Rainfall and groundwater recharge rate parameter identification auxiliary system, and rainfall and groundwater recharge rate parameter identification auxiliary method

Abstract

To prevent extension of the time for identifying a plurality of parameters including a rainfall and groundwater recharge rate when performing identification for the parameters.SOLUTION: A rainfall and groundwater recharge rate parameter identification auxiliary system is used for a parameter identification system having: an observation data acquisition unit that acquires observation data including a measurement result of measurement of water inflow; a site condition acquisition unit that acquires site condition setting information; a hydrological parameter storage unit that stores a hydrological parameter including a coefficient of permeability; a three-dimensional seepage flow analysis unit that takes in the observation data, the site condition setting information, and the hydrological parameter, and performs three-dimensional seepage flow analysis on the basis of a geological model near a tunnel and an analysis model; and a data assimilation processing unit that identifies two hydrological parameters including at least a rainfall and groundwater recharge rate. The rainfall and groundwater recharge rate parameter identification auxiliary system has a range setting unit that decreases an upper limit and a lower limit of the rainfall and groundwater recharge rate as the amount of precipitation in the observation data increases, and causes the data assimilation processing unit to identify the rainfall and groundwater recharge rate.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a rainfall recharge rate parameter identification assistance system and a rainfall recharge rate parameter identification assistance method during tunnel excavation. [Background technology]

[0002] Groundwater analysis may be conducted to predict the amount of spring water in a tunnel and the groundwater environment of the tunnel ground. In groundwater analysis, a hydrogeological model is created based on a geological survey, and a 3D seepage analysis is performed using techniques such as the finite element method. However, conducting groundwater analysis requires the advanced knowledge and skills of specialized engineers, such as the creation of a complex hydrogeological model, technical evaluation of reproducible and predictive analyses, and review of analytical conditions, and the creation of the model and the analysis itself require a great deal of time and effort. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-058296 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, since the groundwater field prediction system is a system that can identify multiple parameters such as the specific storage coefficient and rainfall recharge rate in addition to the hydraulic conductivity of each region, the more parameters there are to be identified, the more likely it is that there will be an infinite number of solutions for parameter combinations that match the observed data. Therefore, parameter identification can take a long time or can be difficult in itself. This is especially true when the system involves simultaneous identification of hydraulic conductivity and recharge rate parameters, which can lead to error messages indicating that the hydrogeological model is not accurate enough and that modifications need to be made to the model.

[0005] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide a rainfall recharge rate parameter identification assistance system and a rainfall recharge rate parameter identification assistance method that can prevent the time required for parameter identification from being prolonged when identifying multiple parameters including at least rainfall recharge rate. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, one aspect of the present invention is a precipitation recharge rate parameter identification assistance system for use in a parameter identification system having: an observation data acquisition unit that acquires observation data including at least measurement results of the amount of spring water generated at the face of a tunnel; a site condition acquisition unit that acquires site condition setting information including at least the position of the face indicating the excavation section; a hydraulic parameter storage unit that stores hydraulic parameters including a permeability coefficient; a three-dimensional seepage analysis unit that takes in the observation data, the site condition setting information, and the hydraulic parameters, and performs a three-dimensional seepage analysis based on a geological model and an analysis model in the vicinity of the tunnel; and a data assimilation processing unit that identifies at least two hydraulic parameters including a rainfall recharge rate, the rainfall recharge rate parameter identification assistance system having a range setting unit that reduces the upper and lower limit values ​​of the rainfall recharge rate as the amount of precipitation in the observation data increases, and causes the rainfall recharge rate to be identified by the data assimilation processing unit.

[0007] Another aspect of the present invention is a rainfall recharge rate parameter identification assistance method executed by a computer, which acquires observation data including at least measurement results of an amount of spring water generated at a tunnel face, acquires site condition setting information including at least a position of the face indicating an excavation section, imports the observation data, the site condition setting information, and hydraulic parameters including a permeability coefficient, performs a three-dimensional seepage analysis based on a geological model and an analytical model in the vicinity of the tunnel, reduces upper and lower limit values ​​of the rainfall recharge rate as the amount of precipitation in the observation data increases, and identifies the rainfall recharge rate by a data assimilation processing unit, thereby identifying at least two hydraulic parameters including the rainfall recharge rate. Effect of the Invention

[0008] As described above, according to the present invention, when identifying a plurality of parameters including at least the rainfall recharge rate, it is possible to prevent the time required for identifying the parameters from being prolonged. [Brief description of the drawings]

[0009] [Figure 1] 1 is a schematic block diagram showing the configuration of a groundwater field prediction system S according to an embodiment of the present invention. [Diagram 2] 4 is a diagram showing an example of condition data stored in a condition storage unit 35. FIG. [Diagram 3] 13 is a flowchart explaining the operation of the precipitation recharge rate parameter identification auxiliary system in the groundwater field prediction system S. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] A groundwater field prediction system according to an embodiment of the present invention will now be described with reference to the drawings. Fig. 1 is a schematic block diagram showing the configuration of a groundwater field prediction system S according to an embodiment of the present invention. The groundwater field prediction system S includes an on-site terminal 1, a cloud server 2, and a large-scale parallel computer 3.

