Fine imaging method of transparent working face by using inter-hole seismic subdivision dynamic detection

Abstract

The application relates to a transparent working face fine imaging method adopting inter-hole seismic subdivision dynamic detection, relates to the technical field of mineral geological exploration, and comprises the following steps: constructing an inter-hole seismic observation network of a target region; selecting a first coal mining subdivision region according to a preset interval, performing seismic monitoring by taking vibration generated by a coal cutter cutting a coal wall as a seismic source, and acquiring an original seismic wave signal sequence; pre-processing the original seismic wave signal sequence according to an adaptive signal processing strategy, and acquiring a standard seismic wave signal sequence; performing transparent working face imaging on the first coal mining subdivision region, and outputting a first transparent working face; and continuously performing subsequent coal mining region division and transparent working face imaging based on the first transparent working face according to a coal mining process. The application solves the problems that the traditional coal mine geological exploration has low precision, cannot update geological information in real time along with the coal mining process, and is difficult to meet the dynamic and high-precision geological navigation demand of intelligent mining.

Description

Technical Field

[0001] This application relates to the field of mineral geological exploration, and in particular to a method for fine imaging of transparent working faces using dynamic detection of inter-hole seismic subdivisions. Background Technology

[0002] With the continuous development of intelligent coal mining technology, the requirements for the precision and timeliness of geological structure information of the working face are constantly increasing, and high-quality geological exploration results have become a key technical support for ensuring mining efficiency and safety.

[0003] Currently, traditional coal mine geological exploration methods are unable to achieve accurate detection of mining areas and cannot dynamically update geological information according to the coal mining process. This not only easily leads to errors in the judgment of coal seam structure and geological anomalies, but also reduces the scientific nature of mining planning and operation adjustments, and increases safety risks and efficiency losses in the process of intelligent mining. Summary of the Invention

[0004] This application provides a transparent working face fine imaging method using inter-hole seismic subdivision dynamic detection, which improves the accuracy of mining area detection in traditional coal mine geological exploration, realizes the ability to dynamically update geological information according to the coal mining process, and reduces safety risks and efficiency losses in intelligent mining.

[0005] The embodiments of this application disclose the following technical solutions: This application provides a method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection, the method comprising: Before coal mining operations are carried out in the target area, an inter-hole seismic observation network for the target area is constructed. A first coal mining sub-region is selected within the target area according to a preset interval. The vibration generated by the coal mining machine cutting the coal wall is used as the seismic source. Seismic monitoring is performed on the first coal mining sub-region to obtain the original seismic wave signal sequence. The original seismic wave signal sequence is preprocessed according to an adaptive signal processing strategy to obtain a standard seismic wave signal sequence; Based on the standard seismic wave signal sequence, a transparent working face image is generated for the first coal mining subdivision area, and the first transparent working face is output. According to the coal mining process, the subsequent coal mining area division and transparent working face imaging will continue to be performed based on the first transparent working face.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application proposes a method for fine imaging of transparent working faces using dynamic detection of inter-bore seismic subdivisions. By constructing an inter-bore seismic observation network, subdividing regional seismic monitoring, adaptive signal preprocessing, transparent working face imaging, and dynamic cyclic detection in stages, it achieves accurate representation and full-process dynamic tracking of the geological structure of the coal mining target area. First, before coal mining operations in the target area, two rows of parallel horizontal boreholes are drilled from the transport roadway and return airway towards the coal seam, and seismic detectors are deployed to construct an inter-bore seismic observation network. Then, the first subdivided coal mining area is selected at preset intervals, and monitoring is conducted using the vibration of the coal mining machine as the seismic source to obtain the original seismic wave signal sequence. Subsequently, the geological state fluctuation is analyzed in conjunction with historical transparent working faces in adjacent areas, the appropriate dimensionality reduction ratio is calculated, and the ray path is optimized. The original signal is preprocessed to obtain a standard seismic wave signal sequence. Next, a seismic tomography algorithm is used to invert the three-dimensional seismic wave velocity field distribution, interpret the geological structure information, and integrate it into the initial geological model to generate the first transparent working face. Finally, based on the first transparent working face, the subsequent regional subdivision intervals and signal processing strategies are dynamically adjusted, and the monitoring, preprocessing, and imaging processes are repeated until the entire target area coal mining operation is completed.

[0007] The technical solution proposed in this application solves the problems of accuracy loss due to large-area overall monitoring, inability of fixed parameters to adapt to geological fluctuations, and insufficient coverage of key anomaly areas in traditional seismic exploration during mining. It avoids coal mining risks caused by fuzzy geological information or low processing efficiency, improves the precision and adaptability of transparent working face imaging, and provides reliable geological support for intelligent coal mining. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating the transparent working surface fine imaging method using inter-hole seismic subdivision dynamic detection provided in this application embodiment; Figure 2 This is a flowchart illustrating the process of obtaining the optimal ray path scheme based on the selection and optimization of ray paths according to the sub-region illumination weights provided in this application embodiment. Detailed Implementation

[0010] This application provides a transparent working face fine imaging method using inter-hole seismic subdivision dynamic detection, which is used to solve the technical problems in the existing technology that traditional coal mine geological exploration is unable to achieve accurate detection of mining areas and cannot dynamically update geological information according to the coal mining process.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0013] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0014] Examples, as shown in the appendix Figure 1 As shown, this application provides a method for fine imaging of a transparent working surface using dynamic detection of inter-hole seismic subdivisions. The method includes the following steps: S110: Before carrying out coal mining operations in the target area, construct an inter-hole seismic observation network for the target area; In this embodiment of the application, in the scenario of realizing fine imaging of transparent working face based on dynamic detection of inter-hole seismic subdivision, in order to provide a stable and dense signal acquisition foundation for subsequent seismic monitoring of coal mining subdivision areas, and to ensure that the vibration signals generated by the coal mining machine cutting the coal wall can be accurately captured to support the interpretation of geological structures, it is necessary to drill holes from the transport roadway and return airway of the working face in the target area to the coal seam construction level and deploy seismic detectors to generate a comprehensive inter-hole seismic observation network with strong signal reception capability.

