Three-dimensional visual geothermal resource dynamic exploration method and device and storage medium

Abstract

This invention discloses a three-dimensional visualization method, device, and storage medium for dynamic geothermal resource exploration, relating to the field of geophysical exploration technology. The method includes deploying multiple types of exploration equipment in a grid distribution, simultaneously acquiring multi-source data, and setting acquisition frequency and accuracy standards; performing preprocessing on the data such as filtering, anomaly removal, interpolation, and standardization; constructing a three-dimensional model of stratigraphic structure, temperature field, and reserve assessment based on the preprocessed data, and establishing a geological parameter correlation database; acquiring data at preset intervals, comparing and analyzing differences, and adjusting model parameters to achieve dynamic iterative updates; using visualization technology to present the model and dynamic change trends, supporting multi-view interactive operation; classifying and storing various types of data and update records, and establishing an indexing mechanism to support rapid retrieval. This invention improves data quality through multi-source data fusion and refined preprocessing; the three-dimensional visualization function enhances the readability of exploration results, providing reliable support for sustainable development.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, and in particular to a three-dimensional visualization method, apparatus, and storage medium for dynamic exploration of geothermal resources. Background Technology

[0002] Geothermal resources, as a clean and renewable new energy source, occupy an important position in the transformation of the energy structure. Their efficient development and utilization rely on precise exploration technology. Currently, geothermal resource exploration technology suffers from weak multi-source data integration capabilities. During the exploration process, geothermal sensors, ground-penetrating radar, and electromagnetic detection equipment operate independently, lacking unified planning for data acquisition. Inconsistent acquisition frequencies and accuracy standards among different devices result in scattered and inconsistent data sources and formats for key data such as underground temperature fields, stratigraphic lithology, and permeability. Data preprocessing often relies on simple filtering and manual outlier removal, which is insufficient to effectively eliminate equipment interference and environmental noise. Methods for supplementing missing data are simplistic, and the standardization of data with different dimensions is incomplete, directly affecting the accuracy of subsequent modeling and analysis. Furthermore, the correlation and consistency between multi-source data cannot be guaranteed.

[0003] Existing 3D geological models are mostly statically constructed, lacking dynamic update mechanisms and failing to adapt to the spatiotemporal variations of geothermal resources. Once constructed, models rely solely on single exploration data to determine parameters, failing to reflect the dynamic processes of formation temperature field evolution, geothermal fluid flow, and reserve consumption and replenishment. Geothermal reserve assessments are often based on fixed formulas, without dynamic adjustments using real-time exploration data, leading to discrepancies between assessment results and actual resource conditions, thus failing to provide a reliable basis for sustainable development planning. Furthermore, model updates do not consider the effects of data bias and time decay, simply replacing data to complete updates, resulting in insufficient accuracy and failing to meet the needs of dynamic exploration.

[0004] Significant shortcomings exist in the 3D visualization and data management aspects. Existing visualization technologies largely remain at the basic model display level, lacking features such as multi-view interaction and anomaly area identification, and failing to intuitively present the dynamic trends of geothermal resources. The flexibility of visualization parameter configuration is insufficient, making it difficult to adapt to the display needs of different users, and the ability to export results and display them synchronously on multiple terminals is lacking. Data storage adopts a traditional centralized architecture, lacking a scientific classification, storage, and indexing mechanism. The retrieval and access efficiency of massive amounts of exploration data, model data, and update records is low, and the standardization and convenience of data management are insufficient, hindering the efficient application and sharing of exploration results. Furthermore, existing technologies have not formed a collaborative system of methods, devices, and storage media; each link is independent, affecting the overall efficiency and accuracy of geothermal resource exploration. Summary of the Invention

[0005] The present invention proposes a three-dimensional visualization method, device, and storage medium for dynamic exploration of geothermal resources, in order to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a three-dimensional visualization method for dynamic exploration of geothermal resources, comprising the following steps: Multi-source exploration data acquisition involves deploying geothermal sensors, ground-penetrating radar, electromagnetic detection equipment, borehole temperature measuring devices, and meteorological monitoring equipment. Data collection points are set up in a grid distribution within the exploration area to simultaneously collect underground temperature field data, stratigraphic lithology parameters, permeability data, geothermal fluid flow data, topographic data, and regional meteorological data. Data preprocessing involves sequentially filtering noise from the collected multi-source data, using an adaptive filtering algorithm to eliminate equipment interference and environmental noise, identifying and removing outlier data points through statistical analysis, and supplementing missing data using radial basis function interpolation. Three-dimensional geological model construction: Based on preprocessed data, three-dimensional modeling algorithms are used to construct stratigraphic structure model, temperature field distribution model and geothermal reserve assessment model; Dynamic updates and optimizations are performed by repeatedly collecting exploration data at preset cycles, comparing the differences between newly collected data and historical data, analyzing the spatiotemporal variation patterns of geothermal resources, and adjusting the parameters and structure of the three-dimensional model. The 3D visualization display uses visualization rendering technology to present the 3D geological model, temperature field distribution, geothermal reserve distribution and dynamic change trend in the form of a 3D scene, and supports model rotation, scaling and sectioning operations. Data storage and management categorizes and stores raw survey data, preprocessing results, 3D model data, and dynamic update records, and establishes a data indexing mechanism to support rapid retrieval and access of data by time, region, and data type.

