Three-dimensional micro-motion detection method, device and medium for goaf of subway line

Abstract

The application provides a three-dimensional micro-motion detection method, device and medium for a mined-out area of a subway line, wherein the method comprises the following steps: superimposing a borehole column within a preset threshold range of a micro-motion detection line on a micro-motion apparent velocity model of a detection area, and calibrating a corresponding relationship between an apparent velocity and a borehole stratum lithology; qualitatively explaining the lithology, tracking and dividing strata including a cover layer and a bedrock based on the corresponding relationship between the apparent velocity and the borehole lithology and according to profile variation characteristics of the apparent velocity; delineating a range of a low-velocity anomaly in the bedrock based on profile characteristics of the apparent velocity and according to a background value of the apparent velocity, a velocity anomaly form and a gradient variation thereof; discriminating a property of the low-velocity anomaly in the bedrock according to a relationship between the low-velocity anomaly and surrounding rock, and explaining and delineating a spatial range of the mined-out area. The application can more intuitively and reliably detect the mined-out area, better meet the engineering geological survey demand, improve detection construction efficiency and ensure the safety of shield construction of a subway tunnel.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geology and geophysics, and particularly to a three-dimensional microtremor detection method, device and medium for a goaf of a subway line. BACKGROUND

[0002] Traditional geophysical exploration methods for goaf can be divided into two categories: seismic wave method and electromagnetic method (Xue Guoqiang et al., 2018). The most commonly used seismic reflection method in seismic wave method is widely used in goaf detection (Cheng Jianyuan et al., 2003, 2008; Yang Shuang'an et al., 2001). When seismic wave encounters a goaf, the dynamics characteristics (waveform, amplitude, main frequency, etc.) of the seismic wave will be significantly distorted due to the disappearance of the coal seam and the interruption of the strong wave impedance interface (Cheng Jianyuan et al., 2003, 2008), thus becoming an important indicator for identifying goaf. Methods of electromagnetic wave, including high-density resistivity method, transient electromagnetic method, direct current method, etc., use the electromagnetic and electrical differences between goaf and surrounding rock to achieve the purpose of detection. High-density resistivity method has high detection accuracy and has become an effective means for detecting coal seam goaf (Xue Guoqiang et al., 2018, Yuan Wen Tao, 2008; Yang Jingming, 2012). Ground transient electromagnetic method is sensitive to low-resistance water-bearing goaf, has the advantages of easy penetration of high-resistance layer, convenient construction, high efficiency, and exploration depth more suitable for coal mine goaf, and has become the preferred method for detecting water-filled goaf (Chen Weiyin and Xue Guoqiang, 2013).

[0003] However, in the survey area with complex topography and geological conditions, high and low voltage power lines, industrial stray current and serious vibration interference, the traditional artificial source seismic and electromagnetic wave detection method is very difficult to collect high-quality raw data in the field, and the detection effect is difficult to guarantee. The microtremor detection method extracts the surface wave dispersion characteristics from the natural source ground weak vibration (noise), and uses the corresponding relationship between dispersion and stratum medium structure to achieve the detection purpose. Since it does not need artificial source, it has natural anti (vibration, electromagnetic) interference ability, and the construction is convenient. Microtremor detection has become a new technology for goaf geophysical exploration, and has achieved satisfactory engineering application effect in goaf detection of Shanxi Huozhou Railway, Henan Zheng-Deng-Luo Intercity Railway, Shaanxi Tongchuan East Slope Coal Mine and newly-built Mudanjiang-Jiamusi Passenger Dedicated Line.

[0004] The boundary range, spatial structure of goaf, and the characteristics of overburden and surrounding rock will directly affect the design and construction of subway tunnel. However, in the busy downtown area, the traditional artificial source geophysical exploration method is difficult to carry out work due to the influence of strong vibration and strong electromagnetic interference, and the site conditions are restricted. Therefore, in view of the special technical requirements of high detection accuracy and shallow detection depth for goaf detection of urban subway line, the microtremor detection method is researched. SUMMARY

[0005] The application provides a three-dimensional microseismic detection method, device and medium for a mined-out area of a subway line.

