Mine aquifer water yield evaluation method and system considering spatial characteristics

Abstract

The invention discloses a mine aquifer water yield evaluation method and system considering spatial characteristics, and the method comprises the following steps: carrying out the weight assignment of evaluation indexes of mine aquifer water yield evaluation through employing a subjective and objective comprehensive weighting method, and obtaining a comprehensive weight; calculating a water yield index value of each drill hole by utilizing the obtained comprehensive weight; constructing an optimized water yield evaluation model considering spatial features, and calculating a water yield index correction value based on spatial interaction; and combining the obtained water yield property index value with the water yield property index correction value to obtain a water yield property index optimization value, and judging a water yield property evaluation result of the research area according to the size of the water yield property index optimization value. According to the mine aquifer water yield evaluation method and system considering the spatial characteristics, the influence of spatial characteristic factors on the water yield evaluation result is considered, the mutual spatial influence between the water yield indexes of the adjacent drill holes is analyzed, and the accuracy of water yield evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluation of water-richness of aquifers, and in particular, to a method and system for evaluating the water-richness of mine aquifers considering spatial characteristics. Background Art

[0002] The continuous progress of Geographic Information System (GIS) technology has made it a core tool for management and decision-making in the coal mining field. GIS provides advanced analysis means for the analysis of stratum structures and the spatial relationships between geographical entities, plays a key role in the construction of intelligent mines and the prevention and control of mine water disasters, and particularly shows important value and broad application prospects in the evaluation of the water-richness of aquifers.

[0003] The research on the evaluation of the water-richness of aquifers includes pumping tests, geophysical prospecting, and multi-factor comprehensive analysis methods. Pumping tests are an important means for evaluating the hydraulic characteristics of aquifers. However, in actual production, the number of pumping boreholes is small, the cost is high, and the limited pumping boreholes may not be able to fully cover the study area, with low control degree and it is difficult to accurately describe the overall hydrographic information of the study area; geophysical prospecting is greatly affected by factors such as topography and landforms, resulting in rough data and difficult to interpret.

[0004] Although the above two types of methods have achieved some good results, they also have certain limitations. Therefore, the current main method for evaluating the water-richness of aquifers is the multi-factor comprehensive analysis method, that is, selecting appropriate evaluation indicators and models to evaluate the water-richness of aquifers. And GIS has the ability to integrate and fuse multi-source heterogeneous spatial data and comprehensively analyze various factors in the geographical space. Domestic and foreign scholars have done rich research in this regard, as follows:

[0005] Prior art document 1 (Wu Qiang, Fan Zhenli, Liu Shouqiang, et al. Information fusion-based evaluation method for water richness of aquifers - water richness index method [J]. Journal of China Coal Society, 2011, 36(07): 1124-1128) and prior art document 2 (Zhou K. Water richness zoning and evaluation of the coal seam roof aquifer based on AHP and multisource geological information fusion [J]. Geofluids, 2021, 2021(1): 1097600.) established an evaluation model for the distribution law of water richness of aquifers based on GIS - the water richness index method by combining single weighting methods and multisource geological information fusion, providing scientific guidance for the prevention and control of mine water hazards; prior art document 3 (Jiang Q, Liu Q, Chai H, et al. GIS-based water inrush risk evaluation of the 10th coal seam floor in Zhuxianzhuang Coal Mine, northern Anhui Province, China [J]. Water Supply, 2024, 24(5): 1714-1733) quantified complex geological data by combining GIS and the vulnerability index method to evaluate the risk of water inrush from the coal seam floor, providing a scientific and reliable quantitative analysis model; prior art document 4 (Li Y, Yin H, Dong F, et al. Study on potential groundwater yield zone in sandstone aquifer based on a dual dynamic variable weight model: A case study in Shuangma Coal Mine of Ordos Basin [J]. Ecological Indicators, 2023, 155: 111059.) analyzed the interaction mechanism of hydrogeological parameters to accurately evaluate the groundwater potential of aquifers 100 meters underground, and constructed a dual dynamic variable weight model that changes with multi-parameter entanglement and single-parameter state values to characterize the water storage potential of underground aquifers; prior art document 5 (Priya U, Iqbal M A, Salam M A, et al.Sustainable groundwater potential zoning with integrating GIS, remote sensing, and AHP model: a case from North-Central Bangladesh[J].Sustainability, 2022, 14(9): 5640) Using GIS, remote sensing technology and hierarchical analysis model, a comprehensive assessment and zoning of groundwater potential in Mymensingh, north-central Bangladesh was carried out. .

[0006] In the innovative research of water-richness calculation models and evaluation methods, some scholars have also made significant progress, as follows:

[0007] Prior art document 6 (Cheng W, Dong F, Tang R, et al. Improved combination weighted prediction model of aquifer water abundance based on a cloud model [J]. ACS omega, 2022, 7 (40): 35840-35850.) proposed a combined weighted prediction model of aquifer water abundance based on an improved cloud model, which improved the model's ability to process spatial data, but the cloud model has high requirements on the quantity and quality of sample data; Prior art document 7 (Bi Y, Wu J, Tang L, et al. Water abundance comprehensive evaluation of coal mine aquifer based on projection pursuit model [J]. Lithosphere, 2022, 2021 (Special 4): 3259214.) uses a projection pursuit comprehensive evaluation method to optimize the optimal projection direction and calculate the comprehensive projection value to characterize the water abundance of the bottom aquifer in the study area, but this method may produce projection directions and comprehensive projection values ​​that are difficult to intuitively interpret, thereby limiting the interpretability of the model results.

