A method for detecting and analyzing a water-conducting fractured zone of a terminal working face of a mining area

Abstract

The application relates to a kind of working face water-conducting fissure zone detection and analysis method of terminal mining area, comprising the following steps: step S1, the height of water-conducting fissure zone development is predicted, the target horizon of drilling is determined;Step S2, drilling construction to target horizon;Step S3, install sensor;The sensor includes optical fiber sensor;Step S4, install underground microseismic detection equipment;Step S5, optical fiber data processing: step S6, microseismic data processing;Step S7, determine the height of water-conducting fissure zone development;Step S8, coupling analysis is carried out to optical fiber data and microseismic data;Step S9, the height of water-conducting fissure zone development is verified.The application improves the accuracy and applicability of drilling to detect the height of water-conducting fissure zone, to meet the needs of terminal mining face of mining area for water-conducting fissure zone exploration and meet the production needs of mining area.

Description

Technical Field

[0001] This application belongs to the field of underground coal mining technology, specifically relating to a method for detecting water-conducting fracture zones at the final working face of a mining area. Background Technology

[0002] In underground mining operations such as coal mining, water-conducting fracture zones are a key factor triggering mine water inrushes. After coal seams are mined, the equilibrium of the overlying strata is disrupted, forming water-conducting fracture zones. If these fracture zones connect with aquifers, such as roof sandstone or karst aquifers, groundwater will surge into the mine tunnels under pressure. Statistics show that mine water inrush accidents account for a significant proportion of major coal mine accidents. Accurately identifying water-conducting fracture zones can predict water inrush channels in advance, allowing for effective water control measures such as drainage and pressure reduction, and grouting to plug water, thereby greatly reducing the risk of water inrushes. Therefore, the detection of water-conducting fracture zones is crucial.

[0003] Currently, the methods for observing the height of the "two zones" (collapse zone and water-conducting fracture zone) in the field of water-conducting fracture zone exploration are mainly divided into surface drilling observation methods, underground drilling methods, geophysical methods, direct observation methods at the working face, and observation methods in the tunnels within the overburden fracture zone. In actual mining operations, the observation of the height of the "two zones" still mainly relies on surface and underground drilling methods. Meanwhile, to study the morphology of the "two zones" and the characteristics of cracking and water leakage in the mined rock mass, and to improve the accuracy of determining the height of the "two zones," these methods can be combined with borehole television and geophysical methods. However, the direct observation methods at the working face and the observation methods in the tunnels within the overburden fracture zone are rarely used nowadays due to certain safety hazards, large workloads, and strict requirements.

[0004] Generally, water-conducting fracture zone detection is performed using underground double-ended water plugs. Boreholes are drilled after the second pressure cycle of the working face (generally more than 100m from the initial cut, and 5-20m inside the return airway and transport roadway). Water injection is then carried out after drilling to a certain depth. The development height of the water-conducting fracture zone is determined based on the water flow. However, this method has certain drawbacks; the measured data cannot accurately reflect the actual height, and the borehole location is limited to the actual conditions of the mine working face. Since there are no adjacent roadways supporting water-conducting fracture zone detection projects near the unmined working face in the mining area, surface drilling is chosen for detection. However, surface drilling faces the problem of changes in surface subsidence.

[0005] Therefore, there is a need to provide a new method for exploring water-conducting fracture zones at the final working face of a mining area, in order to meet the needs of the final working face of the mining area for exploring water-conducting fracture zones and to meet the production needs of the mining area. Summary of the Invention

[0006] To address the aforementioned shortcomings in the existing technology, this invention provides a method for detecting and analyzing water-conducting fracture zones at the end of a mining area, the improvement of which lies in that the method includes the following steps:

[0007] Step S1: Estimate the height of the water-conducting fracture zone and determine the target stratum for drilling;

[0008] Step S2: Drilling to the target layer;

[0009] Step S3, install the sensor; the sensor includes an optical fiber sensor;

[0010] Step S4: Install downhole microseismic detection equipment;

[0011] Step S5, Fiber Optic Data Processing:

[0012] Step S6, microseismic data processing;

[0013] Step S7, determine the development height of the water-conducting fracture zone, including:

[0014] Step S7-1: Determine the location of the fracture point;

[0015] Step S7-2: Draw a fracture diagram to determine the distribution of microseismic strike, dip, and vertical directions;

[0016] Step S7-3, analyze the dynamic characteristics of the goaf and the evolution characteristics of the overlying rock fracture: by observing the fracture diagram drawn in step S7-2, analyze the morphological changes of the goaf at different mining stages;

[0017] Step S7-4: Determine the development height of the water-conducting fracture zone based on the evolution characteristics of the overlying fractures: The development height of the water-conducting fracture zone = the distance from the point of maximum amplitude energy in the vertical direction to the top of the coal seam.

[0018] Step S8: Perform coupled analysis on fiber optic data and microseismic data;

[0019] Step S9 verifies the development height of the water-conducting fracture zone.

[0020] Step S1 includes:

[0021] The predicted height of the water-conducting fracture zone is determined based on the different lithological hardness of the coal seam; the lithological hardness grades of the coal seam include hard lithology, medium-hard lithology, soft lithology, and extremely soft lithology;

[0022] The height of the water-conducting fracture zone located in hard rock is shown in the following formula:

[0023]

[0024] The height of the water-conducting fracture zone in medium-hard lithology is shown in the following formula:

[0025]

[0026] The height of the water-conducting fracture zone located in weak lithology is shown in the following formula:

[0027]

[0028] The height of the water-conducting fracture zone located in extremely soft lithology is shown in the following formula:

[0029]

[0030] Where ΣM represents the cumulative thickness.

