A method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses

Abstract

The present invention discloses a method for quickly identifying and delineating abnormal structural areas in deep rock masses. The method comprises determining information such as the number and location of selected stations and microseismic events; drawing all propagation path rays between the microseismic source and the station; and solving the medium characteristic parameter δ on each propagation path ray. ij ; Determine the minimum grid unit and use the grid to divide the target area; calculate the parameter α at the intersection of each propagation path ray i , α ii ,……,α X And calculate the parameter Δ at all grid cells Ⅰ , Δ Ⅱ ,……,Δ Y Because the medium in areas with abnormal rock structures differs from that in areas with normal structures, and the calculated results of the grid cell parameter Δ differ between the two, this method allows for rapid identification of abnormal rock structures in deep rock masses. Meshes at the edges of these areas are sequentially connected using smooth curves to delineate the boundaries of these areas. This eliminates potential safety hazards in actual projects and has significant practical significance for resolving various geotechnical engineering implementation issues.

Description

Technical Field

[0001] The present invention relates to the field of abnormal area detection in geotechnical engineering, and in particular to a method for quickly identifying and delineating abnormal structural areas in deep rock masses. Background Art

[0002] The development and utilization of natural resources and underground space are steadily advancing deeper into the Earth. The complex geological environment presents significant challenges to the construction and operation of underground projects. Man-made excavated structures and unique geological structures characterized by fragmented rock mass and fragile structures are widespread within the underground space. Frequent engineering disturbances in these areas trigger engineering disasters such as rockbursts and fault slips, causing significant losses. Furthermore, the accumulation of toxic gases, combustible minerals, and water within these abnormal geological structures creates potential secondary hazards, leading to frequent engineering disasters such as water inrush and mudslides.

[0003] Geological phenomena within rock masses present objective factors such as complexity, discontinuity, and uncertainty, presenting significant difficulties and challenges to the design and safety of geological engineering projects, including tunnel construction, bridge design, building quality inspection, and underground power plant construction. Rock mass structural quality issues significantly impact project design and construction, duration and cost, and the safety of construction workers. Rock masses are widely distributed with typical structures such as soil layers, karst crevices, and fissures. These structures vary in occurrence, form, and area, determining their structural quality. Therefore, detecting and locating typical rock mass structures within geological strata, accurately detecting the structural state of underground rock masses, and extracting precise geological structural information are of great practical significance for resolving various geotechnical engineering implementation issues.

[0004] Detecting abnormal geotechnical structures, water sources, and voids, and visualizing the interior of "invisible" underground geotechnical structures, is crucial for eliminating safety hazards in practical engineering projects. To obtain accurate information about underground rock mass structure, geological exploration projects often use drilling equipment to drill cores underground. The cores' condition is then used to determine the structure of the underlying rock mass. Borehole imaging technology utilizes cone reflection to reflect a circular borehole wall image into a flat optical image, providing a 360-degree visual representation of the rock's color and texture. Borehole geological radar (GPR) uses an antenna deep within the borehole to detect electromagnetic waves reflected from various geological structures. However, as the complexity of the geological environment being studied increases, images obtained by a single detection technology are unable to fully describe the geological structure. For example, borehole optical images only reflect the structural state of the borehole wall, providing an incomplete and incomplete picture of the entire geological area within which the borehole is located. Borehole radar images also provide indirect information, resulting in low recognition accuracy. While direct and reliable, borehole data is limited in quantity and cannot accurately assess rock mass structures far from the borehole.