[0008] Existing research has greatly promoted the evaluation of water-bearing capacity of aquifers. Currently, water-bearing capacity evaluation is mostly based on the water-bearing capacity index method, and often focuses on the analysis of geological structure and hydrogeological parameters. As a quantitative concept, the selection and calculation of water-bearing capacity index is important, but it is also deeply affected by the potential distribution characteristics of water-bearing capacity in space. Existing technologies do not consider the impact of spatial characteristic factors on prediction results, resulting in insufficient accuracy of evaluation results. Summary of the invention

[0009] To address the deficiencies in the existing technologies, the present invention provides a method and system for evaluating the water-richness of mine aquifers considering spatial characteristics, taking into account the influence of spatial characteristic factors on the water-richness evaluation results, analyzing the mutual spatial influence between the water-richness indices of adjacent boreholes from a surveying and mapping perspective, and being able to improve the accuracy of the water-richness evaluation.

[0010] The present invention adopts the following technical solutions.

[0011] In a first aspect, the present invention provides a method for evaluating the water-richness of mine aquifers considering spatial characteristics, comprising the following steps: using a subjective and objective comprehensive weighting method to assign weights to the evaluation indicators for evaluating the water-richness of mine aquifers to obtain a comprehensive weight; calculating the water-richness index values of each borehole using the obtained comprehensive weight; constructing an optimized water-richness evaluation model considering spatial characteristics to calculate the corrected value of the water-richness index based on spatial interaction; combining the obtained water-richness index values with the corrected value of the water-richness index to obtain an optimized value of the water-richness index, and judging the water-richness evaluation result of the research area based on the magnitude of the optimized value of the water-richness index.

[0012] Preferably, the step of using a subjective and objective comprehensive weighting method to assign weights to the evaluation indicators for evaluating the water-richness of mine aquifers to obtain a comprehensive weight comprises the following steps: constructing an aquifer water-richness evaluation hierarchical structure model including an objective layer, a criterion layer, and a decision layer; wherein, the decision layer is the evaluation indicators for evaluating the water-richness of mine aquifers; the criterion layer is related to the decision layer and is the criterion for measuring and comparing different evaluation indicators; the objective layer is the water-richness evaluation result of the aquifer; using the fuzzy analytic hierarchy process, according to the hierarchical structure divided by the aquifer water-richness evaluation hierarchical structure model, calculating the subjective weight of each evaluation indicator in the decision layer; using the entropy weight method to calculate the objective weight of each evaluation indicator in the decision layer; combining the obtained subjective weight and objective weight to construct a comprehensive weight model based on multi-strategies of game theory to obtain a comprehensive weight.

[0013] Preferably, the decision layer includes: 4 -2 The equivalent thickness of sandstone in the overlying strata of coal, the core recovery rate, the thickness ratio of brittle and plastic rocks, slope, normalized difference vegetation index, influence of surface water system distribution, and 3 -1 The equivalent thickness of sandstone in the overlying strata of coal, the core recovery rate, the thickness ratio of brittle and plastic rocks; the criterion layer includes: water storage performance and water source recharge; wherein, the water storage performance is used to measure and compare the evaluation indicators: 4 -2 The equivalent thickness of sandstone in the overlying strata of coal, the core recovery rate, the thickness ratio of brittle and plastic rocks; water source recharge is used to measure and compare the evaluation indicators: slope, normalized difference vegetation index, influence of surface water system distribution, and 3 -1 The equivalent thickness of sandstone in the overlying strata of coal, the core recovery rate, the thickness ratio of brittle and plastic rocks.

[0014] Preferably, the subjective weights of each evaluation index in the decision-making layer are calculated using the fuzzy analytic hierarchy process according to the hierarchical structure divided by the evaluation hierarchical structure model of aquifer water abundance, including the following steps: pairwise comparison is carried out between the criterion layer and the decision-making layer to obtain the comparison results; the "0.1-0.9 scale" method is used to score the comparison results to obtain the membership degree between two evaluation indexes; the fuzzy analytic hierarchy process is used to construct the fuzzy complementary judgment matrix A of pairwise evaluation indexes n×n ,

[0015]

[0016] where a ij is the element in the i-th row and j-th column of the fuzzy complementary judgment matrix A n×n , representing the membership degree that evaluation index P i is more important than evaluation index P j ; according to the hierarchical structure divided by the evaluation hierarchical structure model of aquifer water abundance, the subjective weights of each evaluation index are calculated as follows:

[0017]

[0018] where W i represents the subjective weight of each evaluation index; n is the number of evaluation indexes. When calculating the weight of each standard in the criterion layer, n takes 2, and when calculating the weight of each evaluation index in the decision-making layer, n takes 9

[0019] Preferably, the objective weights of each evaluation index in the decision-making layer are calculated using the entropy weight method, including the following steps: standardization processing is carried out on each evaluation index in the decision-making layer; based on the standardized data, the information entropy of each evaluation index is calculated; according to the information entropy of each evaluation index, its objective weight is calculated as follows:

[0020]

[0021] where W j represents the objective weight of each evaluation index; e j represents the information entropy, p ij is the proportion of the characteristics of the j-th evaluation index of the i-th borehole, y ij is the standardized data of the j-th evaluation index of the i-th borehole

[0022] Preferably, the calculation formula for the water abundance index value of each borehole is: where, represents the water abundance index of the i-th borehole; λ i represents the comprehensive weight of the i-th evaluation index; y iIt represents the value of the i-th evaluation index after standardization.