[0031] Step S2 includes: constructing a first initial borehole at a position corresponding to the ground and close to the return airway at a distance of 5 to 20 meters; simultaneously constructing a second initial borehole at a position corresponding to the ground and close to the transport roadway at a distance of 5 to 20 meters; and continuing to drill towards the center of the working face at intervals of 20 meters from the first initial borehole and the second initial borehole until the center of the final working face is reached; all boreholes are constructed to the target stratum.

[0032] Step S3 includes: installing the optical fiber wrapped in PVC pipe into each completed borehole and sealing the borehole; leaving a 0.2-0.5m optical fiber interface at the borehole opening for later optical fiber monitoring.

[0033] Step S4 includes:

[0034] Determine the location of microseismic monitoring; deploy geophones in the transport roadway and return air roadway, and delineate the location of rock mass fracture points based on the peak duration of the geophone changes; deploy four sensors around the monitoring area and at the strata with the expected development height of the water-conducting fracture zone.

[0035] Step S7-1, determining the location of the fracture point, includes:

[0036] Step S7-1-1: Construct the trajectory equation between the earthquake source location and the i-th sensor, as shown in equation (1):

[0037] (X i -X) 2 +(Y i -Y) 2 +(Z i -Z) 2 =v 2 (t i -t) (1)

[0038] Where X, Y, and Z are the spatial coordinates of the earthquake source location; X iY i Z i Let be the coordinates of the i-th sensor; v be the average velocity of sound wave propagation; t be the time when the micro-vibration occurs; t i Let i be the time when the micro-vibration is detected by the i-th sensor;

[0039] Step S7-1-2, subtract the trajectory equation of the kth sensor point from the trajectory equation of the i-th sensor point to obtain equation (2):

[0040] [(X i -X) 2 +(Y i -Y) 2 +(Z i -Z) 2 ]-[(X k -X) 2 +(Y k -Y) 2 +(Z k -Z) 2 ] = v 2 (t i -t)-v 2 (t k -t)(2); X k Y k Z k Let t be the coordinates of the k-th sensor; k The moment when the micro-vibration was detected by the k-th sensor;

[0041] Step S7-1-3, decompose equation (2) to obtain equation (3):

[0042]

[0043] Step S7-1-4: Utilize the time difference between the received signals from the sensors to calculate the propagation distance of the micro-vibration signal between the two sensors, as shown in equation (4):

[0044] d ik =v×(t) k -t i (4)

[0045] Where, d ik t represents the propagation distance between the i-th sensor and the k-th sensor; k The moment when the micro-vibration was detected by the k-th sensor;

[0046] Step S7-1-5, obtain the formula for the distance between the earthquake source location and the i-th sensor, as shown in equation (5):

[0047]

[0048] The linear equation generated by the combination of i and k is used to locate the source coordinates (x, y, z) using four sensors.

[0049] Step S7-2 includes:

[0050] Step S7-2-1: Draw the direction of fracture breakage: Establish a coordinate system with the goaf strike as the abscissa and the overburden depth as the ordinate; mark the determined source location (X, Y, Z) in the coordinate system, and then connect the fracture points with lines according to the monitored fracture development to draw the distribution pattern of overburden fractures in the strike direction.

[0051] Step S7-2-2, draw the dip direction of the fracture: establish a coordinate system according to the dip direction and draw the fracture situation of the overlying rock fracture in the dip direction;

[0052] Step S7-2-3, draw the vertical direction of fracture: use the direction perpendicular to the coal seam as the vertical axis and the horizontal position as the horizontal axis to draw the fracture situation in the vertical direction.

[0053] Step S8 includes:

[0054] Step S8-1, Feature Selection, includes:

[0055] Feature selection was performed using a combination of principal component analysis (PCA) and correlation analysis. PCA was used to analyze the correlation between strain rate of change and the height of water-conducting fracture zones, strain gradient and the height of water-conducting fracture zones, magnitude and the height of water-conducting fracture zones, and frequency and the height of water-conducting fracture zones.

[0056] Step S8-2: Construct a data fusion model using a weighted fusion algorithm.

[0057] The principal component analysis model determines the contribution rates of strain change rate, strain gradient, magnitude, and frequency to the development of water-conducting fracture zones. The strain change rate of the fiber optic sensor is set as the first data feature W1, and the strain gradient of the fiber optic sensor is set as the second data feature W2, where W1 + W2 = 1.

[0058] Let the magnitude data from microseismic monitoring be the third data feature, W3, and the frequency data be the fourth data feature, W4, where W3 + W4 = 1. Using data information relationships and geological conditions, determine the weights of W1, W2, W3, and W4, as well as the weights of the fiber optic sensor data (Q1) and the microseismic monitoring data (Q2), to determine the height of the water-conducting fracture zone, the fiber optic detection height, and the microseismic detection height.

[0059] h 导水裂隙带高度 =Q1h光纤探测高度 +Q2h 微震探测高度

[0060] h 光纤探测高度 =W1h 导水裂隙带高度 +W2h 导水裂隙带高度 .

[0061] h 微震探测高度 =W3h 导水裂隙带高度 +W4h 导水裂隙带高度

[0062] Step S9 includes:

[0063] Step S9-1: Construct a Bayesian network model, using magnitude and frequency data features as parent nodes and the development height of the water-conducting fracture zone as child nodes. Determine the conditional probability distribution between nodes based on prior knowledge and historical data. When the fiber strain change rate is large and the frequency of microseismic events is high, the probability of the water-conducting fracture zone being at a higher development height is obtained by using the probabilistic inference of the Bayesian network.