[0005] In addition to using drilling equipment to obtain rock mass structural information, other geophysical methods include seismic, electromagnetic, and resistivity. Seismic wave detection, based on the propagation characteristics of elastic waves in various geological bodies, inverts the distribution, geometry, and structural characteristics of anomalous geological bodies within areas of hundreds of meters, effectively identifying geological interfaces. However, in practice, anomalous structures are often hidden, spatially uneven, and easily affected by engineering activities, making detection accuracy insufficient for engineering requirements. In particular, traveltime tomography and waveform tomography, representative seismic methods, require extensive data iterations, suffer from poor solution stability, and inevitably introduce a priori models. The inversion results are significantly affected by various factors and parameters, limiting their reliability and adaptability in practical applications. Resistivity or apparent resistivity of rock masses obtained using resistivity or electromagnetic methods can reveal not only the properties of the rock itself, but also the porosity, fracture development, and water content of the rock mass. High-density resistivity is primarily used to detect the spatial distribution of shallow, heterogeneous geological bodies and has been widely used to determine overburden thickness, weathering boundaries, and the distribution of hidden faults. Transient electromagnetic methods, through directly measuring secondary fields, can reveal the distribution characteristics of underground geological bodies. Due to their ability to conduct close-range observations, minimal impact from static displacement, and simple and efficient geodetic work, they are widely used in engineering surveys. However, both resistivity and electromagnetic methods require survey line layout, making them unsuitable for detecting deep rock mass properties.

[0006] In addition, traditional equipment such as geological radar or acoustic wave detectors can only carry out geological surveys over a large range. For weak interlayers or weak structural surfaces in specific areas of geotechnical engineering, traditional methods are difficult to achieve in terms of accuracy. Moreover, the time cost of using these rock structure monitoring methods is also high, which seriously affects the stability and safety of geotechnical engineering. There is an urgent need for a new method to achieve rapid identification and boundary delineation of abnormal structural areas in deep rock masses. Summary of the Invention

[0007] In order to make up for the shortcomings of the existing technology, the present invention provides a method for quickly identifying and delineating the boundaries of abnormal structural areas in deep rock masses, which can achieve rapid identification and delineation of abnormal structural areas in deep rock masses and is an accurate and feasible method.

[0008] The present invention is achieved through the following technical solutions:

[0009] A method for rapidly identifying and delineating abnormal structural areas in deep rock masses is characterized by comprising the following steps:

[0010] S1: The target area is covered by a monitoring network with a certain number of stations and microseismic events. Based on the accuracy requirements and cost constraints of the calculation, the number and location of the selected stations and microseismic events are determined, where there are n stations and m microseismic events.

[0011] S2: Draw all the propagation path rays between the microseismic source and the station (a total of m*n rays), and solve the medium characteristic parameter δ on each propagation path ray between the microseismic source and the station based on the magnitude M of the microseismic event, the peak ground velocity PGV of the microseismic event recorded at the station location, and the distance R between the microseismic source and the station. 11 ,……,δ ij ,……,δ mn , where i represents a microseismic event and j represents a station;

[0012] S3: Determine the minimum grid unit and divide the target area into grids;

[0013] S4: Each propagation path ray will have a large number of intersections in the target area. Let the parameter at the intersection of the propagation path ray be α, and calculate the medium characteristic parameter δ of all propagation path rays passing through the intersection ij The mean of all intersection points is solved for the parameter α i , α ii ,……,α X (Assume that there are X intersection points of propagation path rays in the target area, namely intersection point i, intersection point ii, ..., intersection point X);

[0014] S5: There are a certain number of propagation path ray intersections in each grid cell. The average value of the parameter α at each intersection in the grid cell is taken as the parameter Δ of the grid cell. The parameter Δ at all grid cells is calculated. Ⅰ , Δ Ⅱ ,……,Δ Y (Assume that the target area has a total of Y grid cells, namely grid cell I, grid cell II, ..., grid cell Y);

[0015] S6: Since the medium in the abnormal structure area of ​​the rock mass is different from that in the normal structure area, the parameter Δ of the grid unit calculated in the abnormal area is different from the parameter Δ in the normal area. Based on the difference in the calculation results, the abnormal structure area of ​​the deep rock mass can be quickly identified. The abnormal structure area is composed of a finite number of grid units. The grids at the edge of the abnormal structure area are connected in sequence using a smooth curve to delineate the boundary range of the abnormal area.

