A method for evaluating water-richness of weathered bedrock based on multi-source information fusion and multi-model integration

Abstract

The present application discloses a method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration. The analytic hierarchy process, coefficient of variation method, and grey correlation analysis method are coupled to establish a comprehensive evaluation model for the water-richness of aquifers. The sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD, and apparent resistivity that affect the water-richness of weathered bedrock aquifers are selected as the evaluation index system for the water-richness of aquifers, and the prediction results of the water-richness zoning of weathered bedrock aquifers are obtained by ArcGIS spatial overlay. The obtained comprehensive water-richness zoning map is compared with the unit water yield result. The obtained water-richness zoning evaluation results are consistent with the actual situation, which can improve the accuracy of the water-richness evaluation of aquifers.

Description

Technical Field

[0001] The present application relates to the field of mine technology, and in particular to a method for evaluating the water richness of weathered bedrock based on multi-source information fusion and multi-model integration. Background Art

[0002] Coal mine water damage is one of the five major disasters in mine safety production. With the continuous mining of coal mines in my country, the problem of water damage has become increasingly prominent. Therefore, prevention and control measures for water damage are particularly urgent. The water-richness of the aquifer directly affects the problem of mine water damage. In the process of coal mining, the water-richness of the aquifer is directly related to the amount of water inflow in the mine, which in turn affects the production safety and economic benefits of the mine. As one of the main rock formations overlying coal mines, the evaluation of the water-richness of the aquifer of weathered bedrock is of great significance for predicting mine water damage and formulating prevention and control measures.

[0003] Traditional methods for evaluating the water-richness of weathered bedrock aquifers mainly rely on geological exploration data, hydrogeological test data, and empirical judgment. Although these methods can reflect the water-richness characteristics of aquifers to a certain extent, they have problems such as low prediction accuracy, single evaluation index, and inability to fully reflect the complexity of aquifers.

[0004] In order to improve the accuracy and reliability of the water-richness evaluation of weathered bedrock aquifers, domestic and foreign scholars and research institutions have begun to explore new evaluation methods and technical means in recent years. However, traditional evaluation methods often rely on only a limited number of information sources, such as geological exploration data and hydrogeological test data, and lack the comprehensive use of other relevant information. A single evaluation model is often difficult to fully reflect the complexity and uncertainty of aquifers, resulting in biased evaluation results. In addition, existing evaluation methods often lack systematicity and have not formed a complete evaluation process and system, resulting in poor comparability and repeatability of evaluation results. Summary of the invention

[0005] The embodiment of the present application provides a method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration, and adopts a combined weighting approach to improve the accuracy of the water-richness prediction results of weathered bedrock aquifers.

[0006] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0007] The embodiment of the present invention provides a method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration, including: determining multiple evaluation indicators of the water-richness of a target weathered bedrock aquifer, the evaluation indicators at least including sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability, RQD and apparent resistivity; using a hierarchy analysis method and a coefficient of variation method to weight the evaluation indicators respectively, to obtain a subjective weight and an objective weight of each evaluation indicator; weighting the subjective weight and the objective weight to obtain a combined weight of each evaluation indicator; taking unit water consumption as a reference series, calculating the grey correlation coefficient of each evaluation indicator relative to the reference series by a grey correlation analysis method; calculating the weighted grey correlation coefficient of each evaluation indicator and the reference series according to the combined weight and the grey correlation coefficient; and comprehensively evaluating the water-richness of the target weathered bedrock aquifer according to the weighted grey correlation coefficient to obtain a water-richness evaluation result.

[0008] In some possible implementations, the analytic hierarchy process and the coefficient of variation method are used to respectively weight the evaluation indicators to obtain the subjective weight and objective weight of each evaluation indicator, including: using the analytic hierarchy process to subjectively weight the evaluation indicators to obtain the subjective weight of each evaluation indicator; using the coefficient of variation method to objectively weight the evaluation indicators to obtain the objective weight of each evaluation indicator.

[0009] In some possible implementations, the analytic hierarchy process is used to subjectively weight the evaluation indicators to obtain the subjective weight of each evaluation indicator, including: according to the analytic hierarchy process, the evaluation indicators that affect the water-richness of the target weathered bedrock aquifer are divided into three levels, including: taking the water-richness of the target weathered bedrock aquifer as the model target layer, taking the aquifer lithology, the hydrodynamic conditions of the aquifer and the geophysical parameters of the aquifer as the model criterion layer, taking the sand-to-ground ratio, sandstone thickness, the number of sandstone layers, the sand-mud ratio, the unit water yield, the permeability coefficient, the RQD and the apparent resistivity as the model decision layer; constructing a judgment matrix based on the degree of influence of each evaluation indicator on the water-richness of the aquifer; solving the judgment matrix weights through the subjective weight calculation formula to obtain the subjective weights of each evaluation indicator.