[0007] Furthermore, it also includes a multi-source data fusion optimization step, which completes data integration by constructing a data fusion weight calculation model, the calculation formula being... ,in For the first Type 1 sensor The fusion weight of each data point For the first The accuracy coefficient of the sensor-like sensor, For the first The timeliness coefficient of the data type For the first The accuracy coefficient of the sensor-like sensor, For the first The timeliness coefficient of the data type It is the sum of the products of the accuracy coefficients and timeliness coefficients of all sensors.

[0008] Furthermore, it also includes a dynamic assessment step for geothermal reserves. By combining three-dimensional model parameters with dynamically acquired data, a reserve assessment model is constructed. By analyzing changes in temperature field, fluid flow rate, and formation parameters, the extraction and replenishment of geothermal resources are quantified, the sustainable development potential of geothermal resources is quantified, and a reserve change trend curve is generated simultaneously.

[0009] Furthermore, it also includes a dynamic update deviation correction step for the 3D model, which optimizes the update effect through deviation correction model calculation. Adjustments for model updates This is the weighting coefficient for reserve deviation. This is the geothermal reserve prediction value from the current model. These are the latest measured values ​​of geothermal reserves obtained from exploration. The time decay coefficient, This represents the time interval between the current survey and the previous survey.

[0010] Furthermore, it also includes steps to optimize visualization interaction, supports user-defined visualization parameters, provides an abnormal area marking function, automatically identifies areas and highlights them, and supports exporting visualization results as image or video formats, and supports simultaneous display on multiple terminals.

[0011] Furthermore, it includes the following modules: Multi-source data acquisition module: integrates multiple monitoring devices, grid-deployed acquisition units, supports simultaneous acquisition of multiple types of data and custom parameter configuration; Data preprocessing module: Built-in multiple processing units to complete data noise reduction, anomaly removal, missing data filling and unit unification, and output standardized data; 3D modeling module: Equipped with modeling algorithms and parameter mapping units, it constructs stratigraphic, temperature field, and reserve assessment models, and establishes a database linking geological and geothermal parameters; Dynamic update module: Includes a periodic data acquisition and comparison unit, which collects data periodically, analyzes spatiotemporal variation patterns, and drives iterative model updates; Visualization module: Equipped with a rendering engine and interactive units, it supports model operation and multi-view viewing, completes 3D data visualization, and supports parameter customization and result export; Data storage management module: Adopts a distributed storage architecture to classify and store various types of data and update records, and supports fast retrieval and access.

[0012] Furthermore, it also includes a data fusion optimization module, which has a built-in weight calculation unit and a data integration unit. It dynamically allocates the fusion weights of various types of data through a weight calculation model, integrates multi-source data to generate consistent data results. This module is connected to the data preprocessing module, receives multi-source data after standardization processing, and outputs fused and optimized data for 3D modeling.

[0013] Furthermore, it also includes a reserve assessment module and a deviation correction module. The reserve assessment module combines the three-dimensional model parameters with dynamically acquired data to quantify the geothermal resource extraction and replenishment, and generate a reserve change trend curve. The deviation correction module calculates the model update adjustment amount through the deviation correction model and feeds it back to the dynamic update module.

[0014] Furthermore, it includes an exploration terminal, a data processing unit, a storage unit, a display unit, and a communication unit. The exploration terminal integrates multiple types of exploration equipment; the data processing unit carries various functional modules to complete data preprocessing, 3D modeling, dynamic updating, data fusion, and reserve assessment; the storage unit adopts distributed storage devices; the display unit is a high-definition visualization display device that supports 3D scene display and interactive operation; and the communication unit supports data transmission between the exploration terminal and the data processing unit, and between the data processing unit and the storage unit.

[0015] Furthermore, the storage medium stores a computer program, which, when executed by a processor, completes the steps of the three-dimensional visualization dynamic exploration method for geothermal resources, including multi-source exploration data acquisition, data preprocessing, three-dimensional geological model construction, dynamic updating and optimization, three-dimensional visualization display and data storage and management, and also completes additional steps such as data fusion optimization, dynamic assessment of geothermal reserves, deviation correction and visualization interaction optimization.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The present invention provides a three-dimensional visualization storage medium device and method for dynamic exploration of geothermal resources. Addressing numerous shortcomings of existing technologies, it achieves a comprehensive technological breakthrough, significantly improving the accuracy, dynamism, and visualization level of geothermal resource exploration. At the data support level, the method significantly improves data quality by unifying the acquisition frequency and accuracy standards of multi-source devices and combining refined preprocessing techniques such as adaptive filtering and radial basis function interpolation. The innovative multi-source data fusion optimization strategy, through dynamic allocation of data fusion weights, strengthens the consistency and reliability of multi-source data, providing a solid data foundation for subsequent modeling and solving the core pain point of weak data integration capabilities in existing technologies.

[0017] At the model construction and dynamic updating level, the method constructs a three-dimensional geological model that integrates geological structure and geothermal parameters, achieving a correlation mapping of multi-dimensional information. The dynamic updating mechanism collects data at a preset cycle, combines it with a deviation correction model, and comprehensively considers reserve deviation and time decay factors to optimize model parameters and structure, enabling the model to accurately reflect the spatiotemporal variation patterns of geothermal resources. The dynamic assessment of geothermal reserves quantifies extraction and recharge volumes, generating trend curves to provide scientific support for development planning and solve the problem that existing static models cannot adapt to dynamic resource changes. The modular design of the device enables the coordinated operation of various functional units, further improving the efficiency of model construction and updating.