[0023] Preferably, to construct an optimized water-richness evaluation model considering spatial characteristics and calculate the correction value of the water-richness index based on spatial interaction, the following steps are included: calculating the geographical spatial distance between existing borehole points in the mining area; calculating the spatial weight between each borehole point according to the geographical spatial distance between the borehole points, and constructing a spatial weight matrix; calculating the correction value of the water-richness index based on spatial interaction according to the constructed spatial weight matrix.

[0024] Preferably, the constructed spatial weight matrix is:

[0025]

[0026] where W n represents the spatial weight matrix; is the spatial weight between the i-th borehole point and the j-th borehole point; d ij is the geographical spatial distance between the i-th borehole point and the j-th borehole point; a represents the distance attenuation coefficient, b i represents the dip angle of the aquifer between two boreholes.

[0027] Preferably, calculating the dip angle of the aquifer between two boreholes includes the following steps: interpolating and statistically analyzing the starting depth value of the target formation aquifer and converting it into raster data; calculating the standard deviation σ i ,

[0028] where N is the total number of raster cells in the neighborhood; hi is the elevation value of the i-th raster cell; is the average elevation value of all raster cells in the neighborhood;

[0029] According to the standard deviation σ i of the elevation values within the 3×3 neighborhood of each point, calculate the dip angle b i of the aquifer between two boreholes,

[0030]

[0031] where L i is the horizontal distance between two boreholes.

[0032] Preferably, the calculation formula for the correction value of the water-richness index is as follows:

[0033]

[0034] where, represents the correction value of the water-richness index; Z jDenote the water-richness index obtained from the j-th borehole. λ j Denote the comprehensive weight of the j-th evaluation index; y j Denote the value of the j-th evaluation index after standardization.

[0035] Preferably, the calculation formula for the optimized value of the water-richness index is as follows:

[0036]

[0037] Where, Z i ′ denotes the optimized value of the water-richness index; μ denotes the weight relationship between the obtained water-richness index and the correction value, which is between 0 and 1; Denote the water-richness index of the i-th borehole, λ i Denote the comprehensive weight of the i-th evaluation index; y i Denote the value of the i-th evaluation index after standardization.

[0038] In a second aspect, the present invention provides a mine aquifer water-richness evaluation system considering spatial characteristics for the aforementioned mine aquifer water-richness evaluation method considering spatial characteristics. The system includes: a hierarchy construction module for constructing an aquifer water-richness evaluation hierarchy model including a target layer, a criterion layer, and a decision layer; a weight calculation module for calculating the comprehensive weight of each evaluation index in the decision layer using the subjective and objective comprehensive weighting method according to the constructed aquifer water-richness evaluation hierarchy model; a water-richness index calculation module for calculating the water-richness index value of each borehole using the obtained comprehensive weight and calculating the correction value of the water-richness index based on spatial interaction; and finally combining the obtained water-richness index value with the correction value of the water-richness index to obtain the optimized value of the water-richness index; an evaluation module for judging the water-richness evaluation result of the research area based on the magnitude of the optimized value of the water-richness index.

[0039] The beneficial effect of the present invention is that, compared with the prior art, the present invention uses subjective and objective comprehensive weighting to assign weights to the main control factors, initially quantitatively describes and simulates the distribution of the overall water-richness of the aquifer, and obtains a preliminary water-richness index; subsequently, in order to fully consider the spatial heterogeneity and spatial proximity of the aquifer water-richness, based on the previous research, from the perspective of surveying and mapping, the present invention analyzes the mutual spatial influence between the water-richness indices of adjacent boreholes, constructs an aquifer water-richness evaluation model integrating spatial characteristics, and corrects the water-richness index, thereby improving the accuracy of the water-richness evaluation.