[0011] <Configuration of field terminal 1> The on-site terminal 1 is communicably connected to a cloud server 2 via a network such as the Internet. The on-site terminal 1 is, for example, a computer. The on-site terminal 1 is installed in a site office at a site where tunnel excavation work is carried out. The on-site terminal 1 has a communication function for communicating with the cloud server 2, a control unit for controlling each unit of the on-site terminal 1, etc. In addition to these functions, the on-site terminal 1 has an observation data acquisition unit 11, a site condition acquisition unit 12, an observation data complementation unit 13, and a display unit 14.

[0012] The observation data acquisition unit 11 acquires observation data including at least the measurement results of the amount of spring water occurring at the tunnel face. An observation device is installed at the site where the tunnel excavation work is carried out. The observation device performs observations according to the observation items at the observation location and outputs the observation data that is the observation results. Tunnel excavation work is carried out daily. As this excavation work is carried out, observation data measured on that day is output from the observation device every day (or every few hours) as data including date and time data indicating the date and time of observation. The observation data acquisition unit 11 acquires the observation data including the date and time data every time observation data is output from the observation device. The observation items of the observation data include, for example, the amount of water that springs up inside the mine near the face, the amount of water that springs up inside the mine in the already excavated section, the groundwater level in the observation hole, and the amount of precipitation. The amount of water springing up inside the mine, which is one of the observation data, can be the amount of water springing up only near the face, the total amount of water springing up in the already excavated section, etc. The observation data acquisition unit 11 needs to acquire at least one of the amount of water springing up only near the face and the total amount of water springing up in the already excavated section, depending on the measurement conditions at the site, and may acquire both.

[0013] The observation data complementation unit 13 determines whether there are missing values ​​or abnormal values ​​in the observation data, and if it is determined that there are missing values ​​or abnormal values, extracts the missing values ​​or abnormal values ​​and performs correction processing on the data, such as removing the data or complementing the data using previously obtained observation data.

[0014] The site condition acquisition unit 12 acquires site condition setting information including at least the position of the face indicating the excavation section.

[0015] The display unit 14 receives the results of the three-dimensional seepage analysis output from the large-scale parallel computer 3 via a communication function, and displays them on the display screen.

[0016] Cloud Server 2 Configuration The cloud server 2 is communicatively connected to the on-site terminal 1, and is communicatively connected via a network to a massively parallel computer 3. The cloud server 2 is, for example, configured with at least one server device constructed on the Internet. The cloud server 2 has a communication function for communicating with the on-site terminal 1 and with the massively parallel computer 3. The cloud server 2 also has an analysis data storage unit 21, a data management unit 22, a hydraulic parameter setting unit 23, and a prediction result storage unit 24.

[0017] The analysis data storage unit 21 stores analysis data used for performing three-dimensional seepage analysis. The analysis data includes, for example, observation data, on-site condition setting information, hydraulic parameters, a geological model, an analysis model, analysis conditions, and the like.

[0018] The observation data is acquired by the observation data acquisition unit 11. The site condition setting information is site condition setting information acquired by the site condition acquisition unit 12 . Hydraulic parameters include hydraulic conductivity, rainfall infiltration rate (rainfall recharge rate, recharge amount), specific storage coefficient, water characteristic curve, relative permeability, porosity, etc. Among these hydraulic parameters, the hydraulic conductivity, rainfall infiltration rate (recharge amount), and specific storage coefficient can be the parameters to be identified. Here, the case where the hydraulic conductivity and rainfall recharge rate are identified as an example among these parameters will be described. Note that the parameters to be identified may include at least the rainfall recharge rate and at least one other parameter. The hydraulic conductivity is a coefficient that indicates how easily water passes through soil or rock. Rainfall infiltration rate (rainfall recharge rate, recharge amount) is an index that represents the ability of rain to infiltrate into the ground. The specific storage coefficient represents the amount of water discharged or absorbed from a soil mass per unit volume when the groundwater level fluctuates in the ground, and the higher the specific storage coefficient of the ground, the greater the amount of groundwater discharged or absorbed due to water level fluctuations. The moisture characteristic curve is data that indicates water retention. The relative hydraulic conductivity is the ratio of the unsaturated hydraulic conductivity to the saturated hydraulic conductivity, and is introduced into the analysis through its relationship with the volumetric water content. The above-mentioned water characteristic curve and the relationship between the relative hydraulic conductivity and the volumetric porosity are the unsaturated seepage characteristics required as input data for saturated-unsaturated seepage flow analysis. Porosity represents the degree of gaps and cracks between particles present in rock mass. Volumetric moisture content is the ratio of the volume of water to the volume of a substance.

