Coal mine GIS transparent geological management system fusing multi-source geological exploration data

Abstract

The present application relates to the technical field of geological exploration, in particular to a coal mine GIS transparent geological management system fusing multi-source geological exploration data, comprising a multi-source geological and gas monitoring data acquisition module: obtaining structural geological data and dynamic gas data, forming a structure-gas joint observation data set; a structure-gas coupling modeling module: constructing a three-dimensional geometric model, dynamically adjusting the fracture development zone and the stress concentration area, and generating a structure-gas double-field coupling three-dimensional geological model; a model dynamic updating and local feedback correction module: when the gas sensor detects abnormal changes or new drilling data is accessed, triggering an automatic updating mechanism; a GIS transparent visualization and risk labeling module: using a multi-layer fusion and asynchronous drawing mechanism for real-time rendering, and labeling and displaying high-risk areas. The present application can truly reflect the dynamic correlation between geological structure and gas evolution, and has stronger prediction ability and safety evaluation adaptability.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and in particular to a transparent geological management system for coal mines that integrates multi-source geological exploration data. Background Technology

[0002] During coal mining, the complex and variable geological structure and significant differences in gas occurrence conditions can easily induce major disasters such as gas outbursts and rock bursts. Therefore, accurately grasping the characteristics of coal seam occurrence, fault distribution, and gas enrichment is an important prerequisite for achieving safe mining and scientific layout of coal mines. Although most coal mining enterprises have deployed various detection methods, such as borehole exploration, seismic reflection, geophysical imaging, and gas monitoring, these data often come from different sources, have inconsistent coordinate systems, and have lagging update mechanisms. This results in significant spatial deviations and timeliness gaps between the data, making it difficult to form a unified three-dimensional geological representation and even more difficult to achieve real-time identification and dynamic early warning of high-risk areas.

[0003] Furthermore, traditional coal mine geological information systems are mostly based on static maps or two-dimensional data displays, lacking in-depth integrated modeling capabilities and high-performance visualization rendering mechanisms. They cannot intuitively reflect the coupling relationship between fault zones, gas-bearing layers, and gas fields, thus limiting the ability to accurately identify and control key risk areas. Although some existing three-dimensional geological platforms have modeling functions, they generally lack dynamic update mechanisms and risk assessment models, making them unable to adapt to the actual needs of high-frequency changes in mine sites. Therefore, there is an urgent need for an intelligent geological management system that can integrate multi-source geological and gas data, has dynamic modeling capabilities, and supports three-dimensional risk visualization annotation to improve the transparency and inherent safety level of coal mines. Summary of the Invention

[0004] This invention provides a transparent geological management system for coal mines that integrates multi-source geological exploration data.

[0005] A transparent geological management system for coal mines that integrates multi-source geological exploration data includes the following modules:

[0006] Multi-source geological and gas monitoring data acquisition module: acquires structural geological data from borehole, seismic, geophysical and laser scanning equipment, as well as dynamic gas data from downhole gas sensors, performs spatial coordinate unification and timestamp alignment, and forms a joint structural-gas observation dataset;

[0007] Structure-Gas Coupled Modeling Module: Input the joint observation dataset into the coupled modeling engine, construct three-dimensional geometric models of three structural units, namely coal seam, fault and gas-bearing layer, through attribute parameter modeling method, introduce spatial gas field distribution constraints, dynamically adjust the fracture development zone and stress concentration zone according to the changes in gas pressure gradient and gas content, and generate structure-gas dual field coupled three-dimensional geological model.

[0008] Model dynamic update and local feedback correction module: When the gas sensor detects abnormal changes or new borehole data is connected, the automatic update mechanism of the local sub-region model is triggered. The local difference inversion method is used to correct the gas field and structure model of the corresponding region, so as to realize the dynamic evolution of the three-dimensional geological model.

[0009] GIS Transparent Visualization and Risk Labeling Module: Loads the structure-gas dual-field coupled 3D model into the GIS platform, performs real-time rendering using a multi-layer fusion and asynchronous drawing mechanism, and labels and displays high-risk areas based on the gas enrichment degree and structural mutation intensity in the structure-gas dual-field coupled 3D model.

[0010] Optionally, the dynamic gas data from the downhole gas sensor includes gas pressure, gas concentration, and temperature.

[0011] Optionally, the multi-source geological and gas monitoring data acquisition module includes:

[0012] Structural geological data acquisition: Structural geological data, including columnar sections, core descriptions, well logging curves, seismic profiles, resistivity, and tunnel point clouds, were acquired through borehole, seismic, geophysical, and laser scanning equipment.

[0013] Dynamic gas data stream acquisition: Real-time acquisition of gas concentration, pressure and temperature data at each measuring point through the underground gas monitoring network to form a dynamic gas data stream;

[0014] Spatial coordinate unification and location registration: Establish a unified mine spatial coordinate system and perform spatial coordinate transformation and location registration for structural geological data and gas monitoring data;

[0015] Time synchronization and unified time stamp addition: A hardware clock and software timestamp synchronization mechanism is adopted to assign a unified time stamp to all data;

[0016] Construction of the joint structure-gas observation dataset: Multi-source data with aligned locations and synchronized times are fused to generate a joint structure-gas observation dataset that includes spatial, temporal, and attribute information.

[0017] Optionally, the spatial coordinate unification and position registration include:

[0018] Establishment of a unified coordinate system: Establish a unified coordinate system for the mine space, determine the spatial benchmark for various geological and gas monitoring data, and perform coordinate transformation on structural geological data based on control points and measurement benchmark surfaces in the mining area;

[0019] Spatial registration: Spatial registration of the locations of each monitoring point in the dynamic gas data stream is performed under a unified coordinate system.