[0064] Step S9-2, Cross-validation of fusion results: Cross-validation using microseismic detection data, fiber optic data, and other factors is used to verify the height of the water-conducting fracture zone; based on the cross-validation results, the parameters of the data fusion model are adjusted and optimized;

[0065] Step S9-3: Verify the predicted water-conducting fracture zone development height results of the data fusion model and the Bayesian network model: If the predicted water-conducting fracture zone height results have an absolute error greater than 10% compared with the field measurement results, it is necessary to analyze the reasons, optimize the structure and parameters of the data fusion model, and improve the selection and fitting method of the Bayesian network model.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0067] This application proposes a method for detecting and analyzing water-conducting fracture zones at the final working face of a mining area. This method fully utilizes the advantages of fiber optic sensors and geophysical information (microseismic detection) while overcoming the disadvantages of difficult and time-consuming surface drilling. By combining fiber optic data with geophysical detection information (microseismic data) to comprehensively detect the height of water-conducting fracture zones, the detection accuracy can be further improved. Finally, multi-data fusion of fiber optic data and geophysical detection data (microseismic monitoring data) further improves the accuracy and applicability of drilling for detecting the height of water-conducting fracture zones. Attached Figure Description

[0068] Figure 1 The flowcharts involved in this application;

[0069] Figure 2 This is a schematic diagram of the drilling involved in this application;

[0070] Figure 3 This is a schematic diagram of the development morphology of the water-conducting fracture zone involved in this application;

[0071] Figure 4 A schematic diagram showing the location of the earthquake source involved in the application;

[0072] Among them, 1. Return airway; 2. Transport roadway; 3. First initial borehole; 4. Second initial borehole; 5. First sensor; 6. Second sensor; 7. Third sensor; 8. Fourth sensor; 9. Location of seismic source. Detailed Implementation

[0073] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0074] The main difficulties in the current methods for exploring water-conducting fracture zones at the final working face of a mining area lie in the following two aspects:

[0075] Firstly, real-time monitoring is challenging: the mining process at the final working face of the mining area is dynamic, and water-conducting fracture zones are constantly forming and changing. Therefore, real-time monitoring technology is needed to track the dynamic development of these fracture zones. However, many existing detection methods struggle to achieve real-time monitoring. For example, some geophysical exploration methods, such as electrical resistivity tomography and seismic exploration, while capable of acquiring information about underground geological structures, are typically static detections conducted at a specific point in time. To obtain real-time information on changes in water-conducting fracture zones during mining, such as fracture propagation rates and the location of new fractures, requires real-time monitoring equipment capable of operating stably for extended periods in harsh underground environments. Furthermore, ensuring the real-time nature and accuracy of data transmission places high demands on both technology and equipment.

[0076] Secondly, the complexity of multi-source data fusion: To comprehensively and accurately explore water-conducting fracture zones, it is often necessary to integrate data obtained from multiple detection methods, such as borehole data and geophysical data. However, these data from different sources have different formats, precision, and physical meanings. Borehole data can provide direct core descriptions and local fracture information, while geophysical data is geological structure information inferred based on changes in physical fields; the two differ in spatial scale and precision. Fusion of these multi-source data to establish a unified model of water-conducting fracture zones is a complex process. Mutual verification is required to ensure that the fused data accurately reflects the actual situation of the water-conducting fracture zones.

[0077] The use of fiber optic technology can maximize the use of fiber optic sensors to detect the development process of water-conducting fracture zones and improve the accuracy of detecting the height of water-conducting fracture zones.

[0078] This paper proposes a method for detecting and analyzing water-conducting fracture zones at the final working face of a mining area. This method fully utilizes the advantages of fiber optic sensors and geophysical information (microseismic detection) while overcoming the disadvantages of difficult and time-consuming surface drilling. By combining fiber optic data with geophysical detection information (microseismic data) to comprehensively detect the height of water-conducting fracture zones, the detection accuracy can be further improved. Finally, multi-data fusion of fiber optic data and geophysical detection data (microseismic monitoring data) further enhances the accuracy and applicability of drilling for detecting the height of water-conducting fracture zones.

[0079] The purpose of this invention is to provide a method for detecting and analyzing water-conducting fracture zones at the end of a mining area. To address the issues of detection height and analysis of water-conducting fracture zones at the end of a mining area, fiber optic sensors are introduced into the detection process. A method combining fiber optics and geophysical information (microseismic detection) is established for detecting water-conducting fracture zones. This approach combines the advantages of geophysical information (microseismic detection) detection with the characteristics of fiber optic sensors, avoiding the influence of underground roadways and surface subsidence on water-conducting fracture zone detection. Based on fiber optic data, geophysical data, and other information, and through multi-data fusion analysis, the height of the water-conducting fracture zone is analyzed and studied, ultimately forming a method suitable for detecting and analyzing water-conducting fracture zones at the end of a mining area.

[0080] like Figure 1 As shown, this application relates to a method for detecting and analyzing water-conducting fracture zones in the final working face of a mining area. The final working face is the last working face to be mined, and its completion signifies the basic completion of coal resource mining in that mining area. The method includes the following steps:

[0081] Step S1 involves estimating the height of the water-conducting fracture zone and determining the target stratum for drilling. This includes determining the estimated height of the water-conducting fracture zone based on different coal seam lithological hardness levels. Coal seam lithological hardness grades include hard, medium-hard, soft, and very soft lithology. The height of the water-conducting fracture zone located in hard lithology is shown in the following formula:

[0082]

[0083] The height of the water-conducting fracture zone in medium-hard lithology is shown in the following formula:

[0084]

[0085] The height of the water-conducting fracture zone located in weak lithology is shown in the following formula:

[0086]

[0087] The height of the water-conducting fracture zone located in extremely soft lithology is shown in the following formula:

[0088]

[0089] Specifically, the expected height of the water-conducting fracture zone is determined based on the coal seam hardness, as shown in the table below. The expected height of the water-conducting fracture zone helps determine the target mining thickness for drilling.

[0090] Table 1. Formulas for calculating the height of water-conducting fracture zones in layered mining of thick coal seams.