[0015] In the method provided in this application embodiment, before coal mining operations are carried out in the target area, two rows of parallel and densely distributed horizontal boreholes are drilled from the transport roadway and return airway of the working face of the target area into the coal seam, and seismic detectors are installed in the boreholes to generate an inter-bore seismic observation network for the target area.

[0016] Specifically, the key engineering locations of the working face in the target area are first identified, namely the transport roadway and the return air roadway. These two roadways, as important channels for coal seam mining, are ideal starting points for drilling into the coal seam, ensuring that the drilling direction is parallel to the working face cut, thus laying the foundation for uniform ray coverage of subsequent subdivided area exploration.

[0017] Furthermore, two rows of horizontal boreholes are drilled into the coal seam from the transport roadway and the return airway, respectively, and the distribution of the boreholes is strictly controlled to ensure that the two rows of boreholes are parallel to each other and densely arranged. For example, the borehole spacing can be set according to the coal seam thickness and geological complexity of the target area. If the geological structure of the coal seam is relatively complex, the borehole spacing can be reduced to 5-8 meters to increase the coverage density of the boreholes on the working face and avoid blind spots in detection.

[0018] Furthermore, seismic detectors are installed one by one in the completed horizontal boreholes. The detectors in each borehole must be placed evenly at preset intervals to ensure that the detectors can effectively receive seismic wave signals from different coal mining sub-areas.

[0019] For example, a seismic detector can be deployed every 3-5 meters so that the signal receiving range of each detector can be effectively connected with the receiving range of the adjacent detector, reducing signal loss.

[0020] Meanwhile, after all the geophones are deployed, their operating status will be adjusted to ensure that they can stably acquire seismic wave signals, ultimately forming a complete inter-hole seismic observation network for the target area.

[0021] This step, through precise selection of the borehole construction starting point, control of borehole distribution morphology, and scientific deployment of seismic detectors, constructed a high-quality inter-bore seismic observation network. This provided reliable hardware support for subsequent acquisition of raw seismic wave signal sequences, adaptive signal processing, and transparent working face imaging, effectively ensuring the accuracy of inter-bore seismic subdivision dynamic detection.

[0022] S120: Select a first coal mining subdivision area within the target area according to a preset interval, use the vibration generated by the coal mining machine cutting the coal wall as the seismic source, perform seismic monitoring on the first coal mining subdivision area, and obtain the original seismic wave signal sequence; In this embodiment of the application, in order to achieve progressive and accurate detection of the geological structure of the target area, avoid the loss of accuracy caused by overall detection, and obtain basic data to support subsequent signal processing and imaging, it is necessary to divide the first detection area according to a preset interval and use the vibration of the coal mining machine as the original seismic source to carry out monitoring in order to obtain the original seismic wave signal sequence that can reflect the geological characteristics of the area.

[0023] Specifically, firstly, based on the overall scope of the target area and the preliminary geological exploration, a suitable preset spacing is determined. For example, based on the requirements of coal seam stability and detection accuracy, the preset spacing is set to 20 meters. Using this as a standard, the first independent detection range, namely the first coal mining subdivision area, is delineated within the target area.

[0024] The preset spacing ensures both the detection accuracy of individual subdivided areas and avoids low detection efficiency caused by excessively small spacing, thus ensuring the adaptability of detection work to the coal mining progress.

[0025] Furthermore, there is no need to deploy additional dedicated seismic sources. Instead, the vibration generated when the coal mining machine cuts the coal wall in the first subdivided mining area is used directly as the seismic source. This seismic source originates from the coal mining operation itself, which not only reduces additional equipment investment and construction costs, but also allows the location of the seismic source to be synchronized with the coal mining operation in real time, ensuring that the seismic wave signal can truly reflect the geological conditions of the current mining area.

[0026] Furthermore, relying on the pre-established inter-hole seismic observation network, the seismic detectors deployed in the boreholes were activated to continuously monitor the propagation process of seismic waves generated by the cutting vibration of the coal mining machine in the first subdivided coal mining area.

[0027] During the monitoring process, the seismic detector captures the signal changes of seismic waves as they pass through different geological structures in the coal seam in real time. For example, when the seismic wave encounters a crack in the coal seam, the signal propagation speed and amplitude will fluctuate accordingly. These fluctuations will be completely recorded by the detector, and finally form the original seismic wave signal sequence containing the geological information of the area.

[0028] This step involved scientifically setting intervals to divide the first detection area, making reasonable use of the seismic sources from coal mining operations, and conducting monitoring in conjunction with an observation network. This process yielded the original seismic wave signal sequence for the first subdivided coal mining area, providing data support for the subsequent generation of standard seismic wave signals through adaptive signal processing strategies, thereby achieving transparent working face imaging.

[0029] S130: Preprocess the original seismic wave signal sequence according to an adaptive signal processing strategy to obtain a standard seismic wave signal sequence; In this embodiment of the application, in order to ensure that the seismic wave signal effectively reflects the geological characteristics while improving the signal processing efficiency to meet the real-time requirements of coal mining operations, it is necessary to determine the signal dimensionality reduction ratio by combining the historical geological fluctuations of adjacent mined areas, and then perform targeted preprocessing on the original seismic wave signal to obtain a standard seismic wave signal sequence that can support subsequent imaging and has higher processing efficiency.

[0030] Specifically, the first step is to obtain historical transparent working faces in adjacent areas of the target region that have already been mined. Geological information, such as the distribution and characteristics of different geological structures, is extracted from these historical transparent working faces. Based on this, geological state fluctuation analysis is carried out, and the historical geological state fluctuation degree is output.

[0031] Furthermore, the preset standard geological state fluctuation degree is compared with the calculated historical geological state fluctuation degree, and the ratio of the two is determined as the signal dimensionality reduction compensation coefficient. This coefficient is then multiplied by the initial dimensionality reduction ratio to obtain the first adaptive dimensionality reduction ratio.

