information, second information and third information, the first information comprises spatial coordinate information of the microseismic sensor array, the second information comprises a microseismic signal arrival time sequence, and the third information comprises three-dimensional geometric parameters of the tunnel empty area.

[0200] The construction module is configured to initialize a particle swarm according to the first information, the second information and the third information, and obtain an initial particle swarm parameter set.

[0201] The discrimination module is configured to discriminate a diffraction path according to the particle swarm parameter set, reduce the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional cross section through projection method, and verify whether the straight line path passes through the tunnel empty area projection area point by point along the tunnel axis direction, to obtain a judgment result of the diffraction occurrence condition.

[0202] The calculation module is configured to calculate a minimum diffraction path according to the diffraction discrimination result, and obtain a shortest path length set of actual propagation.

[0203] The inversion module is configured to perform wave velocity inversion according to the shortest path length set and the second information, and obtain an optimal wave velocity value and a source spatial position.

[0204] The solving module is configured to solve the origin time according to the optimal wave velocity value and the source spatial position, and obtain origin time data by back calculating the theoretical arrival time of each sensor and minimizing the measured time difference residual.

[0205] In an embodiment of the present application, the construction module comprises:

[0206] The first construction unit is configured to perform search space boundary definition processing according to the first information, the second information and the third information, and perform hierarchical positioning picking of the PS wave, determine three-dimensional space boundary parameters in which the microseismic source is likely to exist by analyzing the microseismic sensor coordinate distribution and the tunnel empty area geometric range, and obtain a search space boundary set;

[0207] The second construction unit is configured to perform particle position initialization processing according to the search space boundary set, obtain an initial particle position set by randomly generating a group of candidate microseismic source coordinate points in the three-dimensional space boundary;

[0208] The third construction unit is configured to perform wave speed parameter initialization processing according to the initial particle position set, obtain an initial particle swarm parameter set containing position and wave speed by setting a wave speed value range and assigning values.

[0209] In an embodiment of the present application, the discrimination module comprises:

[0210] The first discrimination unit is configured to perform projection cross section setting processing according to the particle swarm parameter set, obtain a cross section sequence by selecting a vertical cross section along the tunnel axis direction at a predetermined interval, and generating a series of two-dimensional projection planes perpendicular to the tunnel axis;

[0211] The second discrimination unit is configured to perform three-dimensional path projection processing according to the cross section sequence, obtain a two-dimensional projection point set by substituting the straight line equation between the microseismic source and the sensor into each cross section plane equation to solve the intersection coordinates, and mapping the three-dimensional space point to the two-dimensional projection plane;

[0212] The third discrimination unit is configured to perform region inclusion discrimination processing according to the two-dimensional projection point set, determine whether the straight line path passes through the tunnel empty area by checking whether each projection point falls within the arched region or the straight wall region of the tunnel cross section, and obtain a determination result of the diffraction occurrence condition.

[0213] In an embodiment of the present application, the calculation module comprises:

[0214] The first calculation unit is configured to perform diffraction type identification processing according to the diffraction discrimination result, obtain a diffraction path type set to be calculated by distinguishing the three-dimensional space geometric relationship into two propagation modes of the vault diffraction path and the bottom plate diffraction path;

[0215] The second calculation unit is configured to perform segmented path geometric analysis processing according to the diffraction path type set, obtain geometric length data of each type of diffraction path by solving the tangent point coordinates by simultaneously solving the space plane equation and the tunnel cylindrical equation, and respectively calculating the straight line segment length and the curve segment arc length;

[0216] The third calculation unit is configured to perform minimum path selection processing according to the geometric length data, to take the minimum value as the shortest path length of actual propagation by comparing the numerical values of the vault diffraction path and the floor diffraction path, and to obtain a shortest path length set.

[0217] In an embodiment of the present application, the inversion module comprises:

[0218] The first inversion unit is configured to perform fitness function construction processing according to the shortest path length set and the signal arrival time sequence, to establish a fitness evaluation function of an optimization problem by calculating the matching deviation between the path length difference of each sensor and the arrival time difference, and to obtain a fitness function model;

[0219] The second inversion unit is configured to perform particle swarm iterative optimization processing according to the fitness function model, to dynamically update the particle speed and position by tracking the individual historical optimal position and the group historical optimal position, to simultaneously optimize the wave velocity parameter and the source coordinate parameter, and to obtain an updated parameter set;

[0220] The third inversion unit is configured to perform convergence judgment processing according to the updated parameter set, to determine whether the convergence condition is met by evaluating whether the fitness function value tends to zero, and to obtain the optimal wave velocity value and the source spatial position.

