Leakage channel identification method based on multi-source data fusion and geophysical exploration
By integrating multi-source data and geophysical exploration, a three-dimensional seepage channel identification model was constructed, which solved the problem of inaccurate identification of seepage channels in karst reservoir seepage investigation and achieved accurate identification and scientific management of seepage channels.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-07
AI Technical Summary
Existing methods for investigating seepage in karst reservoirs are limited, have multiple interpretations, and lack three-dimensional understanding, leading to inaccurate identification of seepage channels, blind verification, and a lack of scientific basis for governance decisions.
Through multi-source data fusion and geophysical exploration, including the collection and fusion of regional geological maps, remote sensing images, hydrological and meteorological data and topographic data, a three-dimensional coupled geological structure model is constructed. High-density electrical resistivity tomography and audio-frequency magnetotellurics are used for joint inversion exploration. Combined with groundwater dynamic response and tracer experiments, a three-dimensional visualization model of seepage channels is constructed and quantitatively evaluated.
It enables accurate identification and quantitative assessment of leakage channels, improves interpretation accuracy, ensures the scientific nature and accuracy of engineering design, and reduces treatment costs.
Smart Images
Figure CN121806147A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of geological exploration and leakage prevention, and particularly relates to a leakage channel identification method based on multi-source data fusion and geophysical exploration. BACKGROUND
[0002] When a water conservancy and hydropower project is constructed in a karst area, reservoir leakage is a core geological problem affecting the safety and benefits of the project, and river bend type leakage is particularly difficult due to its strong concealment and complex spatial structure. Current surveying techniques have the following limitations: geological mapping is limited by surface cognition and cannot reveal deep leakage paths; drilling projects are costly and provide only a "one-hole view", making it difficult to capture complex leakage networks; geophysical exploration has multiple solutions and is prone to misjudgment of low-resistance rock layers or fracture zones as leakage channels; hydrogeological tests lack the guidance of three-dimensional geological models, and the selection of injection and receiving points is blind, limiting the effectiveness of verification; survey results are often expressed in two-dimensional drawings, making it difficult to accurately reflect the three-dimensional heterogeneous characteristics of the karst system, resulting in problems such as inaccurate curtain positioning and insufficient depth in subsequent anti-seepage engineering design, leading to poor treatment results and high costs. Therefore, there is an urgent need for a surveying method that can systematically integrate multi-source data, reduce multiple solutions, and accurately identify and quantitatively evaluate leakage channels. SUMMARY
[0003] The present application provides a leakage channel identification method based on multi-source data fusion and geophysical exploration, which solves the problems of inaccurate leakage channel identification, blind verification, and lack of scientific basis for treatment decisions caused by the single survey method, strong multiple solutions, and insufficient three-dimensional cognition of existing karst reservoir leakage survey methods.
[0004] The object of the present application can be achieved by the following technical solutions: The first aspect of the present application provides a leakage channel identification method based on multi-source data fusion and geophysical exploration, comprising: S1: Collect and fuse regional geological maps, remote sensing images, hydro-meteorological data, and topographic and geomorphic data, analyze karst development rules, and delineate a primary leakage target area; S2: Based on field mapping and drilling data, construct a three-dimensional coupled geological structure model of strata-structure-karst corresponding to the river bend block, and identify potential dominant flow paths; the three-dimensional coupled geological structure model includes a basic control layer, a structure transformation layer, and a karst development layer; S3: In the leakage target area, high-density electrical method and audio magnetotelluric method are used for joint inversion detection, and the dominant dissolution potential field in the three-dimensional geological structure model is used as a priori constraint to delineate the spatial position of karst pipes and fracture dense zones; S4: Based on the spatial position, the dynamic response relationship between groundwater and reservoir water level is analyzed by using long-term observation data of groundwater, and the connectivity of the leakage channel is verified and quantified by combining with the tracer test; S5: The data and results of S1 to S4 are integrated to construct a three-dimensional visualization model of the leakage channel, and a dynamic equivalent hydraulic conductivity coefficient is assigned to the model unit for quantitative evaluation of the leakage amount.