[0006] The technical scheme adopted by the application is as follows:

[0007] According to a first aspect of the application, a three-dimensional microseismic detection method for a mined-out area of a subway line is provided, the method comprising: superimposing drill hole columns within a preset threshold range of a distance microseismic survey line on a microseismic apparent velocity model of a detection area to calibrate the correspondence between apparent velocity and drill hole stratum lithology; based on the correspondence between the apparent velocity and the drill hole lithology, qualitatively explaining the lithology according to the profile velocity variation characteristics of the apparent velocity, tracking and dividing strata, the strata including a cover layer and a bedrock; based on the profile characteristics of the apparent velocity, determining a low-velocity anomaly range according to the background value of the apparent velocity, the velocity anomaly shape and the gradient variation thereof; and according to the relationship between the low-velocity anomaly and surrounding rock, distinguishing the nature of the low-velocity anomaly in the bedrock and explaining the spatial range of the mined-out area.

[0008] According to a second aspect of the application, a three-dimensional microseismic detection device for a mined-out area of a subway line is provided, the device comprising: a first calibration unit configured to superimpose drill holes within a preset threshold range of a distance microseismic survey line on an apparent velocity profile model of a detection area to calibrate the correspondence between apparent velocity and drill hole stratum lithology; a second tracking and dividing unit configured to qualitatively explain the lithology according to the profile velocity variation characteristics of the apparent velocity based on the correspondence between the apparent velocity and the drill hole lithology, track and divide strata, the strata including a cover layer and a bedrock; a third determination unit configured to determine a low-velocity anomaly range according to the background value of the apparent velocity, the velocity anomaly shape and the gradient variation thereof based on the profile characteristics of the apparent velocity; and a fourth explanation and delineation unit configured to distinguish the nature of the low-velocity anomaly range in the bedrock according to the relationship between the low-velocity anomaly and surrounding rock and explain the spatial range of the mined-out area.

[0009] According to a third aspect of the application, a computer-readable storage medium having computer-readable instructions stored thereon is provided, when the computer-readable instructions are executed by a processor of a computer, the computer executes the three-dimensional microseismic detection method for a mined-out area of a subway line described in various embodiments of the application.

[0010] The application has at least the following technical effects:

[0011] The application converts the detected phase velocity into a microtremor apparent velocity model, and determines the basic corresponding relationship between the apparent velocity and the lithology in combination with the drilling data of the detection area. Based on this, the properties of the low-velocity anomaly in the bedrock are determined according to the relationship between the low-velocity anomaly and the surrounding rock, the spatial range of the goaf is explained and circled, the goaf can be detected more intuitively, more reliably and effectively, the engineering detection demand can be better met, the construction efficiency is improved, and the safety of the subway shield construction is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0012] The drawings incorporated into the specification and forming a part thereof show, to the extent necessary, embodiments consistent with the present application, and, together with the specification, serve to explain the principles of the present application. It is apparent that the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without creative labor on the basis of these drawings.

[0013] Figure 1 is a whole flowchart of the three-dimensional microtremor detection method for the goaf of the subway line according to the embodiment of the present application;

[0014] Figure 2a is an engineering layout according to the embodiment of the present application;

[0015] Figure 2b is an observation array schematic diagram according to the embodiment of the present application;

[0016] Figure 3 is a frequency dispersion curve diagram obtained by calculating the microtremor data collected by three different radius arrays according to the embodiment of the present application;

[0017] Figure 4a is a microtremor detection result profile of the LG2 line according to the embodiment of the present application;

[0018] Figure 4b is a microtremor detection result profile of the LG1 line according to the embodiment of the present application;

[0019] Figure 5 is a three-dimensional microtremor detection method for the goaf of the subway line, and a flowchart for determining a three-dimensional apparent velocity model of the detection area according to the embodiment of the present application;

[0020] Figure 6 is an array layout schematic diagram according to the embodiment of the present application;

[0021] Figure 7 is a structure diagram of the three-dimensional microtremor detection device for the goaf of the subway line according to the embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application, that is, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings herein can be arranged and designed in various different configurations.

[0023] Therefore, the detailed description of the embodiments of the present application provided below 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 of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] It should be noted that the relational terms such as "first" and "second" and the like are used only to distinguish one entity or operation from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus including the element.

[0025] Please refer to Figure 1 is the overall flowchart of the three-dimensional microseismic detection method for the mined-out area of the subway line in the embodiments of the present application. The embodiments of the present application provide a three-dimensional microseismic detection method for the mined-out area of the subway line, which starts from step S100, and the drill hole columnar superposition within the preset threshold range of the distance microseismic survey line is superposed on the microseismic apparent velocity model of the detection area to calibrate the corresponding relationship between the apparent velocity and the drill hole stratum lithology. The microseismic survey line is composed of a plurality of detection points, that is, at least two detection points are included on one microseismic survey line. The microseismic survey line can be determined according to the setting of the subway line, or according to the needs of the detection purpose. The preset threshold range is reasonably selected according to different geological conditions to find the corresponding drill hole, and the existing microseismic apparent velocity profile model of the detection area, the corresponding area drill hole and the geological data are comprehensively analyzed to finally obtain the corresponding relationship between the apparent velocity and the drill hole lithology.