[0091]

[0092] Note: 1. ΣM represents the cumulative sampling thickness, i.e., the sum of the sampling thicknesses M of each layer. 2. Scope of application of the formula: single layer sampling thickness 1-3m, cumulative sampling thickness not exceeding 15m. 3. The ± sign in the calculation formula represents the mean square error.

[0093] Both the first and second formulas can be used to calculate the height of the water-conducting fracture zone in layered coal seam mining. The appropriate formula can be selected for calculation based on the actual engineering needs.

[0094] Step S2: Drilling to the target stratum. Specifically, before the coal seam is mined, in the unmined working face, based on the geological conditions of the mining area, surface construction conditions, mining engineering plan, and above-ground and underground comparison diagrams, initial boreholes are drilled at positions 5-20m within the corresponding return airway 1 and transport roadway 2 on the surface. These initial boreholes are then moved towards the center at 20m intervals. All boreholes are drilled to the target stratum. Specifically, as follows... Figure 2 As shown, a first initial borehole 3 is constructed at a location corresponding to the ground and close to the return airway, at a position of 5m to 20m; simultaneously, a second initial borehole 4 is constructed at a location corresponding to the ground and close to the transport roadway, at a position of 5m to 20m. From both the first initial borehole 3 and the second initial borehole 4, drilling continues towards the center of the unmined working face at intervals of 20m until the center of the unmined working face is reached. All boreholes are constructed to the target stratum.

[0095] Step S3: Install fiber optic sensors. Specifically, install fiber optic cables, wrapped in PVC pipes, into each completed borehole and seal the borehole. Leave a 0.2-0.5m fiber optic interface at the borehole opening for later fiber optic monitoring. During the mining process, the fiber optic sensors can detect changes in fiber optic parameters caused by vibration. Connecting to monitoring equipment (detectors) via the fiber optic interface, the sensors collect data on changes and reflections during mining to determine the final development value and morphology of the water-conducting fracture zone.

[0096] Fiber optic sensors are used to collect data on rock deformation and temperature changes. Specifically, high-precision distributed fiber optic strain and temperature sensors are selected and strategically deployed along the borehole or in the water-conducting fracture zone to form a dense monitoring network. The sensors should possess high sensitivity and stability, capable of capturing minute deformations and temperature changes in the rock strata in real time. Fiber Bragg grating (FBG) sensors are employed, achieving sub-micro-strain measurement accuracy and temperature resolution below 0.1℃. During data acquisition, an appropriate sampling frequency is set, selected between 10 and 100 Hz based on the expected development rate of the water-conducting fracture zone and actual monitoring requirements. This ensures accurate recording of physical changes related to the formation and development of the water-conducting fracture zone, such as temperature, displacement, and other detection data. Excessive redundant data is avoided. Figure 3 As shown, the optical fiber amplitude is significant at depths of 300 and 400, indicating that the water-conducting fracture zone of the coal seam has developed to this stratum.

[0097] Step S4, install downhole microseismic detection equipment, including:

[0098] Determine the locations for microseismic monitoring. Geophones are deployed in the transport and return air roadways. The locations of rock mass fracture points are determined based on the duration of peak values ​​observed by the geophones. The geophones are used to receive changes in the fiber optic parameters of the fiber optic sensors.

[0099] Transport roads and return air roads are underground mining roadways excavated in coal mines to recover coal seams. Transport roads are typically located below and closely adjacent to the coal face. Their primary function is to transport coal and other materials extracted from the face. They are usually equipped with bridge conveyors, crushers, and belt conveyors to quickly and efficiently transport coal to the bottom coal bunker or the surface. Additionally, transport roads may also serve as air intake channels, providing fresh air to the coal face. Return air roads are usually located above the coal face and are primarily used to exhaust stale air from the face, forming an important component of the return air system. They may also be used to house monitoring equipment and pipelines, ensuring safe production at the coal face. Together, transport roads and return air roads surround the coal face, forming a relatively complete production operating space. They work in conjunction with other roadways to constitute the transportation, ventilation, and personnel systems of the coal face, providing the necessary conditions for coal mining operations.

[0100] Microseismic monitoring is used to collect arrival time, magnitude, and frequency data of microseismic events. Specifically:

[0101] Multiple fiber optic sensors are deployed around the monitoring area and at the expected height of the water-conducting fracture zone, forming a three-dimensional sensor array to ensure comprehensive monitoring of microseismic events. The selection of sensors should be optimized based on the geological conditions of the monitoring area and the expected intensity of microseismic activity, possessing a wide frequency response range (e.g., 10–1000 Hz) and high sensitivity, capable of accurately recording parameters such as arrival time, magnitude, and frequency of microseismic events. Simultaneously, to reduce the interference of environmental noise on microseismic data, advanced signal processing techniques are employed, such as wavelet transform-based denoising methods and adaptive filtering algorithms, to effectively separate microseismic signals from noise signals, improving the signal-to-noise ratio and quality of the microseismic data.

[0102] Step S5, fiber optic data processing, including fiber optic sensor data feature extraction, specifically:

[0103] From fiber optic strain data, characteristic parameters such as strain rate of change and strain gradient are calculated. These parameters reflect the severity and heterogeneity of rock strata deformation and are closely related to the development of water-conducting fracture zones. When water-conducting fracture zones begin to form and expand, the strain rate of change in nearby rock strata increases significantly. By analyzing the strain rate of change at different locations, the possible development areas of water-conducting fracture zones can be preliminarily determined. Simultaneously, time-series characteristics of strain are extracted, and periodic and trend changes are analyzed using autocorrelation functions and power spectral density to reveal the dynamic laws governing the development of water-conducting fracture zones. For fiber optic temperature data, attention is paid to areas of abnormal temperature changes and temperature gradient characteristics. Since groundwater flow in water-conducting fracture zones causes heat exchange, leading to abnormal temperature distribution, extracting these characteristics can help determine the location and extent of water-conducting fracture zones.