[0032] The greater the fluctuation in geological conditions, the higher the requirement for real-time analysis, the larger the dimensionality reduction ratio, and the less data needs to be processed.

[0033] Furthermore, based on the obtained first adaptation dimensionality reduction ratio, a first signal processing strategy is set, and the original seismic wave signal sequence is preprocessed using the first signal processing strategy, such as filtering out seismic wave signals corresponding to the optimal ray path subset, and finally obtaining a standard seismic wave signal sequence.

[0034] This step dynamically adjusts the dimensionality reduction ratio of signal processing by associating it with the geological fluctuations of the historical mining area. This achieves efficient and targeted preprocessing of the original seismic wave signal, providing a high-quality data foundation for subsequent transparent working face imaging using standard seismic wave signals. It ensures the effective preservation of geological information and improves the timeliness of data processing.

[0035] Step S130 in the method provided in this application embodiment includes: Obtain historical transparent working surfaces in adjacent areas of the target area, perform geological state fluctuation analysis based on the historical transparent working surfaces, and output the historical geological state fluctuation degree. The ratio of the preset standard geological state fluctuation to the historical geological state fluctuation is used as the signal dimensionality reduction compensation coefficient, and the product of the signal dimensionality reduction compensation coefficient and the initial dimensionality reduction ratio is used as the first adaptive dimensionality reduction ratio. Based on the first adaptation dimensionality reduction ratio, a first signal processing strategy is set to preprocess the original seismic wave signal sequence to obtain a standard seismic wave signal sequence.

[0036] In this embodiment of the application, in order to ensure that the preprocessing of the original seismic wave signal can accurately preserve the key information of the geological structure and adapt to the real-time analysis requirements of coal mining operations, it is necessary to dynamically determine the signal dimensionality reduction ratio by combining the geological fluctuations of adjacent mined areas of the target area, and then process the original seismic wave signal in a targeted manner to obtain a standard seismic wave signal sequence that can support the subsequent fine imaging of the transparent working face.

[0037] Specifically, the first step is to obtain historical transparent working faces in adjacent areas of the target area that have already been mined. Geological structure-related information, such as the distribution range of faults and the magnitude of coal seam thickness changes, is extracted from these historical transparent working faces. Based on this, geological state fluctuation analysis is carried out.

[0038] For example, if during the mining process, the displacement of the coal seam around the fault in a certain adjacent area of ​​a historical transparent working face changes frequently and the amplitude reaches more than 0.5 meters, and the coal seam thickness fluctuates more than 0.3 meters in a local area, the historical geological state fluctuation degree can be output by analyzing the variation pattern and amplitude of these geological structures during historical mining.

[0039] Furthermore, the pre-set standard geological state fluctuation degree is compared with the calculated historical geological state fluctuation degree, and the ratio of the two is determined as the signal dimensionality reduction compensation coefficient.

[0040] The standard geological state fluctuation is set based on the general requirements of the coal mining industry for geological exploration accuracy, the geological stability threshold in the safety specifications for coal mining operations, and the typical fluctuation range under similar geological conditions in the past. This provides a unified reference benchmark for geological state fluctuation analysis and ensures that there is a consistent measurement standard for geological fluctuation assessment in different regions and at different times.

[0041] Furthermore, the obtained signal dimensionality reduction compensation coefficient is multiplied by the initial dimensionality reduction ratio to obtain the first suitable dimensionality reduction ratio. The greater the fluctuation in geological conditions and the higher the requirement for real-time analysis, the larger the determined dimensionality reduction ratio will be. For example, if the initial dimensionality reduction ratio is 50%, and the signal dimensionality reduction compensation coefficient is 1.2, then the first suitable dimensionality reduction ratio will be 60% (50% × 1.2), thereby reducing the amount of data that needs to be processed.

[0042] Furthermore, a first signal processing strategy is set based on the obtained first adaptation dimensionality reduction ratio, so as to prioritize the preservation of seismic wave signals in geologically anomalous areas while controlling the amount of signal data and meeting the real-time requirements of coal mining operations.

[0043] In the method provided in this application embodiment, "setting a first signal processing strategy based on the first adaptation dimensionality reduction ratio" includes: Based on the historical transparent working face, geological anomaly coefficient analysis is performed on several first sub-regions within the first coal mining sub-region, and several sub-region anomaly coefficients are output. Several sub-region illumination weights are configured according to the aforementioned sub-region anomaly coefficients, wherein the sub-region illumination weights and sub-region anomaly coefficients are positively correlated; Using the first adaptation dimensionality reduction ratio as the signal screening constraint and maximizing the total illumination of the first coal mining sub-region as the optimization objective, the ray path selection optimization is performed based on the illumination weights of the several sub-regions, and the optimal ray path scheme is output as the first signal processing strategy.

[0044] In this embodiment of the application, in order to ensure that the ray path selection can focus on geological anomaly areas to ensure detection accuracy, and meet the real-time data processing requirements of coal mining operations, it is necessary to determine the anomaly weight of sub-regions in combination with historical geological conditions, and constrain the signal quantity with an appropriate dimensionality reduction ratio, so as to achieve a balance between comprehensive coverage and efficient processing by optimizing the ray path.

[0045] First, based on the obtained historical transparent working faces, geological anomaly coefficient analysis is performed on several first sub-regions within the first coal mining sub-region to output multiple sub-region anomaly coefficients.

[0046] Specifically, if a sub-region shows small fault distribution in the historical transparent working face and the coal seam structure is relatively broken, it can be determined that its geological anomaly coefficient is high; while another sub-region has an intact coal seam structure and no obvious tectonic changes, and its geological anomaly coefficient is low. In this way, the anomaly coefficients of several sub-regions are output.