[0026] It should be noted that the microseismic apparent velocity model described herein can be a two-dimensional apparent velocity model or a three-dimensional apparent velocity model.

[0027] Taking a two-dimensional apparent velocity model as an example, the microtremor data collected by multiple exploration points on a survey line and different radius arrays of each exploration point are used to calculate corresponding dispersion curves, the dispersion curves of each exploration point are combined to obtain a complete dispersion curve of each exploration point, and the dispersion curves of multiple exploration points on the survey line are subjected to velocity transformation and imaging to obtain a two-dimensional apparent velocity model of the survey line. The two-dimensional apparent velocity profile model can be used to calculate and process a three-dimensional microtremor apparent velocity model of a detection area through spatial interpolation smoothing.

[0028] More specifically, the present embodiment takes the mined-out area between Lejialu Station and Gangbei Station of Guangzhou Metro Line 14 as an example, a main survey line LG2 is arranged along the Airport Road (above the tunnel of the metro line), 20 exploration points (LG2-1 to LG2-20) are arranged, and an auxiliary survey line LG1 perpendicular to the main survey line is arranged at the large end of the survey line, 7 exploration points (LG1-3 to LG1-9) are arranged, the point distance of the exploration points is 10 m, and the engineering arrangement is shown in Figure 2a , and the schematic diagram of the observation array is shown in Figure 2b .

[0029] According to the detection task requirements, the present embodiment adopts the "T-shaped" observation array shown in FIG. 2, and the three observation radii are 0.9-6-18 m to meet the 50 m detection depth requirement. The advantage of this observation array is that the small array with a center of 0.9 m contains 1-4 observation points and only needs to occupy one lane, the observation points 9, 6, 5 and 8 along the bottom side of the large triangle can be arranged on the sidewalk, and the observation points 7 and 10 are arranged by borrowing a lane and a sidewalk on the opposite side of the road. The microtremor data is collected on the Airport Road, a main urban road in Guangzhou, and the collection work is carried out at night. The use of such an array basically does not affect traffic, improves construction efficiency and ensures construction safety.

[0030] Figure 3 For the dispersion curves obtained by calculating the microtremor data collected by three different radius arrays, it can be seen that the high-frequency microtremor data collected by the small array with a radius of 0.9 m can extract the dispersion curve (segment) of 8-38 Hz, which plays a decisive role in detecting the near-surface stratum structure and improving the resolution of small-scale geological bodies in the shallow part. The measured dispersion curves obtained by the two observation arrays with radii of 5 m and 15 m are combined to form a complete dispersion curve of 4-38 Hz, which can meet the 50 m detection depth requirement.

[0031] The consistency of the observation instrument is tested before formal data collection, and the power spectrum, power spectrum ratio, coherence coefficient and phase difference consistency of each instrument are better than 95%, meeting the requirements of microtremor detection for instrument consistency. The sampling frequency of the microtremor data is 100 Hz, and the amplification factor is 16. During actual construction, the observation system is designed to observe along the measuring line point by point. Each single point observation time is 16 minutes, and after the observation is completed, the instrument is moved to the next exploration point, and the microtremor data collection of the whole measuring line is completed point by point. Based on the collected microtremor data, a two-dimensional apparent velocity model is obtained and constructed.

[0032] The drill holes within the preset threshold range from the microtremor measuring line are superimposed on the two-dimensional apparent velocity model of the detection area to calibrate the lithology of the stratum, delineate the low-velocity anomaly, and make geological interpretation of the microtremor detection profile, as shown in Figure 4a and Figure 4b . The specific method can be to collect drill hole data in the airport road measuring area, and the drill hole columns within the preset threshold range from the microtremor measuring line (exploration point) are adopted and superimposed on the microtremor profile (see Figure 4a and Figure 4b ), which can play an important role in lithology calibration of the microtremor profile. By comparing with the lithology of the drill hole, the basic correspondence between the apparent S-wave velocity and the lithology in the measuring area is summarized (as shown in Table 1), which provides a reference basis for lithology interpretation and identification of the goaf low-velocity anomaly of the microtremor profile.

[0033] Table 1 Correspondence between lithology and apparent S-wave velocity in the measuring area

[0034]

[0035] In step S200, based on the correspondence between the apparent velocity and the lithology of the drill hole, the lithology is qualitatively interpreted according to the profile velocity variation characteristics of the apparent velocity, and the rock layer is tracked and divided, including the overburden and the bedrock.

