Method for detecting slurry diffusion range in grouting process based on microseism

Abstract

The application discloses a method for detecting slurry diffusion range in a grouting process based on microseismic exploration. Microseismic data is collected, all microseismic events are screened, and effective microseismic events are obtained. The positions of the effective microseismic events and the energy sizes of the effective microseismic events are obtained. The improved DBSCAN clustering analysis method is used to analyze and process the effective microseismic events. The discovered characteristics are used. The field epsilon is set to change with the energy. The greater the energy of a microseismic event is, the greater the field epsilon range of the microseismic event is. The smaller the energy of a microseismic event is, the smaller the field epsilon range of the microseismic event is. Finally, the microseismic events generated by the grouting impact can be distinguished by using the method. The positions of the distinguished microseismic events are obtained, so that the slurry diffusion range in the microseismic data collection is determined. The method can distinguish the microseismic events generated by the grouting impact under the premise of having a large detection depth, so that the detection precision of the slurry diffusion range can be ensured.

Description

Technical Field

[0001] This invention relates to a method for detecting the grout diffusion range during grouting based on microseismic detection, belonging to the field of grouting monitoring technology. Background Technology

[0002] Grouting of fracture zones is a commonly used engineering technique aimed at repairing and reinforcing fracture zones in soil or rock, thereby improving their load-bearing capacity and stability. Determining the grout diffusion range is crucial during the grouting process, as it allows for prediction of grouting effectiveness and control of subsequent grouting parameters. Currently, commonly used methods for detecting grout diffusion range include grouting hole observation, seepage pressure measurement, probe tracking, and groundwater level monitoring. However, these methods all have certain limitations, such as limited observation range, complex layout requirements, susceptibility to interference, and delayed response.

[0003] Geophysical methods offer significant advantages in confirming the extent of grout diffusion, such as non-invasiveness, high resolution, high sensitivity, real-time capability, rapid and accurate monitoring of grout diffusion, and the provision of spatial distribution information through three-dimensional imaging. However, while ground-penetrating radar (GPR) and geoelectric methods are widely used in geophysical exploration to detect the grouting extent in structural fracture zones, they still have limitations. GPR suffers from the skin effect, limiting its detection depth; and geoelectric methods may fail to distinguish between fine fractures due to small differences in resistivity between the grout and the surrounding rock. Microseismic monitoring technology can monitor microseismic events generated by internal rock fractures. It has a large detection depth and is easy to install. When grouting a fracture zone, high-pressure injection is required, which causes the grout to impact the fracture, resulting in the rock within the fracture being compressed and broken, generating microseismic events. Collecting and locating these microseismic events can determine the grout diffusion range. However, during the grouting process, other underground activities or vibrations will generate microseismic data (i.e., non-microseismic events), as well as microseismic events caused by the rock mass itself being affected by geostress (i.e., interfering microseismic events). All of these will be collected during the monitoring process. Currently, there is no good way to distinguish the microseismic events generated by the grouting impact, which leads to extremely low detection accuracy of the grout diffusion range, making microseismic monitoring unsuitable for monitoring the grout diffusion range.

[0004] To address these issues, one of the research directions in this industry is to develop a new microseismic detection method that can distinguish microseismic events generated by grouting impact while maintaining a large detection depth, thereby ensuring the accuracy of the detection range of grout diffusion. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method for detecting the grout diffusion range during grouting based on microseismic detection. Under the premise of having a large detection depth, it can also distinguish microseismic events generated by grouting impact, thereby ensuring the detection accuracy of the grout diffusion range.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for detecting the grout diffusion range during grouting based on microseismic detection, the specific steps of which are as follows:

[0007] Step 1: Deploy the microseismic monitoring system: First, determine the fracture zone area, and then deploy multiple geophones in the surrounding roadways. Each geophone is connected to the microseismic monitoring host through a microseismic signal acquisition line to complete the deployment of the microseismic monitoring system.

[0008] Step 2: Grouting the fracture zone area: Drill multiple grouting holes into the fracture zone area from the tunnel rock wall. Each grouting hole extends into the fracture zone area. After all grouting holes have been drilled, grouting is carried out in the fracture zone area simultaneously through each grouting hole.