[0047] For example, if a sub-region within the first coal mining sub-region has two small faults with a length exceeding 5 meters and a coal seam fracture zone with a width of 3 meters, and its geological structure is clearly unstable as shown in the historical transparent working face, its geological anomaly coefficient is set to 0.9; while another adjacent sub-region shows a continuous and intact coal seam with no fault or fracture record in the historical transparent working face, its geological anomaly coefficient is set to 0.2. Through the differentiated assignment of the above steps, the geological risk level of different sub-regions can be accurately distinguished, and the corresponding sub-region anomaly coefficient can be output.

[0048] This ensures that subsequent ray path optimization prioritizes coverage of areas with higher geological risks, guaranteeing sufficient acquisition of seismic signals from anomalous areas. Specifically, the sub-region illumination weight is positively correlated with the sub-region anomaly coefficient; that is, the higher the geological anomaly coefficient of a sub-region, the greater the allocated illumination weight.

[0049] For example, the illumination weight of a sub-region with a geological anomaly coefficient of 0.9 is set to 0.9, and the illumination weight of a sub-region with a geological anomaly coefficient of 0.2 is set to 0.2. This makes the contribution of ray coverage in the anomaly region higher when calculating the total illumination in the future, and guides the optimization of ray paths to tilt towards high-risk areas.

[0050] Furthermore, using the first adaptation dimensionality reduction ratio as a signal screening constraint (e.g., screening 60% of the ray path numbers), and taking maximizing the total illumination of the first coal mining sub-region as the optimization objective, the ray path selection optimization is performed based on the illumination weights of several sub-regions to output the optimal ray path scheme.

[0051] As attached Figure 2 As shown, in the method provided in this application embodiment, "optimizing ray path selection based on the illumination weights of the plurality of sub-regions and outputting the optimal ray path scheme" includes: Using the first adaptation dimensionality reduction ratio as a signal screening constraint, the ray path is randomly selected in the ray path selection space of the first coal mining subdivision area, and the first ray path scheme is output. Based on the first ray path scheme, calculate the sub-region illumination of the plurality of first sub-regions and output the plurality of sub-region illumination, wherein the illumination is the length of a single ray passing through the sub-region; Based on the illuminance weights of the aforementioned sub-regions, the illuminance of the aforementioned sub-regions is weighted and summed to obtain the first total illuminance; Continue to randomly select ray path schemes and calculate total illumination within the ray path selection space, and output the ray path scheme corresponding to the maximum total illumination as the optimal ray path scheme.

[0052] In this embodiment of the application, in order to control the amount of seismic wave signal processing data and adapt to the real-time requirements of coal mining operations, while ensuring that the ray path can preferentially cover sub-regions with high geological anomaly risk and that geological information in key areas is not missed, it is necessary to determine the optimal ray path by randomly selecting paths, calculating illumination, and iteratively optimizing, with the dimensionality reduction ratio as a constraint, so as to provide a basis for the accurate screening of subsequent original seismic wave signals.

[0053] Specifically, the first adaptation dimensionality reduction ratio is used as the signal screening constraint. The ray path is randomly selected in the ray path selection space of the first coal mining subdivision area, and the first ray path scheme is output.

[0054] The first adaptation dimensionality reduction ratio is the optimal data processing ratio determined by combining the historical geological state fluctuation. For example, if the first adaptation dimensionality reduction ratio is set to 60%, then 60% of the total number of rays are randomly selected in the ray path selection space to form the first ray path scheme. This avoids both selecting too many rays, which would lead to low data processing efficiency, and also prevents too few rays from causing a lack of geological information.

[0055] Furthermore, based on the output first ray path scheme, the sub-region illumination of several first sub-regions is calculated, and the sub-region illumination is output.

[0056] Illuminance is defined as the length of a single ray passing through a sub-region. For example, if a ray starts from a borehole in a transport roadway and passes through sub-region A (geological anomaly coefficient 0.8) in the first coal mining sub-region, the actual passing length is 12 meters. When it passes through sub-region B (geological anomaly coefficient 0.3), the passing length is 8 meters. Then, the illuminance of the ray corresponding to sub-region A is 12 meters, and the illuminance of the ray corresponding to sub-region B is 8 meters. The illuminance of all rays in each sub-region is calculated in this way, and the total illuminance of each sub-region is obtained by superimposing them.

[0057] Furthermore, based on the obtained illuminance weights of several sub-regions, the illuminance of several sub-regions is weighted and summed to obtain the first total illuminance.

[0058] Among them, since the sub-region illumination weight is positively correlated with the sub-region anomaly coefficient, the higher the geological anomaly coefficient, the greater the illumination weight of the sub-region. For example, the illumination weight of sub-region A is 0.8 and the illumination weight of sub-region B is 0.3. If the total illumination of sub-region A is 150 meters and the total illumination of sub-region B is 200 meters, then the first total illumination is (150×0.8)+(200×0.3)=180. By assigning weights, the illumination of the anomaly area is made to account for a higher proportion of the total illumination, ensuring that the optimization direction is focused on high-risk areas.

[0059] Furthermore, the process of randomly selecting ray paths, calculating sub-region illumination, and weighted summing of total illumination is repeated within the ray path selection space. For example, 10 different 60% ray path schemes are selected successively, and the total illumination corresponding to each scheme is calculated. Finally, the scheme with the largest total illumination among the 10 schemes is determined as the optimal ray path scheme.

[0060] For example, the total illumination of the 7th scheme reaches 220, which is significantly higher than the 160-200 of other groups. Moreover, the illumination of sub-region A in this scheme accounts for 65%, which can fully cover the geological anomaly area. Therefore, it is set as the optimal ray path scheme and used as the first signal processing strategy.

[0061] Through the above iterative optimization process, we can strictly follow the first adaptation dimensionality reduction ratio to control the amount of data, and guide the ray path to tilt towards the geological anomaly area through illumination weight. This ensures that the optimal ray path scheme can not only meet the real-time processing requirements, but also provide a reliable path basis for subsequent screening of effective seismic wave signals and accurate generation of transparent working faces.