[0036] Specifically, the overburden in the measuring area is composed of two layers of plain fill and silty clay layer, and the apparent velocity v x = 200-300 m / s. The profile of the main measuring line (LG2) is relatively uniform, with an average thickness of about 2.8 meters, and the silty clay layer is 4.5-10.1 meters thick, with low-velocity thin layers (high clay content or water content) sandwiched between the layers, and the bottom interface fluctuates greatly. The profile of the auxiliary measuring line (LG1) has an average thickness of about 9 meters, and a low-velocity layer about 1 meter thick is developed at a depth of 5 meters, which is stable and continuous, with high mud content / muddy content / water content.

[0037] The bedrock in the measuring area is silty sandstone, and the soil-rock interface is flat with small fluctuations. According to the value of v x and its variation characteristics, the bedrock can be divided into upper and lower segments with interface I as the boundary, and the upper segment has a v x= 400 ~ 700 m / s, local low-velocity anomaly develops, especially concentrated in LG2-01 ~ 04 below, indicating that the bedrock near the soil-rock interface in the survey area is significantly different in weathering. The lower section x > 700 m / s, the rock mass is relatively dense, and the rock mass below LG2-08 ~ 01 section x The value is significantly reduced, and a local low-velocity thin interlayer is developed, indicating that the rock mass strength is reduced and the local weathering is moderate.

[0038] In step S300, based on the profile characteristics of apparent velocity, the range of low-velocity anomaly is determined according to the background value of apparent velocity, the shape of velocity anomaly and the gradient change. The background value of apparent velocity specifically refers to the average value of apparent velocity in a certain local or overall range, the shape of velocity anomaly includes increase or decrease of apparent velocity, and the gradient change represents the amplitude of increase or decrease of apparent velocity.

[0039] Finally in step S400, according to the relationship between the low-velocity anomaly range and the surrounding rock, the nature of the low-velocity anomaly in the bedrock is determined, and the spatial range of the goaf is explained and delineated.

[0040] In some embodiments, according to the relationship between the low-velocity anomaly and the surrounding rock, the nature of the low-velocity anomaly in the bedrock is determined, and the spatial range of the goaf is explained and delineated, specifically including: determining the spatial range of the goaf according to the low-velocity anomaly; and determining the nature of the goaf according to the velocity difference between the goaf and the surrounding rock.

[0041] In some embodiments, the determination of the nature of the goaf according to the velocity difference between the goaf and the surrounding rock specifically includes: if the velocity difference between the goaf and the surrounding rock is within a first threshold range, the goaf is determined to be a coal seam goaf; and if the velocity difference between the goaf and the surrounding rock is within a second threshold range, the goaf is determined to be a rock roadway. It should be noted that the first threshold range and the second threshold range are different in different survey areas and different geological conditions, and need to be determined according to local conditions. The second threshold range should be greater than the first threshold range, that is, the velocity difference between the rock roadway and the surrounding rock should be significantly greater than the velocity difference between the coal seam goaf and the surrounding rock.

[0042] Combined Figure 4a and Figure 4b , a low-velocity anomaly zone (outlined with white dotted lines) is seen below the survey points LG2-17 ~ LG2-19, combined with drilling data, it is explained as "2# coal seam goaf", with an elevation of 0 ~ -7 meters. Corresponding to this, on the LG1 profile, the 2# coal seam is basically a belt-shaped low-velocity anomaly (outlined with white solid lines and labeled as "2# coal seam goaf") along the layer position, and can be connected with the suspected goaf G1 revealed at a depth of 10 ~ 12.6 m in LG-Y2-6 hole, it is reasonable to explain that "2# coal seam goaf".

[0043] Another low-velocity anomaly below LG1-03~04 segment is explained as "4# coal seam goaf" (see Figure 4a The white solid line marked "4# coal seam goaf" in the figure). The boreholes MN2Z2-DL15 and MN2Z3-LG-158 drilled the goaf at 16.2~19.8m (G2) and 9~10.8m (G3) respectively, and the two goafs are connected, which is consistent with the low-velocity anomaly.

[0044] On the LG2 profile, the "2# coal seam goaf" corresponds to a significant low-velocity anomaly, and in addition, there is no obvious velocity anomaly in the goaf (G1, G3 and G4) seen by the borehole, which is related to the fact that the goaf is filled and the velocity difference with the surrounding rock is reduced. For example, MN2Z2-DL15 revealed the suspected goaf G2, the filling material is mainly weathered siltstone, containing siltstone and a small amount of coal slag, and wood chips can be seen sporadically, and water leaks when drilling.