[0005] Further, the step S1 specifically comprises: The multi-source data is spatially registered and integrated by using the GIS platform, remote sensing karst interpretation is performed to identify linear structures, ring images and vegetation anomaly areas; the distribution of soluble rocks, the occurrence of strata and the direction of structures are analyzed in combination with geological maps; the topographic and geomorphic rules of river bay shape, ancient river channel and karst depression are analyzed; and 1 to 3 first-level leakage target areas are comprehensively judged and circled.
[0006] Further, in the ternary coupled geological structure model: The lithology-permeability probability matrix is introduced into the basic control layer to calculate the comprehensive initial permeability of the stratum unit ; wherein, ; In the formula, is the weight of the first kind of rock, is the representative permeability value of the first kind of rock, is the permeability evolution probability of the first kind of rock under the action of dissolution; wherein, the representative permeability value and the permeability evolution probability of each kind of rock are data in the lithology-permeability probability matrix; Wherein, the weight of each kind of rock is determined by statistical volume percentage or area ratio of different rocks in the corresponding stratum unit; the representative permeability value of each kind of rock is an empirical value; the permeability evolution probability of each kind of rock under the action of dissolution is determined by indoor dissolution test analysis; Wherein, the stratum unit is a three-dimensional spatial grid unit divided when the ternary coupled geological structure model of stratum-structure-karst is constructed; The structure reconstruction layer defines a structure connectivity index, which is a multivariate linear function of fault density, mechanical property tensor and intersection angle, for quantifying the reconstruction strength of structure on permeability; The karst development layer is dynamically generated based on the basic control layer and the structure reconstruction layer through the dominant dissolution potential field DCPF; Wherein, the dominant dissolution potential field DCPF is specifically represented as:
[0007] In the formula, representing the hydraulic gradient field, representing the connectivity index, representing the rock formation content, representing the compressive strength of the rock formation, representing the dominant dissolution potential field, representing an activation function for data normalization; wherein the ratio of the water head difference to the distance at different positions is obtained by the data of the reservoir and the long-term observation network of groundwater; the rock formation The content is obtained by field portable XRF spectrometer test; the compressive strength of the rock formation is obtained by field rock point load test conversion.
[0008] Further, the method for identifying the potential dominant seepage path in the step S2 is: The connectivity index data of the tectonic reconstruction layer is loaded on the basic control layer to generate a preliminary permeability heterogeneity model; the paleo and present hydrological network data are input as boundary conditions to drive the calculation of the dominant dissolution potential field DCPF, and the spatial line satisfying the preset DCPF value automatically outlines the potential dominant seepage path.
[0009] Further, the objective function for joint inversion in the step S3 is:
[0010] In the formula, representing the geophysical model parameter vector, representing the data fitting term of the high-density electrical method, representing the data fitting term of the audio magnetotelluric method, representing the regularization parameter, representing the change of the resistivity lateral gradient guided by and constrained by the dominant dissolution potential field DCPF the term of m.
[0011] Further, in the step S4, the dynamic response relationship between the groundwater and the reservoir water level is analyzed by calculating the dynamic response intensity factor DRI:
[0012] In the formula, representing the change amount of the groundwater level, representing the change amount of the reservoir water level, representing the lag time of the water level change, representing the dynamic response intensity factor.
[0013] Further, in the step S4, the connectivity is verified by calculating the tracer migration path dispersion index PDI:
[0014] In the formula, Indicates the first Concentration contribution rate of each peak Indicates the first The flow velocity corresponding to each peak This indicates the number of all peaks. This indicates the diffusion index of the tracer transport path.
[0015] Furthermore, in step S5, the dynamic equivalent hydraulic conductivity coefficient The calculation method is as follows:
[0016] In the formula, Indicates the basic permeability coefficient. This represents the weighting function. It represents the dynamic equivalent hydraulic conductivity coefficient.
[0017] Furthermore, in step S5, the formula for evaluating the leakage amount Q is:
[0018] In the formula, This is a three-dimensional spatial mesh index, representing a single cell; This represents the unit's dynamic equivalent hydraulic conductivity coefficient. Represents the hydraulic gradient of the unit. Indicates the flow area of the unit. Indicates the amount of leakage; This represents the sum of all units.
[0019] A second aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for identifying leakage channels through multi-source data fusion and geophysical exploration.
