Karst groundwater main runoff zone analysis method and system

Abstract

The invention relates to the technical field of karst hydrogeological exploration, in particular to a karst groundwater main runoff zone analysis method and system. The method comprises the following steps: dividing and pre-analyzing a hydrogeological structure of an exploration area; performing data acquisition to obtain a high-density resistivity profile map and an audio induced polarization anomaly map; selecting an area with abnormally low resistivity or abnormally high audio induced polarization as a charging point, and arranging a charging array around the charging point to obtain a first charging anomaly graph; according to the target layer and the constraint condition, establishing models of different lithology combinations; inputting current to the karst aquifer in the type through the charging array, and measuring the distribution and attenuation conditions of the current to obtain a second charging anomaly graph; according to the relative height difference of apparent resistivity in the second charging anomaly graph, the position and the scale of a hidden karst gap are judged, and the range of a karst intense development section and an underground runoff zone of the karst intense development section is delineated in combination with constraint conditions of the model. According to the technical scheme, the exploration precision and efficiency of the karst groundwater main runoff zone can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of karst hydrogeological exploration, and in particular to a method and system for analyzing a main runoff zone of karst groundwater. Background Art

[0002] Karst groundwater is an important component of the groundwater system, and its storage and migration are often affected by complex geological structures and karst development. Accurately identifying and analyzing the main runoff zone of karst groundwater is of great significance for water resource development and groundwater pollution prevention and control. However, due to the characteristics of karst development such as heterogeneity, concealment and multi-layer, traditional exploration methods are generally difficult to accurately and efficiently determine the main runoff zone of karst groundwater.

[0003] In the prior art, methods such as hydrogeological surveys, hydrogeological tests and geophysical surveys are usually used for the exploration of karst groundwater. These methods can reveal the basic characteristics of karst geology and the flow patterns of groundwater to a certain extent, but are limited by the exploration accuracy and efficiency. For example, the hydrogeological survey method relies on the observation of geological structures and surface karst phenomena, and it is difficult to accurately reflect the development of underground karst; the hydrogeological test method needs to be carried out in areas with large changes in groundwater levels, which is time-consuming and costly; and traditional geophysical exploration methods, such as electrical exploration and seismic exploration, although they can obtain the physical parameters of underground media, are not ideal when applied under complex karst geological conditions. Summary of the invention

[0004] The purpose of the present invention is to propose a method for analyzing the main runoff zone of karst groundwater. The technical solution can improve the exploration accuracy and efficiency of the main runoff zone of karst groundwater.

[0005] To achieve the above-mentioned objectives, in a first aspect, an embodiment of the present disclosure provides a method for analyzing the main runoff zone of karst groundwater, comprising: dividing and pre-analyzing the lithology and its combination characteristics and hydrogeological structure characteristics of the exploration area; conducting comprehensive geophysical exploration to collect data and obtain a high-density resistivity profile and an audio-frequency induced polarization anomaly map; selecting areas with abnormally low resistivity or abnormally high audio-frequency induced polarization as charging points, and deploying charging arrays around the charging points to obtain a first charging anomaly map; establishing a hydrogeological structure model of different lithology combinations according to the target layer and constraints; inputting current into the karst aquifer in the hydrogeological structure model through the charging array and measuring the distribution and attenuation of the current to obtain a second charging anomaly map; judging the location and scale of hidden karst voids according to the relative height difference of apparent resistivity in the second charging anomaly map, and delineating the scope of the karst strongly developed section and its underground runoff zone in combination with the constraints of the hydrogeological structure model.

[0006] Beneficial effects of the basic scheme: By dividing the stratum lithology and its combination characteristics and hydrogeological structure characteristics of the exploration area in detail, and using high-density resistivity profiles and audio-frequency induced polarization anomaly maps for data collection, the lithology, distribution and karst development of the concealed strata can be more accurately reflected. High-density resistivity profiles and audio-frequency induced polarization anomaly maps can be used to determine key work areas, and targeted exploration can be carried out in areas where karst development may exist, thereby improving exploration efficiency.