[0045] Significant local low-velocity anomalies are found in the bedrock outside the coal seam outcrop area, which are explained as rock tunnels (such as R1~R8). Compared with the weak low-velocity / no low-velocity anomaly of the 2# and 4# coal seam goaf, the rock tunnel corresponds to a significant low-velocity anomaly, which is related to the fact that the rock tunnel still retains a relatively complete tunnel space and has a large velocity difference with the surrounding rock.

[0046] (1) The low-velocity anomaly R2 on the LG1 profile is between -26~-28.5m, 1.5~2m high, and about 25m long; R1 is between -18.5~-20m, 1.5~2m high, and about 12.5m long. The two horizontal rock tunnels are penetrated by a vertical low-velocity anomaly R3, which is explained as a rock tunnel about 2.6m wide. It is speculated that R3 is a vertical shaft that penetrates the two horizontal tunnels R1 and R2. According to the spatial position relationship between the two goafs G1 and G2 revealed by the borehole and R3, it is speculated that the rock tunnel G3 may be a raise at a depth of -5m and connected with G4 and G1.

[0047] (2) Similar low-velocity anomalies R4 and R5 are also seen below LG2-14 point on LG2 profile, R4 is located between -15~-17m, about 27m long, and R5 is connected with R4 (rock tunnel) at the lower part, vertically extending from -15.5m to -5m level, about 5m wide.

[0048] (3) R6, R7 and R8 low-velocity anomalies on LG2 profile are all explained as rock tunnels.

[0049] The detailed information of goaf and rock tunnel is shown in Table 2.

[0050] Table 2 Detailed information of goaf and rock tunnel interpreted by micro-oscillation detection

[0051]

[0052] Compared with the historical data of No. 1 and No. 3 shafts in Xiaogang of Jiahe Mine, the R1, R4, R6 and R7 rock roadway can be compared with the-16m mining level, and the R2 and R8 can be compared with the-30m mining level.

[0053] In some embodiments, a three-dimensional apparent velocity model of a detection area is specifically obtained. Please refer to Figure 5 , Figure 5 The flow of constructing the three-dimensional apparent velocity model of the detection area is shown. Specifically, the three-dimensional apparent velocity model of the detection area is determined by the following method:

[0054] Step S1001, extracting observation stations to form an equivalent array according to a preset array radius from any arranged observation stations.

[0055] For example, please refer to Figure 6 , Figure 6 is a schematic diagram of array arrangement of the embodiments of the present application. The observation stations are extracted to form an equivalent array according to a preset array radius from any arranged observation stations, and the specific method is as follows:

[0056] Taking the exploration point as the center, scanning in a circular shape with a radius R, and the stations in the circular area form an equivalent array. The R value is generally 1 / 3-1 / 5 of the exploration depth H. Figure 3 In the figure, the hollow circle represents the observation station, the solid and filled circle represents the station forming the equivalent array, and the circle at the center of the equivalent array is both the exploration point and the observation point.

[0057] Of course, the corresponding microseismic data can also be obtained based on Figure 2b the schematic diagram of array arrangement shown.

[0058] Step S1002, calculating the spatial autocorrelation function of each station pair in the equivalent array, which is formed by combining the observation stations in the equivalent array two by two.

[0059] Specifically, the microseismic vertical vibration waveform data of each station pair (such as A, B) is subjected to fast Fourier transform (FFT) to calculate the power spectrum and the cross power spectrum in the same time period, and the spatial autocorrelation function of each data segment is calculated, and the calculation formula is as follows:

[0060]

[0061] Wherein, S B (ω), S A (r, ω) are the Fourier spectrum of the microseismic signals B(0, 0) and A(r, θ) at points B and A respectively, * represents complex conjugate, represents the complex conjugate of the Fourier spectrum of the microseismic signal at point B, represents the complex conjugate of the Fourier spectrum of the micro-motion signal at point A, and Real{} represents taking the real part of {}.

[0062] The spatial autocorrelation function of the station pair is obtained by averaging the spatial autocorrelation function over all time periods.

[0063] Step S1003: Classify station pairs according to the station spacing r, and perform azimuth averaging on the spatial autocorrelation functions of each type of station pair to obtain the array spatial autocorrelation coefficient ρ(ω,r). i ), forming r i ~ρ(r) i The curve, where ω is the angular frequency and r is the angular frequency. i For the station spacing, ρ(r) i ) represents the spatial autocorrelation coefficient.