[0018] At the visualization and data management level, the 3D visualization technology supports multiple interactive operations such as model rotation, scaling, and sectioning, while the highlighting function for abnormal areas enhances the readability of exploration results. Users can customize visualization parameters, and the results support export in multiple formats and simultaneous display on multiple terminals, adapting to the application needs of different scenarios. Data storage adopts a distributed architecture and a classification indexing mechanism, enabling efficient retrieval and access to various types of data, and improving the standardization and convenience of data management. The design of the storage medium ensures the stable operation of the exploration methods and programs, achieving synergistic optimization of methods, devices, and storage media. Overall, this invention, through end-to-end technological innovation, constructs a complete system integrating data acquisition, processing, modeling, updating, visualization, and storage, significantly improving the intelligence level of geothermal resource exploration, providing a reliable guarantee for the efficient and sustainable development and utilization of geothermal resources, and possessing significant practical value and promotional significance. Attached Figure Description

[0019] Figure 1 This is a schematic block diagram of the three-dimensional visualization method for dynamic exploration of geothermal resources proposed in this invention; Figure 2 This is a schematic block diagram of the apparatus for the three-dimensional visualization method for dynamic exploration of geothermal resources proposed in this invention; Figure 3 This is a comparison chart of the errors of various parameters before and after multi-source data fusion; Figure 4 A comparison chart of reserve assessment errors before and after dynamic updating of the 3D model; Figure 5 This is a comparison chart of data retrieval efficiency. Detailed Implementation

[0020] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0022] Furthermore, 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, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0023] Reference Figures 1 to 5 A three-dimensional visualization method for dynamic exploration of geothermal resources includes the following steps: Multi-source exploration data acquisition involves deploying geothermal sensors, ground-penetrating radar, electromagnetic detection equipment, borehole temperature measuring devices, and meteorological monitoring equipment. Data collection points are set up in the exploration area according to a grid distribution to simultaneously collect underground temperature field data, stratigraphic lithology parameters, permeability data, geothermal fluid flow data, topographic data, and regional meteorological data. Data collection frequency and data accuracy standards for various equipment are set. Data preprocessing involves sequentially filtering noise from the collected multi-source data, using an adaptive filtering algorithm to eliminate equipment interference and environmental noise, identifying and removing outlier data points through statistical analysis, supplementing missing data using radial basis function interpolation, and standardizing data of different dimensions to unify data format and coordinate system. The construction of a three-dimensional geological model is based on preprocessed data. A three-dimensional modeling algorithm is used to construct a stratigraphic structure model, a temperature field distribution model, and a geothermal reserve assessment model. A geological attribute database is established by integrating parameters such as stratigraphic lithology and permeability to realize the correlation mapping between geological structure and geothermal parameters. Dynamic updates and optimizations are achieved by repeatedly collecting exploration data at preset cycles, comparing the differences between newly collected data and historical data, analyzing the spatiotemporal variation patterns of geothermal resources, adjusting the parameters and structure of the 3D model, and completing the dynamic iterative updates of the model. The 3D visualization display uses visualization rendering technology to present the 3D geological model, temperature field distribution, geothermal reserve distribution and dynamic change trend in the form of a 3D scene. It supports model rotation, scaling and sectioning operations and provides multi-view viewing function. Data storage and management categorizes and stores raw survey data, preprocessing results, 3D model data, and dynamic update records, and establishes a data indexing mechanism to support rapid retrieval and access of data by time, region, and data type.

[0024] This invention also includes a multi-source data fusion optimization step, which achieves accurate data integration by constructing a data fusion weight calculation model, the calculation formula being: ,in For the first Type 1 sensor The fusion weight of each data point For the first The accuracy coefficient of this type of sensor is determined through factory calibration and actual test data. For the first The timeliness coefficient of the data is dynamically calibrated based on the interval between the data collection time and the current time. For the first The accuracy coefficient of the sensor-like sensor, For the first The timeliness coefficient of the data type The sum of the products of the accuracy coefficients and timeliness coefficients of all sensors is used to improve the consistency and reliability of multi-source data integration by dynamically allocating data fusion weights.

[0025] This invention also includes a dynamic assessment step for geothermal reserves. By combining three-dimensional model parameters with dynamically acquired data, a reserve assessment model is constructed. By analyzing changes in temperature field, fluid flow rate, and formation parameters, the extraction and replenishment of geothermal resources are quantified, the sustainable development potential of geothermal resources is quantified, and a reserve change trend curve is generated simultaneously to provide data support for development planning.

[0026] This invention also includes a dynamic update deviation correction step for the three-dimensional model, which optimizes the update effect through deviation correction model calculation. Adjustments for model updates The reserve deviation weighting coefficient is calibrated through historical update error statistics. This is the geothermal reserve prediction value from the current model. These are the latest measured values ​​of geothermal reserves obtained from exploration. This is the time decay coefficient, which is positively correlated with the thermal conductivity of the formation. The time interval between this exploration and the previous exploration is used to improve the accuracy and timeliness of model updates by combining reserve deviation and time factors.