[0007] By establishing a hydrogeological structure model with different lithology combinations and inputting current into the karst aquifer in the hydrogeological structure model through a charging array, the distribution and attenuation of current in the karst aquifer can be simulated. This method provides a strong physical model support for explaining the characteristics of underground karst development and helps to more accurately understand the flow law of karst groundwater.

[0008] The location and scale of hidden caves, karst gullies or karst fissures are determined based on the relative height difference of apparent resistivity in the second charging anomaly map, and the scope of the intensively developed karst section and its underground runoff zone is delineated in combination with the constraints of the hydrogeological structure model. This not only improves the accuracy of identifying the main runoff zone, but also provides a reliable scientific basis for subsequent groundwater exploitation, water resources management and groundwater pollution prevention and control.

[0009] As an implementable preferred solution, data acquisition is performed to obtain a high-density resistivity profile and an audio frequency induced polarization anomaly map, including the following contents: The exploration profile is arranged according to the hydrogeological conditions and geographical features of the exploration area; high-density resistivity meter and audio frequency induced polarization meter are used for data collection; the high-density resistivity meter measures the resistivity value of each measuring point by laying out multiple measuring lines, setting multiple measuring points on each measuring line; the audio frequency induced polarization meter measures the induced polarization effect of rock formations by emitting alternating electromagnetic fields.

[0010] As an implementable preferred solution, a charging array is arranged around the charging point to obtain a first charging anomaly map, including the following contents: Multiple electrodes are arranged around the charging point to form a charging array; current is input into the underground medium through the electrodes, and the distribution and attenuation of the current in the underground medium are measured; the collected charging data are processed and analyzed to obtain a first charging anomaly map.

[0011] As an implementable optimal solution, a hydrogeological structure model with different lithology combinations is established according to the target layer and constraint conditions, including the following contents: The karst aquifer is taken as the target layer, and the resistivity characteristics and position of the target layer are determined by identifying the resistivity according to the high-density resistivity profile and the first charging anomaly map; the hydrogeological structure model is constrained by taking the lithologic segment interface and its bottom as constraints; and the hydrogeological structure model is inverted using a two-dimensional or three-dimensional inversion algorithm.

[0012] As an implementable preferred solution, it also includes: The collected geological, hydrogeological and geophysical data are cleaned; features indicative of karst development and groundwater are extracted from the raw data; the first deep learning model is trained using the preprocessed data; the data in the survey area are predicted using the first deep learning model to predict and identify potential karst water storage system areas.

[0013] As an implementable preferred solution, it also includes: The prediction results are given in the form of probability or score, indicating the probability or confidence that the area is a karst water storage system; the exploration priority of the area is ranked according to the probability or score of the prediction results.

[0014] As an implementable preferred solution, the first deep learning model is trained using a deep learning model. The convolutional neural network extracts features through convolutional layers and pooling layers, and performs classification or regression through fully connected layers; the recurrent neural network captures the time dependency in sequence data through recurrent units; the training data includes areas with known karst water storage systems and areas with known non-karst water storage systems. Cross-validation and data enhancement are used to improve the generalization ability of the model, and evaluation and optimization are performed after the model training is completed.

[0015] As an implementable preferred solution, it also includes: Read the resistivity measurement data and perform cleaning and normalization to convert the data into image form; Randomly generate N electrode position and quantity combinations as the initial population; Perform cluster analysis or image recognition on each individual to identify abnormal resistivity areas; Calculate the fitness value, select excellent individuals to enter the next generation according to the fitness value, perform crossover and mutation operations on the selected individuals, and generate a new electrode layout plan; Dynamically adjust the crossover probability and mutation probability according to the fitness value distribution of the current population; Check whether the maximum number of iterations is reached or whether the population fitness value reaches the preset threshold. If the termination condition is met, the optimal electrode layout plan is output.

[0016] As an implementable optimal solution, the crossover probability and mutation probability are dynamically adjusted according to the fitness value distribution of the current population, including the following: Calculate the mean and standard deviation of the fitness values ​​of all individuals in the current population; increase the crossover probability when the fitness value distribution of the population is dispersed; and reduce the crossover probability when the fitness value distribution of the population is concentrated; The variance of the calculated population fitness reflects the degree of difference between individuals; when the difference in population fitness is large, the probability of mutation increases; when the difference in population fitness is small, the probability of mutation decreases.