[0020] Optionally, the structure-gas coupling modeling module includes:

[0021] Initial structural unit three-dimensional geometric model construction: The structure-gas joint observation dataset is input into the coupled modeling engine, and the borehole lithology data, seismic tectonic data and geophysical property parameters are extracted. The attribute parameter modeling method is used to construct the coal seam roof and floor interface model, fault geometric model and gas-bearing layer spatial distribution model respectively, forming the initial structural unit three-dimensional geometric model.

[0022] Establishment of spatial gas field distribution constraints: Dynamic gas monitoring data from the structure-gas joint observation dataset are introduced to establish a gas pressure field and gas concentration field distribution model based on Kriging space interpolation, thereby generating spatial gas field distribution constraints;

[0023] Dynamic adjustment of fracture development zone: Based on the characteristics of gas pressure gradient change in the spatial gas field distribution constraint, high pressure gradient zone and low pressure gradient zone are identified. Combined with the coal seam and fault distribution in the three-dimensional geometric model of the initial structural unit, the spatial distribution range and development density of fracture development zone are dynamically adjusted.

[0024] Stress concentration area correction: Based on the gas content variation law in the spatial gas field distribution constraints, analyze the correspondence between gas anomaly areas and geological structures, add stress monitoring points in stress concentration areas, and correct the stress distribution pattern of fault zones and coal seam variation areas.

[0025] Structure-Gas Dual-Field Coupled Model Fusion: The modified three-dimensional geometric model of the structural unit and the optimized gas field distribution model are deeply fused through the dual-field coupling algorithm to generate a structure-gas dual-field coupled three-dimensional geological model that reflects the interaction between geological structure and gas distribution.

[0026] Optionally, the Kriging spatial interpolation predicts the gas concentration and pressure values ​​of unsampled areas based on the data distribution trend of existing points, and is used to spatially interpolate and complete the gas concentration and gas pressure in the dynamic gas monitoring data, and establish a continuous gas pressure field and gas concentration field distribution model.

[0027] Optionally, the model dynamic update and local feedback correction module includes:

[0028] Model update triggering mechanism construction: Real-time monitoring of data streams from gas sensors and borehole data interfaces. When a sudden change in gas concentration is detected that exceeds the gas concentration change triggering threshold or new borehole data is accessed, the update mechanism is automatically triggered to generate a model update command.

[0029] Local sub-region location: Based on the model update instructions, the affected area is determined according to the spatial location correlation, and the local sub-region to be updated is dynamically delineated in the structure-gas dual-field coupled three-dimensional geological model;

[0030] Local gas field model update: Within a defined local sub-region, the local difference inversion method is used, with newly acquired gas monitoring data and borehole data as constraints, to recalculate the gas field parameters of the region and generate an updated local gas field model.

[0031] Structural model parameter correction: Based on the changes in gas pressure distribution in the updated local gas field model, combined with geological structural features, the inversion algorithm is used to correct the fracture development parameters and stress distribution parameters in the local sub-region, and generate an updated local structural model.

[0032] Model fusion and evolution: The updated local gas field model and local structure model are seamlessly fused with the unchanged model region to generate a new generation of structure-gas dual-field coupled three-dimensional geological model, realizing the dynamic evolution of the three-dimensional geological model.

[0033] Optionally, the local difference inversion method is used to update the affected local areas in the three-dimensional geological model after the gas sensor detects abnormal changes or new borehole data is added. The local difference inversion method takes the latest acquired gas pressure, gas concentration and borehole lithology data as input, combines the parameter distribution already existing in the structure-gas dual-field model, compares the differences between the "new and old data", estimates the changes in gas pressure field and gas content in the local area through inversion calculation, and further derives the correction values ​​of fracture development density and stress distribution in the local area.

[0034] Optionally, the GIS transparency visualization and risk labeling module includes:

[0035] Layer data loading and parsing: The structure-gas dual-field coupled 3D geological model is loaded through the data interface of the GIS platform, and the model data is parsed into three independent data layers: geological structure layer, gas distribution layer, and risk labeling layer;

[0036] Multi-layer fusion rendering mechanism: The multi-layer fusion mechanism is adopted to overlay and fuse the geometric models of coal seams, faults and gas-bearing layers in the geological structure layer with the pressure field and concentration field data in the gas distribution layer to generate a comprehensive display model with transparent visualization effect;

[0037] Asynchronous rendering mechanism: Based on the asynchronous rendering mechanism, the rendering task of the comprehensive display model is decomposed into geometric rendering task and attribute rendering task, which are processed in parallel by different threads to achieve real-time and smooth rendering of the 3D scene.

[0038] High-risk area identification: Extract gas enrichment index and structural abrupt change intensity index from the structure-gas dual-field coupled three-dimensional geological model;

[0039] Risk level assessment and labeling display: Based on predefined risk assessment rules, the gas enrichment degree index and structural mutation intensity index are weighted and fused to generate regional risk levels, and high-risk areas are displayed with color coding and boundary labeling in the risk labeling layer.

[0040] Optionally, the identification of high-risk areas includes:

[0041] Gas enrichment index extraction: Gas concentration field and pressure field data are extracted from the structure-gas dual-field coupled three-dimensional geological model, and gas enrichment index is calculated based on the gas concentration gradient and pressure value of each monitoring point.

[0042] Structural mutation intensity index extraction: Extract fault distribution and rock stratum dip angle data from the structure-gas dual-field coupled three-dimensional geological model, calculate fault density and rock stratum dip angle change rate, and generate structural mutation intensity index.