[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: It collects and integrates regional geological maps, remote sensing images, hydrological and meteorological data, and topographic data to analyze karst development patterns and delineate primary seepage target areas; through multi-source data fusion and analysis, it achieves rapid macroscopic screening and target area delineation of seepage risk areas, providing direction for subsequent exploration; based on field surveying and borehole data, it constructs a ternary coupled geological structure model of strata-structure-karst corresponding to the river bend block, identifying potential dominant seepage paths; the ternary coupled geological structure model includes: a basic control layer, a structural modification layer, and a karst development layer; it constructs a dynamically coupled three-dimensional geological model, realizing intelligent identification and prediction from geological structure to potential seepage paths; within the seepage target area, it uses high-density electrical resistivity tomography and audio-frequency magnetotellurics for joint inversion detection, utilizing the dominant dissolution potential field in the three-dimensional geological structure model as a priori constraints to delineate the space of karst conduits and densely fractured zones. Location; By utilizing geophysical inversion under geological prior constraints, the interpretation accuracy was significantly improved, and the precise spatial location of seepage channels was achieved; Based on the spatial location, the dynamic response relationship between groundwater and reservoir water level was analyzed using long-term groundwater observation data, and the connectivity of seepage channels was verified and quantified by combining tracer experiments; Through hydrological dynamics and tracer experiments, the functional confirmation and quantitative evaluation of hydraulic connectivity from physical anomalies were achieved; All data and results were integrated to construct a three-dimensional visualization model of seepage channels, and dynamic equivalent hydraulic conductivity coefficients were assigned to model units for quantitative assessment of seepage volume; By constructing an integrated visualization model and conducting parameterized quantitative assessment, the intuitive display of seepage channels and scientific prediction of seepage volume were achieved, directly supporting engineering design; This solved the problems of inaccurate seepage channel identification, blind verification, and lack of scientific basis for governance decisions caused by the single, multifaceted, and insufficient three-dimensional understanding of existing karst reservoir seepage investigation methods. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 This invention provides a flowchart illustrating the steps of a method for identifying leakage channels using multi-source data fusion and geophysical exploration. Detailed Implementation
[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.
[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0025] To address the problems existing in the background technology, a method for identifying seepage channels by multi-source data fusion and geophysical exploration was designed, which has important practical significance.
[0026] like Figure 1 As shown, the first aspect of this invention is to provide a method for identifying leakage channels through multi-source data fusion and geophysical exploration, comprising the following steps: Step S1: Collect and integrate regional geological maps, remote sensing images, hydrological and meteorological data, and topographic data to analyze karst development patterns and delineate primary seepage target areas.
[0027] Specifically, the GIS (Geographic Information System) platform is used to spatially register and integrate multi-source data, and remote sensing karst interpretation is performed to identify linear structures, ring images, and vegetation anomaly areas; combined with geological maps, the distribution of soluble rocks, stratigraphic attitude, and structural orientation are analyzed; the topographic and geomorphological patterns of river bends, paleochannels, and karst depressions are analyzed; and 1 to 3 primary seepage target areas are comprehensively identified and delineated.
[0028] The system collects regional geological maps at scales of 1:50,000 to 1:10,000, high-resolution satellite remote sensing images, digital elevation models, hydrological and meteorological data, existing exploration reports, and literature within the work area.
[0029] Data Processing and Fusion: ① Utilize a GIS platform to spatially register and integrate the aforementioned multi-source data. ② Perform remote sensing interpretation of karst topography, focusing on identifying linear structures (such as faults and densely jointed zones), ring-shaped images (potentially related to depressions and sinkholes), and areas of abnormal vegetation. ③ Combine geological maps to analyze the distribution boundaries, stratigraphic attitudes, and directions of major structural lines between soluble rocks (such as pure limestone and dolomite) and insoluble rocks (such as sandstone, shale, and impermeable layers). ④ Analyze topography and geomorphology, particularly river bend morphology, bank symmetry, paleochannels, denudation surfaces, karst depressions, and the distribution patterns of sinkholes.
[0030] Step S2: Based on field surveying and drilling data, construct a ternary coupled geological structure model of strata-structure-karst corresponding to the river bend block, and identify potential dominant seepage paths; the ternary coupled geological structure model includes: basic control layer, tectonic modification layer and karst development layer.