[0104] Step S6, microseismic data processing, including microseismic monitoring data feature extraction, specifically:

[0105] The magnitude-frequency distribution characteristics of microseismic events were analyzed, and parameters such as the b-value were obtained using Gutenberg-Richter law fitting. Changes in the b-value reflect variations in the scale and intensity of rock fracturing, providing important evidence for determining the development degree of water-conducting fracture zones. As water-conducting fracture zones gradually develop, rock fracturing activity intensifies, and the b-value may change significantly. Simultaneously, the spatiotemporal distribution characteristics of microseismic events were extracted, and cluster analysis was used to identify concentrated areas of microseismic activity. These areas are often highly correlated with the location of water-conducting fracture zones. Statistical characteristics such as microseismic energy release rate and event intervals were calculated to reflect the activity and patterns of microseismic activity from different perspectives, providing rich information for the monitoring and analysis of the development process of water-conducting fracture zones.

[0106] Step S7, determine the development height of the water-conducting fracture zone, including:

[0107] Step S7-1, determine the location of the fracture point:

[0108] Step S7-1-1: When acoustic emission and microseismic phenomena occur in the rock mass, the detector can acquire the coordinates of each fiber optic sensor and the time when it receives the signal, but it cannot acquire the location and time of the acoustic emission and microseismic events. Therefore, let the spatial coordinates of the seismic source be (X, Y, Z), the time of the microseismic event be t, and the spatiotemporal parameters of the seismic source be (X, Y, Z, t). The coordinates of the i-th sensor are then (X, Y, Z, t). i Y i Z i The time of occurrence of the micro-vibration detected by the i-th sensor is t. i The average speed of sound wave propagation is v, and the trajectory equation between the source and the i-th sensor is shown in equation (1):

[0109] (X i -X) 2 +(Y i -Y) 2 +(Z i -Z) 2 =v 2 (t i -t) (1)

[0110] Where i is the i-th sensor measurement point, i = 1, 2, ..., m; m is the number of sensors receiving signals.

[0111] Step S7-1-2, introduce equation (1) into the linear system to solve the equation, as follows: Subtract the trajectory equation of the kth sensor point from the trajectory equation of the i-th sensor point, as shown in equation (2):

[0112] [(X i -X) 2 +(Y i -Y) 2 +(Z i -Z) 2 ]-[(X k -X) 2 +(Y k -Y) 2 +(Z k -Z) 2 ] = v 2 (t i -t)-v 2 (t k -t) (2)

[0113] Step S7-1-3, decompose equation (2) to obtain equation (3):

[0114]

[0115] Where i = 1, 2, ..., m; k = 1, 2, ..., m.

[0116] Step S7-1-4: Utilize the time difference between the received signals from the sensors to calculate the propagation distance of the micro-vibration signal between the two sensors, as shown in equation (4):

[0117] d ik =v×(t) k -t i (4)

[0118] Where, d ik The distance the microseismic signal travels between two sensors represents the distance the microseismic signal travels between the i-th and k-th sensors; v represents the average velocity of sound wave propagation; t i t: The time detected by the i-th sensor; k The time detected by the k-th sensor. Wherein, the time when the signal arrives at the first sensor is t1, and in this application, it is expressed as the propagation time (t1) and distance (d) from the source to the first sensor. 1k Using this as a reference, the relative distance from the seismic source to other sensors is calculated.

[0119] Step S7-1-5: Obtain the distance between the earthquake source location and the i-th sensor, as shown in equation (5):

[0120]

[0121] Based on the positional relationship, linear equations are generated through different combinations of i and k. The system of equations is solved using four sensors, and the source coordinates (x, y, z) can be obtained to locate the source.

[0122] Where, Δd 0i This represents the distance between the earthquake source location and the i-th sensor.

[0123] Specifically, assuming the coordinates of the fiber optic sensors are as follows: first sensor 5A (X1,Y1,Z1), second sensor 6B (X2,Y2,Z2), third sensor 7C (X3,Y3,Z3), and fourth sensor 8D (X4,Y4,Z4), a coordinate system is established with the first sensor as the origin (i.e., the reference point) (other suitable points can also be chosen as the origin).

[0124] And according to equations (4) and (5), the distance between the earthquake source location 9 and the second sensor is:

[0125]

[0126] Meanwhile, the earthquake source (x, y, z) satisfies the following equation (3): the direction equation of the first and second sensors, taking: i = 1, k = 2;

[0127]

[0128] According to equations (4) and (5), the distance between the earthquake source location and the third sensor is:

[0129]

[0130] Meanwhile, the earthquake source (x, y, z) satisfies the following equation (3): the direction equation passing through the first and third sensors, taking: i = 1, k = 3;

[0131]

[0132] And according to equations (4) and (5), the distance between the earthquake source location and the fourth sensor is:

[0133]

[0134] Meanwhile, the earthquake source (x, y, z) satisfies the following equation (3): the direction equation passing through the first and fourth sensors, taking: i = 1, k = 4;

[0135]

[0136] Based on the above three sets of equations, the location of the earthquake source (X, Y, Z) can be determined.

[0137] Step S7-2: Draw a fracture diagram to determine the distribution of microseismic strike, dip, and vertical directions, including:

[0138] Using different fracture points, fracture maps of the overlying strata in the strike, dip, and vertical directions were drawn to further analyze the dynamic characteristics of the goaf and the evolution characteristics of the overlying strata fractures. The amplitude energy detected at the working face was combined with CAD and GIS software, using the calculated source location (X, Y, Z) and the detected amplitude energy, to plot the amplitude intensity.