[0043] The beneficial effects of this invention are:

[0044] This invention introduces gas field distribution constraints during the modeling process and combines the dual action mechanism of coal seam-fault structure with gas pressure field and concentration field to construct a structure-gas dual-field coupled three-dimensional model. At the same time, based on the gas pressure gradient and gas-bearing anomaly characteristics, it realizes dynamic correction of fracture development zone and stress concentration zone, which can truly reflect the dynamic relationship between geological structure and gas evolution, and has stronger predictive ability and safety assessment adaptability.

[0045] This invention achieves efficient and transparent rendering of structures and gas fields through multi-layer fusion and asynchronous rendering mechanisms, improving the loading efficiency of 3D scenes and the user interaction experience. At the same time, it innovatively introduces a risk level assessment method based on "gas enrichment degree index" and "structural mutation intensity index", and marks and colors high-risk areas in the GIS layer, effectively supporting real-time early warning and accurate decision-making in coal mine geological management, and improving the intelligent level of coal mine safety production. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a system block diagram of an embodiment of the present invention;

[0048] Figure 2 This is a dual-field modeling diagram according to an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0050] like Figures 1-2 As shown, a transparent geological management system for coal mines that integrates multi-source geological exploration data includes the following modules:

[0051] Multi-source geological and gas monitoring data acquisition module: acquires structural geological data from borehole, seismic, geophysical and laser scanning equipment, as well as dynamic gas data from downhole gas sensors, performs spatial coordinate unification and timestamp alignment, and forms a joint structural-gas observation dataset;

[0052] The multi-source geological and gas monitoring data acquisition module specifically includes:

[0053] Structural geological data acquisition: Structural geological data from various sources is acquired through multiple data interfaces and devices, specifically including:

[0054] (1) Obtain the borehole dataset through the borehole data interface. The borehole dataset includes the Lithological Column, Core Description, and WellLogging Curve.

[0055] (2) Obtain earthquake datasets through earthquake data interfaces. Earthquake datasets include seismic profile data (SeismicSection) and acoustic impedance information (Acoustic Impedance).

[0056] (3) Obtain geophysical datasets through the geophysical data interface. The geophysical datasets include resistivity measurements, electromagnetic response data, and gravity anomaly data.

[0057] (4) Obtain laser point cloud datasets through laser scanning equipment. The laser point cloud datasets include tunnel point cloud data.

[0058] Based on the acquired borehole dataset, seismic dataset, geophysical dataset, and laser point cloud dataset, a structural geological data set is ultimately formed, represented as:

[0059] ;

[0060] in, It is a borehole dataset. It is an earthquake dataset. It is a geophysical dataset. It is a laser point cloud dataset. Represents a set of structural geological data;

[0061] Dynamic gas data stream acquisition: Utilizing a gas monitoring network, gas-related parameters at various monitoring points downhole are collected in real time to construct a dynamic gas data stream, represented as follows:

[0062] ;

[0063] in, Represents dynamic gas data stream, It is the first The three-dimensional coordinates of each gas monitoring point The range of values ​​is , The range of values ​​is Coal mine ventilation and safety monitoring systems commonly use underground coordinate systems to ensure that the layout of monitoring points is consistent with the model space. It is the first The methane concentration (volume fraction) at each gas monitoring point ranges from [value range missing]. Normally, the methane concentration in downhole air should be less than 1%. A concentration exceeding 1.5% is a warning level, and a concentration exceeding 5% poses an explosion risk. In some gas-rich areas, the methane concentration can accumulate to over 10%. It is the first The absolute gas pressure at each gas monitoring point, with a value range of [value missing]. Formation gas pressure increases with depth. It is the first The gas temperature at each gas monitoring point has a range of values. Downhole temperature increases with depth. It is the collection timestamp;

[0064] Spatial coordinate unification and location registration: To ensure that data from different sources have a unified spatial representation capability, a unified spatial reference coordinate system for the mine is constructed. The structural data and gas data are then coordinate transformed and registered, as follows:

[0065] ;

[0066] ;

[0067] in, This represents a coordinate transformation function that performs affine or projective transformations based on control points and a spatial transformation matrix. This represents the spatial interpolation and grid registration algorithm;

[0068] Time synchronization and unified timestamp addition: A unified time dimension is added to all data. A hardware real-time clock and software timestamp fusion mechanism is used to add a unified timestamp to each data entry, represented as follows:

[0069] ;

[0070] ;

[0071] ;

[0072] in, It is the absolute time provided by a hardware real-time clock, possessing high stability and long-term drift characteristics, with a value range of [value missing]. Real-time clocks typically operate with second-level or higher precision and are used for time alignment across devices to prevent data misalignment caused by time offsets at different points in time. It is a local timestamp added by the data collection program, and its value range is... The data acquisition program automatically marks each data frame triggered during operation, resulting in high accuracy and suitability for high-frequency data acquisition (such as gas concentration and pressure). This indicates a time fusion operation, employing either a weighted average or a synchronization queue correction mechanism. This is the result of temporal fusion, used for the alignment and unified identification of multi-source data in the time dimension, with a value range of [value range missing]. This is used to ensure the temporal consistency between structural geological data and gas monitoring data;

[0073] Construction of the Structure-Gas Joint Observation Dataset: Data with unified spatial and temporal dimensions are fused to generate a multi-source dataset, represented as follows:

[0074] ;

[0075] The final structure-gas joint observation dataset This includes three-dimensional spatial location, synchronous acquisition timestamps, geological structure attribute information, as well as gas concentration, air pressure, and temperature.