[0062] Finally, based on the determined optimal ray path scheme (i.e. the first signal processing strategy), the previously acquired raw seismic wave signal sequence is preprocessed to filter out the seismic wave signals corresponding to the subset of the optimal ray path, thereby obtaining the standard seismic wave signal sequence.

[0063] Specifically, the original seismic wave signal sequence contains signals propagating along different ray paths. These include effective signals that propagate along the optimal ray path and accurately reflect geological structures, as well as signals that propagate along non-optimal paths and may carry redundant information or noise. During preprocessing, the original signal sequence needs to be matched and filtered one by one according to the ray trajectory, propagation direction, and coverage area specified in the optimal ray path scheme.

[0064] For example, if the optimal ray path scheme includes rays from borehole 3 in the transport tunnel to borehole 5 in the return air tunnel, covering sub-region A, and rays from borehole 6 in the transport tunnel to borehole 8 in the return air tunnel, covering sub-region C (geological anomaly coefficient 0.7), then the seismic wave signals generated during the propagation of these two types of rays are extracted from the original signal, and signals unrelated to the optimal path are removed.

[0065] Simultaneously, during the screening process, it is necessary to perform a secondary verification of the signal's integrity and validity by combining the illumination characteristics of the sub-region corresponding to the optimal ray path. For example, for a ray signal covering sub-region A (illumination 12 meters), it is necessary to confirm whether the signal contains wave velocity change information corresponding to geological features such as coal seam structure and small faults within that sub-region. If the signal is incomplete or the wave velocity fluctuates abnormally beyond a reasonable range, it is determined to be an invalid signal and further eliminated to ensure that the screened signals can truly reflect the geological state of the corresponding sub-region.

[0066] Through the preprocessing steps described above, the final standard seismic wave signal sequence not only eliminates redundant data and noise interference, meeting the data volume control requirements of the first adaptation dimensionality reduction ratio, but also focuses on the geological anomaly areas covered by the optimal ray path, preserving key geological information. This provides data support for subsequent transparent working face imaging and accurate interpretation of geological structures in the first coal mining subdivision area based on this standard signal sequence.

[0067] S140: Perform transparent working face imaging on the first coal mining subdivision area according to the standard seismic wave signal sequence, and output the first transparent working face; In this embodiment of the application, in the scenario of using inter-hole seismic subdivision dynamic detection to achieve fine imaging of transparent working face, in order to transform the pre-processed standard seismic wave signal into an intuitive geological model that can guide coal mining operations, it is necessary to use technologies such as seismic tomography to invert and integrate the signal in order to generate a transparent working face model that can clearly present the coal seam and its internal geological structure.

[0068] Specifically, the seismic tomography algorithm is first used to perform inversion calculations on the coal and rock mass in the first subdivided coal mining area, using information such as seismic wave travel time and amplitude in the standard seismic wave signal sequence as input data, to generate the three-dimensional seismic wave velocity field distribution within the area.

[0069] Furthermore, based on the generated three-dimensional seismic wave velocity field distribution, wave velocity anomaly zones are identified and delineated. By setting wave velocity thresholds and combining them with geological patterns, geological structural information is obtained. For example, high-velocity anomaly zones are interpreted as hard rock layers, gangue zones, and other geological bodies, while low-velocity anomaly zones are interpreted as fault fracture zones, fractured areas, or water-bearing structures.

[0070] Furthermore, the deciphered geological structural information is integrated into the initial geological model of the working face, thereby constructing a first transparent working face with clear geological meaning that covers the first coal mining subdivision area. This first transparent working face can clearly show the spatial morphology and location of the coal seam, roof and floor, and internal hidden geological structures.

[0071] Finally, the first transparent working face is output in a visual graphic format, and the key model parameters and geological structure coordinate information are connected to the intelligent coal mining control system to provide geological basis for the initial decision-making of the coal mining machine.

[0072] Step S140 in the method provided in this application embodiment includes: Using a seismic tomography algorithm, wave velocity field inversion is performed based on the standard seismic wave signal sequence to generate a three-dimensional seismic wave velocity field distribution. Geological structure interpretation is performed based on the three-dimensional seismic wave velocity field distribution to obtain geological structure information; The geological structure information is integrated into the initial geological model of the first coal mining subdivision area to generate the first transparent working face.

[0073] In this embodiment of the application, in order to transform the preprocessed standard seismic wave signal into a geological model that can intuitively guide coal mining operations and clearly present the coal seam, roof and floor and hidden geological structures in the first coal mining subdivision area, it is necessary to construct a first transparent working face with clear geological meaning through the process of seismic wave inversion, geological interpretation and model integration.

[0074] Specifically, the existing seismic tomography algorithm is first used to perform inversion calculations on the coal and rock mass in the first subdivided coal mining area, using information such as seismic wave travel time and amplitude in the standard seismic wave signal sequence as input data.

[0075] For example, if a standard seismic wave signal sequence contains seismic wave signals from multiple boreholes in the transport roadway to multiple boreholes in the return airway, passing through different locations in the area, the wave velocity distribution of the coal and rock mass can be calculated by inverting the differences in travel time and amplitude caused by these signals encountering different geological structures during propagation. When seismic waves pass through fault fracture zones, the wave velocity decreases and the travel time increases accordingly due to the loose medium; while when passing through hard sandstone interlayers, the wave velocity increases and the travel time shortens.

[0076] Furthermore, through extensive analysis and inversion calculations of the aforementioned seismic wave signals, a three-dimensional seismic wave velocity field distribution within the region is generated. This three-dimensional seismic wave velocity field distribution can intuitively demonstrate the spatial variation of the wave velocity in the coal and rock mass using numerical gradients.

[0077] Furthermore, geological structures are interpreted based on the generated three-dimensional seismic wave velocity field distribution. That is, by setting a reasonable wave velocity threshold and combining it with the geological characteristics of coal mines, wave velocity anomaly zones are identified and delineated.