[0010] In some possible implementations, the subjective weight calculation formula is expressed as:

[0011]

[0012] Among them, Ci is the subjective weight of the i-th evaluation index, α ij is the value of the i-th row and j-th column in the judgment matrix, and m is the number of evaluation indicators in the judgment matrix.

[0013] In some possible implementations, the coefficient of variation method is used to objectively weight the evaluation indicators to obtain the objective weight of each evaluation indicator, including: calculating the coefficient of variation of each evaluation indicator, and the coefficient of variation is expressed as:

[0014]

[0015] According to the coefficient of variation, the objective weight of each evaluation index is calculated, and the objective weight is expressed as:

[0016]

[0017] Among them, v i is the coefficient of variation of the i-th evaluation index, s i is the standard deviation of the i-th indicator, x i is the average value of the i-th indicator, A i is the objective weight of the ith indicator.

[0018] In some possible implementations, the combined weights are expressed as:

[0019] W i =βA i +(1-β)C i ,

[0020] Among them, W i is the combined weight of the i-th evaluation index, and β is the preference coefficient.

[0021] In some possible implementations, the unit water consumption is used as a reference series, and the grey correlation coefficient of each evaluation index relative to the reference series is calculated by the grey correlation analysis method, including: taking the unit water consumption as a reference series, and taking the sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, permeability coefficient, RQD and apparent resistivity as comparison series; the initial data of the reference series and the comparison series are dimensionless; wherein the dimensionless is realized by the following formula:

[0022]

[0023] Di is the dimensionless data, and xi is the dimensionless value. The grey correlation coefficient of each evaluation index for the reference series is calculated by the following formula:

[0024]

[0025] Among them, ξ i (k) is the grey relational coefficient of the i-th evaluation index for the reference quantity, k is the number of evaluation indexes, n is the number of objects to be evaluated, ρ is the discrimination coefficient, which is 0.5, x0(k) is the k-th element of the reference sequence, x i (k) is x i The kth element of the sequence.

[0026] In some possible implementations, the weighted association coefficient is expressed as:

[0027]

[0028] Among them, r i It is the weighted grey correlation coefficient between the index to be evaluated and the reference series.

[0029] In some possible implementations, the water-richness of the target weathered bedrock aquifer is comprehensively evaluated according to the weighted grey correlation coefficient to obtain a water-richness evaluation result, including: according to the value of the weighted grey correlation coefficient, each evaluation index is superimposed using ARCGIS, the obtained results are zoned by the natural break method, and a comprehensive zone map of the water-richness of the target weathered bedrock aquifer is drawn to obtain the water-richness evaluation result of the target weathered bedrock aquifer.

[0030] In some possible implementations, after obtaining the water-richness evaluation result, the method further includes: verifying the accuracy of the water-richness evaluation result of the target weathered bedrock aquifer by comparing the measured unit water yield with the water-richness comprehensive zoning map.

[0031] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0032] In the embodiment of the present invention, the analytic hierarchy process, the coefficient of variation method, and the grey correlation analysis method are coupled to establish a comprehensive evaluation model for the water-richness of aquifers. The sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD, and apparent resistivity that affect the water-richness of weathered bedrock aquifers are selected as the evaluation index system for the water-richness of aquifers, and the prediction results of the water-richness zoning of weathered bedrock aquifers are obtained by ArcGIS spatial overlay. The obtained comprehensive water-richness zoning map is compared with the unit water yield result, and the obtained water-richness zoning evaluation results are consistent with the actual situation, which can improve the accuracy of the water-richness evaluation of aquifers. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention, the accompanying drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0034] Figure 1 A schematic flow chart of an embodiment of a method for evaluating water-richness of weathered bedrock based on multi-source information fusion and multi-model integration provided for the implementation of the present invention;

[0035] Figure 2a It is the sand-land ratio zoning map;

[0036] Figure 2bThis is the sandstone thickness zoning map;

[0037] Figure 2c This is the zoning diagram of sandstone layers;

[0038] Figure 2d It is the sand-mud ratio zoning diagram;

[0039] Figure 2e It is the unit water yield zoning map;

[0040] Figure 2f is the permeability coefficient zoning diagram;

[0041] Figure 2g is the RQD value partition diagram;

[0042] Figure 2h is the apparent resistivity zoning map;

[0043] Figure 3 It is a structural diagram of the hierarchical analysis method model in an embodiment of the present invention;

[0044] Figure 4 This is a comprehensive zoning map of water-richness of a target weathered bedrock aquifer in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In the relevant description of this embodiment, the terms "including, containing, having" and the like are open terms and are generally understood to include but not be limited to; the term "at least one" is generally understood to mean one or more, where "plurality" refers to two or more; the term "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items, for example, "at least one of a, b or c", or "at least one of a, b and c", can all represent: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple, respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, generally indicating an "or" relationship before and after.