[0019] The geological model is a three-dimensional geological model that represents the geological structure of the ground when the tunnel to be constructed is excavated. The geological model is created, for example, by using the results of a geological survey of the ground in which the tunnel to be constructed is excavated. The three-dimensional geological model includes, for example, models that represent a plurality of strata, and the elements corresponding to each stratum are assigned physical property values ​​(e.g., physical property values ​​such as permeability coefficient) corresponding to the stratum based on the permeability characteristics of the bedrock (ground). Here, the components that make up the stratum have permeability coefficients according to the type of the components. It is known that such permeability coefficients have a typical value range for each type of component. The groundwater field prediction system S assigns permeability coefficients to the elements corresponding to each stratum in the three-dimensional geological model based on the permeability characteristics of the bedrock (ground) input as the reproduction conditions, based on the typical value range of the permeability coefficient. By appropriately assigning this permeability coefficient, the spring water volume can be calculated with high accuracy. The hydraulic conductivity is a parameter to be identified, and by repeating the predictive analysis, the parameter is corrected in the massively parallel computer 3 and updated to approach a more appropriate value. The analytical model is a model for reproducing the observation results obtained as observation data. For example, the analytical model is a model in which an analytical mesh is applied to the geological model and a permeability coefficient is assigned to each rock mass in the geological model. The analysis conditions are various conditions given for analysis.

[0020] The data management unit 22 acquires observation data and site condition setting information obtained from the on-site terminal 1, and writes them into the analysis data storage unit 21. When the data management unit 22 obtains observation data from the on-site terminal 1, it writes the observation data into the analysis data storage unit 21 together with the date and time when the observation data was observed. Furthermore, the data management unit 22 receives various types of information from the outside and writes them into the analysis data storage unit 21 . The hydraulic parameter setting unit 23 writes the hydraulic parameters into the analysis data storage unit 21 .

[0021] The prediction result storage unit 24 obtains the prediction result from the large-scale parallel computer 3 and stores it.

[0022] The analysis data storage unit 21 and the prediction result storage unit 24 are each configured with a storage medium, such as a hard disk drive (HDD), a flash memory, an electrically erasable programmable read only memory (EEPROM), a random access read / write memory (RAM), a read only memory (ROM), or any combination of these storage media. The analysis data storage unit 21 and the prediction result storage unit 24 can be configured with a non-volatile memory, for example.

[0023] <Configuration of Large-Scale Parallel Computer 3> The large-scale parallel computer 3 has a communication function for communicating with the cloud server 2. The massively parallel computer 3 has a data acquisition unit 31, a three-dimensional seepage flow analysis unit 32, a data assimilation processing unit 33, an analysis result output unit 34, a condition memory unit 35, a range setting unit 36, a message output unit 37, and a data setting unit 38.

[0024] The data acquisition unit 31 acquires from the cloud server 2 various types of data used for performing three-dimensional seepage analysis in the large scale parallel computer 3. For example, the data acquisition unit 31 acquires data indicating the position of a face indicating an excavation section.

[0025] The 3D seepage analysis unit 32 retrieves the observation data, on-site condition setting information, and hydraulic parameters from the cloud server 2, and performs 3D seepage analysis based on the geological model and analysis model of the vicinity of the tunnel. For the 3D seepage analysis, a commonly used existing system (software) can be applied.

[0026] The data assimilation processor 33 performs data assimilation by obtaining analytical values ​​reflecting the observation data using the observation data obtained by the observation data acquisition unit 11 and estimated values ​​(prediction results). When the observation data is obtained, the data assimilation processor 33 performs data assimilation according to a sequential data assimilation method that performs data assimilation. The data assimilation processing unit 33 performs data assimilation processing, outputs the results of the three-dimensional infiltration flow analysis, and updates the parameters to be identified (e.g., hydraulic conductivity, rainfall infiltration rate (rainfall recharge rate, recharge amount), specific storage coefficient, etc.), and writes the updated parameters into the hydraulic parameter memory unit for updating. The data assimilation processing unit 33 modifies at least one of the geological model and the analytical model so as to reduce the difference between the predicted results obtained from the three-dimensional seepage analysis and the observed data if the difference is relatively large, and so as to reduce the difference between the predicted results and the hydraulic parameters if the difference is relatively large. The data assimilation processor 33 is provided with a function for identifying multiple parameters such as the permeability coefficient as a basic parameter for reproducing the observation data by data assimilation, and the rainfall infiltration rate (rainfall recharge rate, recharge amount) and specific storage coefficient, which are also related to the amount of water seeping into the mine. In this embodiment, the parameters to be identified may include at least the rainfall recharge rate and at least one other parameter. When multiple parameters are identified, the more parameters to be identified, the more likely it is that there will be an infinite number of solutions for parameter combinations that match the observation data, so that parameter identification may take time or the identification of parameters may be difficult. Here, the time required to identify parameters can be shortened by restricting the search range when identifying the rainfall recharge rate according to the amount of precipitation. In addition, it is possible to make it difficult to issue an error message indicating that the model needs to be modified due to insufficient accuracy of the hydrogeological analysis model. When performing data assimilation processing, the data assimilation processor 33 performs data assimilation by sequentially repeating time updates (predictions by simulation) and observation updates (model corrections) using parameters that are ensemble members using an ensemble Kalman filter.