[0016] Furthermore, in step S2, the peak ground velocity PGV of the microseismic event recorded at the station location depends only on the magnitude M, epicenter distance R, and propagation path of the microseismic event. Among these parameters, PGV, magnitude M, and epicenter distance R are all known, and the size of PGV is positively correlated with the magnitude M and negatively correlated with the epicenter distance R. A parameter δ is defined to describe the medium characteristics on the propagation path ray between the source and the station. The value of δ at each location is fixed and can be considered a constant. However, M and R are not constants and will vary with different sources. The calculation formula of PGV is shown in formula (1):

[0017]

[0018] According to formula (1), the calculation formula of the medium characteristic parameter δ on the propagation path ray between the earthquake source and the station can be obtained, as shown in formula (2):

[0019]

[0020] In the target area, the number of microseismic events and sensors is more than one. For the medium parameter δ on the propagation path between microseismic event i and station j, ij The calculation formula is shown in formula (3):

[0021]

[0022] Where: PGV j represents the peak ground velocity generated by station j under the action of microseismic event i; R ij M represents the distance between station j and microseismic event i; i represents the magnitude of microseismic event i.

[0023] Furthermore, in step S3, the size of the grid is determined according to the conditions of the area to be measured and the accuracy requirements. Generally speaking, the denser the grid division, the higher the accuracy of the result, but the amount of calculation will also increase, and the processing time will increase.

[0024] Furthermore, in step S4, for an intersection point x in the target area, it is assumed that n source and sensor propagation path rays pass through this point, that is, x is the intersection point of n propagation path rays, and the medium characteristic parameters on these propagation path rays are recorded as δ1, δ2, δ3, ..., δ n , then the parameter α at the intersection point x x The calculation formula is shown in formula (4):

[0025]

[0026] Where, δ p is the medium characteristic parameter on the propagation path ray passing through the desired intersection point, δp The method for calculating is as shown in formula (3), where the line connecting microseismic event i and station j must pass through the spatial point x.

[0027] According to the calculation formula (4), the parameter α at all intersection points in the deep rock mass target area is obtained. i , α ii ,……,α X .

[0028] Furthermore, in step S5, there are a certain number of propagation path ray intersections in each grid unit, and the average value of the parameter α at each intersection is taken as the parameter Δ of the grid unit.

[0029] Assume that the grid cell contains the intersection points of N propagation paths, and the intersection points are recorded as 1, 2, 3, ... N respectively. The parameter Δ of the grid cell is the mean value of the parameters at the N intersection points, and the calculation formula is shown in formula (5):

[0030]

[0031] Where, α q represents the parameter value at the intersection point q of the propagation path ray in the grid cell being sought, α q The method to calculate is shown in formula (4).

[0032] Calculate the parameter Δ at all grid cells according to formula (5): Ⅰ , Δ Ⅱ ,……,Δ Y .

[0033] Beneficial effects

[0034] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the existing technology: relative to the existing technology, the present invention is not only suitable for monitoring shallow rock masses, but also for deep rock masses; the present invention can realize the rapid identification of abnormal structure areas and the delineation of boundary ranges, with high recognition accuracy and low use cost, which not only improves the monitoring timeliness, but also has strong reliability and adaptability in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the locations of stations, microseismic events, and abnormal structural areas in the method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses of the present invention;

[0036] Figure 2 A schematic diagram of all propagation paths between the microseismic source and the station for the method for rapid identification and boundary delineation of abnormal deep rock structure areas of the present invention;

[0037] Figure 3Schematic diagram of grid division for the method of rapid identification and boundary delineation of abnormal structural areas in deep rock masses according to the present invention;

[0038] Figure 4 This is a schematic diagram of the results of the identification and boundary delineation of abnormal structural areas in deep rock masses according to the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions of various embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] A method for rapidly identifying and delineating abnormal structural areas in deep rock masses comprises the following steps:

[0041] S1: The target area is covered by a monitoring network with a certain number of stations and microseismic sources. Based on the accuracy requirements and cost constraints of the calculation, the number and location of the selected stations and microseismic sources are determined, where there are n stations and m microseismic events.