[0139] Specifically, drawing fracture diagrams includes:

[0140] Step S7-2-1: Draw the strike direction of the fracture: Establish a coordinate system with the goaf strike as the abscissa and the overburden depth as the ordinate. Mark the determined epicenter location (X, Y, Z) on the coordinate system. Then, based on the monitored fracture development, connect the fracture points with lines to draw the distribution pattern of the overburden fractures along the strike direction. Different colors or line types are needed to represent fractures of different developmental stages, such as solid red lines representing primary fractures and dashed blue lines representing secondary fractures. Generally, more fractures develop near the goaf, and the fractures gradually become smaller and sparser as they move away from the goaf. The relative size and density of the fractures are judged by comparing the shades of the colors or the density of the lines. At the same time, fracture intersections and areas of concentrated fracture development are stress concentration points, which can easily lead to local instability of the overburden, and are of great significance to the safe production of coal mines.

[0141] Step S7-2-2: Draw the dip direction of the fracture: Establish a coordinate system similar to the dip direction, i.e., use the dip direction of the coal seam as the abscissa and the vertical direction (representing the coal seam height) as the ordinate, and draw the fracture situation of the overburden fractures along the dip direction. The influence of the coal seam dip angle on the fracture distribution and the changes in the boundary conditions of the goaf along the dip direction must be considered. The dip direction of the fractures usually slopes inwards towards the goaf, due to the collapse of the overburden above the goaf under gravity. The extension direction of the fractures can reflect the movement trend of the overburden and the direction of stress transmission, which is of important reference value for judging surface deformation and potential geological hazards.

[0142] Step S7-2-3: Draw the vertical direction of fracture failure: Using the direction perpendicular to the coal seam as the ordinate and the horizontal position as the abscissa, draw the fracture failure situation in the vertical direction. The vertical fracture failure diagram mainly shows the distribution and failure of fractures in the overburden in the vertical direction. Pay close attention to the fracture development at different rock strata interfaces, especially the upward expansion of fractures along the vertical direction as mining progresses. Determining the vertical development height of fractures is an important indicator for assessing the impact of coal seam mining on the overburden. Generally, the fracture development height gradually increases with the increase of mining thickness and mining area. Analyzing the relationship between fracture development height and mining parameters in subsequent steps can predict the upper limit of overburden damage, providing a basis for mine water hazard prevention, gas drainage, and other work.

[0143] Step S7-3 analyzes the dynamic characteristics of the goaf and the evolution characteristics of overlying rock fractures, specifically:

[0144] Analyzing the dynamic characteristics of goaf areas includes: analyzing the morphological changes of goaf areas at different mining stages by observing the fracture fracture diagrams drawn in step S7-2. For example, changes in the range and shape of the goaf: observing fracture fracture diagrams at different times, the range of the goaf will gradually expand in the strike and dip directions as mining progresses. Note the changes in the goaf boundary and whether its shape is regular. If the goaf shape is asymmetrical or has local protrusions, it may indicate uneven mining or changes in geological conditions during the mining process. Roof subsidence and collapse characteristics: In the vertical fracture fracture diagrams, the amount of subsidence and the collapse pattern of the roof strata are important analytical contents. As the goaf forms, the roof strata will gradually subside and eventually collapse. Observe the speed and magnitude of roof subsidence, as well as the height and range of the collapse zone. If the roof subsidence speed is fast or the collapse zone height is abnormal, it may mean that the roof stability is poor and support measures need to be strengthened. Stress distribution and concentration areas: The distribution and development degree of fractures are closely related to stress distribution. In fracture fracture diagrams, densely fractured areas typically correspond to stress concentration zones. By observing fracture fracture diagrams along their strike, dip, and vertical, locations of stress concentration around the goaf can be identified, such as near the coal face or at the goaf's edges. Stress concentration zones can easily lead to coal face spalling and roof fractures, significantly impacting the stability of the goaf.

[0145] The analysis of the evolution characteristics of overburden fractures includes examining their evolutionary patterns from both temporal and spatial dimensions. Temporally, the development of fractures at different mining stages is observed, summarizing the process from initial appearance to gradual expansion and connection, and analyzing the rate and stage-specific characteristics of fracture development. Generally, with increasing mining time, fractures gradually expand and connect, forming a complex fracture network. Spatially, the distribution patterns of fractures in their strike, dip, and vertical directions are studied, such as how fracture height, width, and density change with spatial location, and the mutual influence and connectivity between fractures in different areas. Specifically, the height of the overburden fracture zone at different times is measured and compared; an increase in fracture zone height reflects the upward expansion of the overburden fractures. The relationship between fracture zone height and factors such as mining thickness and mining speed is analyzed, summarizing the quantitative laws governing the evolution of overburden fractures, providing a basis for mine design and safe production. Simultaneously, the influence of overburden lithology and geological structure on fracture evolution characteristics is summarized. To more accurately analyze and interpret the results, numerical simulation, physical simulation, and other methods can be combined to simulate the coal seam mining process. By comparing the simulation results with actual monitoring data and drawn fracture diagrams, we can gain a deeper understanding of the dynamic characteristics of the goaf and the evolution characteristics of overlying rock fractures.

[0146] Step S7-4: Determine the development height of the water-conducting fracture zone based on the evolution characteristics of overburden fractures. Specifically, the development height of the water-conducting fracture zone is equal to the distance from the point of maximum amplitude energy in the vertical direction to the roof of the coal seam. The development height of the water-conducting fracture zone can be determined based on the amplitude energy mentioned earlier. By plotting the relevant energy and combining it with the borehole columnar section, the development height of the water-conducting fracture zone can be determined.

[0147] Step S8: Perform coupled analysis on fiber optic data and microseismic data;

[0148] Step S8-1, Feature Selection:

[0149] Feature selection was performed using a combination of principal component analysis (PCA) and correlation analysis. PCA analyzed the correlations between strain rate of change and the height of the water-conducting fracture zone, strain gradient and the height of the water-conducting fracture zone, magnitude and the height of the water-conducting fracture zone, and frequency and the height of the water-conducting fracture zone. Correlation analysis was used to determine the degree of linear correlation between the above features and the height of the water-conducting fracture zone, removing features with low correlation and determining the relationships between the various factors.