[0047] In the following description of the present embodiment, the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0048] Those skilled in the art should understand that in the following description of the embodiments of the present application, the order of serial numbers does not mean the order of execution, some or all of the steps can be executed in parallel or sequentially, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0049] Those skilled in the art will appreciate that the numerical ranges in the embodiments of the present application are to be construed as also specifically disclosing each intermediate value between the upper and lower limits of the scope. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the range is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the scope.

[0050] Unless otherwise specified, the technical / scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs. Although this application only describes preferred methods and materials, any methods and materials similar or equivalent to these may also be used in the implementation or testing of this application. All documents mentioned in this specification are incorporated by reference to disclose and describe methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0051] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0052] Coal mine water damage is one of the five major disasters in mine safety production. With the continuous mining of coal mines in my country, the problem of water damage has become increasingly prominent. Therefore, prevention and control measures for water damage are particularly urgent. The water-richness of the aquifer directly affects the problem of mine water damage. In the process of coal mining, the water-richness of the aquifer is directly related to the amount of water inflow in the mine, which in turn affects the production safety and economic benefits of the mine. As one of the main rock formations overlying coal mines, the evaluation of the water-richness of the aquifer of weathered bedrock is of great significance for predicting mine water damage and formulating prevention and control measures.

[0053] Traditional methods for evaluating the water-richness of weathered bedrock aquifers mainly rely on geological exploration data, hydrogeological test data, and empirical judgment. Although these methods can reflect the water-richness characteristics of aquifers to a certain extent, they have problems such as low prediction accuracy, single evaluation index, and inability to fully reflect the complexity of aquifers.

[0054] In order to improve the accuracy and reliability of the water-richness evaluation of weathered bedrock aquifers, domestic and foreign scholars and research institutions have begun to explore new evaluation methods and technical means in recent years. However, traditional evaluation methods often rely on only a limited number of information sources, such as geological exploration data and hydrogeological test data, and lack the comprehensive use of other relevant information. A single evaluation model is often difficult to fully reflect the complexity and uncertainty of aquifers, resulting in biased evaluation results. In addition, existing evaluation methods often lack systematicity and have not formed a complete evaluation process and system, resulting in poor comparability and repeatability of evaluation results.

[0055] Based on this, an embodiment of the present invention provides a method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration, which adopts a combined weighting approach to improve the accuracy of the water-richness prediction results of weathered bedrock aquifers.

[0056] Figure 1 A schematic diagram of an embodiment of a method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration provided for the implementation of the present invention, see Figure 1 As shown, the above-mentioned weathered bedrock water-richness evaluation method based on multi-source information fusion and multi-model integration may include:

[0057] S101, determining multiple evaluation indicators of water richness of the target weathered bedrock aquifer, the evaluation indicators at least including sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, rock quality index (RQD) and apparent resistivity;

[0058] It should be noted that, since the water-richness intensity of weathered bedrock aquifers is affected by a variety of complex factors, it is difficult to establish a single deterministic evaluation model when studying the water-richness of aquifers, and the water-richness of aquifers is usually determined by the size of unit water inflow. However, in actual production, due to the small number of hydrological holes in coal mines, the unit water inflow obtained is difficult to reflect the changes in the water-richness of aquifers. Therefore, in order to achieve a comprehensive and objective evaluation of the water-richness of aquifers, the present invention fully exploits the existing hydrogeological data, not only considering direct water-richness indicators (such as unit water inflow), but also incorporating multiple dimensions reflecting geological structures, permeability and physical properties, selecting sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water inflow, permeability coefficient, RQD and apparent resistivity, a total of 8 indicators are used as evaluation indicators, and the water-richness of aquifers is comprehensively evaluated, thereby improving the accuracy and practicality of the evaluation.

[0059] Among them, the sand-to-formation ratio is the ratio of the total thickness of sandstone to the total thickness of the formation. The sand-to-formation ratio can determine the source direction. The larger the sand-to-formation ratio, the higher the proportion of sandstone in the formation, the thicker the sandstone layer or the more numerous it is, thus providing more water storage space and potential water flow channels. Such geological conditions are usually conducive to the storage and migration of groundwater, which enhances the water-richness of the aquifer, so the water-richness of the aquifer is stronger.

[0060] Sandstone thickness is the most intuitive factor that characterizes the water richness of an aquifer. The water in an aquifer is mainly stored in pores or cracks. Due to its unique physical and chemical properties, sandstone has more developed primary pores and cracks than mudstone. These pores and cracks not only provide storage space for groundwater, but also constitute channels for groundwater migration. When sandstone is affected by tectonic stress, it is more likely to form surface network cracks, which further increase the water storage space of the sandstone. Therefore, as the thickness of sandstone increases, its internal water storage space also increases, and the water richness of the aquifer naturally increases gradually.