[0076] Structure-Gas Coupled Modeling Module: Input the joint observation dataset into the coupled modeling engine, construct three-dimensional geometric models of three structural units, namely coal seam, fault and gas-bearing layer, through the attribute parameter modeling method, and introduce spatial gas field distribution constraints. According to the changes in gas pressure gradient and gas content, the fracture development zone and stress concentration zone are dynamically adjusted to generate a structure-gas dual-field coupled three-dimensional geological model.

[0077] The structure-gas coupling modeling module specifically includes:

[0078] Initial structural unit 3D geometric model construction: The structure-gas joint observation dataset is input into the coupled modeling engine, and borehole lithology data, seismic tectonic data, and geophysical parameters are extracted. Using attribute parameter modeling methods, the coal seam roof and floor interface model, fault geometry model, and gas-bearing layer spatial distribution model are established respectively, forming the initial structural unit 3D geometric model, represented as:

[0079] ;

[0080] in, It is the initial structural unit three-dimensional geometric model. This is a coal seam roof and floor interface model, a three-dimensional geometric model describing the morphology and depth variations of the upper and lower boundaries of the coal seam. It is used to determine the coal seam thickness and dip angle, with thickness values ​​ranging from 0.5 to 8.0° and dip angle values ​​ranging from 0 to 25°. Thin coal seams have a weaker impact on gas occurrence. This is a fault geometry model, representing the spatial distribution and elevation changes of fault planes. It is used to identify tectonic fracture zones and deformation zones. The elevation ranges from 0 to 80, and the extension length is 100 to 3000 mm, determined based on the structural characteristics of coal-bearing strata. Micro-faults with a elevation less than 10 mainly affect local gas accumulation, while large faults can form independent gas-separating zones, significantly impacting modeling accuracy. It is a spatial distribution model of gas-bearing strata, which characterizes the spatial range and thickness variation of gas-bearing strata in coal seams. It is used to constrain the gas field distribution. The thickness is 0.5-6.0 mm, the burial depth is 400-1400 m. The gas pressure in shallow gas-bearing strata is lower and the gas escape is obvious, while the gas-bearing strata in deep gas-bearing strata are rich in gas and have strong interaction with faults and fractures.

[0081] Establishment of spatial gas field distribution constraints: Dynamic gas monitoring data from the structure-gas joint observation dataset are introduced, and the gas pressure field is generated using the Kriging spatial interpolation method. With gas concentration field distribution model ;

[0082] in, The coordinates representing the location of geological entities or observation points in the three-dimensional space of the mine are unified in the mine coordinate system, and the horizontal range is [value range missing]. The vertical range of values ​​is It can cover the main mining and monitoring areas. This represents the distribution of the gas pressure field in three-dimensional spatial coordinates, reflecting the pressure distribution of coal seam gas at different spatial locations. Gas pressure generally increases with depth, and its value ranges from 0.01 to 2.50. This represents the distribution of the gas concentration field in three-dimensional spatial coordinates, reflecting the concentration distribution of gas at different spatial locations in the coal seam. The value ranges from 0.01% to 10.00%. The two spatial field models together constitute the spatial gas field distribution constraint, which is used for subsequent dynamic adjustment of the geometric model.

[0083] Dynamic adjustment of fracture development zone: Based on the characteristics of pressure gradient change in the space gas field distribution constraint, using the space pressure field... Based on the gradient, identify high-pressure gradient regions. The high-pressure gradient region is combined with the coal seam geometric model. and fault model Spatial overlay is performed to dynamically adjust the spatial distribution and fracture density of the fracture development zone, i.e., to adjust the distribution range of the fracture zone. and density function This allows the model to reflect the tectonic strain evolution characteristics under gas pressure, expressed as:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] in, and This is the spatial gradient (vector) of the gas pressure field, representing the rate of pressure change. It is used to identify high-pressure gradient regions, and its value ranges from 0.001 to 0.02. When it is less than 0.005, it indicates that the gas pressure change is gradual and the fractures are stable. When it exceeds 0.02, it is usually in a region of sudden pressure change, and the fractures tend to extend towards the high-pressure direction. It is a high-pressure gradient region, referring to a set of points in space with drastic pressure changes, and is a key area for fracture development and adjustment. This is the high-risk threshold for pressure gradients, a threshold used to determine high-pressure gradient regions and trigger dynamic fracture adjustment. The value ranges from 0.01 to 0.02. Values ​​below this are considered to indicate stable pressure changes and do not require adjustment. This refers to the distance from the fault, the spatial distance between the monitoring point or model unit and the fault plane, with a value ranging from 0 to 500. The closer to the fault, the stronger the influence of fracture development and gas seepage. This represents the extent of fracture propagation, characterizing the spatial coverage of the fractured zone within the model. It is used to delineate the fracture zone area, and its value ranges from 100 to 100,000. The wider the fracture propagation zone, the more developed the gas seepage channels. This refers to the adjusted spatial distribution range of the fracture development zone, representing the area or volume occupied by the fracture region in the model. This is the fracture development density function, reflecting the degree of fracture development per unit volume. It is used to characterize the fragility of coal seams, and its value ranges from 0.1 to 2.0. This is the updated fracture development density, reflecting the intensity of fracture distribution per unit volume of coal.