[0027] This invention also includes a visualization interaction optimization step, which supports user-defined visualization parameters, including color mapping schemes, transparency settings, and data display thresholds. It provides an abnormal area marking function, automatically identifies areas with abnormal temperatures, sudden changes in reserves, etc., and highlights them. It also supports exporting visualization results as image or video formats, supports simultaneous display on multiple terminals, and adapts to the needs of displaying exploration results in different scenarios.

[0028] This invention includes the following modules: The multi-source data acquisition module integrates geothermal sensors, ground-penetrating radar, electromagnetic detection equipment, borehole temperature measurement devices, and meteorological monitoring equipment. It deploys acquisition units according to a grid distribution strategy, has the ability to acquire multiple types of data simultaneously, and supports custom configuration of acquisition frequency and accuracy parameters. The data preprocessing module has built-in adaptive filtering unit, outlier identification unit, data interpolation unit and standardization processing unit, which sequentially completes data noise filtering, outlier removal, missing data supplementation and unit unification, and outputs standardized data. The 3D modeling module is equipped with a 3D geological modeling algorithm and a geothermal parameter mapping unit. Based on standardized data, it constructs a stratigraphic structure model, a temperature field distribution model, and a geothermal reserve assessment model, and establishes a database linking geological attributes and geothermal parameters. The dynamic update module sets up a periodic acquisition control unit and a data comparison and analysis unit. It triggers data acquisition according to a set period, compares the differences between new and old data, analyzes the spatiotemporal variation of geothermal resources, and drives the iterative update of the three-dimensional model parameters and structure. The visualization module is equipped with a 3D rendering engine and an interactive control unit. It supports 3D model rotation, scaling and sectioning operations, provides multi-view viewing function, realizes 3D visualization of geothermal resource-related data, and supports custom visualization parameters and export of results. The data storage management module adopts a distributed storage architecture, setting up data classification storage units and index management units to classify and store raw data, preprocessing results, 3D model data and update records, and supporting fast data retrieval and access.

[0029] This invention also includes a data fusion optimization module, which has a built-in weight calculation unit and a data integration unit. It dynamically allocates the fusion weights of various types of data through a weight calculation model, integrates multi-source data to generate consistent data results, and improves the accuracy and consistency of data support. This module is connected to the data preprocessing module, receives multi-source data after standardization processing, and outputs fused and optimized data for 3D modeling.

[0030] This invention also includes a reserve assessment module and a deviation correction module. The reserve assessment module combines the parameters of the three-dimensional model with the dynamically acquired data to quantify the amount of geothermal resource extraction and replenishment, and generate a reserve change trend curve. The deviation correction module calculates the model update adjustment amount through the deviation correction model and feeds it back to the dynamic update module to optimize the three-dimensional model update effect, so that the three-dimensional model accurately matches the actual geothermal resource status.

[0031] This invention includes an exploration terminal, a data processing unit, a storage unit, a display unit, and a communication unit. The exploration terminal integrates multiple types of exploration equipment for collecting multi-source data related to geothermal resources. The data processing unit carries various functional modules to complete data preprocessing, 3D modeling, dynamic updating, data fusion, and reserve assessment. The storage unit uses distributed storage devices to classify and store various types of data and model files. The display unit is a high-definition visualization display device that supports 3D scene display and interactive operation. The communication unit supports data transmission between the exploration terminal and the data processing unit, and between the data processing unit and the storage unit, achieving real-time and stable data transmission.

[0032] In this invention, the storage medium stores a computer program. When the computer program is executed by a processor, it implements the steps of a three-dimensional visualized dynamic geothermal resource exploration method, including multi-source exploration data acquisition, data preprocessing, three-dimensional geological model construction, dynamic updating and optimization, three-dimensional visualization display, and data storage and management. It also implements additional steps such as data fusion optimization, dynamic assessment of geothermal reserves, deviation correction, and visualization interaction optimization, thereby achieving accuracy, dynamism, and visualization effects in geothermal resource exploration.

[0033] The following two examples further illustrate the specific implementation of this system: Example 1: Application of Dynamic Exploration of Geothermal Resources in Plain Sedimentary Basins This embodiment focuses on a geothermal exploration scenario in a plain sedimentary basin with an area of ​​10 square kilometers. The geological structure of this area is relatively flat, mainly containing sedimentary rocks such as sandstone and mudstone. The geothermal resources are mainly layered thermal reservoirs. The method, device and storage medium of this invention enable precise dynamic exploration.

[0034] In the multi-source exploration data acquisition phase, the device's multi-source data acquisition module integrates geothermal sensors, ground-penetrating radar, electromagnetic detection equipment, borehole temperature measuring devices, and meteorological monitoring equipment. Acquisition points are distributed in a 500m x 500m grid, with a total of 400 acquisition units deployed. Geothermal sensors are deployed in boreholes at depths of 50-500 meters, with an acquisition node every 50 meters, collecting underground temperature field data at different depths. Ground-penetrating radar moves along the grid lines to collect stratigraphic lithology parameters and geological structure data. Electromagnetic detection equipment acquires stratigraphic permeability data. Borehole temperature measuring devices simultaneously record geothermal reservoir temperature and geothermal fluid flow data. Meteorological monitoring equipment is deployed at the edge of the exploration area to collect regional meteorological data such as air temperature and precipitation. The acquisition frequency for each device is set as follows: geothermal sensors once per minute, ground-penetrating radar and electromagnetic detection equipment collect one set of data every 100 meters, borehole temperature measuring devices once every 30 minutes, and meteorological monitoring equipment once per hour. The data accuracy standard is set to a temperature error of no more than ±0.2℃ and a permeability data error of no more than ±5%.