[0061] The more layers of sandstone there are, the greater the difference in sandstone grain size. Medium-grained sandstone is more water-rich, while silty sandstone is less water-rich. The number of sandstone layers has a certain impact on the water content of the aquifer. The fewer sandstone layers, the stronger the water-richness. Medium-grained sandstone has moderate grains and well-developed pores and cracks, so it is more water-rich; while silty sandstone has relatively small grains and relatively few pores and cracks, so it is relatively weaker in water-richness. When there are more sandstone layers, sandstones of different grain sizes are intertwined, which may form a more complex water storage structure, but it may also increase the difficulty of groundwater migration. However, if there are fewer sandstone layers and the medium-grained sandstone with stronger water-richness is dominant, the water-richness of the aquifer is usually stronger.

[0062] The sand-mud ratio is the ratio of the thickness of sandstone to the thickness of mudstone. Sandstone has strong permeability, while mudstone has weaker permeability. The larger the sand-mud ratio, the higher the proportion of sandstone in the aquifer, and the stronger its permeability and water storage capacity.

[0063] The unit water yield of a borehole can directly reflect the water richness of an aquifer. It indicates the amount of water extracted from an aquifer per unit time and per unit area. This indicator can directly reflect the water richness and water resources of an aquifer. When the unit water yield is large, it means that the groundwater resources in the aquifer are abundant, the water storage capacity is strong, and the water richness is naturally relatively strong.

[0064] The permeability coefficient is a constant that indicates the permeability of rock formations. It reflects the connectivity of pores and cracks in rocks and the migration speed of groundwater. The larger the permeability coefficient, the better the connectivity of rock cracks and the faster the migration speed of groundwater in rocks. Such geological conditions are conducive to the storage and migration of groundwater, which enhances the water-richness of aquifers.

[0065] The rock quality index (RQD) can reflect the integrity of the rock mass. It represents the ratio of the sum of the lengths of intact rock segments greater than 10 cm (or as specified) in the drill core to the total core length. The larger the RQD value, the better the integrity of the rock mass, the fewer cracks and pores in the rock, and the stronger the water-richness of the rock formation.

[0066] Apparent resistivity reflects the changes in the conductivity of rocks and ores to infer the actual distribution of different electrical bodies underground. The geoelectric section obtained through geophysical inversion can intuitively show the resistivity distribution characteristics of underground rock formations. The low-resistance anomaly can reflect the water content of the geological body to a certain extent. The smaller the apparent resistivity, the greater the water content of the rock formation and the better the water-richness of the rock formation.

[0067] In some embodiments, since the evaluation indicators selected for evaluating the water-richness of aquifers usually cover multiple different aspects such as geology, hydrology, and physical properties, such as the above-mentioned 8 evaluation indicators. Since these evaluation indicators have measurement standards, their dimensions are often inconsistent. For example, the thickness of sandstone may be measured in meters or centimeters, while the permeability coefficient may be measured in centimeters per second or meters per day. If this dimensional inconsistency is used directly for data analysis, it may cause deviations or errors in the research results. Therefore, in order to avoid the negative impact of dimensional inconsistency on the research results, the statistical values ​​of each evaluation indicator can be dimensionless before data analysis.

[0068] Dimensionless processing is a mathematical method that converts raw data into relative values ​​without dimensions so that data of different dimensions can be compared and analyzed on the same scale. This processing can eliminate the influence of dimensional differences on research results, making the analysis results more accurate and reliable. Through dimensionless processing, the statistical values ​​of each main controlling factor can be converted into relative values ​​comparable on the same scale, so as to more accurately evaluate their influence on the water-richness of the aquifer. In actual operation, there are many methods of dimensionless processing, such as standardization, normalization, extreme difference, etc. The specific method can be selected according to the needs of the actual application process.

[0069] Exemplarily, Table 1 and Table 2 are a set of example data statistically obtained based on drilling data in an embodiment of the present invention and a data statistical table of each evaluation index after normalization.

[0070] Table 1:

[0071]

[0072]

[0073] Table 2:

[0074]

[0075] In some embodiments, after obtaining the actual detection data of each evaluation index, the corresponding partition diagrams can be drawn according to the corresponding data. For example, according to the contents shown in Table 1 and Table 2 above, the drawn partition diagrams can be respectively as follows: Figure 2a to Figure 2h As shown. Among them, Figure 2a This is the sand-land ratio zoning map. Figure 2b is the sandstone thickness zoning map, Figure 2c The sandstone layer zoning diagram is shown in Figure 1. Figure 2d is the sand-mud ratio zoning diagram, Figure 2e The unit water yield zoning diagram is: Figure 2f is the permeability coefficient zoning diagram, Figure 2g is the RQD value partition diagram, Figure 2h This is the apparent resistivity zoning map.