[0089] Stress concentration area correction: Based on the variation law of gas content in the space gas field, the correspondence between gas-bearing anomaly areas and geological structures is analyzed, and the gas concentration field is used to correct the stress concentration area. Identifying gas-bearing anomaly zones by spatial changes Add stress monitoring points in stress concentration areas, and combine them with existing stress models. The stress distribution pattern of fault zones and coal seam variation zones is modified, and the dynamic response relationship between gas anomalies and structural stress is established, expressed as:

[0090] ;

[0091] ;

[0092] ;

[0093] in, This represents a set of points indicating gas-bearing anomalies. In three-dimensional space, it is the set of regions where the gas concentration gradient exceeds a set gas-bearing anomaly threshold, used to identify spatial locations of drastic changes in gas distribution. This represents the gas-bearing gradient, characterizing the rate of change in gas concentration, and is used to identify areas with abnormal gas content. Its value ranges from 0.001% to 0.05%. This represents the original stress field model, characterizing the original tectonic stress state of the rock mass in three-dimensional space. Its value ranges from 0.5 to 8.0, with values ​​below 2 in shallow regions and reaching 6-8 in deep stress concentration areas. This represents the stress correction caused by the gas field, with a value range of [value missing]. Positive values ​​represent increased stress, while negative values ​​represent stress release. This represents the gas content anomaly threshold, the threshold for identifying abnormal gas content changes in regions, used to trigger stress correction, and its value ranges from 0.02 to 0.05. It refers to the corrected spatial stress field, specifically the three-dimensional geological structure stress distribution obtained after incorporating the influence of gas. This represents the gas-structure influence function, describing the relationship between gas diffusion, burial depth, and tectonic gradient on stress variation. This is a gas diffusion capacity parameter, a physical parameter that measures the seepage and migration capacity of gas in a coal seam. It is affected by coal quality, fractures, etc. For tight coal seams, the gas diffusion capacity is relatively low, and its value range is [value missing]. In coal bodies with obvious fractures or weak structures, the diffusion capacity can reach [amount missing]. The gas diffusion capacity is mainly affected by coal porosity, fracture connectivity, and coal metamorphism. In tight coal seams or undisturbed areas, gas migration is slow and diffusion capacity is low; while in tectonic coal seams, soft coal seams, or fault fracture zones, the fracture network is well-developed, making gas more easily infiltrated and accumulated, thus significantly enhancing diffusion capacity. Gas diffusion capacity parameters directly affect the range of gas's regulatory effect on stress and are important control factors in constructing structure-gas coupling models. Burial depth, referring to the vertical distance from the current location to the surface, is a major geological factor affecting the magnitude of in-situ stress. Its value ranges from 300 to 1500 mm; for shallow coal seams, the range is 300-800 mm, and for deep, high-gas coal seams, it is 800-1500 mm. Burial depth is a crucial factor controlling in-situ stress and gas pressure. As burial depth increases, the gravitational stress of the strata and the compressive force of the surrounding rock intensify, leading to increased coal body fragmentation and increased gas storage pressure. Deep coal seams are more prone to forming closed, gas-rich regions, resulting in localized stress anomalies. Therefore, in coupled modeling, burial depth is used not only for estimating the original stress field but also as a gain factor in the stress correction function. The tectonic gradient represents the degree of drastic change in geological structure in space, such as the rate of geometric change like coal seam undulations and fault abrupt changes. Its value ranges from 0 to 1.0. The tectonic gradient reflects the severity of geological structural changes, such as coal seam undulations, fault cutting, and dense folds. The more drastic the structural abrupt changes, the more likely they are to lead to stress concentration or the formation of loose zones, thus affecting gas enrichment and seepage paths. In coupled modeling, regions with high tectonic gradients require the introduction of stress correction terms and are treated as areas to enhance model sensitivity.

[0094] Structure-Gas Dual-Field Coupled Model Fusion: After adjusting the fracture distribution and correcting the stress in the structural unit model, the dual-field coupling algorithm is invoked to deeply fuse the corrected three-dimensional geometric model of the structural unit with the optimized gas field distribution model, generating a structure-gas dual-field coupled three-dimensional geological model that simultaneously possesses spatial structure, gas occurrence, and mechanical response, represented as:

[0095] ;

[0096] in, This indicates a coupled three-dimensional model that, after fusion, can simultaneously reflect both the geological structure and the spatial distribution of gas. It is a structural geometric model that has been optimized based on fracture distribution and fault structure.

[0097] Model dynamic update and local feedback correction module: When the gas sensor detects abnormal changes or new borehole data is connected, the automatic update mechanism of the local sub-region model is triggered. The local difference inversion method is used to correct the gas field and structure model of the corresponding region, so as to realize the dynamic evolution of the three-dimensional geological model.

[0098] The model dynamic update and local feedback correction module specifically includes:

[0099] Model update trigger mechanism construction: Real-time monitoring of gas concentration change sequences from gas sensors And the new entry identifier for the borehole data interface When a sudden change in gas concentration exceeding the safety threshold is detected, or when new borehole data is accessed, the model update mechanism is activated, generating a model update command. Activation conditions include:

[0100] The time derivative of the gas concentration exceeds the threshold for triggering a sudden change in gas concentration.

[0101] This indicates that new borehole data has been received.

[0102] in, Indicates time gas concentration, This represents the threshold for triggering sudden changes in gas concentration, with a value ranging from 0.3 to 0.5. This indicates the number of new borehole data entries; 0 indicates no new data, and 1 indicates new data.