[0040] In addition, to ensure the accuracy of the evaluation of water abundance, the present invention also designs a hierarchical structure model for evaluating the water abundance of aquifers. This hierarchical structure model fully considers the existing geological data, remote sensing image data, etc. in the study area, and selects nine evaluation indicators from two aspects of the water storage performance and water source recharge of the aquifer, namely, "equivalent thickness of sandstone in the overlying strata of the No. 4-2 coal seam, core recovery rate, thickness ratio of brittle and plastic rocks, slope, normalized difference vegetation index, influence of surface water system distribution, and equivalent thickness of sandstone in the overlying strata of the No. 3-1 coal seam, core recovery rate, thickness ratio of brittle and plastic rocks". The selection of evaluation indicators is more comprehensive and objective. At the same time, by designing the hierarchical structure model for evaluating the water abundance of aquifers, a complex decision-making problem is decomposed into multiple levels, making the problem clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flow chart of the method for evaluating the water abundance of mine aquifers considering spatial characteristics in the present invention;

[0042] Figure 2 is a schematic diagram of the hierarchical structure for evaluating the water abundance of aquifers constructed in the method for evaluating the water abundance of mine aquifers considering spatial characteristics in the present invention;

[0043] Figure 3 is a schematic diagram of the degree of formation inclination in the present invention;

[0044] Figure 4 is a schematic diagram of the evaluation result of water abundance obtained by using the method for evaluating the water abundance of aquifers considering spatial characteristics in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0046] As Figure 1 shown, Embodiment 1 of the present invention provides a method for evaluating the water abundance of mine aquifers considering spatial characteristics, including the following steps:

[0047] Step 1: Use the subjective and objective comprehensive weighting method to assign weights to the evaluation indicators for evaluating the water abundance of mine aquifers, obtain the comprehensive weights, and calculate the water abundance index of each borehole. Step 1 specifically includes the following steps:

[0048] Step 1.1: Use the fuzzy analytic hierarchy process to subjectively assign weights to the evaluation indicators.

[0049] The determination of subjective weights draws on the idea of the Fuzzy Analytic Hierarchy Process (FAHP), which specifically includes the following steps:

[0050] As Figure 2 shown, Step 1.1.1: Construct an evaluation hierarchy model for the water-richness of aquifers that includes an objective layer, a criterion layer, and a decision layer.

[0051] Among them, the objective layer includes: the evaluation result of the water-richness of aquifers; the criterion layer includes: water storage performance and water source recharge, which are the criteria used to measure and compare different evaluation indicators; the decision layer includes: 4 -2 equivalent thickness of sandstone in the overlying strata of coal, core recovery rate, thickness ratio of brittle-plastic rocks, slope, normalized difference vegetation index, influence of surface water system distribution, and 3 -1 equivalent thickness of sandstone in the overlying strata of coal, core recovery rate, thickness ratio of brittle-plastic rocks. These 9 alternative evaluation indicators will be evaluated and compared according to the criteria of the criterion layer.

[0052] Specifically, the equivalent thickness of sandstone in the overlying strata of coal seam 4-2, core recovery rate, and thickness ratio of brittle-plastic rocks are evaluation indicators related to water storage performance; slope, normalized difference vegetation index, influence of surface water system distribution, and the equivalent thickness of sandstone in the overlying strata of coal seam 3-1, core recovery rate, and thickness ratio of brittle-plastic rocks are evaluation indicators related to water source recharge.

[0053] Based on the perspective of surveying and mapping, this invention analyzes the mutual spatial influence between the water-richness indices of adjacent boreholes, comprehensively considers the existing geological data, remote sensing image data, etc. in the study area, combines the query of relevant data and the opinions of relevant experts, selects the above 9 evaluation indicators from the two aspects of the water storage performance and water source recharge of the aquifer, and constructs an evaluation hierarchy model for the water-richness of aquifers, decomposing a complex decision-making problem into multiple levels to make the problem clearer.

[0054] Step 1.1.2: Conduct pairwise comparisons in the criterion layer and the decision layer.

[0055] Specifically, first, compare the two criterion layers to determine which one has a more important influence on the evaluation of water-richness; second, pairwise compare all the evaluation indicators in the decision layer under each criterion layer to determine which one has a more important influence on the upper-level criterion layer.

[0056] Step 1.1.3: Use the "0.1 - 0.9 scale" method to score the comparison results in Step 1.1.2 to obtain the membership degree between two evaluation indicators.

[0057] Step 1.1.4: Use the fuzzy analytic hierarchy process to construct a fuzzy complementary judgment matrix between pairwise evaluation indicators.

[0058] Specifically, when making pairwise comparison judgments between different evaluation indicators in the fuzzy analytic hierarchy process, it is necessary to represent the importance degree of one evaluation indicator relative to another.

[0059] The constructed fuzzy complementary judgment matrix is as follows:

[0060]

[0061] In the formula, the element a in the i-th row and j-th column ij represents the membership degree that evaluation indicator P i is more important than evaluation indicator P j

[0062] Step 1.1.5: Calculate the weights of the evaluation indicators in each layer according to the hierarchical structure divided by the aquifer water-richness evaluation hierarchical structure model to obtain the subjective weight W i .

[0063] Specifically, first, calculate the subjective weights of these two criteria in the criterion layer, and then calculate the subjective weights of each evaluation indicator in the decision-making layer. The calculation formula for the subjective weight is as follows:

[0064]

[0065] In the formula, n is the number of evaluation indicators. When calculating the subjective weights of each criterion in the criterion layer, n is 2 for all; when calculating the subjective weights of each evaluation indicator in the decision-making layer, n is 9 for all.

[0066] Step 1.2: Use the entropy weight method to assign objective weights to each evaluation indicator in the decision-making layer.