[0027] The calculation program that realizes the functions of the 3D seepage analysis unit 32 and the data assimilation processing unit 33 is a combination of 3D seepage analysis and a sequential data assimilation method with relatively low calculation costs. This prediction analysis (calculation program) can be executed using a large-scale parallel computer.

[0028] The analysis result output unit 34 outputs the analysis result obtained by the three-dimensional seepage analysis. For example, the analysis result output unit 34 transmits the analysis result to the cloud server 2, and causes the prediction result storage unit 24 of the cloud server 2 to write the prediction result.

[0029] The massively parallel computer 3 can perform three-dimensional seepage analysis processing by the three-dimensional seepage analysis unit 32 and data assimilation processing by the data assimilation processing unit 33.

[0030] The above-mentioned groundwater field prediction system S is a system that automatically performs a series of processes from acquiring observation data to outputting the results of predictive analysis, thereby enabling predictions to be made in real time. In general, predicting groundwater conditions requires advanced knowledge and skills of analytical specialists (e.g., technical evaluation of the results of reproduction and prediction analyses, methods for reviewing analytical conditions, etc.), and also takes a great deal of time (e.g., several days). According to the above-mentioned groundwater field prediction system S, by combining three-dimensional seepage analysis with a sequential data assimilation method with relatively low calculation costs and performing these calculations on a large-scale parallel computer 3, it is possible to reproduce observed data and obtain prediction results such as the amount of underground spring water and groundwater levels with high accuracy in a short time. This makes it possible to perform predictive analysis without requiring the advanced knowledge, skills, and judgment of analytical specialists.

[0031] The observation data acquired by the groundwater field prediction system S can be any data that can be handled by the calculation program in the groundwater field prediction system S. In addition, a check system is provided to complement and process abnormal values ​​in the observation data, so accuracy can be expected to be maintained and improved. Regarding the amount of water that seeps into the mine, which is one of the observation data, the calculation program and system can handle either the amount of water that seeps in only near the face or the entire amount of water that has already been excavated, depending on the on-site measurement conditions, etc. In addition, in the groundwater field prediction system S, the parameter identified during predictive analysis is the permeability coefficient, but it is also possible to identify multiple parameters other than the permeability coefficient, such as rainfall infiltration rate (rainfall recharge rate, recharge amount) and specific storage coefficient, which are also related to the amount of water that seeps into the mine.

[0032] The message output unit 37 outputs an error message indicating that the model needs to be corrected due to insufficient accuracy of the hydrogeological analysis model. The message output unit 37 outputs a message to the large-scale parallel computer 3, thereby allowing the message to be output to the on-site terminal 1 via the cloud server 2. When the large-scale parallel computer 3 and the on-site terminal 1 are connected, the message output unit 37 may output the message to the on-site terminal 1 without going through the cloud server 2.

[0033] The condition storage unit 35 stores condition data including a plurality of different precipitation amounts and a parameter range that is set so that the upper and lower limit values ​​of the rainfall recharge rate parameter become smaller as the precipitation amount increases. The parameter range stored in the condition storage unit 35 is such that the greater the amount of precipitation, the narrower the range determined by the upper and lower parameter limits of the rainfall recharge rate.

[0034] The range setting unit 36 ​​reduces the upper and lower limits of the rainfall recharge rate as the amount of precipitation in the observation data increases, and causes the data assimilation processor 33 to identify the rainfall recharge rate. The range setting unit 36 ​​reads out a parameter range corresponding to the amount of precipitation obtained as observation data from the condition memory unit 35, and causes the data assimilation processing unit 33 to identify the rainfall recharge rate so that it is any value between the upper and lower limit values ​​of the rainfall recharge rate indicated by the read-out range.