[0221] In an embodiment of the present application, the solving module comprises:

[0222] The first solving unit is configured to perform theoretical arrival time calculation processing according to the optimal wave velocity value and the source spatial position, to inversely deduce the theoretical arrival time of each sensor by the ratio relationship between the wave velocity and the shortest path length, and to obtain a theoretical arrival time sequence;

[0223] The second solving unit is configured to perform residual calculation processing according to the theoretical arrival time sequence and the second information, to calculate the time difference residual of each sensor by comparing the difference between the theoretical arrival time and the measured arrival time sequence, and to obtain a residual data set;

[0224] The third solving unit is configured to perform earthquake time optimization processing according to the residual data set, to obtain earthquake time data by solving the earthquake time parameter that minimizes the residual.

[0225] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A deep-buried tunnel microseismic source positioning method, characterized in that, The method comprises the following steps: acquiring first information, second information and third information, wherein the first information comprises spatial coordinate information of a microseismic sensor array, the second information comprises a microseismic signal arrival time sequence, and the third information comprises three-dimensional geometric parameters of a tunnel empty area; performing particle swarm initialization according to the first information, the second information and the third information to obtain an initial particle swarm parameter set; performing diffraction path discrimination according to the particle swarm parameter set, reducing the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional cross section through a projection method, and checking whether the straight line path crosses the tunnel empty area projection region point by point along the tunnel axis direction to obtain a judgment result of the diffraction occurrence condition; performing minimum diffraction path calculation according to the diffraction judgment result to obtain a shortest path length set of actual propagation; performing wave velocity inversion according to the shortest path length set and the second information to obtain an optimal wave velocity value and a source spatial position; performing earthquake time calculation according to the optimal wave velocity value and the source spatial position, and obtaining earthquake time data by inversely calculating the theoretical arrival time of each sensor and minimizing the measured time difference residual.

2. The deep-buried tunnel microseismic source positioning method according to claim 1, characterized in that, The method comprises the following steps: performing search space boundary definition processing according to the first information, the second information and the third information, determining three-dimensional space boundary parameters of the possible existence of the microseismic source by analyzing the microseismic sensor coordinate distribution and the tunnel empty area geometric range, and performing hierarchical positioning picking of the PS wave to obtain a search space boundary set; performing particle position initialization processing according to the search space boundary set, and obtaining an initial particle position set by generating a group of candidate microseismic source coordinate points in the three-dimensional space boundary; performing wave velocity parameter initialization processing according to the initial particle position set, and obtaining an initial particle swarm parameter set containing position and wave velocity by setting the wave velocity value range and assigning values.

3. The deep-buried tunnel microseismic source positioning method according to claim 1, characterized in that, The method comprises the following steps: performing diffraction path discrimination according to the particle swarm parameter set, reducing the three-dimensional propagation path between the microseismic source and the sensor to a two-dimensional cross section through a projection method, and checking whether the straight line path crosses the tunnel empty area projection region point by point along the tunnel axis direction to obtain a judgment result of the diffraction occurrence condition, comprising the following steps: performing projection cross section setting processing according to the particle swarm parameter set, selecting a vertical cross section along the tunnel axis direction at a predetermined interval to generate a series of two-dimensional projection planes perpendicular to the tunnel axis to obtain a cross section sequence; performing three-dimensional path projection processing according to the cross section sequence, mapping a three-dimensional space point to a two-dimensional projection plane by substituting the straight line equation between the microseismic source and the sensor into each cross section plane equation to solve the intersection coordinates to obtain a two-dimensional projection point set; 4. The deep-buried tunnel microseismic source positioning method according to claim 1, characterized in that, performing region inclusion discrimination processing according to the two-dimensional projection point set, checking whether each projection point falls within the arched region or the straight wall region of the tunnel cross section to determine whether the straight line path crosses the tunnel empty area to obtain a judgment result of the diffraction occurrence condition. The method comprises the following steps: According to the diffraction identification result, diffraction type recognition processing is performed, the three-dimensional space geometric relationship is divided into two propagation modes of vault diffraction path and bottom plate diffraction path, and a diffraction path type set to be calculated is obtained; According to the diffraction path type set, segmented path geometric analysis processing is performed, the cut point coordinates are solved by simultaneously solving the spatial plane equation and the tunnel cylindrical equation, the straight line segment length and the curve segment arc length are calculated respectively, and geometric length data of various diffraction paths are obtained; According to the geometric length data, minimum path selection processing is performed, the numerical values of the vault diffraction path and the bottom plate diffraction path are compared, the minimum value is taken as the shortest path length of actual propagation, and a shortest path length set is obtained.

5. The deep-buried tunnel microseismic source positioning method according to claim 1, characterized in that, According to the shortest path length set and the second information, wave velocity inversion is performed, and optimal wave velocity value and source space position are obtained, including: According to the shortest path length set and the signal arrival time sequence, fitness function construction processing is performed, the matching deviation between the path length difference of each sensor and the arrival time difference is calculated, the fitness evaluation function of the optimization problem is established, and a fitness function model is obtained; According to the fitness function model, particle swarm iterative optimization processing is performed, the particle velocity and position are dynamically updated by tracking the individual historical optimal position and the group historical optimal position, the wave velocity parameter and the source coordinate parameter are simultaneously optimized, and an updated parameter set is obtained; According to the updated parameter set, convergence judgment processing is performed, whether the fitness function value tends to zero is evaluated, whether the convergence condition is met is judged, and optimal wave velocity value and source space position are obtained.