[0035] In the data preprocessing stage, the data preprocessing module processes multi-source data according to the workflow. An adaptive filtering algorithm with a window size of 5 sampling points is used to eliminate noise caused by equipment vibration and environmental electromagnetic interference; outlier data points exceeding the mean ± 3 standard deviations are identified and removed using the 3σ criterion; missing data in areas with large borehole spacing is supplemented using the radial basis function interpolation method with a Gaussian kernel function; and the z-score method is used to standardize data of different dimensions such as temperature, permeability, and flow rate, unifying the data format to JSON and adopting the National Geodetic Coordinate System 2000.

[0036] In the multi-source data fusion optimization stage, the data fusion optimization module allocates fusion weights through a weight calculation model, the formula of which is: Set the accuracy coefficient of the geothermal sensor. The time interval between the data collection time and the current time is 1 day, and the timeliness coefficient is... Accuracy coefficient of ground-penetrating radar Timeliness coefficient The accuracy coefficient of electromagnetic detection equipment Timeliness coefficient Accuracy coefficient of borehole temperature measuring device Timeliness coefficient Accuracy coefficient of meteorological monitoring equipment Timeliness coefficient .calculate Geothermal sensor The fusion weight of each data point The weights of various data points are calculated and fusion is completed in this manner.

[0037] In the 3D geological model construction phase, the 3D modeling module uses the MarchingCubes algorithm to construct a stratigraphic structure model based on the fused data, restoring the distribution range and thickness of sandstone and mudstone in layers with a depth of 50 meters; it uses volume rendering technology to construct a temperature field distribution model, using a gradient of red, orange, yellow, green and blue colors to represent the temperature distribution from 50 to 150℃; and it combines the volume method to construct a geothermal reserve assessment model, integrating parameters such as stratigraphic lithology, permeability, and porosity to establish a geological attribute database, achieving a one-to-one mapping between geological structure and geothermal parameters.

[0038] In the dynamic update and optimization phase, the dynamic update module triggers data collection monthly according to a preset cycle. Comparing the newly collected data with historical data reveals an average increase of 0.3℃ in deep geothermal reservoir temperature and an average decrease of 2% in geothermal fluid flow. The update adjustment amount is calculated using a deviation correction model, with the following formula: Among them, the reserve deviation weighting coefficient The model's geothermal reserve predictions have been calibrated through statistical analysis of historical updates over the past year. Latest measured values ​​of geothermal reserves Time decay coefficient This is positively correlated with the thermal conductivity characteristics of the strata in this region; the time interval between this exploration and the previous one... Calculated ,according to Adjust the model's storage parameters to complete the dynamic iterative update of the model.

[0039] In the 3D visualization section, the visualization module uses OpenGL rendering technology to present 3D scenes. It supports dragging the mouse to rotate the model 360 degrees, using the scroll wheel to control the zoom level, and clicking the mouse to select a cutting plane to cut the model along any direction of the x, y, or z axes to view the internal strata and temperature distribution. Users can customize the color mapping scheme, adjust the color threshold for high-temperature areas to 90℃, and set the model transparency to 60%. The system automatically identifies abnormal areas with temperatures exceeding 90℃ and highlights them in red. It supports exporting visualization results as PNG images or MP4 videos, and enables simultaneous display on multiple terminals such as computers and tablets via a local area network.

[0040] In the data storage and management phase, the data storage management module adopts a distributed storage architecture, classifying and storing raw exploration data, preprocessing results, 3D model data, and dynamic update records on different nodes, establishing a dual indexing mechanism of time and region. Data folders are divided by exploration date, and regional data is associated by grid number. It supports quick retrieval of corresponding data by inputting time range and regional coordinates, with a response time of no more than 1 second. When the computer program stored on the storage medium is executed by the processor, it fully implements all steps of the aforementioned exploration method.

[0041] Table 1 Comparison of errors of various parameters before and after data fusion Table 1 clearly demonstrates the significant effects of multi-source data fusion optimization. Before fusion, various data types generally had high errors due to differences in sensor accuracy and timeliness. The error in the underground temperature field data reached 5.2%, and the accuracy of stratigraphic lithology identification was only 91.5%, directly affecting the accuracy of subsequent modeling. By dynamically allocating fusion weights through a weight calculation model, fully considering sensor accuracy and data timeliness, the errors of various parameters were significantly reduced. The error in the temperature field data decreased to 1.8%, and the accuracy of lithology identification improved to 97.3%. This effect ensured the quality of the basic data of the 3D geological model, enabling the model to accurately reflect the distribution of geothermal resources and providing reliable support for subsequent dynamic updates and reserve assessments. It also solved the core problem of poor consistency of multi-source data in traditional technologies.

[0042] Example 2: Application of Dynamic Exploration of Geothermal Resources in Mountain Fault Zones This embodiment focuses on a geothermal exploration scenario in a mountainous fault zone covering an area of ​​8 square kilometers. The terrain in this area is highly undulating, with three main fault zones. The geothermal resources are mainly of the fault-controlled heat type. The method, device, and storage medium of this invention enable precise dynamic exploration in complex terrain.