[0042] S2: Draw all the propagation path rays between the microseismic source and the station (a total of m*n rays), and solve the medium characteristic parameter δ on each propagation path ray between the microseismic source and the station based on the magnitude M of the microseismic event, the peak ground velocity PGV of the microseismic event recorded at the station location, and the distance R between the microseismic source and the station. 11 ,……,δ ij ,……,δ mn , where i represents a microseismic event and j represents a station;

[0043] S3: Determine the minimum grid unit and divide the target area into grids;

[0044] S4: Each propagation path ray will have a large number of intersections in the target area. Let the parameter value at the intersection of the propagation path ray be α. By calculating the medium characteristic parameter δ of all propagation path rays passing through the intersection ij The mean of all intersection points is solved for the parameter α i , α ii ,……,α X (Assume that there are X intersection points of propagation path rays in the target area, namely intersection point i, intersection point ii, ..., intersection point X);

[0045] S5: There are a certain number of propagation path ray intersections in each grid cell. The average value of the parameter α at each intersection in the grid cell is taken as the parameter Δ of the grid cell. The parameter Δ at all grid cells is calculated. Ⅰ, Δ Ⅱ ,……,Δ Y (Assume that the target area has a total of Y grid cells, namely grid cell I, grid cell II, ..., grid cell Y);

[0046] S6: Since the medium in the abnormal structure area of ​​the rock mass is different from that in the normal structure area, the parameter Δ of the grid unit calculated in the abnormal area is different from the parameter Δ in the normal area. Based on the difference in the calculation results, the abnormal structure area of ​​the deep rock mass can be quickly identified. The abnormal structure area is composed of a finite number of grid units. The grids at the edge of the abnormal structure area are connected in sequence using a smooth curve to delineate the boundary range of the abnormal area.

[0047] In step S1, the target area is covered by a monitoring network with a certain number of stations and microseismic sources. Based on the information recorded at each station, the magnitude of each microseismic event and the peak ground velocity (PGV) from the microseismic event to the station location can be determined. The number and location of stations and microseismic events required for the calculation are selected based on actual needs. A greater number of stations and microseismic events yields more accurate results, but the computational effort increases, increasing time and cost. A smaller number of stations and microseismic events reduces computational cost, but also reduces accuracy, potentially failing to meet accuracy requirements.

[0048] Furthermore, in step S2, the peak ground velocity PGV of the microseismic event recorded at the station location depends only on the magnitude M, epicenter distance R, and propagation path of the microseismic event. Among these parameters, PGV, magnitude M, and epicenter distance R are all known, and the size of PGV is positively correlated with the magnitude M and negatively correlated with the epicenter distance R. A parameter δ is defined to describe the medium characteristics on the propagation path ray between the source and the station. The value of δ at each location is fixed and can be considered a constant. However, M and R are not constants and will vary with different sources. The calculation formula of PGV is shown in formula (1):

[0049]

[0050] According to formula (1), the calculation formula of the medium characteristic parameter δ on the propagation path ray between the earthquake source and the station can be obtained, as shown in formula (2):

[0051]

[0052] In the target area, the number of microseismic events and sensors is more than one. For the medium parameter δ on the propagation path between microseismic event i and station j, ij The calculation formula is shown in formula (3):

[0053]

[0054] Where: PGV j represents the peak ground velocity generated by station j under the action of microseismic event i; R ij M represents the distance between station j and microseismic event i; i represents the magnitude of microseismic event i.