[0031] Among these, detailed geological mapping includes: conducting large-scale hydrogeological mapping at scales of 1:2000 to 1:500 within the target area delineated in S1. This involves a detailed investigation of stratigraphic lithology, geological structures (fault and fracture statistics), and karst morphology (caves, fissures, springs), including their occurrence, scale, and infilling conditions. Actual measurements of the flow rate, water temperature, pH value, and electrical conductivity of major springs will also be taken.
[0032] Supplementary exploration: As needed, deploy a small number of control boreholes. Core logging and photography will be conducted in the boreholes, along with comprehensive logging (such as sonic logging, resistivity logging, and natural gamma logging) and stratified water pressure / injection tests to obtain parameters of rock mass integrity and permeability.
[0033] Model Construction: ① Using borehole data, geological profiles, and topographic data, construct an initial 3D geological structure model of the river bend block, including its stratigraphy, structure, and karst features, using GOCAD (Geological Computer Aided Design), Petrel (Petrel, Haiyan Exploration and Development Software Platform), or similar 3D geological modeling software. ② The model must clearly represent: key stratigraphic interfaces (especially the top of the aquitard), major faults, dominant fracture groups, large karst caves, and areas of strong karst development. ③ Path Identification: Based on the 3D model and combined with groundwater contour maps (if available), analyze potential dominant seepage paths. For example, along fault zones, contact zones between soluble and insoluble rocks, and the dip direction of strata.
[0034] Specifically, in the ternary coupled geological structure model: A lithology-permeability probability matrix is introduced into the basic control layer to calculate the overall initial permeability of the stratigraphic unit. ;in, In the formula, For the first The weight of lithology, For the first Representative permeability values for this type of lithology For the first The permeability evolution probability of a certain lithology under dissolution; wherein, the representative permeability value and permeability evolution probability of the lithology are data from the lithology-permeability probability matrix; The weight of each lithology is determined by statistically analyzing the volume percentage or area proportion of different lithologies within the corresponding stratigraphic unit; the representative permeability value of each lithology is an empirical value; and the permeability evolution probability of each lithology under dissolution is determined by indoor dissolution test analysis. Among them, the stratigraphic unit is the three-dimensional spatial grid unit divided when constructing the ternary coupled geological structure model of the stratigraphy-structure-karst; The structural modification layer defines a structural conductivity index, which is a multivariate linear function of fault density, mechanical property tensor, and tangency angle, used to quantify the intensity of structural modification of permeability. The karst development layer is based on the basic control layer and the tectonic modification layer, and is dynamically generated through the dominant dissolution potential field DCPF. The dominant dissolution potential field DCPF is specifically represented as follows:
[0035] In the formula, This represents the hydraulic gradient field (a spatial scalar field, with a gradient value at each spatial point). Indicates the conduction index, Indicates rock strata content, Indicates the compressive strength of the rock strata. Indicates the dominant dissolution potential field. This represents the activation function, used for data normalization.
[0036] This was achieved by calculating the ratio of water head difference to distance at different locations using data from a long-term reservoir and groundwater monitoring network; rock strata The content was obtained by field portable XRF spectrometer (X-ray Fluorescence Spectrometer); the compressive strength of the rock strata was obtained by conversion from field rock point load test.
[0037] It should be noted that the rock strata The higher the content, the lower the compressive strength. Under the current hydraulic-tectonic conditions, the rock has a higher potential to be dissolved and develop into a seepage channel in the future, which means that the corresponding dominant dissolution potential field DCPF should be larger.
[0038] The method for identifying potential advantageous seepage paths in step S2 is as follows: The conductivity index data of the structural modification layer is loaded onto the basic control layer to generate a preliminary permeability heterogeneity model; ancient and modern hydrological network data are used as boundary conditions to drive the calculation of the dominant dissolution potential field DCPF, and the spatial connection that meets the preset DCPF value automatically delineates the potential dominant seepage path.
[0039] Step S3: Within the seepage target area, a joint inversion detection is performed using high-density electrical resistivity tomography and audio-frequency magnetotellurics. The dominant dissolution potential field in the three-dimensional geological structure model is used as a priori constraint to delineate the spatial location of karst conduits and densely fractured zones.