[0064] Step S1004: Fit the r using the Bessel function. i ~ρ(r) i The surface wave phase velocity is obtained from the curve, and the dispersion curve of the center point (exploration point) of the equivalent array is obtained.

[0065] In some embodiments, r is fitted using a first-order zero Bessel function J0(x). i ~ρ(r) i From the curve, find the argument x of the Bessel function; then from... Find the i-th frequency f i phase velocity v r (f i ), to obtain the phase velocity dispersion curve of the center point (exploration point) of the equivalent array.

[0066] To improve the resolution of micro-motion detection and obtain detailed detection results, we need to return to... Figure 2b or Figure 6 The equivalent array can be subdivided into smaller subarrays with radii of R / 2 and R / 4. Stations located on the circumference can be used simultaneously for calculations of both the large and small subarrays. Steps S1002-S1004 are performed on the subarrays separately. After obtaining the segmented dispersion curves, they are then stitched together to form the complete dispersion curve.

[0067] Step S1005, the Rayleigh wave phase velocity v r Perform velocity transformation processing to obtain the apparent velocity v c and v r ~f curve converted to v c The ~h curve, where f represents the frequency and h represents the detection depth.

[0068] In some embodiments, the Rayleigh wave phase velocity v is expressed by the following formula (2). rThe velocity transform processing is performed to obtain the apparent velocity v c :

[0069]

[0070] where v c,i is the i-th apparent velocity, v r,i is the i-th Rayleigh wave phase velocity, and f i is the i-th frequency.

[0071] In some embodiments, the detection depth where v r represents the Rayleigh wave phase velocity, and f is the frequency.

[0072] Finally, in step S1006, the v c ~ h curve of the entire detection area is interpolated and smoothed to form a velocity image of the detection area, and a three-dimensional apparent velocity model of the entire detection area is obtained.

[0073] Referring to Figure 7 is a structural diagram of a three-dimensional microseismic detection device for a mined-out area of a subway line according to an embodiment of the present application. The embodiment of the present application also provides a three-dimensional microseismic detection device for a mined-out area of a subway line, which comprises:

[0074] The first calibration unit 701 is configured to superimpose the borehole within a preset threshold range of the distance microseismic survey line on the three-dimensional apparent velocity model of the detection area, and determine the corresponding relationship between the apparent velocity and the lithology of the borehole.

[0075] The second tracking and dividing unit 702 is configured to qualitatively interpret the lithology according to the profile velocity variation characteristics of the apparent velocity, track and divide the rock strata based on the corresponding relationship between the apparent velocity and the lithology of the borehole, and the rock strata include the overburden and the bedrock.

[0076] The third determination unit 703 is configured to determine the low-velocity anomaly range according to the background value, the velocity anomaly shape and the gradient variation of the velocity anomaly based on the profile characteristics of the apparent velocity.

[0077] The fourth interpretation and delineation unit 704 is configured to determine the nature of the low-velocity anomaly in the bedrock according to the relationship between the low-velocity anomaly and the surrounding rock, and interpret and delineate the spatial range of the mined-out area.

[0078] In some embodiments, the first calibration unit 701 is further configured to calculate the corresponding dispersion curves respectively by using the microseismic data collected by a plurality of different radius arrays, combine the dispersion curves to obtain a complete dispersion curve, and obtain the three-dimensional apparent velocity model of the detection area by velocity transform and imaging of the dispersion curve.

[0079] In some embodiments, the fourth interpretation delineation unit 704 is further configured to:

[0080] determine the spatial range of the goaf according to the low-velocity abnormal range;

[0081] determine the property of the goaf according to the velocity difference between the goaf and the surrounding rock.

[0082] In some embodiments, the fourth interpretation delineation unit 704 is further configured to:

[0083] if the velocity difference between the goaf and the surrounding rock is within a first threshold range, determine the goaf as a coal seam goaf;

[0084] if the velocity difference between the goaf and the surrounding rock is within a second threshold range, determine the goaf as a rock tunnel.