[0103] Local sub-region localization: based on model update instructions Combined with update trigger points Spatial coordinates, defining the radius of influence Using the spatial sphere filtering algorithm, a local sub-region to be updated is defined in the original structure-gas dual-field model, represented as:

[0104] ;

[0105] in, It is the entire model space. It is a partial update of a sub-region. It is the local influence radius, with a value ranging from 20 to 50, which is set according to the sensor deployment density and structural complexity;

[0106] Local gas field model update: To reflect the dynamic changes of the gas field under new monitoring data input, a local sub-region is updated. Within the model, Kriging space interpolation combined with a difference inversion algorithm is used to reconstruct the gas pressure and concentration fields, resulting in an updated gas field model. , is represented as:

[0107] ;

[0108] in, It is the updated gas field model. This represents the combined observed value of gas concentration and pressure at the i-th monitoring point. This is a newly collected gas concentration from an underground sensor, with a value range of 0.01-10.0. This refers to the newly collected gas pressure, which varies with burial depth, ranging from 0.01 to 2.5. This is the regional average trend function. This represents the total number of monitoring points participating in the interpolation, ranging from 10 to 200. The Kriging weights are obtained from the covariance matrix, and they satisfy the covariance constraint. , It refers to the first The known points and others The weighted sum of the spatial covariances between points, It refers to the first The covariance between known points and the target estimated point represents the spatial correlation between the two points (calculated using a semivariogram); the closer the distance, the stronger the correlation. This represents the degree of influence of each known point on the target point;

[0109] The weighting coefficients for Kriging spatial interpolation are solved using the covariance matrix method: First, a covariance matrix is ​​constructed based on the spatial distance between monitoring points, and a suitable semi-variogram model (such as a spherical model) is selected to determine the spatial correlation; second, the covariance vector between the interpolation point and each monitoring point is constructed; finally, the weighting coefficients of each monitoring point are obtained by solving a system of linear equations with unbiased constraints. It is used for interpolation calculation of the spatial distribution of gas pressure and concentration.

[0110] Structural model parameter correction: based on the updated local gas field model The pressure gradient and gas content variation trends within the region were recalculated, and combined with geological structural parameters, the fracture development and stress distribution models were revised to generate an updated structural model. , is represented as:

[0111] ;

[0112] ;

[0113] ;

[0114] in, This represents the density function of the original fracture development, with a value range of 0.1-2.0. This represents the fracture development density function after local area updates, characterizing the change in the degree of fracture development in the rock mass. It is the locally updated stress function, representing the stress distribution in a local region after the disturbance, and is used for structural stress analysis and early warning assessment. This represents the original stress field function, with values ​​ranging from 0.5 to 8.0. This is a local stress correction, with a value range of [value range missing]. , This represents the vertical pressure gradient, with values ​​ranging from 0.001 to 0.02. The gas-bearing gradient has a value range of 0.001-0.05. The coefficient representing the weighting of the vertical gas pressure gradient ranges from 0.1 to 0.5. In coal seam gas fields, when the vertical pressure gradient is large (such as at the roof-floor interface or in areas where coal seam thickness varies), fractures tend to propagate along the stress direction. If the coefficient is too small, the model will be insensitive to pressure disturbances and unable to reflect the actual fracture propagation; if the coefficient is too large, it will amplify local fluctuations and cause numerical instability. The weighting coefficient for the influence of the gas content spatial gradient ranges from 0.05 to 0.3, reflecting the degree of response of fracture development to changes in gas content. The larger the gas content gradient, the more significant the difference between gas diffusion and adsorption in the coal body, and the higher the possibility of fracture expansion or closure. If the value is too small, the model will be slow to respond to changes in gas content and will not be able to reflect the gas-driven microstructure evolution; if the value is too large, it will cause non-physical abrupt changes in fracture density.

[0115] Model Fusion and Dynamic Evolution: The Fuded and Updated Gas Field Model With structural model and the unchanged region model Seamless spatial stitching is achieved through a transition function to realize the continuous evolution of the 3D model, expressed as:

[0116] ;

[0117] in, It is a spatial smoothing weight function. The weight representing the influence of the local gas field increases with the degree of gas concentration anomaly. The weight representing the impact of structural modifications is increased in stress concentration areas. It is a time step The updated 3D coupled model, It's the geological model from the previous iteration. This indicates the weight of the local gas field influence, which increases with the degree of gas concentration anomaly. The weight representing the impact of structural modifications is increased in stress concentration areas. It ensures continuous evolution in space and time, reflecting the adaptive update characteristics of the model.

[0118] GIS Transparent Visualization and Risk Labeling Module: Loads the structure-gas dual-field coupled 3D model into the GIS platform, uses a multi-layer fusion and asynchronous drawing mechanism for real-time rendering, and labels and displays high-risk areas based on the gas enrichment degree and structural mutation intensity in the structure-gas dual-field coupled 3D model;

[0119] The GIS transparency visualization and risk labeling module specifically includes:

[0120] Layer data loading and parsing: The structure-gas dual-field coupled 3D geological model is loaded through the data interface provided by the GIS platform, and the model data is parsed into three independent layers, including:

[0121] (1) Geological structure layer: including three-dimensional geometric models of coal seams, faults, gas-bearing layers, etc.;

[0122] (2) Gas distribution layer: including gas concentration field and pressure field;

[0123] (3) Risk labeling layer: used to store the identification results and graphic labeling information of high-risk areas;

[0124] Multi-layer blending rendering mechanism: adopts The hybrid algorithm fuses the geological structure layer and the gas distribution layer into an image layer to generate a comprehensive display model, represented as follows:

[0125] ;

[0126] in, Indicates spatial location The integrated display model after fusion Represents the image values ​​of the geological structure layer. Represents the image values ​​of the gas layer, by and Generated by color mapping function, It is the transparency coefficient of the geological structure layer, with a fixed value of 0.7. Since the geological structure is the main reference information and plays a dominant role, setting it to 0.7 can preserve the clear visibility of structural features while displaying the gas field overlay effect.

