Multi-source data fusion geological support system

Abstract

The invention discloses application of a'mine multi-source data geological support system 'geological support system, belongs to the field of coal mine geological support, and particularly relates to construction of a mine transparent geological and multi-source geological support system platform. The invention designs a mine multi-source data geological support system, and the system builds a coal measure stratigraphic framework based on three-dimensional seismic data and geostatistical inversion thereof. The geological model is constrained by drilling and horizon data, the geological model is optimized in combination with mine multi-source data detection data, and the geological model is updated and adjusted in real time by utilizing geological data and information which are updated in real time so as to reflect the newest state and change trend of a geological body. In addition, the geological guarantee system also considers module projects such as gas power, hydrogeological prediction and post-mining geological management. The embedded intelligent algorithm can deeply mine various measurement data features, and the workload and professional dependence of manual data analysis are reduced. The working efficiency of mine management personnel is improved through interactive and diverse geological data formats, and more visual geological parameters are provided through the three-dimensional visualization technology. Aiming at the functions of the method of the mine multi-source data geological support system, the mine multi-source data geological support system can be used in the fields of mine geological modeling, geological management and intelligent mining.

Description

Technical fields:

[0001] This invention relates to geological assurance in coal mines, and more particularly to the construction of a transparent geological and multi-source geological assurance system platform for mines. Background technology:

[0002] Coal is the most economical and reliable primary energy resource in my country, and intelligent coal mining is the core technological support for achieving high-quality development of the coal industry. With the accelerating advancement of intelligent coal mining, intelligent transparent mining technology has become a pressing technical challenge in the construction of intelligent coal mines. Compared to digital mines and integrated mine automation systems, intelligent mines place higher demands on the basic platform. Intelligent and precise mining relies on real-time and accurate geological data support, as well as a reliable and rapid recovery analysis system. Embedding real-time updates of transparent mine geology into an intelligent mining platform presents significant challenges. First, the fusion of diverse geological data and the generation of high-precision geological models face numerous problems. For example, data coupling and error correction from drilling, 3D seismic data, underground geophysical exploration, hydrological exploration, and two-way roadway information severely restrict the accuracy of the geological model. Developing effective coupling algorithms is necessary, which relies on professional geological, geophysical, and hydrological engineers. Intelligent and precise geological support systems need to combine geological models with real-time visualized production data, preventing potential geological disasters while also considering real-time coal mining trends.

[0003] To improve the utilization rate of mine exploration and geological data, simplify data analysis processes, visualize geological coupling models, and construct a geological support system platform based on multi-source data fusion, this is an effective way to achieve the goal. Integrating mine water control and gas dynamics data to establish a comprehensive geological database, combined with 5G transmission and dynamically updated visualization technology, is a key measure for intelligent mine construction. Secondly, coupling the geological model to the longwall face is an important foundation for achieving intelligent mining and also an effective way to improve geological data. Therefore, the mine geological support platform is the foundation of intelligent mine construction and a direct and visible result of transparent mining.

[0004] To address the aforementioned problems, this invention designs a multi-source data geological support system for mines. This method, based on 3D seismic data and geostatistical inversion, constructs a coal-bearing stratigraphic framework, uses borehole data to constrain stratigraphic positions, and optimizes the spatial correction model of mining exposure and planar geophysical data. Combined with multi-scale analysis of hydrogeological, gas, and other data, a refined geological model is constructed. Structured and unstructured spatiotemporal data undergo unified formatting, consistency, and spatialization processing. The stored vector data, geophysical data, and geological data are rapidly and timely integrated after updates, and the integrated results are used to update the corresponding data range, automatically transforming the original content into historical data. For data with spatial coordinates, direct coordinate matching is used; for data without spatial coordinates, geological information is extracted based on identification. Combined with 5G transmission, the geological support system is ultimately constructed.

[0005] A 3D geological model constructed based on multi-source data is updated and adjusted in real time using updated geological data and information to reflect the latest state and changing trends of the geological body. Based on computer technology and graphics methods, 3D geological modeling software and related algorithms are used to achieve dynamic updates of the geological model. First, various geological exploration and monitoring technologies are used to acquire real-time updated geological data and information from roadways, face cuts, and coal mining machines, which are then preprocessed and quality controlled. Model update algorithms, such as model repair, model fusion, and model evolution algorithms, are used to update and adjust the original 3D geological model based on the acquired real-time data and information, using relevant algorithms and model update rules. 3D visualization technologies, such as 3D graphics rendering, are used to visualize the updated geological model to better understand the latest state and changing trends of the geological body. Finally, a dynamic update process is established for the actual working face conditions to ensure timely and accurate model updates. This process includes data acquisition, data processing, model updating, and model verification, and can be combined with automation and artificial intelligence technologies to improve the accuracy and efficiency of updates. Summary of the Invention:

[0006] In view of the above problems, this invention proposes a multi-source data geological support system for mines, applicable to various uses such as transparent geology and intelligent construction in coal mines. The technical solution of this invention is as follows:

[0007] The purpose of this invention is to provide a multi-source data geological support system for mines. This system mainly covers modules such as data access, infrastructure, storage layer, basic platform and services, high-performance 3D visualization and digital twin rendering engine, intelligent algorithm, interface layer, application layer, and access layer.