[0055] Furthermore, in step S3, the size of the grid is determined according to the conditions of the area to be measured and the accuracy requirements. Generally speaking, the denser the grid division, the higher the accuracy of the result, but the amount of calculation will also increase, and the processing time will increase.

[0056] Furthermore, in step S4, in the selected target area, since there are many microseismic events and sensors, the propagation path rays must intersect. For the parameter α at the intersection of the propagation path rays, we take the medium characteristic parameter δ of all the propagation path rays passing through the intersection. ij is represented by the mean value of .

[0057] For an intersection point x in the target area, assume that there are n source and sensor propagation path rays passing through this point, that is, x is the intersection point of n propagation path rays, and the medium characteristic parameters on these propagation path rays are recorded as δ1, δ2, δ3, ..., δ n , then the parameter α at the intersection point x x The calculation formula is shown in formula (4):

[0058]

[0059] Where, δ p is the medium characteristic parameter on the propagation path ray passing through the desired intersection point, δ p The method for calculating is as shown in formula (3), where the line connecting microseismic event i and station j must pass through the spatial point x.

[0060] According to the calculation formula (4), the parameter α at all intersection points in the deep rock mass target area is obtained. i , α ii ,……,α X .

[0061] Furthermore, in step S5, there are a certain number of propagation path ray intersections in each grid unit, and the average value of the parameter α at each intersection is taken as the parameter Δ of the grid unit.

[0062] Assume that the grid cell contains the intersection points of N propagation paths, and the intersection points are recorded as 1, 2, 3, ... N respectively. The parameter Δ of the grid cell is the mean value of the parameters at the N intersection points, and the calculation formula is shown in formula (5):

[0063]

[0064] Where, α q represents the parameter value at the intersection point q of the propagation path ray in the grid cell being sought, α q The method to calculate is shown in formula (4).

[0065] Calculate the parameter Δ at all grid cells according to formula (5): Ⅰ , Δ Ⅱ ,……,Δ Y .

[0066] The following combination Figures 1 to 4 The method for quickly identifying abnormal structural areas in deep rock masses and defining their boundaries according to an embodiment of the present invention is described in detail.

[0067] like Figure 1 As shown, this simulation uses a uniform model with a longitudinal wave velocity of 5100 m / s, a shear wave velocity of 2881 m / s, and an average density of the rock mass of 2810 kg / m 3 Take the research area of ​​1000m×1000m, and the preset position of the abnormal structure area is shown in the figure. Set the area as a blank area, the longitudinal wave velocity is 340m / s, the shear wave velocity is 0m / s, and the density is 1kg / m 3 The target area is covered by a monitoring network with a certain number of stations and microseismic sources. Based on the accuracy requirements and cost constraints of the calculation, 8 stations and 81 microseismic events were selected from the area. The distribution of stations and microseismic events is shown in the figure below. Figure 1 shown.

[0068] like Figure 2 As shown in the figure, all propagation path rays between 81 microseismic sources and 8 stations are drawn, totaling 648. According to the magnitude M of the microseismic event, the peak ground velocity PGV of the microseismic event recorded at the station location, and the distance R between the microseismic source and the station, the medium characteristic parameter δ on each propagation path ray is solved. ij , (i represents a microseismic event, j represents a station), and the color of the propagation path rays in the figure is based on the calculated medium characteristic parameter value δ ij Determine the medium characteristic parameter δ on each propagation path ray ij The values ​​are shown in Table 1. In Table 1, the propagation path rays passing through the abnormal area are highlighted, and their δ ij The value is significantly smaller than the δ on the propagation path ray that does not pass through the abnormal area ij value.