[0043] In the multi-source exploration data acquisition phase, the device's multi-source data acquisition module is deployed with acquisition units arranged in a 300m × 300m grid to adapt to the complex mountainous terrain, with a total of 889 acquisition units deployed. Geothermal sensors are deployed in boreholes at depths of 100-800 meters, with an acquisition node every 100 meters, focusing on collecting temperature data at different depths near fault zones. Ground-penetrating radar moves along gentle slopes, avoiding steep areas, and collects data on stratigraphic lithology and fault zone distribution. Electromagnetic detection equipment specifically detects permeability in fault zones. Borehole temperature measuring devices are deployed to the core area of ​​the thermal reservoir, recording temperature and fluid flow. Meteorological monitoring equipment is deployed at mountain peaks and valleys to collect regional meteorological data. The acquisition frequency for each device is set as follows: geothermal sensors every 2 minutes, ground-penetrating radar every 50 meters, borehole temperature measuring devices every hour, and meteorological monitoring equipment every 30 minutes. The data accuracy standard is set to a temperature error of no more than ±0.3℃ and a permeability data error of no more than ±6%.

[0044] In the data preprocessing stage, the data preprocessing module addresses the strong noise characteristics of mountainous environments by employing an adaptive filtering algorithm with a window size of 7 sampling points to eliminate noise caused by terrain vibrations; it identifies and removes abnormal data points near fault zones using the 3σ criterion combined with a local anomaly detection algorithm; it supplements missing data in steep areas using the radial basis function interpolation method with a Gaussian kernel function, with the interpolation radius dynamically adjusted according to the terrain slope; and it standardizes multi-source data using the z-score method, unifying the data format to JSON and adopting the National Geodetic Coordinate System 2000.

[0045] In the multi-source data fusion optimization stage, the data fusion optimization module allocates fusion weights through a weight calculation model, the formula of which is: Set the accuracy coefficient of the geothermal sensor. The time interval between the data collection time and the current time is 2 days, and the timeliness coefficient is... Accuracy coefficient of ground-penetrating radar Timeliness coefficient The accuracy coefficient of electromagnetic detection equipment Timeliness coefficient Accuracy coefficient of borehole temperature measuring device Timeliness coefficient Accuracy coefficient of meteorological monitoring equipment Timeliness coefficient .calculate Geothermal sensor The fusion weight of each data point This method is used to complete the weight allocation and fusion of all data points.

[0046] In the 3D geological model construction phase, the 3D modeling module, based on the fused data, uses the MarchingCubes algorithm combined with a fault zone-specific modeling strategy to construct a stratigraphic structure model containing three main fault zones, clearly restoring the fault zone strike, dip angle, and depth. Volume rendering technology is used to construct a temperature field distribution model, highlighting high-temperature anomaly areas near the fault zones. A geothermal reserve assessment model is constructed by combining the volumetric method with a fault zone thermal conductivity model. A geological attribute database is established by integrating stratigraphic lithology, permeability, and fault zone parameters, realizing the correlation mapping between geological structure, fault zone characteristics, and geothermal parameters.

[0047] In the dynamic update and optimization phase, the dynamic update module triggers data collection every two months according to a preset cycle. Comparing the newly collected data with historical data reveals that the average temperature of the geothermal reservoir near the fault zone has increased by 0.5℃, while the geothermal fluid flow rate has not changed significantly. The update adjustment amount is calculated using a deviation correction model, with the following formula: Among them, the reserve deviation weighting coefficient The model's geothermal reserve predictions have been calibrated through statistical analysis of historical updates over the past year. Latest measured values ​​of geothermal reserves Time decay coefficient This is positively correlated with the thermal conductivity characteristics of the strata in this region; the time interval between this exploration and the previous one... Calculated ,according to Adjust the model's reserve parameters and the thermal conductivity of the fracture zone to complete the dynamic iterative update of the model.

[0048] In the 3D visualization section, the visualization module uses OpenGL rendering technology to present the 3D scene, supporting model rotation, scaling, and sectioning operations. The rendering accuracy of the fault zone area is specifically optimized. Users can customize the fault zone display color to orange and set the transparency to 50%, clearly viewing the spatial relationship between the fault zone and the thermal reservoir. The system automatically identifies anomalous areas with temperatures exceeding 100℃ and highlights them in bright red. The visualization results can be exported as PNG, JPG images, or MP4 and AVI video formats, enabling simultaneous display on multiple terminals via wireless network.

[0049] In the data storage and management phase, the data storage management module adopts a distributed storage architecture, storing data categorized into raw data, preprocessed results, 3D model data, update records, and fault zone-specific data, and establishing a triple indexing mechanism based on time, region, and data type. It supports retrieving exploration data for corresponding regions by fault zone number and querying model update records by time range, with a response time of no more than 1.2 seconds. When the computer program stored on the storage medium is executed by the processor, it fully implements all steps of the aforementioned exploration methods, adapting to the complex exploration needs of mountain fault zones.