[0017] In a second aspect, the disclosed embodiments also provide a karst groundwater main runoff zone analysis system, which utilizes the above-mentioned karst groundwater main runoff zone analysis method. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a logical diagram of the analysis method for the main runoff zone of karst groundwater. DETAILED DESCRIPTION

[0019] In order to make the technical solution and advantages of the present application clearer, the technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings. It will be understood that the specific embodiments described herein are only partial embodiments of the present invention, which are only used to explain the present application, rather than to limit the present application. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. The same reference numerals appearing in the drawings of the following embodiments represent the same features or components, which can be applied to different embodiments.

[0020] In addition, unless otherwise defined, the technical terms or scientific terms used in the description of the present invention should have the common meanings understood by those skilled in the art in the art to which the present invention belongs.

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings: Embodiment 1 Reference Figure 1 , the analysis method of the main runoff zone of karst groundwater includes the following steps.

[0022] Step S100, collect basic data such as geology, hydrogeology, geophysics, etc. of the exploration area (belt), including but not limited to geological maps, hydrogeological maps, geophysical exploration reports, etc. Through the collation and analysis of these data, a preliminary understanding of the geological structure, stratum lithology and its distribution, surface karst development, etc. of the exploration area (belt) is obtained.

[0023] On this basis, the geological, hydrogeological and geophysical conditions in the exploration area (belt) are further clarified, including stratum lithology, geological structure, karst development characteristics, groundwater dynamic changes, etc. Based on this information, the stratum lithology and its combination characteristics and hydrogeological structural characteristics are divided to provide a basis for subsequent exploration work.

[0024] Step S200, performing geophysical exploration, including: Step S201, use high-density resistivity method and audio frequency induced polarization method to conduct exploration. The high-density resistivity method reveals the geological structure and stratum distribution characteristics by measuring the resistivity difference of the underground medium; the audio frequency induced polarization method uses the induced polarization effect of rock strata in an alternating electromagnetic field to detect the karst development, including: Step S201-1, layout of exploration profiles according to the hydrogeological conditions and geographical features of the exploration area (zone) to ensure reasonable distribution of survey lines and comprehensive coverage.

[0025] Step S201-2, using a high-density resistivity meter and an audio frequency induced polarization meter to collect data. The high-density resistivity meter measures the resistivity value of each measuring point by laying out multiple measuring lines and setting multiple measuring points on each measuring line; the audio frequency induced polarization meter measures the induced polarization effect of the rock formation by emitting an alternating electromagnetic field.

[0026] Step S201-3, processing and analyzing the collected data, including steps such as data correction, filtering and denoising, and anomaly extraction, to obtain a high-density resistivity profile and an audio-frequency induced polarization anomaly map.

[0027] Step S202, further adopting the charging method to conduct survey, including: Step S202-1, based on the high-density resistivity profile and the audio-induced polarization anomaly map, select areas with strong karst development, rich groundwater and obvious migration patterns as charging points. These areas are specifically characterized by abnormally low resistivity or abnormally high audio-induced polarization.

[0028] Step S202-2: Multiple electrodes are arranged around the charging point to form a charging array. The number and arrangement of electrodes are adjusted according to the actual conditions such as the resistivity distribution of the underground medium, the degree of karst development, and the exploration depth to ensure the charging effect.

[0029] Step S202-3: inject a current of appropriate intensity into the underground medium through the electrodes, and measure the distribution and attenuation of the current in the underground medium.

[0030] Step S202-4, processing and analyzing the collected charging data to obtain a first charging abnormality graph.

[0031] Step S300, establishing a hydrogeological structure model of different lithology combinations, according to the formation lithology and its combination characteristics and hydrogeological structure characteristics divided in step S100, and the high-density resistivity profile, audio frequency induced polarization anomaly map and first charging anomaly map collected and processed in step S200, establishing a hydrogeological structure model of different lithology combinations. Including: Step S301, taking the karst aquifer as the target layer, and determining the resistivity characteristics and position of the target layer according to the high-density resistivity profile and the first charging anomaly map. Since the resistivity of the karst aquifer is usually low, the position of the target layer can be determined by identifying the area with relatively low resistivity.