[0009] Step 3, Grout diffusion monitoring: During the grouting process, as grout is continuously injected into the fracture zone area, it impacts the rock in the fracture zone area, causing the rock in each fracture to be squeezed and broken, generating micro-vibration events. Multiple geophones collect the micro-vibration events in the fracture zone area and the interfering micro-vibration events in other areas in real time, and feed all the collected micro-vibration events back to the micro-vibration monitoring host.

[0010] Step 4: Monitoring Data Processing: The microseismic monitoring host first filters all collected microseismic events to obtain valid microseismic events, locates each valid microseismic event and determines its energy magnitude, and then uses the improved DBSCAN cluster analysis method to analyze and process the valid microseismic events, thereby distinguishing the microseismic events generated by grouting impact. Finally, the location of the distinguished microseismic events is obtained, thereby determining the range of grout diffusion during this microseismic data acquisition.

[0011] Step 5: Continuous monitoring: Repeat steps 3 and 4 at set intervals, and repeat this process multiple times to obtain the range of slurry diffusion at different time points.

[0012] The principle of this invention for determining the grout diffusion range based on microseismic monitoring is as follows: As is well known, microseismic monitoring technology can monitor microseismic events generated by internal rock fractures. However, when used for grout diffusion monitoring, it cannot distinguish between microseismic events generated by grouting impact and interfering microseismic events. The inventors have discovered that microseismic events generated by grouting impact have a characteristic: during grouting, the impact of the grout along the fracture continuously excites a large number of microseismic events, and these microseismic event clusters extend slowly and continuously along the fracture development direction. Simultaneously, the greater the impact energy on the fracture, the greater the energy of the generated microseismic events. Furthermore, because the initial impact of the grout entering the fracture is large, as… As the grout penetrates the crack, its impact force continuously decreases until it ceases to exist. This indicates that the grout essentially stops flowing deep within the crack. Therefore, the micro-vibrations generated by the grouting impact have higher energy and a larger number of events at the initial location of the crack. As the grout penetrates deeper into the crack, the energy and number of micro-vibrations generated continuously decrease. This characteristic does not exist in interfering micro-vibrations. Therefore, by using this discovery, by defining a range ε ​​that varies with energy, we can identify micro-vibrations with a large number of events around high-energy micro-vibrations. The final set of micro-vibrations formed as the energy decreases is the micro-vibration event generated by the grouting impact.

[0013] Furthermore, the screening process in step four is as follows: By setting an amplitude threshold or a micro-vibration frequency range, when using the amplitude threshold method, all micro-vibration events are compared with the set amplitude threshold. If the amplitude of a micro-vibration event exceeds the amplitude threshold, it is determined to be a valid micro-vibration event; otherwise, the micro-vibration event is discarded, thus completing the screening of valid micro-vibration events. When using the micro-vibration frequency range method, the frequency of all micro-vibration events is compared with the micro-vibration frequency range. If the frequency of a micro-vibration event is within the micro-vibration frequency range, it is determined to be a valid micro-vibration event; otherwise, the micro-vibration event is discarded, thus completing the screening of valid micro-vibration events.

[0014] Furthermore, in step four, a particle swarm localization algorithm is used to locate effective microseismic events.

[0015] Furthermore, the improved DBSCAN clustering analysis method distinguishes microseismic events generated by grouting impact. The specific process is as follows:

[0016] 1) Mark all microseismic events as unprocessed;

[0017] 2) Select the microseismic event F with the highest energy among all unprocessed microseismic events and mark it as processed;

[0018] 3) Define a neighborhood ε, the size of which varies with the energy intensity of the selected microseismic event:

[0019]

[0020] Where ε is the neighborhood size, ε0 is the initial value of the neighborhood, Sigmoid is the activation function, E is the energy of the microseismic event F, E0 is the energy trigger threshold, and C is a manually set neighborhood variation coefficient that changes with the fracture size. The size of the neighborhood ε changes with the strength of the energy E; when E is less than E0, the value of ε approaches ε0; when E is too large, ε approaches ε0+C, making ε adaptively change with the energy of the microseismic event, thus more accurately determining the neighborhood range.