[0008] The data access refers to the collection of multi-source data, including seismic monitoring, drilling, geophysical exploration, surveying, hydrology, gas, etc., to collect multi-source spatial data and multi-source business data of coal mines in preparation for use by upper-level applications.

[0009] The infrastructure layer consists of registration and discovery, centralized configuration, authentication and authorization, log management, monitoring and alerting, caching services, message queues, container management, etc.

[0010] The storage layer, located above the infrastructure, is responsible for the platform's basic storage management. It consists of a message bus, file storage, cache, and databases, and the platform supports mainstream databases such as Oracle, MySQL, and PostgreSQL. The service layer is the core layer of the platform, supporting communication methods such as REST, PRC, and MQ to facilitate integration and interaction between programs.

[0011] The aforementioned basic platform and services refer to general functions such as service configuration, registration and discovery, and log management. Services are further divided into basic services, business services, and component services. Business services are business application module development built upon the basic services. Basic services include fundamental functional modules such as user management, application management, organization management, permission management, process modeling, transaction modeling, approval work orders, form design, development components, statistical mining, and resource management. Business services are customized functional modules developed for the business scope of this project, including data collection, data management, data publishing, statistical querying, and data analysis. Component services include data API interfaces, printing templates, custom BI, workflow engines, message notifications, rule engines, application templates, integration interfaces, and extended development.

[0012] The high-performance 3D visualization and digital twin rendering engine is mainly responsible for providing capabilities such as editing, secondary development, components, and model libraries for visualization scenes. The visualization types include CAD, GIS, small 3D scenes, and large 3D scenes. This engine provides basic visualization capabilities for coal mine geological support systems.

[0013] The intelligent algorithms refer to the modular algorithm capabilities provided by the platform, including intelligent analysis and early warning such as gas early warning, fire early warning, and water hazard early warning; 3D geological modeling algorithms such as roadway updating and modeling, 3D geological modeling, and attribute modeling and analysis; GIS algorithms such as buffer analysis, shortest path analysis, and big data loading algorithms; and data service capabilities such as GIS data coordinate conversion, format conversion, and unified publishing.

[0014] The interface layer refers to the API gateway's external application service interfaces. The API gateway has functions such as unified authentication, API authorization, API rate limiting, traffic analysis, fault isolation, API auditing and analysis, and API testing. Ultimately, it provides interfaces to the application layer using WebSocket, HTTP / HTTPS, and TCP / UDP protocols.

[0015] The application layer refers to the implementation of functions such as data collection, real-time monitoring, 3D display, statistical analysis, intelligent analysis, early warning and forecasting, and reporting, based on specific needs. These application functions support display on hardware and software systems such as browsers, mobile devices, and large screens. This serves geological support application scenarios, such as geological support for fully mechanized transparent working faces, geological support for fully mechanized tunneling, gas geology, hydrogeology, reserve calculation, and numerical simulation.

[0016] The system platform refers to the design, development, deployment, and operation of a microservice architecture. It features separate front-end and back-end development, integrating technologies such as IoT, configuration, GIS, algorithms, workflow, and 3D visualization. Custom development is possible based on the actual functional needs of each subsystem. Furthermore, it supports secondary development and extension of APIs for functional expansion and upgrades.

[0017] The multi-source data fusion geological support system is characterized by providing integrated management of coal mine geological data, geological transparency, real-time processing, and dynamic updates. The system platform can improve the work efficiency of coal mine managers and effectively ensure the safe and efficient operation of mine production.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. This invention relates to a multi-source data geological support system for mines. It constructs a three-dimensional geological model framework using 3D seismic data, geostatistical inversion results, borehole data, well logging data, and two-way roadway information. Based on various mine data sources, including measurement data, geophysical data, hydrological data, and gas dynamic data, the geological model is optimized through spatial coordinate registration and secondary fusion interpretation of geological anomalies. This multi-source data fusion geological model improves the management and application of mine measurement data, and 3D visualization technology intuitively displays the geological characteristics of the mining area.