[0050] Table 2 Comparison of Reserve Assessment Errors Before and After Model Dynamic Update Table 2 visually demonstrates the optimization effects of dynamic model updates and bias correction. The complex terrain of mountain fault zones significantly impacts geothermal resources due to the thermal conductivity of these zones. Traditional static models, failing to consider spatiotemporal variations and time decay, resulted in geothermal reservoir temperature assessment errors of up to 6.8% and geothermal reserve assessment errors exceeding 8%, hindering accurate development planning. This invention, by collecting data at preset intervals and integrating reserve deviations and time interval factors with a bias correction model, dynamically adjusts model parameters and the thermal conductivity of fault zones. After the update, all assessment errors are reduced to below 2.5%, with the fault zone thermal conductivity assessment error at only 2.2%. This effect stems from the dynamic update mechanism's accurate capture of geothermal resource variation patterns and the bias correction model's comprehensive consideration of multiple influencing factors, enabling the model to continuously adapt to the complex geological conditions of mountain fault zones and providing high-precision data support for geothermal resource development planning.

[0051] The data storage and management process has been further optimized. For the specific exploration needs of mountain fault zones, the storage module includes an additional fault zone parameter database, dedicated to storing core data such as fault zone strike, dip angle, and thermal conductivity, and establishing a correlation index with data on stratigraphic structure and temperature field. It supports rapid retrieval of multi-source exploration data and model files for corresponding areas via fault zone numbers, facilitating technical personnel's focused analysis of the distribution and dynamic changes of geothermal resources near fault zones. The storage medium employs a highly stable design, adapting to the complex environment of mountain field exploration, ensuring secure and lossless data storage, smooth computer program operation, and complete replication of the entire exploration process, fully meeting the accuracy and stability requirements of dynamic geothermal resource exploration in mountain fault zones.

[0052] Reference Figure 3 This figure visually illustrates the core effect of multi-source data fusion optimization. In traditional techniques, the lack of unified standards for multi-source data acquisition, coupled with differences in sensor accuracy and inconsistent data timeliness, leads to persistently high errors in various parameters. For example, the error in underground temperature field data reaches 5.2%, and the error in the thermal conductivity of fault zones is as high as 9.1%, severely impacting the accuracy of subsequent modeling. This invention addresses this by constructing a weighted calculation model. The system dynamically allocates fusion weights for each data point, fully considering sensor accuracy and data timeliness to achieve precise integration of multi-source data. After fusion, the errors of various parameters are significantly reduced, all controlled within 2.7%, with errors in underground temperature field and fluid flow data even below 2%. This provides high-quality data support for the construction of three-dimensional geological models, fundamentally solving the core pain point of poor consistency of multi-source data in traditional technologies.

[0053] Reference Figure 4 This figure clearly demonstrates the optimization effect of the dynamic update and deviation correction mechanism. Traditional static models do not consider the spatiotemporal changes of geothermal resources and rely solely on single exploration data, resulting in reserve assessment errors generally exceeding 7%, which cannot meet the needs of dynamic exploration. Although the model before the update in this invention integrates multi-source data, it does not perform deviation correction, and the error remains at 5.9%-8.1%. By collecting data at preset intervals and combining it with the deviation correction model... By comprehensively considering the factors of reserve deviation and time decay, and dynamically adjusting the model parameters, the errors of all assessments were reduced to within 2.5% after the update, with the error in assessing the thermal conductivity of the fault zone being only 2.2%. This effect enables the model to accurately reflect the dynamic processes of geothermal reservoir temperature evolution, reserve consumption and replenishment, etc., providing high-precision data support for the sustainable development planning of geothermal resources.

[0054] Reference Figure 5This diagram visually illustrates the efficiency of the distributed storage architecture and indexing mechanism. Traditional centralized storage lacks a scientific classification and indexing system. As data volume increases, retrieval time rises exponentially, reaching 42.8 seconds for 1000GB of data, severely impacting the efficient application of exploration results. This invention employs a distributed storage architecture, classifying data by raw data, preprocessed results, and model data, and establishing a triple indexing mechanism based on time, region, and data type. Retrieval time increases slowly with data volume, reaching only 3.8 seconds for 1000GB of data, nearly 11 times faster than traditional methods. This efficient data retrieval capability ensures rapid access to massive amounts of exploration data, facilitating timely analysis of geothermal resource dynamics by technical personnel, improving the overall efficiency of exploration work, and solving the problems of cumbersome and inefficient traditional data management and retrieval.

[0055] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A three-dimensional visualization method for dynamic exploration of geothermal resources, characterized in that, Includes the following steps: Multi-source exploration data acquisition involves deploying geothermal sensors, ground-penetrating radar, electromagnetic detection equipment, borehole temperature measuring devices, and meteorological monitoring equipment. Data collection points are set up in a grid distribution within the exploration area to simultaneously collect underground temperature field data, stratigraphic lithology parameters, permeability data, geothermal fluid flow data, topographic data, and regional meteorological data. Data preprocessing involves sequentially filtering noise from the collected multi-source data, using an adaptive filtering algorithm to eliminate equipment interference and environmental noise, identifying and removing outlier data points through statistical analysis, and supplementing missing data using radial basis function interpolation. Three-dimensional geological model construction: Based on preprocessed data, three-dimensional modeling algorithms are used to construct stratigraphic structure model, temperature field distribution model and geothermal reserve assessment model; Dynamic updates and optimizations are performed by repeatedly collecting exploration data at preset cycles, comparing the differences between newly collected data and historical data, analyzing the spatiotemporal variation patterns of geothermal resources, and adjusting the parameters and structure of the three-dimensional model. The 3D visualization display uses visualization rendering technology to present the 3D geological model, temperature field distribution, geothermal reserve distribution and dynamic change trend in a 3D scene, and supports model rotation, scaling and sectioning operations. Data storage and management categorizes and stores raw survey data, preprocessing results, 3D model data, and dynamic update records, and establishes a data indexing mechanism to support rapid retrieval and access of data by time, region, and data type.