[0150] Step S8-2: Construct a data fusion model using a weighted fusion algorithm.

[0151] Based on the principal component analysis (PCA) model described above, the contributions of strain rate of change, strain gradient, magnitude, and frequency to the development of water-conducting fracture zones are determined. Let the strain rate of change from the fiber optic sensor be the first data feature, W1; let the strain gradient from the fiber optic sensor be the second data feature, W2; where W1 + W2 = 1. Let the magnitude data from microseismic monitoring be the third data feature, W3; and let the frequency data from microseismic monitoring be the fourth data feature, W4; where W3 + W4 = 1. Using the principal component analysis method, that is, using data information relationships, geological conditions, and other factors to determine the weights of W1, W2, W3, and W4, as well as the weight Q1 of the fiber optic sensor data and the weight Q2 of the microseismic monitoring data, we obtain:

[0152] h 导水裂隙带高度 =Q1h 光纤探测高度 +Q2h 微震探测高度

[0153] h 光纤导水裂隙带高度 =W1h 导水裂隙带高度 +W2h 导水裂隙带高度 .

[0154] h 微震探测高度 =W3h 导水裂隙带高度 +W4h 导水裂隙带高度

[0155] Step S9: Verify the development height of the water-conducting fracture zone;

[0156] Step S9-1: Based on the Bayesian network, construct a Bayesian network model to analyze the changing trend of the water-conducting fracture zone over time and space.

[0157] A Bayesian network structure is constructed, using magnitude and frequency data as parent nodes and the development height of water-conducting fracture zones as child nodes. The conditional probability distribution between nodes is determined based on prior knowledge and historical data. In Bayesian statistics, prior knowledge is used to determine the prior probability distribution, which can be viewed as a subjective judgment of the conditional probability distribution before the observation data is collected. When the fiber strain rate of change is large and the frequency of microseismic events is high, the probability of the water-conducting fracture zone being at a higher development height can be calculated using the probabilistic inference of the Bayesian network.

[0158] In this application, Bayesian networks can effectively handle uncertain information, comprehensively consider the influence of multiple factors on the development height of water-conducting fracture zones, and continuously update data (i.e., new fiber optic and microseismic data) to adjust the probability judgment of the development height of water-conducting fracture zones in real time, providing more comprehensive and accurate information support for decision-making. In practical applications, the structure and parameters of Bayesian networks can be optimized by combining historical experience and data mining techniques to analyze the changing trend of water-conducting fracture zones over time.

[0159] Step S9-2: Cross-validate the fusion results;

[0160] Specifically, single-factor analysis—combining microseismic detection data, fiber optic data, and other factors—was used to verify the height of the water-conducting fracture zone in cross-validation. Based on the cross-validation results, the parameters of the data fusion model (weights of the PCA model, conditional probability distribution of the Bayesian network, etc.) were adjusted and optimized to improve model performance and prediction accuracy. Cross-validation can fully utilize limited historical data, avoid overfitting of the Bayesian network model, and improve the model's stability and reliability.

[0161] Step S9-3: On-site verification and feedback adjustments;

[0162] Field investigations were conducted to verify the predicted development height of water-conducting fracture zones using data fusion and Bayesian network models. Core samples were obtained by drilling near the predicted fracture zone height to directly observe the degree of fracture development in the rock and compare the results with the model predictions. If a significant deviation (absolute error > 10%) was found between the predicted and field measurements, further analysis was needed to determine the cause. This could be due to errors in the data acquisition process, imperfections in the data fusion model, limitations of the time-series model, or the complexity of geological conditions. To address these issues, improvements were made to the data acquisition methods, increasing the number and accuracy of sensors, optimizing the structure and parameters of the data fusion model, improving the selection and fitting methods of the Bayesian network model, or considering the introduction of more geological information and prior knowledge to further optimize and improve the entire model system, thereby enhancing the accuracy and reliability of water-conducting fracture zone development height detection and prediction. Simultaneously, a monitoring and feedback mechanism was established to continuously update and optimize the model based on actual conditions, ensuring it consistently provides accurate and reliable information on water-conducting fracture zone development, providing strong support for related engineering activities and geological disaster prevention.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for detecting and analyzing a water-conducting fractured zone of a terminal working face of a mining area, characterized in that, The method comprises the following steps: Step S1, predicting the height of the water flowing fractured zone, determining the target horizon for drilling; Step S2, drilling to the target horizon; Step S3, installing a sensor; the sensor comprises an optical fiber sensor; Step S4, installing a downhole microseismic detection device; Step S5, optical fiber data processing: Step S6, microseismic data processing; Step S7, determining the height of the water flowing fractured zone, comprising: Step S7-1, determining the position of the fracture point; Step S7-2, drawing a fracture breakdown map to determine the distribution of the microseismic trend, inclination and vertical direction; Step S7-3, analyzing the dynamic characteristics of the goaf and the evolution characteristics of the overburden rock fracture: by observing the fracture breakdown map drawn in step S7-2, the morphological changes of the goaf at different mining stages are analyzed; Step S7-4, determining the height of the water flowing fractured zone according to the evolution characteristics of the overburden rock fracture: the height of the water flowing fractured zone = the distance from the maximum amplitude energy point in the vertical direction to the coal seam roof; Step S8, coupling analysis of the optical fiber data and the microseismic data; Step S9, verifying the height of the water flowing fractured zone; Step S8 comprises: Step S8-1, feature selection, comprising: Using a method combining principal component analysis model and correlation analysis model for feature selection; the principal component analysis model is used to analyze the correlation between the strain rate and the height of the water flowing fractured zone, the correlation between the strain gradient and the height of the water flowing fractured zone, the correlation between the magnitude and the height of the water flowing fractured zone, and the correlation between the frequency and the height of the water flowing fractured zone; Step S8-2, constructing a data fusion model by a weighted fusion algorithm: The principal component analysis model determines the contribution rate of the strain change rate, the strain gradient, the magnitude, and the frequency to the development of the water flowing fractured zone, and sets the strain change rate of the optical fiber sensor as the first data characteristic , sets the strain gradient of the optical fiber sensor as the second data characteristic , wherein ; The magnitude data of microseismic monitoring is the third data feature, and the frequency data of microseismic monitoring is the fourth data feature , wherein, ; the weights of , , and are determined by using the data information relationship and the geological condition factors, and the weight Q1 of the optical fiber sensor data and the weight Q2 of the microseismic monitoring data are used to determine the height of the water flowing fractured zone, the height of the optical fiber detection, and the height of the microseismic detection. ； Step S9 comprises: Step S9-1, constructing a Bayesian network model, taking the magnitude data and the frequency data features as parent nodes and the height of the water flowing fractured zone as a child node, and determining the conditional probability distribution between the nodes according to prior knowledge and historical data; when the optical fiber strain rate is large and the microseismic event frequency is high, the probability of the water flowing fractured zone being at a high development height is greater by using the probability reasoning of the Bayesian network; Step S9-2, cross-validation of the fusion result: the cross-validation of the coupling of the microseismic detection data, the optical fiber data and other factors is used to verify the height result of the water flowing fractured zone; according to the cross-validation result, the parameters of the data fusion model are adjusted and optimized; Step S9-3, verifying the height result of the water flowing fractured zone predicted by the data fusion model and the Bayesian network model: if there is an absolute error > 10% deviation between the predicted height result of the water flowing fractured zone and the actual measurement result, the reason needs to be analyzed, the structure and parameters of the data fusion model are optimized, and the selection and fitting method of the Bayesian network model is improved.