[0035] In addition, the precipitation recharge rate parameter identification assistant device including the condition storage unit 35 and the range setting unit 36 ​​of the large-scale parallel computer 3 may be provided as a separate device from the large-scale parallel computer 3 and connected to the large-scale parallel computer 3. In other words, the large-scale parallel computer 3 and the precipitation recharge rate parameter identification assistant device may be separated as separate devices and linked to each other. The precipitation recharge rate parameter identification assistant device may execute the functions of the condition storage unit 35 and the range setting unit 36 ​​in response to the three-dimensional seepage analysis being performed. At least one of the condition storage unit 35 and the range setting unit 36 ​​may be provided in the massively parallel computer 3, and configured as a precipitation recharge rate parameter identification auxiliary system. Furthermore, the massively parallel computer 3 may be a parameter identification system as long as it is capable of identifying parameters, and may be composed of a single computer, or the functions of the massively parallel computer 3 may be distributed across multiple computers.

[0036] FIG. 2 is a diagram showing an example of condition data stored in the condition storage unit 35. As shown in FIG. The condition data stored in the condition storage unit 35 is data in which the amount of precipitation is associated with a parameter range. Here, the amount of precipitation includes a plurality of different ranges. For example, the amount of precipitation may be "100 mm or more," "less than 100 mm to 30 mm or more," or "less than 30 mm." The parameter range is determined by an upper parameter limit value and a lower parameter limit value. Here, the parameter range for a precipitation amount of "100 mm or more" is stored as an upper parameter limit value of "0.1" and a lower parameter limit value of "0.05", and the parameter range for a precipitation amount of "less than 30 mm" is stored as an upper parameter limit value of "0.4" and a lower parameter limit value of "0.2". In this way, the condition data is set for a plurality of different precipitation amounts, and the upper and lower limit values ​​of the rainfall recharge rate parameter are set to be smaller as the precipitation amount increases. Also, the parameter range determined by the upper and lower parameter limits becomes narrower as the precipitation amount increases.

[0037] The parameters of the rainfall recharge rate change depending on the surface geology, topography, precipitation, etc. The surface geology depends on the parameters of the hydraulic conductivity of each geology, which is identified at the same time. As for the topography, since the target is a mountain tunnel, most of the area to be analyzed is mountain forest, and the difference due to the topography is considered to be relatively small compared to the change in precipitation. Therefore, in this embodiment, the identification support system takes into account the magnitude of precipitation.

[0038] In general, the more the precipitation per hour (precipitation per hour or precipitation per day, etc.), the smaller the rainfall recharge rate, and conversely, the less the precipitation per hour, the larger the rainfall recharge rate. On the other hand, upper and lower limit values ​​(search range) of the parameters to be identified in data assimilation can be set. In this embodiment, using the setting, the precipitation data is automatically read, and when the precipitation per hour is high, the upper and lower limit values ​​of the rainfall recharge rate parameter are automatically set to small values, and when the precipitation per hour is low, the upper and lower limit values ​​of the rainfall recharge rate parameter are automatically set to large values. Therefore, even when the parameters of the hydraulic conductivity and the rainfall recharge rate are identified simultaneously, the identification of the rainfall recharge rate can be automatically assisted in order to smoothly identify the hydraulic conductivity. In this embodiment, the search range of the rainfall recharge rate parameter to be identified can be changed according to the amount of precipitation, but the classification according to the amount of precipitation can be freely changed, and for example, as shown in Fig. 2 above, when the amount of precipitation in one day exceeds 100 mm, the amount of precipitation and the number of classifications can be set, such as less than 100 mm to 30 mm or more, less than 30 mm, etc. This makes it possible to take into account to some extent the differences in the topography of each tunnel site by changing the settings.

[0039] Next, the operation of the above-mentioned groundwater field prediction system S will be described. FIG. 3 is a flowchart explaining the operation of the precipitation recharge rate parameter identification auxiliary system in the groundwater field prediction system S. By executing the flowchart shown in FIG. 2, in this embodiment, it is possible to automatically determine whether the precipitation recharge rate parameters of the geology of the area being excavated have been identified. The groundwater field prediction system S can make predictions in real time by automatically performing a series of predictive analyses, from acquiring observation data, to storing and transferring information such as data, analysis setting conditions, and model information, to performing data assimilation predictive analysis, and outputting and transferring the analysis results.

[0040] The observation data acquisition unit 11 of the on-site terminal 1 acquires observation data from the observation device (step S1). When a plurality of observation items are measured by existing measuring devices or the like, the observation data acquisition unit 11 automatically acquires the measurement results (e.g., each time the measurement results are output from the measuring device). The acquired data includes, for example, the amount of water springing up in the mine near the face, the amount of water springing up in the mine in the already excavated section, the groundwater level in the observation hole, the amount of precipitation, and the like. The observation data used for data assimilation can be any data that can be handled by the calculation program. In addition, the calculation program and prediction system for the amount of water seeping into the mine, which is one of the observation data, can handle either the amount of water seeping in only near the face or the total amount of water seeping in the already excavated section, making it possible to respond to the situation on site.