[0021] 4) Calculate the distance between each microseismic event using the Euclidean distance calculation formula;

[0022] The formula for calculating Euclidean distance is:

[0023]

[0024] In the above formula, dist(α,β) is the Euclidean distance between any microseismic event α and any microseismic event β, x α y α z α The x, y, and z coordinates of event α are given below. β y β z β These are the x, y, and z coordinates of event β, respectively.

[0025] 5) Obtain the distances between the microseismic event F and all surrounding microseismic events according to step 4). Compare each distance with the neighborhood ε obtained in step 3). If there are several microseismic events within the neighborhood ε of the coordinates of microseismic event F (i.e., there are multiple microseismic events around microseismic event F with distances smaller than the neighborhood ε):

[0026] ① Create a cluster C and add F into C;

[0027] ② Let N be the set of microseismic events within the neighborhood ε. For each unprocessed microseismic event F' within N:

[0028] I. Mark as processing;

[0029] II. Calculate the neighborhood ε of microseismic event F' according to step 3), and obtain the distance between microseismic event F' and each surrounding microseismic event according to step 4). If there are several microseismic events within the neighborhood ε of F' coordinates, add them to N.

[0030] III. If F' does not belong to any cluster, add it to C;

[0031] ③ Save C;

[0032] 6) If there are no other microseismic events within the neighborhood ε of coordinate F, then F is denoted as the interfering microseismic event;

[0033] 7) Repeat steps 2) to 6), with each repetition the neighborhood ε continuously shrinks until there are no unprocessed microseismic events;

[0034] Each repetition will form a cluster C. The cluster containing the most microseismic events is identified as the set of microseismic events generated by grouting impact, thus completing the process of distinguishing microseismic events generated by grouting impact.

[0035] Compared with existing technologies, this invention uses multiple detectors to collect microseismic events in the fracture zone area and interfering microseismic events in other areas during grouting in real time. All collected microseismic events are screened to obtain valid ones. The location and energy magnitude of each valid microseismic event are then acquired. An improved DBSCAN clustering analysis method is then used to analyze and process these valid microseismic events. Utilizing the aforementioned characteristics, a range ε ​​that varies with energy is set. Higher energy microseismic events have a larger range ε, while lower energy microseismic events have a smaller range ε. This aligns with the aforementioned characteristics where higher energy microseismic events are surrounded by more microseismic events, and as energy decreases, the surrounding microseismic events gradually decrease, eventually forming a cluster. This method allows for the differentiation of microseismic events generated by grouting impact. Finally, the location of the differentiated microseismic events is obtained, thus determining the grout diffusion range during the microseismic data acquisition. Continuous monitoring allows for the acquisition of the grout diffusion range at different time points. Therefore, this invention, while having a large detection depth, can effectively differentiate microseismic events generated by grouting impact, thereby ensuring the accuracy of grout diffusion range detection. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the layout of the microseismic monitoring system in this invention;

[0037] Figure 2 This is a schematic diagram of slurry diffusion in this invention.

[0038] In the figure: 1. Microseismic monitoring host, 2. Microseismic signal acquisition line, 3. Geophone, 4. Fissure zone area, 5. Grouting hole, 6. Rock wall, 7. Fissure, 8. Microseismic event. Detailed Implementation

[0039] The present invention will be further described below.

[0040] like Figure 1 As shown, the specific steps of this invention are as follows:

[0041] Step 1: Deploy the microseismic monitoring system: First, determine the fracture zone area 4, and deploy multiple geophones 3 in the surrounding roadways. Each geophone 3 is connected to the microseismic monitoring host 1 through the microseismic signal acquisition line 2 to complete the deployment of the microseismic monitoring system.

[0042] Step 2: Grouting the fracture zone area: Drill multiple grouting holes 5 into the fracture zone area 4 on the tunnel rock wall. Each grouting hole 5 extends into the fracture zone area 4. After all the grouting holes 5 have been drilled, grouting is carried out on the fracture zone area 4 simultaneously through each grouting hole 5.

[0043] Step 3, Grout Diffusion Monitoring: During the grouting process, as the grout is continuously injected into the fracture zone area 4, such as... Figure 2 As shown, the slurry impacts the rock in the fracture zone 4, causing the rock in each fracture to be squeezed and broken, generating micro-seismic events 8. Multiple geophones 3 collect the micro-seismic events 8 in the fracture zone 4 and the interfering micro-seismic events in other areas in real time, and feed all the collected micro-seismic events back to the micro-seismic monitoring host 1.