[0067] Specifically, the objective weights are determined by calculating the information entropy e j of the evaluation indicators, quantifying the information of each evaluation indicator, determining its role degree in the comprehensive evaluation, and objectively considering the contribution of each evaluation indicator to the final evaluation result. Step 1.2 specifically includes the following steps:

[0068] Step 1.2.1: Standardize each evaluation indicator, and its calculation formula is as follows:

[0069]

[0070]

[0071] In the formula, y i is the standardized data; x i is the data before standardization of each evaluation indicator; x min is the minimum value of each evaluation indicator; x max ​is the maximum value of each evaluation index. Equation (1) is applicable to positively correlated indexes, and Equation (2) is applicable to negatively correlated indexes.

[0072] Step 1.2.2: Based on the standardized data, calculate the information entropy e of each evaluation index j , and its calculation formula is as follows:

[0073]

[0074]

[0075] Equation (4) is the information entropy e j calculation formula, where y ij is the standardized data of the j-th evaluation index of the i-th borehole; p ij is the proportion of the characteristics of the j-th evaluation index of the i-th borehole. Calculate its objective weight W respectively through the obtained information entropy of each evaluation index j :

[0076]

[0077] Step 1.3: Construct a comprehensive weight model based on game theory with multiple strategies, and combine the obtained subjective weight and objective weight to form an optimal weight combination.

[0078] Specifically, in the process of determining the comprehensive weight of each evaluation index, introduce the idea of game theory, and thus construct a multi-strategy weight assignment model. Conduct a comprehensive analysis of the weights obtained by the used subjective weight assignment method and objective weight assignment method to identify the optimal weight combination that minimizes the deviation. Step 1.3 specifically includes the following steps:

[0079] Step 1.3.1: Use n weight determination methods to form a weight set w = {w1, w2,... w n} of n weight vectors obtained by using the subjective weight method and the objective weight method, and perform a linear combination on them to obtain the comprehensive weight. The calculation formula of the comprehensive weight is as follows in Equation (6):

[0080]

[0081] In the formula, W represents the comprehensive weight, and θ k is the weight coefficient.

[0082] The game theory model aims to construct a coordinated environment. Through the setting of the environment, promote the fairness and rationality among different evaluation methods. Therefore, it is necessary to obtain the optimal weight coefficient. Through Equation (7), make the deviation between the comprehensive weight and each weight the smallest, and transform it into the form of the linear equation system in Equation (8).

[0083]

[0084]

[0085] The weight linear combination coefficients θ1, θ2, …, θ obtained from the above formula n are normalized, and formula (6) is solved to obtain the comprehensive weight model based on game theory.

[0086] Step 1.4: Use the established comprehensive weight model to calculate the comprehensive weights of each evaluation index.

[0087] Step 1.5: Utilize the obtained comprehensive weights and perform weighted superposition analysis and calculation according to the water-richness evaluation model to obtain the water-richness index of each borehole. Its calculation formula is as shown in formula (9):

[0088]

[0089] In the formula, is the water-richness index of the i-th borehole. The larger its value, the stronger the water-richness; λ i is the comprehensive weight of the i-th evaluation index, and its calculation method is the same as W in formula (6); y i is the value after standardization of the i-th evaluation index.

[0090] Step 2: Construct an optimized water-richness evaluation model considering spatial characteristics and calculate the correction value of the water-richness index under spatial interaction

[0091] Specifically, the present invention uses a spatial analysis method to construct an optimized water-richness evaluation model considering spatial characteristics. This method quantifies and analyzes the mutual influence of water-richness indexes between borehole positions based on spatial distance relationships, including the following steps:

[0092] Step 2.1: Calculate the geographical spatial distances between existing borehole positions in the mining area to evaluate the spatial proximity of each borehole position, and use this as a preliminary quantitative index of spatial proximity.

[0093] Step 2.2: Calculate the spatial weights between each borehole position according to the geographical spatial distances between each borehole position, and construct a spatial weight matrix W n , reflecting the relative distance relationship between borehole positions.

[0094]

[0095]

[0096] In the formula, is the spatial weight between the i-th borehole position and the j-th borehole position; d ijis the geospatial distance between the i-th and j-th drilling points; a represents the distance attenuation coefficient.

[0097] The key of the present invention is to determine the search distance threshold and the distance attenuation coefficient. The setting of the search distance threshold follows the comprehensiveness principle, that is, to ensure that all drilling points in the study area are included in the analysis framework, so as to truly reflect the overall characteristics and internal relationships of the spatial data. The distance attenuation coefficient is used to quantify the interaction strength between spatial units. As the distance increases, the interaction strength decays at a certain rate. In many fields, due to the lack of theoretical models, the selection of the distance attenuation coefficient is often based on empirical values, and the generally set range is

[0098] [1, 2]. However, the distance attenuation coefficient is not fixed. Its selection should consider various influencing factors. In the present invention, the determination of the distance attenuation coefficient should not only be determined based on the spatial distance between two drilling points, but also consider the mutual influence of groundwater in the target formation aquifer with different inclination degrees under the action of gravity. See Figure 2 .