[0020] 2. The intelligent algorithm for multi-source geological data fusion deeply explores the coupling relationship between data, improves the ability to describe geological anomalies, and reduces the basic workload of staff through real-time monitoring and intelligent statistical analysis.

[0021] 3. Basic services transform offline processing into online processing through basic functional modules such as user management, application management, organization management, permission management, process modeling, transaction modeling, approval work orders, form design, development components, statistical mining, and resource management. Management departments at all levels can keep abreast of the latest production dynamics and processing solutions in real time, thereby improving production efficiency.

[0022] 4. Application-layer functions support display on browsers, mobile devices, large screens, and other hardware and software systems. This serves geological support application scenarios, such as geological support for fully mechanized transparent working faces, geological support for fully mechanized tunneling, gas geology, hydrogeology, reserve calculation, numerical simulation, and other application scenarios.

[0023] 5. Intelligent algorithms provide modular algorithm capabilities, including intelligent early warning systems for gas, fire, and water hazards; 3D geological modeling algorithms such as tunnel updating and modeling, 3D geological modeling, and attribute modeling and analysis; GIS algorithms such as buffer analysis, shortest path analysis, and big data loading; and data service capabilities such as GIS data coordinate conversion, format conversion, and unified publishing. Attached image description:

[0024] Figure 1 This is a schematic diagram of the multi-source data fusion and initial geological modeling process.

[0025] Figure 2 It is a flowchart of the process for generating and dynamically updating a refined model.

[0026] Figure 3 This is a flowchart of the geological support system. Detailed implementation method:

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0028] This invention proposes a multi-source data geological support system for mines, which can be applied to mine geological support, multi-source measurement data management and analysis, three-dimensional local or overall visualization, and mine hierarchical management.

[0029] Specifically, such as Figure 1 This paper illustrates the establishment of an initial geological model framework based on three-dimensional seismic data and the Markov chain-Monte Carlo algorithm (MCMC) for geostatistical inversion according to an embodiment of the present invention. The method includes:

[0030] Step 101: Data Processing and Analysis. This step mainly involves organizing and summarizing data from 3D seismic, drilling, well logging, tunnel exposure, well seismic calibration, well logging data processing, rock physics analysis, and multi-source data quality control analysis. It provides preliminary target coal seam roof and floor strata for constructing an initial geological framework to constrain subsequent geostatistical inversion. Strata interpretation is performed based on 3D seismic and well logging data, combined with mining information. Subsequent geostatistical inversion parameters are provided. Well logging data is standardized and optimized to meet geological laws. Gaussian functions are used to fit P-wave impedance and lithology curves, obtaining geostatistical information representing the spatial distribution of lithology and impedance information of the strata.

[0031] Step 102: Establish a geological framework. Based on the interpreted stratigraphic positions of the coal seam top and bottom interfaces, construct an initial geological framework as the initial geostatistical model framework. If there is known real data on the coal seam top and bottom plates (real data from exposure and drilling), a correction algorithm (such as radial basis interpolation) can be used to preliminarily correct the seismically interpreted coal seam top and bottom plate stratigraphic positions, thereby improving the constraint capacity and inversion accuracy of the geological framework.

[0032] Step 103: High-resolution geostatistical inversion, i.e., construction of high-precision three-dimensional geological model data volume.

[0033] Based on the geostatistical inversion principle under Bayesian discriminant and Markov chain-Monte Carlo (MCMC) algorithm, the lithology and P-wave impedance information of well logging (i.e., the statistically fitted lithology and P-wave impedance probability density functions) are used as soft constraints, while the geological framework and 3D seismic data are used as hard constraints. This fully leverages the advantages of high lateral resolution seismic data and high vertical resolution well logging data to obtain a high-resolution 3D geological model data volume that can characterize the lithology, thickness, depth, and distribution of coal-bearing strata.

[0034] Step 104: Dynamically update the 3D geological model data volume, track and acquire measured data of coal seam interface information at different mining stages (which can be obtained from multiple sources such as mine sketches and underground geophysical exploration), use interpolation algorithms such as radial basis interpolation to dynamically correct the geological grid, and perform geostatistical inversion again based on the updated geological grid to obtain a new 3D geological model data volume. In this way, through feedback from multiple measured data and multiple rounds of inversion, the dynamic correction and updating of the 3D geological model data volume is achieved, continuously improving the 3D geological model data volume, reducing the error of the 3D seismic data itself, and improving the accuracy of the geological model.