[0076] In the embodiment of the present invention, the zoning map of each evaluation index can intuitively display the spatial distribution characteristics of each index data. By using different colors, shadows or symbols to represent different numerical ranges, researchers can quickly grasp the distribution of each index in different regions, thereby preliminarily judging the possible spatial differences in the water-richness of the aquifer. When constructing an aquifer water-richness evaluation model, the zoning map can be used as one of the input data. By combining the zoning map of each index with other information such as geology and hydrology, a more accurate and reliable prediction model can be constructed to evaluate the water-richness and water resource potential of the aquifer.

[0077] S102, using the analytic hierarchy process and the coefficient of variation method to assign weights to the evaluation indicators respectively, and obtaining the subjective weight and objective weight of each evaluation indicator;

[0078] In some embodiments, in the above step S102, the hierarchical analysis method can be used to subjectively weight the evaluation indicators to obtain the subjective weight of each evaluation indicator; the coefficient of variation method can be used to objectively weight the evaluation indicators to obtain the objective weight of each evaluation indicator.

[0079] The analytic hierarchy process (AHP) is to decompose a complex problem or decision into several components, and then combine them at different levels according to the interrelated influences and affiliations between the components. Taking the requirements of the previous layer as the criterion, the priority weight of each factor to the previous layer is obtained through pairwise judgment, comparison and calculation.

[0080] Specifically, the hierarchical analysis method is used to subjectively weight the evaluation indicators and obtain the subjective weight of each evaluation indicator, including:

[0081] According to the analytic hierarchy process, the evaluation indicators affecting the water-richness of the target weathered bedrock aquifer are divided into three levels, which can include the following steps:

[0082] S21, with the water-richness of the target weathered bedrock aquifer as the target layer of the model, the lithology of the aquifer, the hydrodynamic conditions of the aquifer and the geophysical parameters of the aquifer as the criterion layer of the model, and the sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD and apparent resistivity as the decision layer of the model;

[0083] For example, Figure 3 Schematic diagram of the structure of the hierarchical analysis method model in the embodiment of the present invention. Figure 3 As shown in the figure, according to the principle of hierarchical analysis method, the various index factors affecting the water-richness of weathered bedrock aquifers are divided into three hierarchical structures. First, the top layer of the model is the target layer (A layer), which represents the final evaluation target, that is, the water-richness evaluation of weathered bedrock aquifers (A). The criterion layer (B layer) of the model is established as a bridge between the target layer and the specific decision layer. In the criterion layer, the three core attributes of the aquifer are comprehensively considered: aquifer lithology (B1), aquifer hydrodynamic conditions (B2) and aquifer geophysical parameters (B3). Finally, the above eight evaluation indicators are divided into the decision layer (C layer), namely, the number of sandstone layers (C1), sandstone thickness (C2), sand-to-ground ratio (C3), sand-to-mud ratio (C4), RQD (C5), unit water yield (C6), permeability (C7) and apparent resistivity (C8). The criterion layer of C1 to C5 is B1, the criterion layer of C6 and C7 is B2, and the criterion layer of C8 is B3.

[0084] Through such hierarchical division, we can more systematically analyze the impact of various indicators on the water-richness of weathered bedrock aquifers, thereby obtaining more accurate and comprehensive evaluation results.

[0085] S22, based on the influence of each evaluation index on the water-richness of the aquifer, a judgment matrix is ​​constructed;

[0086] Exemplarily, also taking the data in Table 1 and Table 2 as an example, the constructed judgment matrix is ​​shown in the following Table 3.

[0087] Table 3:

[0088]

[0089] S23, solving the judgment matrix weight through the subjective weight calculation formula to obtain the subjective weight of each evaluation index.

[0090] In some embodiments, the subjective weight calculation formula can be expressed by the following formula (1):

[0091]

[0092] Among them, C i is the subjective weight of the i-th evaluation index, αij is the value of the i-th row and j-th column in the judgment matrix, and m is the number of evaluation indicators in the judgment matrix.

[0093] Finally, through the above formula (1), taking the data shown in Table 3 as an example, the subjective weights of each indicator can be calculated as shown in the following Table 4.

[0094] Table 4:

[0095]

[0096] In some embodiments, the coefficient of variation method is an objective weighting method based on the law of the data itself, without interference from subjective factors. When assigning weights using the coefficient of variation method, the main focus is on measuring the differences between the observations. The larger the difference, the greater its importance and the greater its weight. In the application of the coefficient of variation method, the coefficient of variation of each observation is first calculated. The coefficient of variation is the ratio of the standard deviation to the mean, which can effectively reflect the degree of dispersion of the data, that is, the size of the difference between the data.

[0097] When using the coefficient of variation method for weighting, if the coefficient of variation of an observation is large, it means that the observation shows a large difference in the overall data, so its importance in the evaluation system is correspondingly higher. Based on this logic, a larger weight can be given to this observation to fully reflect its key position in the overall evaluation. Objectively weighting the evaluation indicators through the coefficient of variation method completely relies on the characteristics of the data itself, without introducing any subjective judgment or preference, thereby minimizing the impact of human factors on the evaluation results.