[0044] Step 4: Monitoring Data Processing: The microseismic monitoring host 1 first filters all collected microseismic events. The specific process is as follows: By setting an amplitude threshold or a microseismic frequency range, when using the amplitude threshold method, all microseismic events are compared with the set amplitude threshold. If the amplitude of a microseismic event exceeds the amplitude threshold, it is determined to be a valid microseismic event; otherwise, the microseismic event is discarded, thus completing the screening of valid microseismic events. When using the microseismic frequency range method, the frequency of all microseismic events is compared with the microseismic frequency range. If the frequency of a microseismic event is within the microseismic frequency range, it is determined to be a valid microseismic event; otherwise, the microseismic event is discarded, thus completing the screening of valid microseismic events.

[0045] Next, the particle swarm optimization algorithm was used to locate each effective microseismic event and determine its energy magnitude. Then, an improved DBSCAN clustering analysis method was used to analyze and process the effective microseismic events, thereby distinguishing the microseismic events generated by grouting impact. The specific process is as follows:

[0046] 1) Mark all microseismic events as unprocessed;

[0047] 2) Select the microseismic event F with the highest energy among all unprocessed microseismic events and mark it as processed;

[0048] 3) Define a neighborhood ε, the size of which varies with the energy intensity of the selected microseismic event:

[0049]

[0050] Where ε is the neighborhood size, ε0 is the initial value of the neighborhood, Sigmoid is the activation function, E is the energy of the microseismic event F, E0 is the energy trigger threshold, and C is a manually set neighborhood variation coefficient that changes with the fracture size. The size of the neighborhood ε changes with the strength of the energy E; when E is less than E0, the value of ε approaches ε0; when E is too large, ε approaches ε0+C, making ε adaptively change with the energy of the microseismic event, thus more accurately determining the neighborhood range.

[0051] 4) Calculate the distance between each microseismic event using the Euclidean distance calculation formula;

[0052] The formula for calculating Euclidean distance is:

[0053]

[0054] In the above formula, dist(α,β) is the Euclidean distance between any microseismic event α and any microseismic event β, x α y α z α The x, y, and z coordinates of event α are given below. β y β z β These are the x, y, and z coordinates of event β, respectively.

[0055] 5) Obtain the distances between the microseismic event F and all surrounding microseismic events according to step 4). Compare each distance with the neighborhood ε obtained in step 3). If there are several microseismic events within the neighborhood ε of the coordinates of microseismic event F (i.e., there are multiple microseismic events around microseismic event F with distances smaller than the neighborhood ε):

[0056] ① Create a cluster C and add F into C;

[0057] ② Let N be the set of microseismic events within the neighborhood ε. For each unprocessed microseismic event F' within N:

[0058] I. Mark as processing;

[0059] II. Calculate the neighborhood ε of microseismic event F' according to step 3), and obtain the distance between microseismic event F' and each surrounding microseismic event according to step 4). If there are several microseismic events within the neighborhood ε of F' coordinates, add them to N.

[0060] III. If F' does not belong to any cluster, add it to C;

[0061] ③ Save C;

[0062] 6) If there are no other microseismic events within the neighborhood ε of coordinate F, then F is denoted as the interfering microseismic event;

[0063] 7) Repeat steps 2) to 6), with each repetition the neighborhood ε continuously shrinks until there are no unprocessed microseismic events;

[0064] Each repetition will form a cluster C. The cluster containing the most microseismic events is identified as the set of microseismic events generated by grouting impact. This completes the process of distinguishing microseismic events generated by grouting impact. Finally, the location of the distinguished microseismic events is obtained, thereby determining the range of grout diffusion during this microseismic data acquisition.

[0065] Step 5: Continuous monitoring: Repeat steps 3 and 4 at set intervals, and repeat this process multiple times to obtain the range of slurry diffusion at different time points.