[0069] Table 1 Medium characteristic parameters δ on each propagation path ray ij value

[0070]

[0071] Figure 3 The target area is divided into grids, and the size of each grid unit is selected as 10m×10m. In this embodiment, 648 propagation path rays actually generate 54079 intersection points in the target area, and the parameter α at each intersection point is X Take the medium characteristic parameter δ of all propagation path rays passing through the intersection ij According to the formula Calculate, where δ p is the medium characteristic parameter on the propagation path ray passing through the desired intersection point, δ p The calculation formula is In this implementation, the parameter value α at each intersection X See Table 2. Due to the large number of intersections, this embodiment only lists the parameter values ​​α at some intersections. X The highlighted parts in Table 2 are the parameter values ​​α of the intersection points that fall in the abnormal area. X , by comparison, the parameter value α of the intersection point falling in the abnormal area X The parameter value α is significantly smaller than the intersection of the normal area X .

[0072] Table 2 Parameter values ​​α at each intersection X

[0073]

[0074]

[0075]

[0076]

[0077] There are a certain number of propagation path ray intersections in each grid cell, and the parameter α at each intersection is taken X The mean of is taken as the parameter Δ of the grid unit. According to the formula Calculate the medium parameter Δ of the grid unit. Where α q represents the parameter value at the intersection point q of the propagation path ray in the grid cell being sought, α q The calculation formula is Solve for the parameter Δ at all grid cells Ⅰ , Δ Ⅱ ,……,Δ YThe grid cell size is chosen to be 10m×10m, resulting in a total of 10,000 grid cells. This example lists only some of the grid cell parameters Δ, as shown in Table 3. In Table 3, grid cell 1 is at coordinates (0, 0), and the grid cells are numbered from left to right and from bottom to top. The highlighted grids in Table 3 are located in the abnormal area, while the remaining grids are located in the normal area. A comparison shows that the parameters Δ for the grid cells in the normal and abnormal areas are significantly different.

[0078] Table 3 Parameters Δ of each grid unit

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] As can be seen in Table 3, the calculated values ​​for the abnormal structure grid area are significantly different from those of the surrounding grid cells, thus enabling rapid identification of abnormal structure areas in deep rock masses. Smooth curves are used to sequentially connect the grids at the edge of the abnormal structure area to delineate the boundaries of the abnormal area. Figure 4 The figure shows the abnormal structure area circled by a smooth curve after calculation. The shape of the circled area is almost consistent with the preset abnormal structure area. In order to quantitatively compare the degree of fit between the abnormal structure area circled by calculation and the preset abnormal structure area, Table 4 lists the vertex coordinates of the preset abnormal structure area and the vertex coordinates of the abnormal structure area circled after calculation. The vertex coordinates are given in the order of clockwise rotation starting from the vertex coordinate of the lower left corner. By comparing the position changes of the same vertex coordinates, it can be seen that the calculated vertex error is within 1m. This error is very small relative to the overall 1000m×1000m study area. The slight difference between the calculated value and the preset value is completely within the acceptable error range. It can be seen that the calculated abnormal area results are well consistent with the preset abnormal structure area and have high accuracy.

[0085] Table 4. Vertex coordinates of the preset and calculated abnormal structure areas and the difference between them

[0086]

[0087]

[0088] Furthermore, in actual operation, if more stations and microseismic events are selected and the grid units are set smaller, the calculation accuracy will be higher, the abnormal structure area can be identified more accurately, and the boundary range of the abnormal structure area can be more precisely delineated.