6. A deep-buried tunnel microseismic source positioning system, characterized in that, Including: An acquisition module is configured to acquire first information, second information, and third information, the first information includes spatial coordinate information of a microseismic sensor array, the second information includes a microseismic signal arrival time sequence, and the third information includes three-dimensional geometric parameters of a tunnel empty area; A construction module is configured to initialize a particle swarm according to the first information, the second information, and the third information, and obtain an initial particle swarm parameter set; A discrimination module is configured to discriminate a diffraction path according to the particle swarm parameter set, reduce the three-dimensional propagation path between a microseismic source and a sensor to a two-dimensional cross section by projection method, and verify whether a straight line path crosses a tunnel empty area projection area along a tunnel axis direction point by point to obtain a judgment result of diffraction occurrence condition; A calculation module is configured to calculate a minimum diffraction path according to the diffraction identification result, and obtain a shortest path length set of actual propagation; An inversion module is configured to perform wave velocity inversion according to the shortest path length set and the second information, and obtain optimal wave velocity value and source space position; A calculation module is configured to calculate a minimum diffraction path according to the diffraction identification result, and obtain a shortest path length set of actual propagation; 7. The deep-buried tunnel microseismic source positioning system according to claim 6, characterized in that, The construction module includes: The first construction unit is configured to perform a search space boundary definition process according to the first information, the second information and the third information, determine three-dimensional space boundary parameters in which a microseismic source is likely to exist by analyzing microseismic sensor coordinate distribution and tunnel empty area geometric range and performing hierarchical positioning picking of PS waves, and obtain a search space boundary set; The second construction unit is configured to perform a particle position initialization process according to the search space boundary set, generate a group of candidate microseismic source coordinate points in the three-dimensional space boundary, and obtain an initial particle position set; The third construction unit is configured to perform a wave velocity parameter initialization process according to the initial particle position set, set a wave velocity value range and assign values, and obtain an initial particle swarm parameter set containing positions and wave velocities.

8. The deep-buried tunnel microseismic source positioning system according to claim 6, characterized in that, The discrimination module includes: The first discrimination unit is configured to perform a projection cross section setting process according to the particle swarm parameter set, select vertical cross sections at a predetermined interval along a tunnel axis direction, generate a series of two-dimensional projection planes perpendicular to the tunnel axis, and obtain a cross section sequence; The second discrimination unit is configured to perform a three-dimensional path projection process according to the cross section sequence, substitute a straight line equation between a microseismic source and a sensor into each cross section plane equation to solve intersection coordinates, map a three-dimensional space point to a two-dimensional projection plane, and obtain a two-dimensional projection point set; The third discrimination unit is configured to perform a region inclusion discrimination process according to the two-dimensional projection point set, check whether each projection point falls within an arch-shaped region or a straight wall region of a tunnel cross section, determine whether a straight line path passes through a tunnel empty area, and obtain a determination result of a diffraction occurrence condition.

9. The deep-buried tunnel microseismic source positioning system according to claim 6, characterized in that, The calculation module includes: The first calculation unit is configured to perform a diffraction type identification process according to the diffraction discrimination result, distinguish three-dimensional space geometric relationships into two propagation modes of a vault diffraction path and a floor diffraction path, and obtain a diffraction path type set to be calculated; The second calculation unit is configured to perform a segmented path geometric analysis process according to the diffraction path type set, solve cut point coordinates by simultaneously solving a space plane equation and a tunnel cylindrical equation, and calculate straight line segment lengths and curve segment arc lengths, respectively, to obtain geometric length data of each type of diffraction path; The third calculation unit is configured to perform a minimum path selection process according to the geometric length data, compare numerical values of the vault diffraction path and the floor diffraction path, take a minimum value as a shortest path length actually propagated, and obtain a shortest path length set.

10. The deep-buried tunnel microseismic source positioning system of claim 6, wherein, The inversion module includes: The first inversion unit is configured to perform an adaptability function construction process according to the shortest path length set and the signal arrival time sequence, calculate a matching deviation between path length differences of each sensor and arrival time differences, establish an adaptability evaluation function of an optimization problem, and obtain an adaptability function model; The second inversion unit is configured to perform a particle swarm iterative optimization process according to the adaptability function model, dynamically update particle velocities and positions by tracking individual historical optimal positions and group historical optimal positions, synchronously optimize wave velocity parameters and source coordinate parameters, and obtain an updated parameter set; A third inversion unit is configured to perform a convergence judgment process according to the updated parameter set, determine whether a convergence condition is met by evaluating whether the fitness function value tends to zero, and obtain the optimal wave velocity value and the spatial position of the seismic source.

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