[0085] In some embodiments, the first calibration unit 701 is further configured to:

[0086] extract observation stations from any deployed observation stations to form an equivalent station array according to a preset station array radius;

[0087] calculate the spatial autocorrelation function of each station pair in the equivalent station array, the station pair being formed by combining the observation stations in the equivalent station array two by two;

[0088] classify the station pairs according to the inter-station distance r, and azimuthally average the spatial autocorrelation functions of the station pairs of each class to obtain the station array spatial autocorrelation coefficient ρ(ω, r i ), which constitutes the r i ~ ρ(r i ) curve, where ω is the angular frequency, r i is the inter-station distance of the station pair, and ρ(r i ) is the spatial autocorrelation coefficient;

[0089] fit the r i ~ ρ(r i ) curve using Bessel functions to obtain the surface wave phase velocity and obtain the dispersion curve of the center point of the equivalent station array;

[0090] perform velocity transformation processing on the Rayleigh wave phase velocity v r to obtain the apparent velocity v c , and convert the v r ~ f curve to the v c ~ h curve, where f represents the frequency and h is the detection depth;

[0091] perform interpolation smoothing processing on the v c ~ h curve of the entire detection area to perform apparent velocity imaging of the detection area and obtain a full-detection-area three-dimensional apparent velocity model.

[0092] In some embodiments, the first calibration unit 701 is further configured to:

[0093] The microtremor vertical vibration waveform data of each station pair is respectively processed, the power spectrum and the cross power spectrum are calculated by fast Fourier transform in the same time period, the spatial autocorrelation function p(w, r) of each data segment is calculated, and the calculation formula is as follows:

[0094]

[0095] wherein S B (ω), S A (r, ω) are the Fourier spectra of the microtremor signals B(0, 0) and A(r, θ) at points B and A respectively, * represents the complex conjugate, represents the complex conjugate of the Fourier spectrum of the microtremor signal at point B, represents the complex conjugate of the Fourier spectrum of the microtremor signal at point A, and Real{} represents the real part in {}.

[0096] In some embodiments, the first calibration unit 701 is further configured to:

[0097] The r i ~ p(r i ) curve is fitted by using the first type of zero-order Bessel function J0(x), and the element x of the Bessel function is obtained;

[0098] According to x = 2πf i r / v r (f i ), the Rayleigh wave phase velocity v i (f r ) of the i-th frequency f i is obtained, and the dispersion curve of the equivalent array center point is obtained.

[0099] In some embodiments, the first calibration unit 701 is further configured to perform velocity transformation processing on the Rayleigh wave phase velocity v i by using the following formula (2) to obtain the apparent velocity v c :

[0100]

[0101] wherein v c,i is the i-th apparent velocity, v r,i is the i-th Rayleigh wave phase velocity, and f i is the i-th frequency.

[0102] It should be noted that the modules described in the embodiments of the present application can be implemented in the form of software or hardware, and the described modules can also be arranged in the processor. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0103] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the method described in the above embodiments. The computer readable storage medium can be included in the electronic device described in the above embodiments, or can exist separately and not be assembled into the electronic device.

[0104] It should be noted that the computer readable medium shown in the embodiments of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component. In the present application, the computer readable signal medium can include a data signal carrying a computer readable computer program in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The computer program contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination of the above.

[0105] The above merely provides preferred exemplary embodiments of the present application, and is not intended to limit the implementation of the present application. Based on the main concept and spirit of the present application, the person skilled in the art can easily make corresponding changes or modifications, and the protection scope of the present application should be subject to the protection scope required by the claims.

Claims

1. A three-dimensional micro-motion detection method for goaf areas along subway lines, characterized in that: The method includes: overlaying a borehole columnar section within a preset threshold range of the distance to the micro-motion survey line onto the micro-motion apparent velocity model of the detection area, and calibrating the correspondence between apparent velocity and borehole strata lithology; Based on the correspondence between apparent velocity and borehole lithology, and according to the profile velocity variation characteristics of apparent velocity, lithology is qualitatively interpreted, and rock strata are traced and divided, including overburden and bedrock. Based on the profile characteristics of apparent velocity, the range of low-velocity anomalies is determined according to the background value of apparent velocity, the morphology of velocity anomalies and their gradient changes. Based on the low-velocity anomaly and its relationship with the surrounding rock, determine the nature of the low-velocity anomaly in the bedrock and interpret the spatial range of the goaf. The dispersion curves are calculated by using micro-motion data collected by multiple arrays with different radii. The dispersion curves are then merged to obtain a complete dispersion curve. The dispersion curve is then transformed by velocity and imaged to obtain a two-dimensional apparent velocity model of the detection area. The step of determining the nature of the low-velocity anomaly in the bedrock and interpreting and delineating the spatial range of the goaf based on the low-velocity anomaly and its relationship with the surrounding rock, specifically includes: determining the spatial range of the goaf based on the range of the low-velocity anomaly. The nature of the goaf is determined based on the velocity relationship between the goaf and the surrounding rock. The method of determining the nature of the goaf based on the velocity difference between the goaf and the surrounding rock specifically includes: if the velocity difference between the goaf and the surrounding rock is within a first threshold range, then the goaf is determined to be a coal seam goaf. If the velocity difference between the goaf and the surrounding rock is within the second threshold range, then the goaf is determined to be a rock tunnel. The three-dimensional micro-motion apparent velocity model of the detection area is determined by the following method: an equivalent array is formed by extracting observation stations from any set up observation stations according to a preset array radius; Calculate the spatial autocorrelation function of each station pair in the equivalent array, wherein the station pair is formed by combining two observation stations in the equivalent array; Station pairs are classified according to the distance between stations, r. The spatial autocorrelation function of each type of station pair is averaged azimuthally to obtain the spatial autocorrelation coefficient ρ(ω,r). i ), forming r i ~ρ(r) i The curve, where ω is the angular frequency and r is the angular frequency. i For the station spacing, ρ(r) i ) represents the spatial autocorrelation coefficient; Fitting r using the Bessel function i ~ρ(r) i The surface wave phase velocity is obtained from the curve, and the dispersion curve of the equivalent array center point v is obtained. r ~f; Rayleigh wave phase velocity v r Perform velocity transformation processing to obtain the apparent velocity v c and v r ~f curve converted to v c ~h curve, where f represents frequency and h is detection depth; v for the entire detection area c The ~h curve is interpolated and smoothed to perform visual velocity imaging of the survey area, thereby obtaining a three-dimensional visual velocity model of the entire survey area.