[0078] For example, areas with wave velocities significantly higher than the average wave velocity of the surrounding coal seams are interpreted as geological bodies such as hard rock strata and gangue zones. Suppose that the wave velocity in a certain area reaches 3500 m / s, which is much higher than the wave velocity of coal seams of about 2500 m / s. Combined with the geological data of the mining area, it can be determined that there are thick sandstone layers. Areas with wave velocities significantly lower than the average wave velocity of the surrounding coal seams are interpreted as fault fracture zones, fractured areas, or water-bearing structures. If the wave velocity in a certain area is only 1800 m / s and the wave velocity distribution is disordered, it can be determined that there are fault fracture zones. In this way, detailed geological structural information in the area can be obtained.

[0079] Furthermore, the interpreted geological structural information is integrated into the initial geological model of the working face. This initial geological model is constructed based on fundamental information obtained from previous geological exploration, such as the approximate spatial morphology and thickness of the coal seam and roof and floor. Now, the newly interpreted fault locations, sandstone distribution ranges, and water-bearing fracture zones are added to the initial geological model according to their actual locations in three-dimensional space.

[0080] For example, in the initial geological model, the coal seam is represented as a continuous layered structure. Now, the interpreted fault locations are marked in the form of clear fracture zones at the corresponding locations in the coal seam. Sandstone interlayers are superimposed on the coal seam or roof and floor in a specific layered structure, thereby constructing a first transparent working face with clear geological significance that covers the first subdivision of the coal mining area.

[0081] Ultimately, the first transparent working face obtained through the above steps can clearly display the spatial morphology and location of the coal seam, roof and floor, and internal hidden geological structures, just like a three-dimensional geological map, so that personnel involved in coal mining operations can intuitively understand the geological conditions of the area.

[0082] S150: According to the coal mining process, the subsequent coal mining area division and transparent working face imaging are carried out based on the first transparent working face.

[0083] In this embodiment of the application, in the scenario of using inter-hole seismic subdivision dynamic detection to achieve fine imaging of transparent working face, in order to enable the geological exploration of the coal mining area to dynamically adapt to the geological changes of the current mining area and ensure that each coal mining subdivision area has accurate geological model support, it is necessary to continuously carry out the subdivision and imaging of subsequent areas based on the first transparent working face that has been generated, so as to ensure that there is reliable geological basis throughout the coal mining operation.

[0084] Specifically, the geological state fluctuation analysis is first performed based on the first transparent working face to output the first geological state fluctuation degree. For example, by observing the distribution and changes of geological structures such as coal seams, faults, and rock strata in the first transparent working face, the fluctuation patterns and amplitudes they exhibit during mining are analyzed, such as the range of changes in coal seam thickness and the displacement of coal and rock masses around faults, thereby determining the first geological state fluctuation degree.

[0085] Furthermore, the ratio of the preset standard geological state fluctuation degree to the first geological state fluctuation degree is used as the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient.

[0086] If the first geological state has a large fluctuation, it indicates that the geological changes are more active and the real-time requirements are higher. In this case, the second signal dimensionality reduction compensation coefficient will make the dimensionality reduction ratio of subsequent signal processing greater, or the second spacing compensation coefficient will make the division spacing of coal mining sub-regions shorter, thus adapting to geological changes.

[0087] Furthermore, a second signal processing strategy is set according to the second signal dimensionality reduction compensation coefficient, and the preset interval is compensated according to the second interval compensation coefficient to obtain the second division interval. Based on the second division interval, a second coal mining subdivision area is selected within the target area.

[0088] Furthermore, seismic monitoring is performed on the obtained second subdivided coal mining area to obtain a second raw seismic wave signal sequence. This sequence is then preprocessed according to a second signal processing strategy to obtain a second standard seismic wave signal sequence. Finally, transparent working face imaging is performed on the second subdivided coal mining area based on the second standard seismic wave signal sequence, outputting the second transparent working face.

[0089] Following the above process, as coal mining progresses, the subsequent coal mining area division and transparent working face imaging operations are repeatedly performed until the coal mining operation in the target area is completed. This dynamic tracking method ensures that each subsequent coal mining area has a transparent working face model that reflects actual geological changes, providing continuous geological support for the safe and efficient conduct of coal mining operations.

[0090] Step S150 in the method provided in this application embodiment includes: Based on the first transparent working face, geological state fluctuation analysis is performed, and the first geological state fluctuation degree is output. The ratio of the preset standard geological state fluctuation to the first geological state fluctuation is used as the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient, and the second signal processing strategy is set according to the second signal dimensionality reduction compensation coefficient. The preset spacing is compensated according to the second spacing compensation coefficient to obtain the second division spacing, and a second coal mining subdivision area is selected in the target area based on the second division spacing; Seismic monitoring is performed on the second coal mining sub-area to obtain the second original seismic wave signal sequence, and the second original seismic wave signal sequence is preprocessed according to the second signal processing strategy to obtain the second standard seismic wave signal sequence; Based on the second standard seismic wave signal sequence, a transparent working face image is generated for the second coal mining subdivision area, and the second transparent working face is output. Continue with the subsequent division of mining areas and imaging of transparent working faces according to the coal mining progress until the coal mining operation in the target area is completed.

[0091] In this embodiment of the application, in order to ensure that the entire coal mining operation can be carried out based on the transparent working face that conforms to the real-time geological changes, and to avoid insufficient accuracy or low efficiency of regional geological exploration due to fixed detection parameters, it is necessary to dynamically adjust the parameters and regional division of subsequent detection based on the first transparent working face that has been generated, and continuously advance the imaging work of each subdivided area, so as to ensure the safety and efficiency of coal mining operations in the entire target area.

[0092] Specifically, the geological state fluctuation analysis is first conducted based on the first transparent working face, outputting the first geological state fluctuation degree. The first transparent working face contains detailed geological information such as coal seam thickness, fault distribution, and strata structure within the first subdivided coal mining area. The analysis should focus on the changing characteristics of these geological structures. For example, observing the fluctuation range of coal seam thickness within the first area, if the coal seam thickness changes from an average of 2.5 meters to 1.8 meters, the fluctuation amplitude reaches 0.7 meters.