[0098] In some embodiments, the coefficient of variation method is used to objectively weight the evaluation indicators to obtain the objective weight of each evaluation indicator, including:

[0099] Calculate the coefficient of variation of each evaluation index; the coefficient of variation can be expressed by the following formula (2):

[0100]

[0101] According to the coefficient of variation, the objective weight of each evaluation index is calculated. The objective weight can be expressed by the following formula (3):

[0102]

[0103] Among them, v i is the coefficient of variation of the i-th evaluation index, s i is the standard deviation of the ith indicator, is the average value of the i-th indicator, A i is the objective weight of the ith indicator.

[0104] For example, according to the above formulas (2) and (3), the objective weights of the evaluation indicators can be obtained by calculation using the data shown in Table 1 and Table 2 as shown in Table 5 below.

[0105] Table 5:

[0106]

[0107] S103, integrating the subjective weight and the objective weight to obtain the combined weight of each evaluation index;

[0108] It should be noted that in step S103, a new weighting result can be obtained by using subjective weights (analytic hierarchy process) and objective weights (coefficient of variation method). This model can not only effectively integrate subjective and objective information, but also reduce the inaccuracy caused by a single weighting method.

[0109] In some embodiments, the combined weight may be expressed by the following formula (4):

[0110] W i =βA i +(1-β)C i (4)

[0111] Among them, W i is the combined weight of the i-th evaluation index, β is the preference coefficient, and the value of β can usually be 0.5.

[0112] Exemplarily, according to the subjective weights and objective weights shown in Tables 4 and 5 above, the combined weights determined by the above formula (4) can be as shown in the following Table 6.

[0113] Table 6:

[0114]

[0115]

[0116] S104, taking the unit water consumption as a reference series, calculating the grey correlation coefficient of each evaluation index relative to the reference series by grey correlation analysis method;

[0117] It should be noted that grey correlation analysis mainly focuses on the analysis of correlation coefficients, which is actually a geometric comparison of a series of data sequences that reflect the characteristics of factor changes, and uses the similarity of geometric shapes to represent the correlation between various factors.

[0118] Specifically, the above step S104 may include:

[0119] S1041, taking unit water consumption as the reference series, and taking sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, permeability coefficient, RQD and apparent resistivity as the comparison series;

[0120] Exemplarily, the reference number series and the comparison number series and their corresponding parameter values ​​may be shown in Table 7 below.

[0121] Table 7:

[0122]

[0123] S1042, performing dimensionless processing on the initial data of the reference sequence and the comparison sequence; wherein the dimensionless processing is implemented by the following formula (5):

[0124]

[0125] Among them, Di is the dimensionless data, and xi is the value before dimensionless;

[0126] S1043, calculate the grey correlation coefficient of each evaluation index for the reference series by the following formula (6):

[0127]

[0128] Among them, ξ i (k) is the grey relational coefficient of the i-th evaluation index for the reference quantity, k is the number of evaluation indexes, n is the number of objects to be evaluated, ρ is the discrimination coefficient, which is 0.5, x0(k) is the k-th element of the reference sequence, x i (k) is x i The kth element of the sequence.

[0129] Exemplarily, taking the above Table 7 as an example, the grey correlation coefficients of the indicators calculated by the above formula (6) can be shown in the following Table 8.

[0130] Table 8:

[0131]

[0132] S105, calculating the weighted grey correlation coefficient of each evaluation index and the reference series according to the combined weight and the grey correlation coefficient;

[0133] In some embodiments, the weighted grey relational coefficient can be expressed by the following formula (8):

[0134]

[0135] Among them, r i It is the weighted grey correlation coefficient between the index to be evaluated and the reference series.

[0136] For example, taking the data shown in Table 8 and Table 6 as an example, the weighted grey correlation coefficient of the index to be evaluated and the reference series is calculated by the above formula (8), and the weighted grey correlation coefficient vector can be obtained as follows: a = (a1, a2, ... a8) = (0.02177, 0.058886, 0.059092, 0.067823, 0.21753, 0.063856, 0.093267, 0.182934).

[0137] S106, based on the weighted grey correlation coefficient, comprehensively evaluate the water-richness of the target weathered bedrock aquifer to obtain a water-richness evaluation result.

[0138] In some embodiments, the water-richness evaluation results include, but are not limited to, water-richness grade classification, water-richness distribution map, and the degree of influence of evaluation indicators.

[0139] Among them, water-richness classification: According to the value of weighted grey correlation coefficient, the water-richness of the target weathered bedrock aquifer is divided into different levels, such as strong water-rich area, relatively strong water-rich area, medium water-rich area, relatively weak water-rich area and weak water-rich area, etc. It helps to intuitively understand the water-richness performance of the aquifer.