2. The three-dimensional micro-motion detection method for mining subsidence areas in subway lines according to claim 1, characterized in that: The calculation of the spatial autocorrelation function of each station pair in the equivalent array specifically includes: performing a fast Fourier transform on the micro-motion vertical vibration waveform data of each station pair within the same time period to obtain the power spectrum and cross-power spectrum, and calculating the spatial autocorrelation function ρ(ω,r) of each data segment. The calculation formula is as follows: Among them, S B (ω), S A (r, ω) are the Fourier spectra of the micro-motion signals B(0, 0) and A(r, θ) at points B and A, respectively. * represents complex conjugate. The complex conjugate of the Fourier spectrum of the micro-motion signal at point B. represents the complex conjugate of the Fourier spectrum of the micro-motion signal at point A, and Real{} represents taking the real part of {}.

3. The three-dimensional micro-motion detection method for mining subsidence areas in subway lines according to claim 2, characterized in that: The Bessel function is used to fit r. i ~ρ(r) i The surface wave phase velocity is obtained by calculating the dispersion curve of the equivalent array center point using the curve. Specifically, this involves fitting the r curve with the first-order zero-order Bessel function J0(x). i ~ρ(r) i Find the argument x of the Bessel function from the curve. According to x = 2πf i r / v r (f i Find the i-th frequency f. i Rayleigh wave phase velocity v r (f i ), thus obtaining the dispersion curve of the center point of the equivalent array.

4. The three-dimensional micro-motion detection method for mining subsidence areas in subway lines according to claim 3, characterized in that: The Rayleigh wave phase velocity v is obtained by using the following formula (2). r Perform velocity transformation processing to obtain the apparent velocity v c : Among them, v c,i Let v be the i-th apparent velocity. r,i Let f be the phase velocity of the i-th Rayleigh wave. i Let i be the i-th frequency.

5. A three-dimensional micro-motion detection device for mining subsidence areas in subway lines, characterized in that: The device includes: a first calibration unit, configured to overlay boreholes within a preset threshold range of distance micro-motion measuring lines onto a two-dimensional apparent velocity profile model of the detection area, and calibrate the correspondence between apparent velocity and borehole strata lithology; The second tracking and segmentation unit is configured to qualitatively interpret the lithology based on the correspondence between the apparent velocity and the borehole lithology, and to track and segment the rock strata according to the profile velocity change characteristics of the apparent velocity. The rock strata include the overburden and the bedrock. The third determining unit is configured to determine the range of low-velocity anomalies based on the profile features of apparent velocity, according to the background value of apparent velocity, the morphology of velocity anomalies and their gradient changes. The fourth interpretation and delineation unit is configured to determine the nature of the low-velocity anomaly in the bedrock based on the low-velocity anomaly and its relationship with the surrounding rock, and to interpret and delineate the spatial range of the goaf.

6. A computer-readable storage medium, characterized in that: It stores computer-readable instructions, which, when executed by the computer's processor, cause the computer to perform the three-dimensional micro-motion detection method for the goaf area of ​​the subway line as described in any one of claims 1 to 4.

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