[0093] At the same time, the distribution density and extension length of fault fracture zones are statistically analyzed. If there are 3 small faults with a length of more than 10 meters in the area and the average width of the fracture zone is 2 meters, then the degree of fluctuation of the geological state in the area is quantitatively analyzed by combining these data, and the first geological state fluctuation degree is finally output.

[0094] Furthermore, the ratio of the preset standard geological state fluctuation to the first geological state fluctuation is used as the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient. The preset standard geological state fluctuation is set based on the general requirements of the coal mining industry for geological exploration of stable coal seams. For example, the standard fluctuation is set as a state where the coal seam thickness fluctuation does not exceed 0.3 meters and the fault fracture zone density is less than 0.5 faults / 100 square meters.

[0095] Specifically, if the first geological state fluctuation is 1.5 times the standard geological state fluctuation, then the ratio of the two (i.e., the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient) is 2 / 3. In this case, if the geological fluctuation is more active and the real-time requirements for signal processing are higher, then the second signal processing strategy is set based on this coefficient, and the signal dimensionality reduction ratio is appropriately increased to reduce the amount of data processing. If it is necessary to cover the fluctuation area more finely, then the preset spacing is compensated based on this coefficient, and the division spacing is shortened to improve the accuracy of subsequent regional detection.

[0096] Furthermore, the preset spacing is compensated according to the second spacing compensation coefficient to obtain the second division spacing, and the second coal mining subdivision area is selected in the target area based on the second division spacing.

[0097] For example, if the preset spacing is originally 20 meters, and the second spacing compensation coefficient is 0.8, then the second division spacing is 20 × 0.8 = 16 meters. By shortening the spacing, the range of the second coal mining subdivision area is made smaller, which can more accurately capture the geological details in the area. When selecting, the boundary of the first coal mining subdivision area should be used as the starting point, and a 16-meter-wide area should be delineated along the coal mining advance direction as the second coal mining subdivision area to ensure that the area is connected without omission and without overlap.

[0098] Furthermore, seismic monitoring was conducted in the second subdivided coal mining area to obtain the second original seismic wave signal sequence. The monitoring process was consistent with that in the first subdivided coal mining area, using the vibration of the coal mining machine cutting the coal face as the seismic source. Through the geophones in the corresponding boreholes of the previously constructed inter-bore seismic observation network, the original signals of seismic waves propagating in the second area were collected. These signals contained information on the reflection and refraction of seismic waves by different geological structures in the area.

[0099] Furthermore, the second original seismic wave signal sequence is preprocessed according to the second signal processing strategy to obtain the second standard seismic wave signal sequence.

[0100] If the second signal processing strategy sets a 70% dimensionality reduction ratio based on the second signal dimensionality reduction compensation coefficient, then 70% of the signals that can reflect key geological information need to be screened out during preprocessing. For example, seismic wave signals that propagate along the optimal ray path and can cover potential fault locations in the second region are preferentially retained, while redundant noise signals are removed, ultimately yielding the second standard seismic wave signal sequence.

[0101] Furthermore, based on the obtained second standard seismic wave signal sequence, transparent working face imaging is performed on the second coal mining subdivision area to output the second transparent working face.

[0102] Similarly, the imaging process needs to follow the seismic tomography algorithm, generate the three-dimensional seismic velocity field distribution of the region through the inversion of the second standard signal, interpret the geological structure information (such as whether there are new fracture zones, whether the coal seam has a further thinning trend, etc.), and integrate this information into the initial geological model of the second region to form a second transparent working face that can clearly show the geological state of the second region.

[0103] Finally, the subsequent coal mining area division and transparent working face imaging are carried out according to the coal mining progress. As the coal mining machine gradually advances to the completion of the second area, the geological condition fluctuation is analyzed based on the second transparent working face. The above process of "calculating compensation coefficient - adjusting division spacing / signal strategy - monitoring preprocessing - imaging" is repeated to generate transparent working faces for the third, fourth and other subsequent subdivided coal mining areas in sequence, until the coal mining operation in the entire target area is completed.

[0104] Ultimately, through the aforementioned dynamic and cyclical detection and imaging process, the detection parameters of each coal mining sub-area can be adapted to its actual geological fluctuations. This ensures the accuracy of geological information detection and allows for flexible adjustment of data processing efficiency based on the degree of fluctuation, providing continuous and reliable geological support for the entire coal mining operation and effectively avoiding mining risks caused by unclear geology.

[0105] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects: This application proposes a method for fine imaging of transparent working faces using dynamic detection of inter-hole seismic subdivision. First, before coal mining operations in the target area, two rows of parallel and densely packed horizontal boreholes are drilled from the transport roadway and return airway into the coal seam. Seismic detectors are deployed to construct an inter-hole seismic observation network, laying the hardware foundation for subsequent signal acquisition. Then, the first subdivision area of ​​coal mining is selected at preset intervals. Using the vibration of the coal cutting machine as the seismic source, the original seismic wave signal sequence of this area is acquired through monitoring the inter-hole seismic observation network. Subsequently, combined with historical transparent working faces of adjacent mined areas in the target area, the geological state fluctuation is analyzed to determine the historical geological state fluctuation degree, and the signal dimensionality reduction compensation coefficient is calculated. The first adaptation dimensionality reduction ratio is used to set the first signal processing strategy, and the original seismic wave signals are screened to obtain the standard seismic wave signal sequence. Then, the seismic tomography algorithm is used to invert the wave velocity field of the standard seismic wave signal sequence, generate the three-dimensional seismic wave velocity field distribution and interpret the geological structure information, integrate it into the initial geological model, and output the first transparent working face. Finally, based on the first transparent working face, the first geological state fluctuation is analyzed, the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient is calculated, the signal processing strategy and the regional division spacing are adjusted, and the second coal mining subdivision area is selected for repeated monitoring, preprocessing, and imaging. The process is cyclically advanced according to the coal mining progress until the coal mining operation in the target area is completed.