[0066] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting the grout diffusion range during grouting based on microseismic detection, characterized in that, The specific steps are as follows: Step 1: Deploy the microseismic monitoring system: First, determine the fracture zone area, and then deploy multiple geophones in the surrounding roadways. Each geophone is connected to the microseismic monitoring host through a microseismic signal acquisition line to complete the deployment of the microseismic monitoring system. Step 2: Grouting the fracture zone area: Drill multiple grouting holes into the fracture zone area from the tunnel rock wall. Each grouting hole extends into the fracture zone area. After all grouting holes have been drilled, grouting is carried out in the fracture zone area simultaneously through each grouting hole. Step 3, Grout diffusion monitoring: During the grouting process, as grout is continuously injected into the fracture zone area, it impacts the rock in the fracture zone area, causing the rock in each fracture to be squeezed and broken, generating micro-vibration events. Multiple geophones collect the micro-vibration events in the fracture zone area and the interfering micro-vibration events in other areas in real time, and feed all the collected micro-vibration events back to the micro-vibration monitoring host. Step 4: Monitoring Data Processing: The microseismic monitoring host first filters all collected microseismic events to obtain valid microseismic events, locates each valid microseismic event and determines its energy magnitude, and then uses an improved DBSCAN clustering analysis method to analyze and process the valid microseismic events, thereby distinguishing the microseismic events generated by grouting impact. The specific process is as follows: 1) Mark all microseismic events as unprocessed; 2) Select the microseismic event F with the highest energy among all unprocessed microseismic events and mark it as processed; 3) Define a neighborhood ε, the size of which varies with the energy intensity of the selected microseismic event: Where ε is the neighborhood size. Let F be the initial value of the neighborhood, Sigmoid be the activation function, and E be the energy of the microseismic event F. The energy trigger threshold is defined as C, which is an artificially set neighborhood variation coefficient that changes with the fracture size; the size of the neighborhood ε changes with the intensity of energy E; when E is less than... When, the value of ε approaches When E is too large, ε approaches +C; 4) Calculate the distance between each microseismic event using the Euclidean distance calculation formula; The formula for calculating Euclidean distance is: In the above formula, dist(α,β) is the Euclidean distance between any microseismic event α and any microseismic event β, x α y α z α The x, y, and z coordinates of event α are given below. β y β z β These are the x, y, and z coordinates of event β, respectively. 5) Obtain the distances between the microseismic event F and all surrounding microseismic events according to step 4). Compare each distance with the neighborhood ε obtained in step 3). If the neighborhood of the coordinates of microseismic event F is... There are several micro-seismic events in memory: Create a cluster Add F to Inside; Neighborhood The set of internal microseismic events is denoted as For Each unprocessed microseismic event : I. Mark as processing; II. Calculate microseismic events based on step 3). neighborhood And obtain microseismic events according to step 4). The distance from the surrounding microseismic events, if neighborhood of coordinates There are several micro-vibration events in memory, which are added to... ; III. If It does not belong to any cluster, so add it to among; Will save; 6) If there are no other microseismic events within the neighborhood ε of coordinate F, then F is denoted as the disturbing microseismic event; 7) Repeat steps 2) through 6) until there are no more unprocessed microseismic events; Each repetition will form a cluster The cluster containing the most microseismic events is identified as the set of microseismic events generated by grouting impact. Finally, the location of the distinguished microseismic events is obtained, thereby determining the range of grout diffusion during this microseismic data acquisition. Step 5: Continuous monitoring: Repeat steps 3 and 4 at set intervals, and repeat this process multiple times to obtain the range of slurry diffusion at different time points.

2. The method for detecting the grout diffusion range during grouting based on microseismic detection according to claim 1, characterized in that, The screening process in step four is as follows: By setting an amplitude threshold or a micro-vibration frequency range, when setting an amplitude threshold, all micro-vibration events are compared with the set amplitude threshold. If the amplitude of a micro-vibration event exceeds the amplitude threshold, it is determined to be a valid micro-vibration event; otherwise, the micro-vibration event is discarded, thus completing the screening of valid micro-vibration events. When setting a micro-vibration frequency range, the frequency of all micro-vibration events is compared with the micro-vibration frequency range. If the frequency of a micro-vibration event is within the micro-vibration frequency range, it is determined to be a valid micro-vibration event; otherwise, the micro-vibration event is discarded, thus completing the screening of valid micro-vibration events.

3. The method for detecting the grout diffusion range during grouting based on microseismic detection according to claim 1, characterized in that, In step four, the particle swarm localization algorithm is used to locate valid microseismic events.

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