2. The method of claim 1, wherein, Step S1 comprises: According to different coal seam lithology hardness, the predicted height of the water flowing fractured zone is determined; the coal seam lithology hardness grade comprises hard lithology, medium-hard lithology, soft lithology and extremely soft lithology; The height of the water flowing fractured zone located in the hard lithology is shown in the following formula: ； The height of the water flowing fractured zone located in the medium-hard lithology is shown in the following formula: ； The height of the water flowing fractured zone located in the soft lithology is shown in the following formula: ； The height of the water flowing fractured zone in the extremely weak lithology is shown in the following formula: ； Wherein, ΣM is the cumulative mining thickness.

3. The method of claim 1, wherein, Step S2 comprises: constructing a first initial borehole at a position corresponding to the ground and close to the return air roadway 5-20 m; simultaneously, constructing a second initial borehole at a position corresponding to the ground and close to the transportation roadway 5-20 m; simultaneously, continuing to drill at every 20 m interval from the first initial borehole and the second initial borehole to the center of the working face until reaching the center of the end-mining working face; all the boreholes are constructed to the target horizon.

4. The method of claim 1, wherein, Step S3 comprises: installing the optical fiber wrapped with the PVC pipe into each of the completed boreholes, and plugging the boreholes; reserving a 0.2-0.5 m optical fiber interface at the borehole mouth for later optical fiber monitoring.

5. The method of claim 1, wherein, Step S4 comprises: Determining the microseismic monitoring position; arranging the geophones in the transportation crossheading and the return air crossheading, and demarcating the position of the rock mass fracture point according to the peak value duration of the geophones; arranging four sensors around the monitoring area and the horizon of the expected water flowing fractured zone development height.

6. The method of claim 1, wherein, Step S7-1, determining the position of the fracture point, comprises: Step S7-1-1, constructing the strike equation between the seismic source position and the ith sensor, as shown in formula (1): （1） Wherein, X, Y, Z are the spatial coordinates of the source location; X i , Y i , Z i are the coordinates of the i-th sensor; v is the average speed of sound wave transmission; t is the time of microseismic occurrence; t i is the time of microseismic occurrence monitored by the i-th sensor; Step S7-1-2, subtracting the strike equation of the kth sensor measuring point from the strike equation of the ith sensor measuring point to obtain formula (2): （2）； X k , Y k , Z k is the kth sensor coordinate; t k is the time of microseismic occurrence monitored by the kth sensor; Step S7-1-3, decomposing formula (2) to obtain formula (3): （3） Step S7-1-4, using the time difference of the sensor received signals to obtain the propagation distance of the microseismic signal between the two sensors, as shown in formula (4): ， （4） wherein d ik represents the propagation distance between the ith sensor and the kth sensor; t k is the microseismic occurrence time monitored by the kth sensor; Step S7-1-5, obtaining the distance formula between the seismic source position and the ith sensor, as shown in formula (5): =d ik （5）； The linear equation generated by the combination of i and k is used to locate the seismic source position coordinates (x, y, z) using the four sensors.

7. The method according to claim 1, wherein, Step S7-2, comprises: Step S7-2-1, drawing the strike direction of the fracture breakage: establishing a coordinate system with the goaf strike as the horizontal coordinate and the overburden depth as the vertical coordinate; marking the determined seismic source position (X, Y, Z) in the coordinate system, and then connecting the fracture points with lines according to the monitored fracture development to draw the distribution form of the overburden fracture in the strike direction; Step S7-2-2, drawing the inclination direction of the fracture breakage: establishing a coordinate system according to the inclination direction to draw the breakage of the overburden fracture in the inclination direction; Step S7-2-3, drawing the vertical direction of the fracture breakage: taking the direction perpendicular to the coal seam level as the vertical coordinate and the horizontal position as the horizontal coordinate to draw the fracture breakage in the vertical direction.

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