[0106] The method provided in this application, through the technical solution of "constructing a seismic observation network - subdividing regional monitoring - adaptive signal preprocessing - transparent working face imaging - dynamic cyclic detection", solves the problems of large overall detection accuracy loss, inability of fixed parameters to adapt to geological dynamic changes, and insufficient signal coverage in key anomaly areas in traditional seismic detection during mining. It improves the precision and adaptability of transparent working face imaging, and provides reliable real-time geological support for geological risk avoidance and coal mining path planning in intelligent coal mining.

[0107] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0108] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0109] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection, characterized in that the method... include: Before coal mining operations are carried out in the target area, an inter-hole seismic observation network for the target area is constructed. A first coal mining sub-region is selected within the target area according to a preset interval. The vibration generated by the coal mining machine cutting the coal wall is used as the seismic source. Seismic monitoring is performed on the first coal mining sub-region to obtain the original seismic wave signal sequence. The original seismic wave signal sequence is preprocessed according to an adaptive signal processing strategy to obtain a standard seismic wave signal sequence; Based on the standard seismic wave signal sequence, a transparent working face image is generated for the first coal mining subdivision area, and the first transparent working face is output. According to the coal mining process, the subsequent coal mining area division and transparent working face imaging will continue to be performed based on the first transparent working face.

2. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 1, characterized in that, Before mining operations are carried out in the target area, two rows of parallel and densely distributed horizontal boreholes are drilled from the transport roadway and return airway of the working face of the target area into the coal seam, and seismic detectors are installed in the boreholes to generate an inter-bore seismic observation network for the target area.

3. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 1, characterized in that, The original seismic wave signal sequence is preprocessed according to an adaptive signal processing strategy to obtain a standard seismic wave signal sequence, including: Obtain historical transparent working surfaces in adjacent areas of the target area, perform geological state fluctuation analysis based on the historical transparent working surfaces, and output the historical geological state fluctuation degree. The ratio of the preset standard geological state fluctuation to the historical geological state fluctuation is used as the signal dimensionality reduction compensation coefficient, and the product of the signal dimensionality reduction compensation coefficient and the initial dimensionality reduction ratio is used as the first adaptive dimensionality reduction ratio. Based on the first adaptation dimensionality reduction ratio, a first signal processing strategy is set to preprocess the original seismic wave signal sequence to obtain a standard seismic wave signal sequence.

4. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 3, characterized in that, Based on the first adaptation dimensionality reduction ratio, a first signal processing strategy is set, including: Based on the historical transparent working face, geological anomaly coefficient analysis is performed on several first sub-regions within the first coal mining sub-region, and several sub-region anomaly coefficients are output. Several sub-region illumination weights are configured according to the aforementioned sub-region anomaly coefficients, wherein the sub-region illumination weights and sub-region anomaly coefficients are positively correlated; Using the first adaptation dimensionality reduction ratio as the signal screening constraint and maximizing the total illumination of the first coal mining sub-region as the optimization objective, the ray path selection optimization is performed based on the illumination weights of the several sub-regions, and the optimal ray path scheme is output as the first signal processing strategy.

5. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 4, characterized in that, Ray path selection optimization is performed based on the illumination weights of the aforementioned sub-regions, outputting the optimal ray path scheme, including: Using the first adaptation dimensionality reduction ratio as a signal screening constraint, the ray path is randomly selected in the ray path selection space of the first coal mining subdivision area, and the first ray path scheme is output. Based on the first ray path scheme, calculate the sub-region illumination of the plurality of first sub-regions and output the plurality of sub-region illumination, wherein the illumination is the length of a single ray passing through the sub-region; Based on the illuminance weights of the aforementioned sub-regions, the illuminance of the aforementioned sub-regions is weighted and summed to obtain the first total illuminance; Continue to randomly select ray path schemes and calculate total illumination within the ray path selection space, and output the ray path scheme corresponding to the maximum total illumination as the optimal ray path scheme.

6. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 1, characterized in that, Based on the standard seismic wave signal sequence, a transparent working face image is generated for the first coal mining subdivision area, and the first transparent working face is output, including: Using a seismic tomography algorithm, wave velocity field inversion is performed based on the standard seismic wave signal sequence to generate a three-dimensional seismic wave velocity field distribution. Geological structure interpretation is performed based on the three-dimensional seismic wave velocity field distribution to obtain geological structure information; The geological structure information is integrated into the initial geological model of the first coal mining subdivision area to generate the first transparent working face.

7. The method for fine imaging of a transparent working surface using dynamic inter-hole seismic subdivision detection according to claim 1, characterized in that, According to the coal mining progress, based on the first transparent working face, subsequent coal mining area division and transparent working face imaging are performed, including: Based on the first transparent working surface, geological state fluctuation analysis is performed, and the first geological state fluctuation degree is output. The ratio of the preset standard geological state fluctuation to the first geological state fluctuation is used as the second signal dimensionality reduction compensation coefficient or the second spacing compensation coefficient, and the second signal processing strategy is set according to the second signal dimensionality reduction compensation coefficient. The preset spacing is compensated according to the second spacing compensation coefficient to obtain the second division spacing, and a second coal mining subdivision area is selected in the target area based on the second division spacing; Seismic monitoring is performed on the second coal mining sub-area to obtain the second original seismic wave signal sequence, and the second original seismic wave signal sequence is preprocessed according to the second signal processing strategy to obtain the second standard seismic wave signal sequence; Based on the second standard seismic wave signal sequence, a transparent working face image is generated for the second coal mining subdivision area, and the second transparent working face is output. Continue with the subsequent division of coal mining areas and imaging of transparent working faces according to the coal mining progress until the coal mining operation in the target area is completed.

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