[0099] The flow and distribution of water in the aquifer are affected by the earth's gravity field. Under the action of gravity, groundwater flows from the high potential energy area to the low potential energy area. As Figure 3 shown, the aquifer at drilling point B is at a higher gravity potential position relative to the aquifer at drilling point A. It can be inferred that the influence of the aquifer at drilling point B on the water flow at drilling point A is greater than the influence of the aquifer at drilling point A on the water flow at drilling point B. Therefore, the steps for determining the distance attenuation coefficient in step 2.2 are as follows:

[0100] Step 2.2.1: Calculate the aquifer dip angle between two adjacent drillings in the target formation aquifer grid data.

[0101] Interpolate and statistically analyze the starting depth value of the target formation aquifer, convert it into raster data, and estimate the formation dip degree by calculating the standard deviation σ i of the elevation values within the 3×3 neighborhood of each point in the raster data. The aquifer dip angle between two drillings is represented by b i here, and its calculation formula is as follows:

[0102]

[0103]

[0104] In the formula, N is the total number of raster cells in the neighborhood; h i is the elevation value of the i-th raster cell; is the average value of the elevation values of all raster cells in the neighborhood; L i is the horizontal distance between two drillings.

[0105] Step 2.2.2: Select the distance attenuation coefficient according to the range of the dip angle of the aquifer between two boreholes.

[0106] When the value of the distance attenuation coefficient a is larger, the proportion of the spatial weight is smaller, and the influence on the target is also smaller. The influence of the formation inclination on the water flow is significant. Generally, the study area shows a monoclinic structure trending westward, and the strike and dip of the formation present a broad and gentle wavy shape, with a general dip angle of 1 - 2°, and up to 3° in local areas. Although there is no fixed value that can be used as an absolute standard for the influence of formation inclination on water flow, for effective evaluation in practical applications and combined with the actual situation of the study area, the present invention takes 2° as the critical value b of the influence of formation inclination on water flow, and selects 1, 2, and 3 as the values of a based on past experience. The specific value-taking situations are classified as follows: i For the critical value of the influence on water flow, and comprehensively considering past experience, 1, 2, and 3 are selected as the values of a, and the specific value-taking situations are classified as follows:

[0107]

[0108] Step 2.3: Calculate the corrected value of the water-richness index based on spatial interaction according to the constructed spatial weight matrix.

[0109] After constructing the spatial weight matrix reflecting the spatial relationship between borehole positions, through the spatial weight matrix, calculate the corrected value of the water-richness index based on spatial interaction. The calculation formula is as follows:

[0110]

[0111] In the formula, Z j represents the water-richness index determined by the j-th borehole based on Equation (9), that is

[0112] Step 3: Combine the water-richness index value calculated in Step 1 with the corrected value of the water-richness index to correct the water-richness index of each target borehole position. The mutual influence between positions is considered in the correction process, and finally the optimized value Z i ' of the water-richness index is obtained. Judge the water-richness of the study area through the optimized value of the water-richness index Z i '. The calculation formula of the optimized value Z

[0113]

[0114] In the formula, μ represents the weight relationship between the obtained water-richness index and the corrected value, which is between 0 and 1.

[0115] In order to ensure a comprehensive and accurate assessment of the water-richness of the aquifer in the study area, 57 sets of borehole data were selected and analyzed. These borehole samples not only ensure that their spatial distribution evenly covers the entire study area, but also that each sample contains detailed information on the geological strata, ensuring the breadth and representativeness of the data.

[0116] The water-richness index of each borehole is calculated by the present invention. In order to comprehensively consider the combined effect of the water-richness index calculated by the prior art document 6 and the water-richness index correction value, μ is taken as 0.5 in formula (16), giving the two equal weights to ensure their equal contribution in the final calculation result, thereby providing a more objective and comprehensive analysis result. The final water-richness index of each borehole obtained in this way is based on the present invention. A comprehensive assessment of the water-richness of the aquifer in the study area is carried out, and a water-richness distribution prediction map is drawn for intuitive expression, see Figure 3 .

[0117] Depend on Figure 4 The prediction results of the optimized water-richness evaluation model shown in the figure show that the areas with higher water-richness index are mainly distributed in the northwest and central and southern parts of the study area. According to the actual situation in the study area, the main landforms in the northeast of the study area are hills, with large fluctuations, and the terrain features such as hills and gullies are staggered. The larger slope causes precipitation to be lost in the form of surface runoff, which is not easy to penetrate, and the thickness of sandstone is relatively thin, so the water-richness is weak. The surface slope in the northwest is low, the terrain is flat, and it is covered by loose sand layers. It is a wind-sand beach area with good surface recharge capacity; the thickness of the stratum sandstone is thick, and the rock is also relatively broken, so the water-richness is strong; the central and southern parts are located in the valley areas of Ulanbulagou and Changjiagou, and the recharge capacity is relatively good. In addition, the core sampling rate in this area is low. Affected by the 3-1 coal fire area, the regional rock strata have higher porosity and better fracture water recharge conditions, so the water-richness here is relatively strong. It can be concluded that the prediction results of the optimized water-richness evaluation model are consistent with the actual situation, indicating that the established optimized water-richness evaluation model is feasible.

[0118] Taking a coal mine in northern Shaanxi as an example, the present invention proposes an optimized water-richness evaluation model integrating spatial characteristics based on previous research. The model aims to improve the accuracy of the evaluation by considering the influence of the mutual relationship between drilling points in combination with GIS spatial analysis ideas. Through comparative experiments, it is concluded that the prediction results based on the present invention show higher accuracy, which provides new ideas for the subsequent research on water-richness evaluation of aquifers.