[0041] The observation data complementation unit 13 judges whether the acquired observation data contains missing values ​​or abnormal values, and if it is judged that there are missing values ​​or abnormal values, extracts the missing values ​​or abnormal values ​​and performs correction processing on the data, such as removing the data or complementing the data using previously obtained observation data. A missing value indicates a case where observation data that should be obtained as a value is missing. An abnormal value is a value that deviates extremely from the observation data obtained this time for an observation item compared to data obtained in the past. Observation data may contain missing values ​​or abnormal values. Therefore, if this data is used as is, it will affect the results of predictive analysis. Therefore, the observation data complementation unit 13 checks the acquired observation data to extract missing values ​​and abnormal values, and performs correction processing on the data, such as removing the data or complementing it with previous values. This enables data assimilation that is not influenced by abnormal values, etc., and makes it possible to make predictions while maintaining accuracy. In addition, the observation data complementation unit 13 has a function to display an error message if the correction processing exceeds the allowable time.

[0042] The site condition acquisition unit 12 acquires the site condition setting information input from the input device of the site terminal 1. The data representing the site condition setting information input from the site terminal 1 may include at least the face position (already excavated section). If the face position (already excavated section) information is input, a 3D seepage analysis, which will be described later, can be performed, and thus prediction results such as the amount of water seeping into the mine and the groundwater level can be obtained. In addition, the data representing the site condition setting information can include the number of days to be predicted, and in this case, it is also possible to obtain prediction results up to the number of days input. In addition, by inputting precipitation information based on the weather forecast for the site area as the site conditions, more accurate prediction results can be obtained.

[0043] The data management unit 22 writes the observation data obtained from the on-site terminal 1, on-site condition setting information, and analysis conditions into the analysis data storage unit 21. The hydraulic parameter setting unit 23 writes hydraulic parameters (permeability coefficient, rainfall infiltration rate (rainfall recharge rate, recharge amount), specific storage coefficient, water characteristic curve, relative permeability coefficient, volumetric water content, porosity, etc.) into the analysis data storage unit 21. Here, the analysis data storage unit 21 stores various information on the hydrogeological model and the analysis model in advance.

[0044] (Step 1) The observation data acquisition unit 11 of the on-site terminal 1 acquires observation data from the observation device. When a plurality of observation items are measured by existing measuring devices or the like, the observation data acquisition unit 11 automatically acquires the measurement results (e.g., each time the measurement results are output from the measuring device). The acquired data includes, for example, the amount of water welling up in the mine near the face, the amount of water welling up in the mine in the already excavated section, the groundwater level in the observation hole, the amount of precipitation, and the like. The observation data used for data assimilation can be any data that can be handled by the calculation program. In addition, the calculation program and prediction system for the amount of water seeping into the mine, which is one of the observation data, can handle either the amount of water seeping in only near the face or the total amount of water seeping in the already excavated section, making it possible to respond to the situation on site.

[0045] (Step S2) The observation data complementation unit 13 judges whether the acquired observation data contains missing values ​​or abnormal values, and if it is judged that there are missing values ​​or abnormal values, extracts the missing values ​​or abnormal values ​​and performs correction processing on the data, such as removing the data or complementing the data using previously obtained observation data. A missing value indicates a case where observation data that should be obtained as a value is missing. An abnormal value is a value that deviates extremely from the observation data obtained this time for an observation item compared to data obtained in the past. Observation data may contain missing values ​​or abnormal values. Therefore, if this data is used as is, it will affect the results of predictive analysis. Therefore, the observation data complementation unit 13 checks the acquired observation data to extract missing values ​​and abnormal values, and performs correction processing on the data, such as removing the data or complementing it with previous values. This enables data assimilation that is not influenced by abnormal values, etc., and makes it possible to make predictions while maintaining accuracy. In addition, the observation data complementation unit 13 has a function to display an error message if the correction processing exceeds the allowable time.

[0046] (Step S3) The site condition acquisition unit 12 acquires the site condition setting information input from the input device of the site terminal 1. The data representing the site condition setting information input from the site terminal 1 may include at least the face position (already excavated section). If the face position (already excavated section) information is input, a 3D seepage analysis, which will be described later, can be performed, and thus prediction results such as the amount of water seeping into the mine and the groundwater level can be obtained. In addition, the data representing the site condition setting information can include the number of days to be predicted, and in this case, it is also possible to obtain prediction results up to the number of days input. In addition, by inputting precipitation information based on the weather forecast for the site area as the site conditions, more accurate prediction results can be obtained.