[0127] Asynchronous rendering mechanism: The rendering task of the overall display model is broken down into geometric rendering subtasks. With property rendering subtask The images are processed in parallel by different threads, and the final image is output through asynchronous fusion of multiple threads, as shown below:

[0128] ;

[0129] in, This is the final output image. This indicates a thread fusion operation to ensure smooth image rendering and synchronization of attribute information;

[0130] High-risk area identification: Gas enrichment index and structural abrupt change intensity index are extracted from the structure-gas dual-field coupled three-dimensional geological model, specifically including:

[0131] (1) Calculation of gas enrichment index: Extracting the gas concentration field from the model and pressure field The gas enrichment index is calculated based on standardized concentration and pressure as follows:

[0132] ;

[0133] ;

[0134] ;

[0135] in, It is the minimum gas concentration. , It is the maximum gas concentration. , This represents the minimum gas pressure. , This represents the maximum gas pressure. , It is the weighting coefficient for gas concentration. Concentration changes are more likely to trigger mutation risks, and are the dominant factor. The weighting coefficient for gas pressure. It assists in measuring potential enrichment drivers and supplements risk assessment dimensions. Indicator of gas enrichment level;

[0136] (2) Calculation of structural abrupt change intensity index: Extraction of fault density field With the rate of change of rock strata dip angle After normalization, the structural mutation intensity index is calculated and expressed as:

[0137] ;

[0138] ;

[0139] ;

[0140] in, This is the fault density, with a value ranging from 0 to 10. It is the normalized fault density. It is the minimum fault density. , It is the maximum fault density. , It is the rate of change of rock strata dip angle, characterizing the abrupt change in lithological interfaces and the possibility of sliding. It is the normalized rate of change of rock strata dip angle. It is the minimum dip angle of the rock strata. , It is the maximum rate of change of rock strata dip angle. , It is the fault density weighting coefficient. It is the dominant factor in the risk of abrupt changes in geological structure. It is the weighting coefficient of the rate of change of rock strata dip angle. Treating them as equally important to cover different types of mutation risks, Indicates the intensity index of structural mutation;

[0141] Risk level assessment and labeling show: based on gas enrichment index With structural mutation index The regional risk level is calculated using a weighted fusion model and is expressed as follows:

[0142] ;

[0143] in, It is the weight of the gas enrichment index. In high-gas mining areas, gas is the primary driving factor. It is the weight of the structural mutation index. As an auxiliary dimension for collaborative judgment of sudden risks, Indicates the regional risk level;

[0144] Set risk threshold ,when At that time, the area was identified as a high-risk area and was color-coded and displayed with a bounding box in the risk labeling layer to achieve spatial visualization risk warning;

[0145] in, This can effectively focus on the spatial location where potential dangers are most concentrated, avoiding the expansion of the scope of misjudgment, thereby reducing the practicality and accuracy of the assessment system.

[0146] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0147] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A transparent geological management system for coal mines that integrates multi-source geological exploration data, characterized in that, Includes the following modules: Multi-source geological and gas monitoring data acquisition module: acquires structural geological data from borehole, seismic, geophysical and laser scanning equipment, as well as dynamic gas data from downhole gas sensors, performs spatial coordinate unification and timestamp alignment, and forms a joint structural-gas observation dataset; Structure-Gas Coupled Modeling Module: Input the joint observation dataset into the coupled modeling engine, construct three-dimensional geometric models of three structural units, namely coal seam, fault and gas-bearing layer, through attribute parameter modeling method, introduce spatial gas field distribution constraints, dynamically adjust the fracture development zone and stress concentration zone according to the changes in gas pressure gradient and gas content, and generate structure-gas dual field coupled three-dimensional geological model. Model dynamic update and local feedback correction module: When the gas sensor detects abnormal changes or new borehole data is connected, the automatic update mechanism of the local sub-region model is triggered. The local difference inversion method is used to correct the gas field and structure model of the corresponding region, so as to realize the dynamic evolution of the three-dimensional geological model. GIS Transparent Visualization and Risk Labeling Module: Loads the structure-gas dual-field coupled 3D model into the GIS platform, performs real-time rendering using a multi-layer fusion and asynchronous drawing mechanism, and labels and displays high-risk areas based on the gas enrichment degree and structural mutation intensity in the structure-gas dual-field coupled 3D model.

2. The coal mine GIS transparent geological management system integrating multi-source geological exploration data according to claim 1, characterized in that, The dynamic gas data from the downhole gas sensor includes gas pressure, gas concentration, and temperature.

3. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 1, characterized in that, The multi-source geological and gas monitoring data acquisition module includes: Structural geological data acquisition: Structural geological data, including columnar sections, core descriptions, well logging curves, seismic profiles, resistivity, and tunnel point clouds, were acquired through borehole, seismic, geophysical, and laser scanning equipment. Dynamic gas data stream acquisition: Real-time acquisition of gas concentration, pressure and temperature data at each measuring point through the underground gas monitoring network to form a dynamic gas data stream; Spatial coordinate unification and location registration: Establish a unified mine spatial coordinate system and perform spatial coordinate transformation and location registration for structural geological data and gas monitoring data; Time synchronization and unified time stamp addition: A hardware clock and software timestamp synchronization mechanism is adopted to assign a unified time stamp to all data; Construction of the structure-gas joint observation dataset: Multi-source data with aligned locations and synchronized times are fused to generate a structure-gas joint observation dataset that includes spatial, temporal, and attribute information.

4. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 3, characterized in that, The spatial coordinate unification and position registration include: Establishment of a unified coordinate system: Establish a unified coordinate system for the mine space, determine the spatial benchmark for various geological and gas monitoring data, and perform coordinate transformation on structural geological data based on control points and measurement benchmark surfaces in the mining area; Spatial registration: Spatial registration of the locations of each monitoring point in the dynamic gas data stream is performed under a unified coordinate system.

5. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 1, characterized in that, The structure-gas coupling modeling module includes: Initial structural unit three-dimensional geometric model construction: The structure-gas joint observation dataset is input into the coupled modeling engine, and the borehole lithology data, seismic tectonic data and geophysical property parameters are extracted. The attribute parameter modeling method is used to construct the coal seam roof and floor interface model, fault geometric model and gas-bearing layer spatial distribution model respectively, forming the initial structural unit three-dimensional geometric model. Establishment of spatial gas field distribution constraints: Dynamic gas monitoring data from the structure-gas joint observation dataset are introduced to establish a gas pressure field and gas concentration field distribution model based on Kriging space interpolation, thereby generating spatial gas field distribution constraints; Dynamic adjustment of fracture development zone: Based on the characteristics of gas pressure gradient change in the spatial gas field distribution constraint, high pressure gradient zone and low pressure gradient zone are identified. Combined with the coal seam and fault distribution in the three-dimensional geometric model of the initial structural unit, the spatial distribution range and development density of fracture development zone are dynamically adjusted. Stress concentration area correction: Based on the gas content variation law in the spatial gas field distribution constraints, analyze the correspondence between gas anomaly areas and geological structures, add stress monitoring points in stress concentration areas, and correct the stress distribution pattern of fault zones and coal seam variation areas. Structure-Gas Dual-Field Coupled Model Fusion: The modified three-dimensional geometric model of the structural unit and the optimized gas field distribution model are deeply fused through the dual-field coupling algorithm to generate a structure-gas dual-field coupled three-dimensional geological model that reflects the interaction between geological structure and gas distribution.

6. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 5, characterized in that, The Kriging spatial interpolation predicts the gas concentration and pressure values ​​in unsampled areas based on the data distribution trend of existing points. It is used to spatially interpolate and complete the gas concentration and gas pressure in dynamic gas monitoring data, and establish a continuous gas pressure field and gas concentration field distribution model.

7. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 1, characterized in that, The model dynamic update and local feedback correction module includes: Model update triggering mechanism construction: Real-time monitoring of data streams from gas sensors and borehole data interfaces. When a sudden change in gas concentration is detected that exceeds the gas concentration change triggering threshold or new borehole data is accessed, the update mechanism is automatically triggered to generate a model update command. Local sub-region location: Based on the model update instructions, the affected area is determined according to the spatial location correlation, and the local sub-region to be updated is dynamically delineated in the structure-gas dual-field coupled three-dimensional geological model; Local gas field model update: Within a defined local sub-region, the local difference inversion method is used, with newly acquired gas monitoring data and borehole data as constraints, to recalculate the gas field parameters of the region and generate an updated local gas field model. Structural model parameter correction: Based on the changes in gas pressure distribution in the updated local gas field model, combined with geological structural features, the inversion algorithm is used to correct the fracture development parameters and stress distribution parameters in the local sub-region, and generate an updated local structural model. Model fusion and evolution: The updated local gas field model and local structure model are seamlessly fused with the unchanged model region to generate a new generation of structure-gas dual-field coupled three-dimensional geological model, realizing the dynamic evolution of the three-dimensional geological model.

8. A coal mine GIS transparent geological management system integrating multi-source geological exploration data according to claim 7, characterized in that, The local difference inversion method is used to update the affected local areas in the three-dimensional geological model after the gas sensor detects abnormal changes or new borehole data is added. The local difference inversion method takes the latest acquired gas pressure, gas concentration and borehole lithology data as input, combines the parameter distribution already existing in the structure-gas dual-field model, compares the differences between the "new and old data", estimates the changes in gas pressure field and gas content in the local area through inversion calculation, and further derives the correction values ​​of fracture development density and stress distribution in the local area.

9. A coal mine GIS transparent geological management system integrating multi-source geological exploration data as described in claim 1, characterized in that, The GIS transparency visualization and risk labeling module includes: Layer data loading and parsing: The structure-gas dual-field coupled 3D geological model is loaded through the data interface of the GIS platform, and the model data is parsed into three independent data layers: geological structure layer, gas distribution layer, and risk labeling layer; Multi-layer fusion rendering mechanism: The multi-layer fusion mechanism is adopted to overlay and fuse the geometric models of coal seams, faults and gas-bearing layers in the geological structure layer with the pressure field and concentration field data in the gas distribution layer to generate a comprehensive display model with transparent visualization effect. Asynchronous rendering mechanism: Based on the asynchronous rendering mechanism, the rendering task of the comprehensive display model is decomposed into geometric rendering task and attribute rendering task, which are processed in parallel by different threads to achieve real-time and smooth rendering of the 3D scene. High-risk area identification: Extract gas enrichment index and structural abrupt change intensity index from the structure-gas dual-field coupled three-dimensional geological model; Risk level assessment and labeling display: Based on predefined risk assessment rules, the gas enrichment degree index and structural mutation intensity index are weighted and fused to generate regional risk levels, and high-risk areas are displayed with color coding and boundary labeling in the risk labeling layer.

10. A coal mine GIS transparent geological management system integrating multi-source geological exploration data according to claim 9, characterized in that, The identification of high-risk areas includes: Gas enrichment index extraction: Gas concentration field and pressure field data are extracted from the structure-gas dual-field coupled three-dimensional geological model, and gas enrichment index is calculated based on the gas concentration gradient and pressure value of each monitoring point. Structural mutation intensity index extraction: Extract fault distribution and rock stratum dip angle data from the structure-gas dual-field coupled three-dimensional geological model, calculate fault density and rock stratum dip angle change rate, and generate structural mutation intensity index.

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