[0119] Embodiment 2 of the present invention provides a system for evaluating the water-richness of a mine aquifer taking into account spatial characteristics, which is used for the method for evaluating the water-richness of a mine aquifer taking into account spatial characteristics in any of the aforementioned embodiments. The system includes: a hierarchical construction module, a weight calculation module, a water-richness index calculation module and an evaluation module.

[0120] Specifically, the hierarchical construction module is used to construct an aquifer water-richness evaluation hierarchical model including a target layer, a criterion layer, and a decision-making layer; the weight calculation module is used to calculate the comprehensive weight of each evaluation index in the decision-making layer using the subjective and objective comprehensive weighting method according to the constructed aquifer water-richness evaluation hierarchical model; the water-richness index calculation module is used to calculate the water-richness index value of each borehole using the obtained comprehensive weight, and calculate the corrected value of the water-richness index based on spatial interaction; finally, the obtained water-richness index value and the corrected value of the water-richness index are combined to obtain the optimized value of the water-richness index; the evaluation module is used to judge the water-richness evaluation result of the study area based on the magnitude of the optimized value of the water-richness index.

[0121] The beneficial effect of the present invention is that, compared with the prior art, the present invention uses subjective and objective comprehensive weighting to assign weights to the main control factors, preliminarily quantitatively describes and simulates the distribution of the overall water-richness of the aquifer, and obtains a preliminary water-richness index; subsequently, in order to fully consider the spatial heterogeneity and spatial proximity of the aquifer water-richness, based on the previous research, from the perspective of surveying and mapping, the present invention analyzes the mutual spatial influence between the water-richness indexes of adjacent boreholes, constructs an aquifer water-richness evaluation model integrating spatial characteristics, and corrects the water-richness index, thereby improving the accuracy of the water-richness evaluation.

[0122] In addition, in order to ensure the accuracy of the water-richness evaluation, the present invention also designs an aquifer water-richness evaluation hierarchical model. This hierarchical model fully considers the existing geological data, remote sensing image data, etc. in the study area, and selects nine evaluation indexes from two aspects of the water storage performance and water source recharge of the aquifer, namely, "equivalent thickness of sandstone in the overlying strata of the 4-2 coal seam, core recovery rate, thickness ratio of brittle and plastic rocks, slope, normalized difference vegetation index, influence of surface water system distribution, and equivalent thickness of sandstone in the overlying strata of the 3-1 coal seam, core recovery rate, thickness ratio of brittle and plastic rocks". The selection of evaluation indexes is more comprehensive and objective. At the same time, by designing the aquifer water-richness evaluation hierarchical model, a complex decision-making problem is decomposed into multiple levels, making the problem clearer.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for evaluating the water richness of a mine aquifer taking into account spatial characteristics, characterized in that: The following steps are involved: The subjective and objective comprehensive weighting method is used to assign weights to the evaluation indicators of the water-richness evaluation of the mine aquifer to obtain the comprehensive weights; The water-richness index value of each borehole is calculated using the obtained comprehensive weight; An optimized water-richness evaluation model considering spatial characteristics was constructed, and the water-richness index correction value based on spatial interaction was calculated; The obtained water-richness index value is combined with the water-richness index correction value to obtain the optimized value of the water-richness index. The water-richness evaluation result of the study area is judged by the size of the optimized value of the water-richness index.

2. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 1, characterized in that: The method of using the subjective and objective comprehensive weighting method to assign weights to the evaluation indicators of the water-richness evaluation of the mine aquifer to obtain the comprehensive weights includes the following steps: A hierarchical model for evaluating the water-richness of aquifers is constructed, which includes a target layer, a criterion layer, and a decision layer. The decision layer is the evaluation index for evaluating the water-richness of aquifers in mines. The standard layer is related to the decision layer and is the standard for measuring and comparing different evaluation indicators. The target layer is the evaluation result of the water-richness of aquifers. The fuzzy analytic hierarchy process is used to calculate the subjective weight of each evaluation index in the decision-making layer according to the hierarchical structure of the aquifer water-richness evaluation hierarchical model. Use the entropy weight method to calculate the objective weight of each evaluation indicator in the decision-making layer; The obtained subjective weights and objective weights are combined to construct a comprehensive weight model of multiple strategies based on game theory to obtain the comprehensive weights.

3. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 2, characterized in that: The decision-making layer includes: -2 Equivalent thickness of sandstone in overlying strata, core sampling rate, brittle-plastic rock thickness ratio, slope, normalized vegetation index, influence of surface water distribution and 3 -1 Equivalent thickness of sandstone in overlying coal formations, core sampling rate, and brittle-plastic rock thickness ratio; The criteria layer includes: water storage performance and water supply; among which, water storage performance is used to measure and compare evaluation indicators: 4 -2 Equivalent thickness of sandstone overlying coal formations, core sampling rate, brittle-plastic rock thickness ratio; water supply is used to measure and compare evaluation indicators: slope, normalized vegetation index, surface water distribution influence and 3 -1 Equivalent thickness of sandstone in overlying coal strata, core sampling rate, and brittle-plastic rock thickness ratio.

4. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 2, characterized in that: The method uses the fuzzy hierarchical analysis method to calculate the subjective weight of each evaluation index in the decision-making layer according to the hierarchical structure divided by the aquifer water-bearing property evaluation hierarchical structure model, including the following steps: Perform pairwise comparisons in the criterion layer and the decision layer to obtain comparison results; The "0.1-0.9 scale" method was used to score the comparison results and obtain the degree of membership between the two evaluation indicators; Using the fuzzy analytic hierarchy process, the fuzzy complementary judgment matrix A between the two evaluation indicators is constructed. n×n , Among them, a ij A is the fuzzy complementary judgment matrix n×n The element in the i-th row and j-th column represents the evaluation index P i Ratio evaluation index P j Important affiliation; According to the hierarchical structure of the aquifer water-richness evaluation hierarchical structure model, the subjective weight of each evaluation index is calculated as follows: Among them, W i It represents the subjective weight of each evaluation indicator; n is the number of evaluation indicators. When calculating the weight of each standard at the criterion layer, n is 2; when calculating the weight of each evaluation indicator at the decision layer, n is 9.

5. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 2, characterized in that: The entropy weight method is used to calculate the objective weight of each evaluation indicator in the decision-making layer, including the following steps: Each evaluation indicator in the decision-making layer is standardized; Based on the standardized data, the information entropy of each evaluation index is calculated; According to the information entropy of each evaluation indicator, its objective weight is calculated as follows: Among them, W j Represents the objective weight of each evaluation index; e j represents information entropy, p ij is the proportion of the features of the jth evaluation index of the i-th borehole, y ij is the standardized data of the jth evaluation index of the ith borehole.

6. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 1, characterized in that: The calculation formula of the water-richness index value of each borehole is: in, represents the water-richness index of the ith borehole; i represents the comprehensive weight of the i-th evaluation index; y i Represents the standardized value of the i-th evaluation index.

7. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 1, characterized in that: The method of constructing an optimized water-richness evaluation model taking into account spatial characteristics and calculating a water-richness index correction value based on spatial interaction comprises the following steps: Calculate the geographic distance between existing drilling points in the mining area; According to the geographic spatial distance between each drilling point, the spatial weight between each drilling point is calculated to construct a spatial weight matrix; According to the constructed spatial weight matrix, the corrected value of the water-richness index based on spatial interaction was calculated.

8. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 7, characterized in that: The constructed spatial weight matrix is: Among them, W n represents the spatial weight matrix; is the spatial weight between the i-th drilling point and the j-th drilling point; d ij is the geographic distance between the i-th drilling point and the j-th drilling point; a is the distance attenuation coefficient, b i It represents the inclination angle of the aquifer between two boreholes.

9. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 8, characterized in that: Calculating the aquifer dip between two boreholes involves the following steps: Interpolate and count the starting depth values ​​of the target stratum aquifer and convert them into raster data; Calculate the standard deviation σ of the elevation values ​​within a 3×3 neighborhood of each point in the raster data i , Where N is the total number of grid cells in the neighborhood; hi is the elevation value of the i-th grid cell; is the average elevation value of all grid cells in the neighborhood; According to the standard deviation σ of the elevation values ​​in the 3×3 neighborhood of each point i , calculate the dip angle b of the aquifer between the two boreholes i , Among them, L i is the horizontal distance between the two boreholes.

10. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 8, characterized in that: The calculation formula of the water richness index correction value is as follows: in, Indicates the water-richness index correction value; Z j Indicates the water-richness index of the jth borehole, λ j represents the comprehensive weight of the jth evaluation index; y j Represents the standardized value of the j-th evaluation index.

11. The method for evaluating the water richness of aquifers in a mine taking into account spatial characteristics according to claim 10, characterized in that: The calculation formula of the optimized value of water richness index is as follows: Among them, Z i ′ represents the optimized value of the water-richness index; μ represents the weight relationship between the obtained water-richness index and the corrected value, which is between 0 and 1; represents the water-richness index of the ith borehole, λ i represents the comprehensive weight of the i-th evaluation index; y i Represents the standardized value of the i-th evaluation index.

12. A mine aquifer water-richness evaluation system taking into account spatial characteristics, used in the mine aquifer water-richness evaluation method taking into account spatial characteristics as claimed in any one of claims 1 to 11, characterized in that: The system comprises: A hierarchical construction module is used to construct a hierarchical model for evaluating the water-bearing quality of an aquifer, which includes a target layer, a criterion layer, and a decision layer; The weight calculation module is used to calculate the comprehensive weight of each evaluation index of the decision-making layer according to the constructed aquifer water-richness evaluation hierarchical structure model using the subjective and objective comprehensive weighting method; The water-richness index calculation module is used to calculate the water-richness index value of each borehole using the obtained comprehensive weight, and calculate the water-richness index correction value based on spatial interaction; finally, the obtained water-richness index value is combined with the water-richness index correction value to obtain the water-richness index optimization value; The evaluation module is used to judge the water-richness evaluation results of the study area based on the size of the optimized value of the water-richness index.

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