[0047] (Step S4) When condition data (parameter range) is input from the field terminal 1 or the cloud server 2, the data acquisition unit 31 acquires the input condition data.

[0048] (Step S5) The data management unit 22 writes the observation data obtained from the field terminal 1, field condition setting information, analysis conditions, and condition data into the analysis data storage unit 21. (Step S6) The hydraulic parameter setting unit 23 writes hydraulic parameters (permeability coefficient, rainfall infiltration rate (rainfall recharge rate, recharge amount), specific storage coefficient, water characteristic curve, relative permeability coefficient, volumetric water content, porosity, etc.) into the analysis data storage unit 21. Here, the analysis data storage unit 21 stores various information on the hydrogeological model and the analysis model in advance.

[0049] (Step S7) The large scale parallel computer 3 imports various data required for analysis from the cloud server 2. Here, the imported condition data is stored in a condition storage unit .

[0050] (Step S8) The range setting unit 36 ​​extracts the amount of precipitation from the observation data, reads out condition data (parameter range) of the rainfall recharge rate corresponding to the extracted amount of precipitation from the condition storage unit 35, and sets the read out condition data in the data assimilation processor 33. In this way, the range setting unit 36 ​​allows the data assimilation processor 33 to identify the rainfall recharge rate to be any value between the upper limit value and the lower limit value of the rainfall recharge rate corresponding to the amount of precipitation obtained as the observation data.

[0051] (Step S9) The three-dimensional seepage analysis unit 32 performs three-dimensional seepage analysis using various data obtained from the cloud server 2. Here, a three-dimensional unsteady seepage analysis is performed.

[0052] (Step S10) When the 3D seepage analysis is performed, the data assimilation processor 33 performs data assimilation processing. Here, for the rainfall recharge rate, the upper and lower limit values ​​of the parameter of the rainfall recharge rate are determined by the range setting unit 36, so the data assimilation processor 33 identifies the rainfall recharge rate so that it is any value between the upper and lower limit values.

[0053] (Step S11) The data assimilation processor 33 determines the deviation between the data at the observation site and the results of the three-dimensional seepage analysis. In the data assimilation process, if there is a large discrepancy between the values ​​of the observation data and the analysis results of the three-dimensional seepage analysis (step S11-YES), or if the discrepancy is small (step S11-NO) but the deviation in the parameter estimates is large (step S12-YES), the data assimilation processor 33 determines that the initially created hydrogeological model and analysis model may be different. In this case, since it is necessary to modify the model to one that can accurately reproduce the observation results, the data assimilation processor 33 interrupts the prediction analysis and outputs data indicating that model modification is necessary (step S13). This output can be displayed on the display unit 14 of the on-site terminal 1 via the cloud server 2, for example. This allows the operator of the on-site terminal 1 to modify the geological model and analysis model, etc.

[0054] On the other hand, if the deviation between the observed data values ​​and the prediction results is small (step S11-NO) and the deviation of the parameter estimates is small (step S12-NO), the data assimilation processor 33 corrects and updates the identified parameters (step S14) and outputs the prediction results of the mine spring water volume, groundwater level, etc. to the cloud server 2 (step S15). The corrected and updated parameters, analysis conditions, etc. are carried over to the next analysis step (for example, step S9 to be executed next time).

[0055] (Step S16) When a prediction result is output from the large-scale parallel computer 3, the prediction result storage unit 24 of the cloud server 2 stores the prediction result. (Step S17) When the prediction result is stored in the prediction result storage unit 24, the on-site terminal 1 acquires the prediction result. The display unit 14 displays the acquired prediction result on the display screen. Based on the prediction result, the on-site terminal 1 can display the amount of water seeping into the mine, the total head distribution, a water level drawdown diagram, and the like on the display unit 14. In this way, the prediction results are output from the large scale parallel computer 3 so that they can be managed and shared on the cloud server 2. Therefore, the prediction results can be displayed on the on-site terminal 1 at the site where it is desired to display them.

[0056] According to the embodiment described above, when predicting and analyzing the groundwater environment of a tunnel ground, the accuracy of the reproduction analysis and prediction analysis can be improved by assimilating the results of the 3D seepage flow analysis from a geological survey with actual sequential observation data (such as the amount of water seepage inside the tunnel, groundwater level, and precipitation).

[0057] Furthermore, according to the embodiment described above, when predicting and analyzing the groundwater environment of a tunnel ground, the accuracy of reproduction analysis and prediction analysis can be improved by assimilating actual sequential observation data (such as the amount of water seepage inside the tunnel, groundwater level, and precipitation) into the results of three-dimensional seepage flow analysis from a geological survey.