[0040] The method selection and combination involved a combined approach within the target area: high-density electrical resistivity tomography (EDS) and audio-frequency magnetotellurics (AFM). ① High-density EDS: Sensitive to shallow (typically <100m) karst and fissures, offering high resolution. A Wenner or dipole-dipole array was used, with a point spacing of 5-10 meters. ② AFM: Profound at great depths (hundreds to thousands of meters), sensitive to deep, low-resistivity bodies (such as water-filled caves and fault zones), compensating for the insufficient depth of high-density EDS. Point spacing was 50-100 meters.
[0041] Survey line layout: Geophysical survey lines should perpendicularly traverse the potential advantageous seepage paths identified by S2, geological structural lines, and the core area of the target zone delineated by S1, forming a "well"-shaped or grid-like survey network.
[0042] Data Acquisition and Processing: Data acquisition was conducted strictly in accordance with specifications. Topographic correction and defect removal were performed on high-density electrical resistivity tomography (EDT) data; denoising, static correction, and robust processing (robust processing / robust estimation method) were performed on AMT (Audio-frequency Magnetotelluric) data.
[0043] Joint Inversion and Interpretation: ① Two-dimensional inversion is performed on the data from both methods to obtain resistivity profiles. ② Comprehensive geological interpretation is conducted: The two resistivity profiles are compared and analyzed. Typically, seepage channels (water-filled caves, water-saturated fracture zones) exhibit obvious low-resistivity anomalies. Through mutual verification of the two methods, local interference, low-resistivity strata, and genuine karst seepage channels can be effectively distinguished. ③ Typical low-resistivity anomaly areas with large scale, good continuity, and consistent with geological structures are delineated as key suspected seepage channels (anomaly zones).
[0044] Specifically, high-density electrical resistivity tomography (ERT) and audio-frequency magnetotelluric (AMT) methods are used in conjunction within the target area to delineate the spatial locations of karst conduits and densely fractured zones through resistivity anomalies. Data acquisition and structured priors: After parallel acquisition of ERT (shallow high-resolution) and AMT (deep low-resolution) data, inversion is not performed independently. This invention first uses the "dominant dissolution potential field (DCPF)" extracted from the 3D geological model as a key structural prior constraint. Regions with high DCPF values are defined as "guiding factors" for the spatial variation of resistivity values during the inversion process. Creative joint inversion process: This invention constructs a new objective function φ(m) for collaborative inversion. Here, ERT stands for Audio-frequency Magnetotelluric; ERT stands for Resistivity Tomography.
[0045] The objective function for the joint inversion is:
[0046] In the formula, Represents the parameter vector of the geophysical model. This represents the data fitting term for high-density electrical resistivity tomography (EDT). This represents the data fitting term for the audio magnetotelluric method. Represents the regularization parameter. This indicates that the lateral resistivity gradient variation is constrained by the dominant dissolution potential field DCPF. The term of m.
[0047] Intelligent Anomaly Delineation: Through the aforementioned collaborative inversion, a "geoelectric structure consensus model" is obtained. The finally delineated karst conduits are no longer a simple superposition of anomalies from the two methods, but rather low-resistivity anomaly networks with high confidence that automatically "emerge" from the joint inversion model under the prior guidance of DCPF. This method effectively overcomes the ambiguity of single methods, and is particularly capable of identifying "hidden" conduits that are loosely filled and whose responses are ambiguous in both ERT and AMT inversions alone.
[0048] Step S4: Based on the spatial location, analyze the dynamic response relationship between groundwater and reservoir water level using long-term groundwater observation data, and verify and quantify the connectivity of the leakage channel by combining tracer experiments.
[0049] The construction of a groundwater dynamic monitoring network includes: setting up long-term groundwater monitoring wells (using existing or newly drilled wells) in and around the target area, and simultaneously establishing reservoir water level monitoring points along the reservoir bank. Monitoring frequency will be increased during the impoundment or flood season.