[0032] Step S302: constraining the hydrogeological structure model by taking the interface and bottom of the lithology segment as constraint conditions. The interface and bottom of the lithology segment are important boundary conditions of the hydrogeological structure model and are of great significance to the accuracy and reliability of the model.

[0033] Step S303: Establish a hydrogeological structural model of different lithology combinations according to the target layer and the constraint conditions. The hydrogeological structural model should include elements such as stratum distribution, geological structure, karst development, etc., and should be able to reflect the migration law of groundwater.

[0034] Step S304, invert the hydrogeological structure model using a two-dimensional or three-dimensional inversion algorithm. The two-dimensional inversion algorithm is suitable for the exploration and analysis of planar areas; the three-dimensional inversion algorithm is suitable for the exploration and analysis of three-dimensional areas. This can more accurately depict the underground geological structure, including rock layers, faults, cracks, etc., and provide detailed geological information. The inversion algorithm can also monitor the distribution of groundwater and the migration path of pollutants, providing a scientific basis for environmental protection.

[0035] Step S400: Based on the establishment of the hydrogeological structure model, the karst aquifer is verified by the charging method. The charging method line verification is to verify the accuracy and reliability of the hydrogeological structure model by laying a charging array above the karst aquifer and inputting current, and measuring the distribution and attenuation of the current in the karst aquifer, including: Step S401, a charging array is arranged above the karst aquifer, and the size and shape of the array should be adjusted according to actual conditions to ensure the charging effect.

[0036] Step S402: inputting current into the karst aquifer through the charging array and measuring the distribution and attenuation of the current.

[0037] Step S403: Process and analyze the collected charging data to obtain a second charging abnormality graph.

[0038] Step S404, according to the relative height difference of the apparent resistivity in the second charging anomaly map, the location and scale of the hidden karst voids are determined, and the karst voids include caves, karst grooves or karst gap zones, etc. Since karst voids usually have a relatively low resistivity, the location and scale of the hidden caves, karst grooves or karst gap zones can be determined by identifying the areas with relatively low resistivity in the second charging anomaly map.

[0039] Step S405, defining the range of the intensely developed karst section and its underground runoff zone according to the distribution and scale of the hidden caves, karst gullies or karst fissure zones and the constraints of the hydrogeological structure model.

[0040] Embodiment 2 The technical feature of this embodiment that is different from the above embodiments is that step S100 further includes: Cleaning of collected data, for example, cleaning of collected geological, hydrogeological and geophysical data requires removal of outliers caused by instrument failure or environmental interference.

[0041] Feature selection and extraction are carried out to extract features that are indicative of karst development and groundwater from the original data, including geological characteristics (stratigraphic lithology, geological structure, fault distribution, etc.), hydrogeological characteristics (groundwater level, groundwater dynamic changes, karst development degree, etc.), and geophysical characteristics (resistivity profile, induced polarization anomaly, charging anomaly, etc.).

[0042] The first deep learning model is trained using the preprocessed data. In this embodiment, the deep learning model is used for training. The convolutional neural network (CNN) extracts features through the convolution layer and the pooling layer, and performs classification or regression through the fully connected layer. The recurrent neural network (RNN) captures the time dependency in the sequence data through the recurrent unit. The training data includes areas with known karst water storage systems (positive samples) and areas with known non-karst water storage systems (negative samples). Cross-validation and data enhancement are used to improve the generalization ability of the model, and evaluation and optimization are performed after the model training is completed.

[0043] The first deep learning model is used to predict the data in the exploration area (zone) to identify potential karst water storage system areas. The prediction results are usually given in the form of probability or score, indicating the probability or confidence that the area is a karst water storage system.

[0044] The exploration priority of the region is ranked according to the probability or score of the prediction results. For karst water storage system areas with greater development potential, exploration and development plans can be arranged in priority; for areas with smaller development potential, exploration and development plans can be postponed or cancelled, thereby improving the accuracy and efficiency of karst water storage system identification.