[0089] The specific embodiments of the present invention described above do not limit the scope of protection of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses, characterized in that: The specific steps include: S1: The target area is covered by a monitoring network with a certain number of stations and microseismic events. The number and location information of the selected stations and microseismic events are determined based on the accuracy requirements and cost constraints of the calculation. n microseismic events m indivual; S2: Draw the ray cosines of all propagation paths between the microseismic source and the station. m*n According to the magnitude of microseismic events M , the peak ground velocity of the microseismic event recorded at the station location PGV and the distance between the microseismic source and the station R , solve the medium characteristic parameters on each propagation path ray between the microseismic source and the station , ,......, ,……, ,……, ,in i represents microseismic events, j Indicates a station; S3: Determine the minimum grid unit and divide the target area into grids; S4: Each propagation path ray will have a large number of intersections in the target area, and the parameters at the intersection of the propagation path rays are α , by calculating the medium characteristic parameters of all propagation path rays passing through the intersection The mean of all intersection points is solved. , ,……, , there are X intersection points of propagation path rays in the target area, namely intersection point i, intersection point ii, ..., intersection point X; S5: There are a certain number of propagation path ray intersections in each grid cell. The parameters at each intersection in the grid cell are taken. α The mean of the grid cells is taken as the parameter of , calculate the parameter Δ at all grid cells Ⅰ , Δ Ⅱ ,……,Δ Y , there are Y grid cells in the target area, namely grid cell I, grid cell II, ..., grid cell Y; S6: Since the medium in the abnormal structure area of ​​the rock mass is different from that in the normal structure area, the parameter Δ of the grid unit calculated in the abnormal area is different from the parameter Δ in the normal area. Based on the difference in the calculation results, the abnormal structure area of ​​the deep rock mass can be quickly identified. The abnormal structure area is composed of a finite number of grid units. The grids at the edge of the abnormal structure area are connected in sequence using a smooth curve to delineate the boundary range of the abnormal area.

2. A method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses according to claim 1, characterized in that: In step S2, the peak ground velocity of the microseismic event recorded at the station location PGV Depends only on the magnitude of the microseismic event M , epicenter distance R And the propagation path, these parameters, PGV , magnitude M and epicentral distance R are all known, and PGV Size and magnitude M The size is positively correlated with the distance from the epicenter. R Negative correlation between size and quantity, define a parameter δ to describe the medium characteristics on the propagation path between the source and the station, where δ The value at each position is fixed and can be considered a constant, but M 、 R is not a constant and will vary with the earthquake source. PGV The calculation formula is shown in formula (1): （1） According to formula (1), the medium characteristic parameters on the propagation path between the source and the station can be obtained as δ The calculation formula is shown in formula (2): （2） In the target area, there are more than one microseismic events and sensors. i and stations j Medium characteristic parameters along the propagation path δ ij The calculation formula is shown in formula (3): （3） Where: PGV j Indicates station j In microseismic events i Peak ground speed under action; R ij Indicates station j In microseismic events i the distance between them; M i Indicates microseismic events i The magnitude of the earthquake.

3. The method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses according to claim 1, characterized in that: In step S3, the size of the divided grid is determined according to the conditions of the area to be measured and the accuracy requirements.

4. The method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses according to claim 1, characterized in that: In step S4, for an intersection point x in the target area, it is assumed that n The source and station propagation path rays pass through this point, that is, x is n The intersection of the propagation path rays, the medium characteristic parameters on these propagation path rays are recorded as , , , ......， , then the parameter at the intersection point x The calculation formula is shown in formula (4): （4） Where, is the medium characteristic parameter on the propagation path ray passing through the desired intersection point, The method for calculating is as shown in formula (3), where the microseismic event i With the station j The line connecting must pass through the spatial point x; According to the calculation formula (4), the parameters of all intersection points in the deep rock mass target area are obtained. , ,……, .

5. The method for rapid identification and boundary delineation of abnormal structural areas in deep rock masses according to claim 1, characterized in that: In step S5, there are a certain number of propagation path ray intersections in each grid unit, and the parameters at each intersection are taken. α The mean of is taken as the parameter Δ of the grid unit; Assume that the grid cell contains N intersection points of propagation path rays, and the intersection points are recorded as 1, 2, 3, ... N respectively. The parameter Δ of the grid cell is the mean value of the parameters at the N intersection points, and the calculation formula is shown in formula (5): （5） Where, Represents the intersection of the propagation path rays in the desired grid cell q The parameter value at The method of finding is shown in formula (4); Calculate the parameter Δ at all grid cells according to formula (5): Ⅰ , Δ Ⅱ ,……,Δ Y .

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