[0140] Water-rich distribution map: By drawing a comprehensive water-rich zoning map of the target weathered bedrock aquifer, the water-rich distribution of the aquifer in different regions can be clearly displayed. This helps to identify areas with strong water-richness and provide guidance for the development and utilization of water resources.

[0141] Influence degree of evaluation index: According to the calculation results of weighted grey correlation coefficient, the influence degree of each evaluation index on water-bearing property can be analyzed. It helps to understand which factors are the key factors affecting the water-bearing property of aquifers and provide a basis for subsequent hydrogeological surveys and water resources management.

[0142] In some embodiments, the above step S106 may specifically include:

[0143] According to the value of weighted grey correlation coefficient, ARCGIS is used to overlay each evaluation index, and the obtained results are partitioned by natural break method. The natural break method can automatically determine the partition boundary based on the statistical characteristics of the data, so that the data difference within each zone is minimized, while the difference between each zone is maximized. According to the partition results, a comprehensive zoning map of the water-richness of the target weathered bedrock aquifer is drawn. By interpreting and analyzing the comprehensive zoning map, information such as the water-richness level of different regions can be determined, thereby obtaining the evaluation results of the water-richness of the target weathered bedrock aquifer.

[0144] For example, Figure 4 This is a comprehensive water-rich zoning map of a target weathered bedrock aquifer in an embodiment of the present invention. Figure 4As shown in the figure, by drawing a zoning map, the target weathered bedrock aquifer is divided into weak water-rich area, strong water-rich area, medium water-rich area and strong water-rich area, and different colors are used to represent different water-rich area levels. Through the drawn zoning map, the water-richness of the target weathered bedrock aquifer can be intuitively observed, and the water-richness of the target weathered bedrock aquifer can be evaluated.

[0145] In some embodiments, after obtaining the water-richness evaluation result, the method may further include:

[0146] By comparing the measured unit water yield with the comprehensive water-richness zoning map, the accuracy of the water-richness evaluation results of the target weathered bedrock aquifer is verified.

[0147] Specifically, first, the unit water yield data of the target weathered bedrock aquifer can be obtained from actual exploration or monitoring work. These data can be obtained through professional hydrogeological testing methods in representative locations or boreholes. After obtaining the measured data, necessary preprocessing can be performed on it, including data cleaning, outlier removal and standardization, etc., to ensure the accuracy and consistency of the data. Then the preprocessed measured unit water yield data is compared with the comprehensive water-richness zoning map. It is used to verify whether the actual water yield of different water-richness grade areas in the zoning map is consistent with expectations. By comparison, it can be found which areas have good water-richness evaluation results consistent with the measured data, and which areas may have deviations or need further investigation. Based on the results of comparative analysis, the accuracy of the water-richness evaluation results can be verified. If it is found that the evaluation results of some areas are significantly deviated from the measured data, these areas can be reviewed and adjusted to ensure the accuracy and reliability of the evaluation results. This includes re-evaluating the weight of the evaluation indicators, adjusting the zoning boundaries or updating the evaluation indicator data. Finally, the results of comparative analysis and verification adjustment can be compiled into a report for subsequent research and decision-making reference. The report can record in detail the source of the measured data, processing methods, results of comparative analysis, and specific measures and basis for verification and adjustment.

[0148] For example, see also Figure 4 As shown in Figure 2, unit water yield is the ultimate indicator for testing the water-richness of aquifers. Figure 4 From the measured unit water yield and the water-rich zoning results, it can be seen that the unit water yield of boreholes SSG-3 and FHG-4 is relatively large, falling in the strong water-rich area; the unit water yield of borehole DCC-2 is relatively small, falling in the weak water-rich area; the unit water yield of the remaining boreholes is medium, falling in the medium and strong water-rich areas, indicating that the GRA-AHP-CV comprehensive evaluation results are consistent with the measured unit water yield distribution, and the evaluation results are relatively accurate.

[0149] In the embodiment of the present invention, the analytic hierarchy process, the coefficient of variation method, and the grey correlation analysis method are coupled to establish a comprehensive evaluation model for the water-richness of aquifers. The sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD, and apparent resistivity that affect the water-richness of weathered bedrock aquifers are selected as the evaluation index system for the water-richness of aquifers, and the prediction results of the water-richness zoning of weathered bedrock aquifers are obtained by ArcGIS spatial overlay. The obtained comprehensive water-richness zoning map is compared with the unit water yield result, and the obtained water-richness zoning evaluation results are consistent with the actual situation, which can improve the accuracy of the water-richness evaluation of aquifers.

[0150] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments.