[0035] Step 105: Structural Information Fusion. This step can be performed before seismic inversion. Based on the actual needs of the coal mine, a combined approach of seismic profile interpretation and 3D seismic multi-attribute interpretation is adopted. For large faults (elevation ≥ 5m), information can be directly obtained through seismic profile interpretation. For small faults that cannot be interpreted through seismic profiles, 3D seismic multi-attribute analysis is used to mine subtle changes in seismic data and fault response characteristics, achieving a refined interpretation of small faults. Finally, the fault structural information is integrated into the constructed geological model data volume to constrain the stratigraphic structure at fault locations, enriching the information in the 3D geological model data volume and further ensuring mining safety.

[0036] Step 106: Spatial Coordinate Coupling and Correction of Multi-Source Mine Data. This step establishes a unified data format and assigns new spatial coordinates (x, y, z) and spatiotemporal coordinates (x, y, z, t) to the planar data from underground geophysical exploration, point data from boreholes, spatial data exposed in the two roadways, hydrogeological data, and gas dynamic data under the unified coordinate format. Combined with intelligent algorithms, secondary interpretation and correction are performed on various types of measurement data, and anomalies in geological, hydrological, and gas anomalies are re-corrected and coupled.

[0037] Step 107: Generation of a detailed geological model of the secondary coal seam. Based on the initial geological model framework described above, and the results of multi-source data coupling and correction, this step uses borehole, two-way roadway, and mining data, along with stratigraphic constraints, to generate a detailed geological model of the secondary coal seam, precisely depicting the structural features within the coal seam. This model is then combined with mine hydrogeological exploration and gas dynamic analysis to predict potential risk zones in the mine.

[0038] Specifically, Figure 2 This invention illustrates the dynamic updating and adjustment process of the geological model. Based on real-time measurement and production data, the model is continuously updated, achieving an organic integration of dynamic model adjustment and data. The data update module mainly includes API interfaces, CAD interaction, manual data entry, and manual upload via Excel, providing a popular data upload and management approach that reduces reliance on specialized technical personnel and simplifies the workflow. In particular, the internally editable workflow can adapt to data input and updates in various scenarios.

[0039] Specifically, Figure 3 The geological support platform construction process of the present invention is illustrated, which includes data access, infrastructure, storage layer, basic platform and services, high-performance 3D visualization and digital twin, intelligent algorithm, interface layer, application layer and access layer.

[0040] The above description is a specific embodiment of the present invention, which further details the purpose, technical solution and beneficial effects of the present invention. It can be understood by those skilled in the art that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. All equivalent, modified or improved versions made without departing from the spirit disclosed in the present invention should be included in the scope of protection of the present invention.

Claims

1. A mine multi-source data geological guarantee system, characterized in that The mine geological guarantee system, three-dimensional geological modeling, three-dimensional seismic data geological statistics inversion, mine multi-source geological data fusion, multi-source data spatial coordinate coupling and correction, and geological guarantee platform construction process, which contains data access, infrastructure, storage layer, basic platform and service, high-performance three-dimensional visualization and digital twin, intelligent algorithm, interface layer, application layer and access layer. The mine multi-source data geological guarantee system takes into account the mine geological structure, mining geological exposure, hydrogeological and gas power risk prediction method.

2. The mine geological guarantee system construction key method according to claim 1, characterized in that: The three-dimensional geological modeling includes an initial geological model based on three-dimensional geological data and borehole horizon data and a fine model dynamically updated by multi-source data.

3. The 3D seismic data geostatistical inversion of claim 1, wherein, The geological statistics inversion is a geological statistics inversion of three-dimensional seismic data and Markov chain-Monte Carlo algorithm (MCMC).

4. The mine multi-source geological data fusion according to claim 1, characterized in that, The mine multi-source geological data fusion includes main multi-source data coupling correction results, using borehole, two lane, mining data and horizon constraints, etc. to generate a secondary coal seam fine geological model and finely depict the structural features in the coal seam. Combined with mine hydrogeological exploration, gas power analysis and other prediction of potential risk areas in the mine.

5. The spatial coordinate coupling and correction of multi-source data according to claim 1, wherein, The spatial coordinate coupling and correction of multi-source data includes formulating a unified data format, assigning new spatial coordinates (x, y, z) and space-time coordinates (x, y, z, t) to the plane data of underground geophysical exploration, point data of boreholes, spatial data of two lane exposure, hydrogeological data, and gas power data under the unified coordinate format, and combining intelligent algorithms to perform secondary interpretation and correction on various measurement data and re-correct and couple abnormal areas of structure, hydrology, gas, etc.

6. The geological assurance platform setup process of claim 1, wherein, Data access, infrastructure, storage layer, basic platform and service, high-performance three-dimensional visualization and digital twin, intelligent algorithm, interface layer, application layer and access layer.

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