[0058] According to the embodiment described above, it is possible to automatically set a limit on the search range of the parameters of the rainfall recharge rate to be identified depending on the amount of precipitation, thereby shortening the time required for parameter identification and reducing the occurrence of error messages in the rainfall recharge rate parameter identification assistance system. Furthermore, according to the above-described embodiment, the classification according to the amount of precipitation can be freely changed, allowing specialized engineers to set the amount of precipitation according to the topography of each tunnel site.

[0059] In addition, the entire process from acquiring observational data to outputting the results of predictive analysis can be carried out automatically, making real-time predictions possible.

[0060] In the above-described embodiment, the allowable values ​​and the control values ​​can be set to arbitrary values ​​according to the conditions of each tunnel site. These settings may be input by an analysis specialist through an input device such as a mouse or keyboard of the on-site terminal 1. The condition data can also be stored in the condition storage unit 35 of the large-scale parallel computer 3 by inputting arbitrary values ​​through the input device of the on-site terminal 1.

[0061] The on-site terminal 1, the cloud server 2, and the large-scale parallel computer 3 in the above-mentioned embodiment may each be realized by a computer. In that case, a program for realizing this function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to realize the function. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. The term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, and storage devices such as hard disks built into a computer system. The term "computer-readable recording medium" may also include a medium that dynamically holds a program for a short period of time, such as a communication line when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, and a medium that holds a program for a certain period of time, such as a volatile memory inside a computer system that is a server or client in that case. The above-mentioned program may be a program for realizing a part of the above-mentioned function, or may be a program that can realize the above-mentioned function in combination with a program already recorded in the computer system, or may be a program that is realized using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0062] Although an embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and designs that do not deviate from the gist of the present invention are also included. [Explanation of symbols]

[0063] 1...on-site terminal, 2...cloud server, 3...large-scale parallel computer, 11...observation data acquisition section, 12...on-site condition acquisition section, 13...observation data complementation section, 14...display section, 21...analysis data storage section, 22...data management section, 23...hydraulic parameter setting section, 24...prediction result storage section, 31...data acquisition section, 32...three-dimensional seepage flow analysis section, 33...data assimilation processing section, 34...analysis result output section, 35...condition storage section, 36...range setting section, 37...message output section, 38...data setting section

Claims

1. an observation data acquisition unit that acquires observation data including at least a measurement result of the amount of spring water generated at the tunnel face; A site condition acquisition unit that acquires site condition setting information including at least the position of a face indicating an excavation section; A hydraulic parameter storage unit that stores hydraulic parameters including a hydraulic conductivity coefficient; a three-dimensional seepage analysis unit that takes in the observation data, the site condition setting information, and hydraulic parameters, and performs a three-dimensional seepage analysis based on a geological model and an analysis model in the vicinity of the tunnel; a data assimilation processor for identifying at least two hydraulic parameters including a rainfall recharge rate; A precipitation recharge rate parameter identification assistance system for use in a parameter identification system having a range setting unit that sets the upper and lower limits of the rainfall recharge rate to be smaller as the amount of precipitation in the observation data increases, and causes the data assimilation processing unit to identify the rainfall recharge rate; A rainfall recharge rate parameter identification assistant system with

2. A condition storage unit stores a plurality of different precipitation amounts and a parameter range set so that the upper and lower limit values ​​of the parameter of the rainfall recharge rate become smaller as the precipitation amount increases, The range setting unit reads out a parameter range corresponding to the amount of precipitation obtained as the observation data from the condition storage unit, and causes the data assimilation processing unit to identify the rainfall recharge rate so that the rainfall recharge rate is any value between an upper limit value and a lower limit value of the rainfall recharge rate indicated by the read out range. A rainfall recharge rate parameter identification assistance system as claimed in claim 1.

3. The parameter ranges stored in the condition storage unit are: The greater the amount of precipitation, the narrower the range determined by the upper and lower parameter limits of the rainfall recharge rate. A rainfall recharge rate parameter identification assistance system as claimed in claim 2.

4. 1. A computer-implemented method for assisting in identifying rainfall recharge rate parameters, comprising: Obtaining observation data including at least the results of measuring the amount of spring water generated at the tunnel face; Obtain site condition setting information including at least the position of a face indicating an excavation section; The observation data, the on-site condition setting information, and hydraulic parameters including a permeability coefficient are input, and a three-dimensional seepage analysis is performed based on a geological model and an analytical model in the vicinity of the tunnel; The upper and lower limits of the rainfall recharge rate are set smaller as the amount of precipitation in the observation data increases, and the rainfall recharge rate is identified by a data assimilation processor, thereby identifying at least two hydraulic parameters including the rainfall recharge rate. A method to assist in identifying rainfall recharge rate parameters.

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