[0045] Embodiment 3 The technical features of this embodiment that distinguish it from the above embodiments are that the position and number of electrodes can be automatically adjusted, including the following: Read the resistivity measurement data, clean and normalize it, and convert the data into image form for subsequent processing.

[0046] N combinations of electrode positions and quantities are randomly generated as the initial population.

[0047] Cluster analysis or image recognition is performed on each individual to identify the abnormal resistivity area. The abnormal area of ​​the resistivity image is identified through the second deep learning model. The second deep learning model uses the same training method as the first deep learning model to output the confidence of the resistivity abnormal area identification, and the confidence value is between 0 and 1.

[0048] The fitness value is calculated based on the recognition results and the number of electrodes. The fitness calculation formula is as follows:

[0049] in, represents the fitness value, Represents the silhouette coefficient, which is used to evaluate the clustering effect. The calculation formula of the silhouette coefficient is:

[0050] in, is the average distance from sample i to other samples in the same cluster, is the minimum average distance from sample i to all samples in other clusters.

[0051] The value range of the silhouette coefficient is between -1 and 1, and the larger the value, the better the clustering effect; It represents the probability of abnormal area predicted by the convolutional neural network (CNN) model, reflecting the confidence of the model in identifying the resistivity abnormal area; Represents the number of electrodes, which serves as a weighting factor of fitness and reflects the density of electrode layout.

[0052] Excellent individuals are selected according to their fitness values ​​to enter the next generation, and crossover and mutation operations are performed on the selected individuals to generate new electrode layout plans.

[0053] According to the fitness value distribution of the current population, parameters such as crossover probability and mutation probability are dynamically adjusted.

[0054] Calculate the mean and standard deviation of the fitness values ​​of all individuals in the current population. When the fitness value distribution of the population is relatively dispersed (i.e., the standard deviation is large), it indicates that there are individuals with good diversity in the population. At this time, the crossover probability should be increased to promote the mixing of excellent genes in the population and improve the exploration capability. When the fitness value distribution of the population is relatively concentrated (i.e., the standard deviation is small), it indicates that the population may be close to the optimal solution or fall into a local optimum. At this time, the crossover probability should be reduced to reduce the destruction of excellent genes and improve the development capability.

[0055] The variance of population fitness is calculated to reflect the degree of difference between individuals. When the variance of population fitness is large, it indicates that there are many different solutions in the population. At this time, the mutation probability should be increased to increase the diversity of the population and avoid premature convergence. When the variance of population fitness is small, it indicates that the difference between individuals in the population is small. At this time, the mutation probability should be reduced to reduce unnecessary search overhead and accelerate convergence.

[0056] Check whether the maximum number of iterations has been reached or whether the population fitness value has reached a preset threshold. If the termination condition is met, the optimal electrode layout plan is output; otherwise, continue iterating.

[0057] The disclosed embodiment also provides a karst groundwater main runoff zone analysis system, which uses the above-mentioned karst groundwater main runoff zone analysis method.

[0058] The above contents are only embodiments of the present invention. The common sense such as the known specific structures and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to explain the content of the claims.

Claims

1. A method for analyzing the main runoff zone of karst groundwater, characterized by: include: Divide and pre-analyze the stratum lithology and its combination characteristics as well as the hydrogeological structure characteristics of the exploration area; Comprehensive geophysical exploration is carried out to collect data and obtain high-density resistivity profiles and audio-frequency induced polarization anomaly maps; areas with abnormally low resistivity or abnormally high audio-frequency induced polarization are selected as charging points, and charging arrays are arranged around the charging points to obtain the first charging anomaly map; based on the target layer and constraints, a hydrogeological structure model with different lithology combinations is established; current is input into the karst aquifer in the hydrogeological structure model through the charging array and the distribution and attenuation of the current are measured to obtain the second charging anomaly map; the location and scale of the hidden karst voids are determined based on the relative height difference of the apparent resistivity in the second charging anomaly map, and the scope of the intensively developed karst section and its underground runoff zone is delineated in combination with the constraints of the hydrogeological structure model.