[0050] Dynamic response analysis: Plot time series curves of reservoir water level and water levels at each observation well. Analyze their correlation and lag time. Observation wells that rise and fall synchronously with the reservoir water level and have short lag times indicate that they are closely hydraulically connected to the reservoir and located on the main seepage path.
[0051] Tracer test: Select the upstream of the most suspected seepage channel (such as the edge of a reservoir, a sinkhole, or a specific borehole) as the source point, and the downstream possible discharge point (such as a spring, borehole, or ditch) as the receiving point. Add tracers such as sodium fluorescein and iodide, monitor their arrival time and peak concentration, calculate the groundwater flow velocity, and directly confirm the connectivity of the seepage channel.
[0052] Specifically, the dynamic response relationship between groundwater and reservoir water level is analyzed by calculating the Dynamic Response Intensity Factor (DRI):
[0053] In the formula, This indicates the change in groundwater level. This indicates the change in reservoir water level. Indicates the lag time of water level changes. This represents the dynamic response intensity factor.
[0054] Connectivity was verified by calculating the tracer transport path dispersion index (PDI).
[0055] In the formula, Indicates the first Concentration contribution rate of each peak Indicates the first The flow velocity corresponding to each peak This indicates the number of all peaks. This indicates the diffusion index of the tracer transport path.
[0056] Each peak represents a concentration peak generated when the tracer reaches the receiving point from different paths (main channel, branch, slit network) or after different transport times; the concentration-time curve (penetration curve) monitored by the tracer experiment is mathematically decomposed (such as Gaussian fitting, deconvolution) to separate the composite curve into multiple independent peaks, each peak corresponding to a main transport path or a dominant flow rate.
[0057] Step S5: Integrate the data and results from S1 to S4, construct a three-dimensional visualization model of the leakage channel, and assign a dynamic equivalent hydraulic conductivity coefficient to the model unit to conduct a quantitative assessment of the leakage.
[0058] Specifically, the dynamic equivalent hydraulic conductivity coefficient The calculation method is as follows:
[0059] In the formula, Indicates the basic permeability coefficient. This represents the weighting function. This represents the dynamic equivalent hydraulic conductivity coefficient. The weighting function is... The coefficients of each parameter are determined using the least squares method. The least squares method is a well-known technique and will not be elaborated upon here.
[0060] The formula for evaluating the leakage rate Q is as follows:
[0061] In the formula, This is a three-dimensional spatial mesh index, representing a single cell; This represents the unit's dynamic equivalent hydraulic conductivity coefficient. Represents the hydraulic gradient of the unit. Indicates the flow area of the unit. Indicates the amount of leakage; This represents the sum of all units.
[0062] This concludes the embodiment.
[0063] A second aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for identifying leakage channels through multi-source data fusion and geophysical exploration.
[0064] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.
[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.
[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.
Claims
1. A method for identifying leakage channels using multi-source data fusion and geophysical exploration, characterized in that, Includes the following steps: S1: Collect and integrate regional geological maps, remote sensing images, hydrological and meteorological data and topographic data to analyze the karst development pattern and delineate the primary seepage target area; S2: Based on field surveying and borehole data, construct a ternary coupled geological structure model of stratigraphy, structure and karst corresponding to the river bend block, and identify potential dominant seepage paths; The ternary coupled geological structure model includes: a basic control layer, a tectonic modification layer, and a karst development layer; S3: Within the seepage target area, a joint inversion detection is performed using high-density electrical resistivity tomography and audio-frequency magnetotellurics. The dominant dissolution potential field in the three-dimensional geological structure model is used as a priori constraint to delineate the spatial location of karst conduits and densely fractured zones. S4: Based on the aforementioned spatial location, analyze the dynamic response relationship between groundwater and reservoir water level using long-term groundwater observation data, and verify and quantify the connectivity of the seepage channel by combining tracer experiments. S5: Integrate the data and results from S1 to S4, construct a three-dimensional visualization model of the leakage channel, and assign dynamic equivalent hydraulic conductivity coefficients to the model units to conduct quantitative assessment of leakage.
2. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 1, characterized in that, Step S1 specifically includes: Using a GIS platform, spatial registration and integration of multi-source data are performed, and remote sensing karst interpretation is conducted to identify linear structures, ring images, and vegetation anomalies. Combined with geological maps, the distribution of soluble rocks, stratigraphic attitude, and structural orientation are analyzed. The topographic and geomorphological patterns of river bends, paleochannels, and karst depressions are analyzed. Based on comprehensive judgment, one to three primary seepage target areas are delineated.
3. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 1, characterized in that, In the aforementioned ternary coupled geological structure model: The basic control layer incorporates a lithology-permeability probability matrix to calculate the overall initial permeability of the formation unit. ;in, ; In the formula, For the first The weight of lithology, For the first Representative permeability values for this type of lithology For the first The permeability evolution probability of a certain lithology under dissolution; wherein, the representative permeability value and permeability evolution probability of the lithology are data from the lithology-permeability probability matrix; The weight of each lithology is determined by statistically analyzing the volume percentage or area proportion of different lithologies within the corresponding stratigraphic unit; the representative permeability value of each lithology is an empirical value; and the permeability evolution probability of each lithology under dissolution is determined by indoor dissolution test analysis. Among them, the stratigraphic unit is the three-dimensional spatial grid unit divided when constructing the ternary coupled geological structure model of the stratigraphy-structure-karst; The structural modification layer defines a structural conductivity index, which is a multivariate linear function of fault density, mechanical property tensor, and tangency angle, used to quantify the intensity of structural modification of permeability. The karst development layer is based on the basic control layer and the tectonic modification layer, and is dynamically generated through the dominant dissolution potential field DCPF. The dominant dissolution potential field DCPF is specifically represented as follows: In the formula, Represents the hydraulic gradient field. Indicates the conduction index, Indicates rock strata content, Indicates the compressive strength of the rock strata. Indicates the dominant dissolution potential field. This represents the activation function, used for data normalization. This was achieved by calculating the ratio of water head difference to distance at different locations using data from a long-term reservoir and groundwater monitoring network; rock strata The content was obtained by field portable XRF spectrometer testing; the compressive strength of the rock strata was obtained by conversion from field rock point load tests.
4. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 3, characterized in that, The method for identifying potential advantageous seepage paths in step S2 is as follows: The conductivity index data of the structural modification layer is loaded onto the basic control layer to generate a preliminary permeability heterogeneity model; ancient and modern hydrological network data are used as boundary conditions to drive the calculation of the dominant dissolution potential field DCPF, and the spatial connection that meets the preset DCPF value automatically delineates the potential dominant seepage path.
5. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 4, characterized in that, The objective function for the joint inversion in step S3 is: In the formula, Represents the parameter vector of the geophysical model. This represents the data fitting term for high-density electrical resistivity tomography (EDT). This represents the data fitting term for the audio magnetotelluric method. Represents the regularization parameter. This indicates that the lateral resistivity gradient variation is constrained by the dominant dissolution potential field DCPF. The term of m.
6. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 3, characterized in that, In step S4, the dynamic response relationship between groundwater and reservoir water level is analyzed by calculating the dynamic response intensity factor (DRI). In the formula, This indicates the change in groundwater level. This indicates the change in reservoir water level. Indicates the lag time of water level changes. This represents the dynamic response intensity factor.
7. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 1, characterized in that, In step S4, connectivity is verified by calculating the tracer transport path dispersion index (PDI). In the formula, Indicates the first Concentration contribution rate of each peak Indicates the first The flow velocity corresponding to each peak This indicates the number of all peaks. This indicates the diffusion index of the tracer transport path.
8. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 6, characterized in that, In step S5, the dynamic equivalent hydraulic conductivity coefficient The calculation method is as follows: In the formula, Indicates the basic permeability coefficient. Represents the weighting function. It represents the dynamic equivalent hydraulic conductivity coefficient.
9. The method for identifying leakage channels through multi-source data fusion and geophysical exploration according to claim 8, characterized in that, In step S5, the formula for evaluating the leakage amount Q is: In the formula, This is a three-dimensional spatial grid index, representing a single cell; This represents the unit's dynamic equivalent hydraulic conductivity coefficient. Represents the hydraulic gradient of the unit. Indicates the flow area of the unit. Indicates the amount of leakage; This represents the sum of all units.
10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for identifying leakage channels by multi-source data fusion and geophysical exploration as described in any one of claims 1-9.