2. The three-dimensional visualization method for dynamic exploration of geothermal resources according to claim 1, characterized in that, It also includes a multi-source data fusion optimization step, which completes data integration by constructing a data fusion weight calculation model, the calculation formula being: ,in For the first Type 1 sensor The fusion weight of each data point For the first The accuracy coefficient of the sensor-like sensor, For the first The timeliness coefficient of the data type For the first The accuracy coefficient of the sensor-like sensor, For the first The timeliness coefficient of the data type It is the sum of the products of the accuracy coefficients and timeliness coefficients of all sensors.

3. The method for dynamic exploration of geothermal resources with three-dimensional visualization according to claim 1, characterized in that, It also includes a dynamic assessment step for geothermal reserves. By combining three-dimensional model parameters with dynamically acquired data, a reserve assessment model is constructed. By analyzing changes in temperature field, fluid flow rate, and formation parameters, the extraction and replenishment of geothermal resources are quantified, the sustainable development potential of geothermal resources is quantified, and a reserve change trend curve is generated simultaneously.

4. The three-dimensional visualization method for dynamic exploration of geothermal resources according to claim 1, characterized in that, It also includes a dynamic update deviation correction step for the 3D model, which optimizes the update effect through deviation correction model calculation. Adjustments for model updates This is the weighting coefficient for reserve deviation. This is the geothermal reserve prediction value from the current model. These are the latest measured values ​​of geothermal reserves obtained from exploration. The time decay coefficient, This represents the time interval between the current survey and the previous survey.

5. The three-dimensional visualization method for dynamic exploration of geothermal resources according to claim 1, characterized in that, It also includes visualization interaction optimization steps, supports user-defined visualization parameters, provides an abnormal area marking function, automatically identifies areas and highlights them, and supports exporting visualization results as image or video formats, and supports simultaneous display on multiple terminals.

6. An apparatus for applying the three-dimensional visualization method for dynamic exploration of geothermal resources according to any one of claims 1-5, characterized in that, Includes the following modules: Multi-source data acquisition module: integrates multiple monitoring devices, grid-deployed acquisition units, supports simultaneous acquisition of multiple types of data and custom parameter configuration; Data preprocessing module: Built-in multiple processing units to complete data noise reduction, anomaly removal, missing data filling and unit unification, and output standardized data; 3D modeling module: Equipped with modeling algorithms and parameter mapping units, it constructs stratigraphic, temperature field, and reserve assessment models, and establishes a database linking geological and geothermal parameters; Dynamic update module: Includes a periodic data acquisition and comparison unit, which collects data periodically, analyzes spatiotemporal variation patterns, and drives iterative model updates; Visualization module: Equipped with a rendering engine and interactive units, it supports model operation and multi-view viewing, completes 3D data visualization, and supports parameter customization and result export; Data storage management module: Adopts a distributed storage architecture to classify and store various types of data and update records, and supports fast retrieval and access.

7. The three-dimensional visualization geothermal resource dynamic exploration device according to claim 6, characterized in that, It also includes a data fusion optimization module, which has a built-in weight calculation unit and a data integration unit. It dynamically allocates the fusion weights of various types of data through a weight calculation model, integrates multi-source data to generate consistent data results. This module is connected to the data preprocessing module, receives multi-source data after standardization processing, and outputs fused and optimized data for 3D modeling.

8. The three-dimensional visualization geothermal resource dynamic exploration device according to claim 6, characterized in that, It also includes a reserve assessment module and a deviation correction module. The reserve assessment module combines the three-dimensional model parameters and dynamically acquired data to quantify the amount of geothermal resource extraction and replenishment, and generate a reserve change trend curve. The deviation correction module calculates the model update adjustment amount through the deviation correction model and feeds it back to the dynamic update module.

9. A three-dimensional visualization device for dynamic exploration of geothermal resources, characterized in that, The system includes an exploration terminal, a data processing unit, a storage unit, a display unit, and a communication unit. The exploration terminal integrates multiple types of exploration equipment. The data processing unit is equipped with the functional modules described in claims 6-8 to complete data preprocessing, 3D modeling, dynamic updating, data fusion, and reserve assessment. The storage unit uses a distributed storage device. The display unit is a high-definition visualization display device that supports 3D scene display and interactive operation. The communication unit supports data transmission between the survey terminal and the data processing unit, and between the data processing unit and the storage unit.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, completes the steps of a three-dimensional visualization method for dynamic exploration of geothermal resources as described in any one of claims 1-5. The steps include multi-source exploration data acquisition, data preprocessing, three-dimensional geological model construction, dynamic updating and optimization, three-dimensional visualization display, and data storage and management. It also completes additional steps such as data fusion optimization, dynamic assessment of geothermal reserves, deviation correction, and visualization interaction optimization.

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