2. The method for analyzing the main runoff zone of karst groundwater according to claim 1, characterized in that: Data collection is performed to obtain high-density resistivity profiles and audio-frequency induced polarization anomaly maps, including the following: Arrange exploration profiles according to the hydrogeological conditions and geographical features of the exploration area; High-density resistivity meter and audio frequency induced polarization meter are used for data collection; the high-density resistivity meter is used to lay out multiple measuring lines, set multiple measuring points on each measuring line, and measure the resistivity value of each measuring point; The audio frequency induced polarization instrument measures the induced polarization effect of rock formations by emitting an alternating electromagnetic field.

3. The method for analyzing the main runoff zone of karst groundwater according to claim 1, characterized in that: A charging array is arranged around the charging point to obtain a first charging anomaly map, including the following contents: Multiple electrodes are arranged around the charging point to form a charging array; Current is input into the underground medium through the electrodes, and the distribution and attenuation of the current in the underground medium are measured; the collected charging data are processed and analyzed to obtain a first charging anomaly map.

4. The method for analyzing the main runoff zone of karst groundwater according to claim 1, characterized in that: According to the target layer and constraint conditions, the hydrogeological structure model of different lithology combinations is established, including the following contents: The karst aquifer is taken as the target layer, and the resistivity characteristics and position of the target layer are determined by identifying the resistivity according to the high-density resistivity profile and the first charging anomaly map; the hydrogeological structure model is constrained by taking the lithologic segment interface and its bottom as constraints; and the hydrogeological structure model is inverted using a two-dimensional or three-dimensional inversion algorithm.

5. The method for analyzing the main runoff zone of karst groundwater according to claim 1, characterized in that: Also includes: Cleaning of collected geological, hydrogeological and geophysical data; Extract features indicative of karst development and groundwater from the raw data; The preprocessed data is used to train the first deep learning model; the first deep learning model is used to predict the data of the exploration area to predict and identify potential karst water storage system areas.

6. The method for analyzing the main runoff zone of karst groundwater according to claim 5, characterized in that: Also includes: The prediction results are given in the form of probability or score, indicating the probability or confidence that the area is a karst water storage system; the exploration priority of the area is ranked according to the probability or score of the prediction results.

7. The method for analyzing the main runoff zone of karst groundwater according to claim 6, characterized in that: The first deep learning model is trained using a deep learning model. The convolutional neural network extracts features through convolutional layers and pooling layers, and performs classification or regression through fully connected layers; the recurrent neural network captures the time dependency in sequence data through recurrent units; The training data includes areas with known karst water storage systems and areas with known non-karst water storage systems. Cross-validation and data augmentation are used to improve the generalization ability of the model, and evaluation and optimization are performed after the model training is completed.

8. The method for analyzing the main runoff zone of karst groundwater according to claim 1, characterized in that: Also includes: Read the resistivity measurement data and perform cleaning and normalization to convert the data into image form; Randomly generate N electrode position and quantity combinations as the initial population; Perform cluster analysis or image recognition on each individual to identify abnormal resistivity areas; Calculate the fitness value, select excellent individuals to enter the next generation according to the fitness value, perform crossover and mutation operations on the selected individuals, and generate a new electrode layout plan; Dynamically adjust the crossover probability and mutation probability according to the fitness value distribution of the current population; Check whether the maximum number of iterations is reached or whether the population fitness value reaches the preset threshold. If the termination condition is met, the optimal electrode layout plan is output.

9. The method for analyzing the main runoff zone of karst groundwater according to claim 8, characterized in that: According to the fitness value distribution of the current population, the crossover probability and mutation probability are dynamically adjusted, including the following: Calculate the mean and standard deviation of the fitness values ​​of all individuals in the current population; increase the crossover probability when the fitness values ​​of the population are dispersed; When the fitness value distribution of the population is concentrated, the crossover probability should be reduced; The variance of the calculated population fitness reflects the degree of difference between individuals; when the difference in population fitness is large, the probability of mutation increases; when the difference in population fitness is small, the probability of mutation decreases.

10. A karst groundwater main runoff zone analysis system, characterized in that: The system uses the karst groundwater main runoff zone analysis method as described in any one of claims 1-9.

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