[0151] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for evaluating the water-richness of weathered bedrock based on multi-source information fusion and multi-model integration, characterized in that: include: Determine multiple evaluation indicators of water richness of the target weathered bedrock aquifer, wherein the evaluation indicators at least include sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD and apparent resistivity; The evaluation indicators are weighted by using the analytic hierarchy process and the coefficient of variation method to obtain the subjective weight and the objective weight of each evaluation indicator; Integrate the subjective weight and the objective weight to obtain the combined weight of each evaluation index; Taking the unit water consumption as a reference series, the grey correlation coefficient of each evaluation index relative to the reference series is calculated by grey correlation analysis method; Calculating the weighted grey relational coefficient of each evaluation index and the reference series according to the combined weight and the grey relational coefficient; According to the weighted grey correlation coefficient, the water-richness of the target weathered bedrock aquifer is comprehensively evaluated to obtain a water-richness evaluation result.

2. The method according to claim 1, characterized in that The method of using the analytic hierarchy process and the coefficient of variation method to respectively weight the evaluation indicators to obtain the subjective weight and objective weight of each evaluation indicator includes: using the analytic hierarchy process to subjectively weight the evaluation indicators to obtain the subjective weight of each evaluation indicator; using the coefficient of variation method to objectively weight the evaluation indicators to obtain the objective weight of each evaluation indicator.

3. The method according to claim 2, characterized in that The step of using the analytic hierarchy process to subjectively weight the evaluation indicators to obtain the subjective weight of each evaluation indicator includes: According to the analytic hierarchy process, the evaluation indicators affecting the water-richness of the target weathered bedrock aquifer are divided into three levels, including: taking the water-richness of the target weathered bedrock aquifer as the model target layer, taking the lithology of the aquifer, the hydrodynamic conditions of the aquifer and the geophysical parameters of the aquifer as the model criterion layer, taking the sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, unit water yield, permeability coefficient, RQD and apparent resistivity as the model decision layer; Based on the influence of each evaluation index on the water-richness of the aquifer, a judgment matrix is ​​constructed; The judgment matrix weight is solved by a subjective weight calculation formula to obtain the subjective weight of each evaluation index.

4. The method according to claim 3, characterized in that The subjective weight calculation formula is expressed as: Among them, Ci is the subjective weight of the i-th evaluation index, α ij is the value of the i-th row and j-th column in the judgment matrix, and m is the number of evaluation indicators in the judgment matrix.

5. The method according to claim 4, characterized in that The method of objectively weighting the evaluation indicators by the coefficient of variation method to obtain the objective weight of each evaluation indicator includes: The coefficient of variation of each evaluation index is calculated, and the coefficient of variation is expressed as: According to the coefficient of variation, the objective weight of each evaluation index is calculated, and the objective weight is expressed as: Among them, v i is the coefficient of variation of the i-th evaluation index, s i is the standard deviation of the ith indicator, is the average value of the i-th indicator, A i is the objective weight of the ith indicator.

6. The method according to claim 5, characterized in that The combined weight is expressed as: IN i =βA i +(1-β)C i , Among them, W i is the combined weight of the i-th evaluation index, and β is the preference coefficient.

7. The method according to claim 6, characterized in that The method of taking the unit water consumption as a reference series and calculating the grey correlation coefficient of each evaluation index relative to the reference series by grey correlation analysis method includes: The unit water consumption is taken as a reference series, and the sand-to-ground ratio, sandstone thickness, number of sandstone layers, sand-mud ratio, permeability coefficient, RQD and apparent resistivity are taken as comparison series; The initial data of the reference sequence and the comparison sequence are dimensionally non-dimensionalized; wherein the dimensionless processing is implemented by the following formula: Among them, Di is the dimensionless data, and xi is the value before dimensionless; The grey correlation coefficient of each evaluation index for the reference series is calculated by the following formula: Among them, ξ i (k) is the grey relational coefficient of the i-th evaluation index for the reference quantity, k is the number of evaluation indexes, n is the number of objects to be evaluated, ρ is the discrimination coefficient, which is 0.5, x0(k) is the k-th element of the reference sequence, x i (k) is the kth element of the xi sequence.

8. The method according to claim 7, characterized in that The weighted correlation coefficient is expressed as: Among them, r i It is the weighted grey correlation coefficient between the index to be evaluated and the reference series.

9. The method according to claim 8, characterized in that The method of comprehensively evaluating the water-richness of the target weathered bedrock aquifer according to the weighted grey correlation coefficient to obtain a water-richness evaluation result includes: According to the value of the weighted grey correlation coefficient, ARCGIS is used to perform superposition processing on each evaluation index, and the obtained results are partitioned by the natural break method to draw a comprehensive zoning map of the water-richness of the target weathered bedrock aquifer, thereby obtaining the evaluation result of the water-richness of the target weathered bedrock aquifer.

10. The method according to claim 9, characterized in that After obtaining the water-richness evaluation result, the method further comprises: By comparing the measured unit water yield with the comprehensive water-richness zoning map, the accuracy of the water-richness evaluation results of the target weathered bedrock aquifer is verified.

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