A device and method for assisting in the identification of hydraulic conductivity parameters.

Abstract

To provide a water permeability coefficient parameter identification support method capable of determining a situation whether or not its hydraulic analysis model is better to be corrected.SOLUTION: A water permeability coefficient parameter identification support device and method comprises: determining whether or not difference between spring water amount gained from a result of a three-dimensional seepage flow analysis and observed data at a cut face position where the spring water amount is gained by carrying out the three-dimensional analysis based on a geological model and an analysis model near a tunnel by inputting the observed data including at least a measured result gained through measurement of the seepage amount generated in the cut face of the tunnel, at-site condition setting information including at least the cut face position representing an excavation section, and a hydraulic parameter including water permeability coefficient is within a first allowance referring the observed data; based on the determination result, when the difference is out of the first allowance, determining whether or not a period out of the first allowance exceeds a first management amount; and outputting a message representing correction of the geological model to be carried out when the first management amount is exceeded based on the result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a device for assisting in identifying permeability coefficient parameters and a method for assisting in identifying permeability coefficient parameters.

Background Art

[0002] When excavating a mountain tunnel, groundwater analysis may be performed to predict the amount of tunnel water inflow and the groundwater environment of the tunnel rock mass. In groundwater analysis, a hydrogeological model is created based on geological surveys and other data, and three-dimensional seepage flow analysis is performed using techniques such as the finite element method. To carry out groundwater analysis, advanced knowledge and techniques of professional engineers are required, such as creating a complex hydrogeological model, technically evaluating and reviewing analysis conditions for reproduction analysis and prediction analysis, and a great deal of time and effort are required for the creation and analysis of the model itself.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As one method for solving such problems, it is conceivable to use a groundwater field prediction system that automatically performs a series from obtaining observation data to outputting the results of prediction analysis by combining three-dimensional seepage flow analysis and a data assimilation technique. In the calculation program of such a groundwater field prediction system, parameters such as the permeability coefficient and specific storage coefficient of each geology are identified using observation data such as the amount of face water inflow during tunnel excavation and the data assimilation technique, and it is possible to predict the in-pit water inflow and water level drop that will occur by future excavation. However, in such groundwater field prediction systems, parameters such as permeability coefficients for each geological region are identified through data assimilation so that observational data can be reproduced. Even when using observational data on seepage volume in the geological region during drilling, there are cases where the observational data cannot be reproduced well. In such cases, parameters can sometimes be identified by increasing the amount of observational data, but sometimes reproduction is not possible even with increased observational data due to insufficient accuracy of the hydrogeological analysis model used in the 3D seepage flow analysis. In such cases, it is desirable to be able to determine whether or not it is necessary to modify the hydrogeological analysis model.

[0005] This invention has been made in view of these circumstances, and its purpose is to provide a device and method for identifying permeability coefficient parameters that can determine whether or not it is appropriate to modify a hydraulic geological analysis model. [Means for solving the problem]

[0006] To solve the above-mentioned problems, one aspect of the present invention is a permeability coefficient parameter identification auxiliary device used in a groundwater field prediction system, comprising: an observation data acquisition unit that acquires observation data including at least measurement results of the amount of seepage occurring at the tunnel face; a field condition acquisition unit that acquires field condition setting information including at least the location of the tunnel face indicating the excavation section; a hydraulic parameter storage unit that stores hydraulic parameters including the permeability coefficient; and a three-dimensional seepage flow analysis unit that takes in the observation data, the field condition setting information and hydraulic parameters and performs a three-dimensional seepage flow analysis based on a geological model and an analysis model of the vicinity of the tunnel. The permeability coefficient parameter identification auxiliary device includes: a first determination unit that determines whether the difference between the amount of spring water obtained from the three-dimensional seepage flow analysis and the observation data at the face where the spring water amount was obtained falls within a first allowable value based on the observation data; a second determination unit that, based on the determination result of the first determination unit, determines whether the period during which the amount of spring water does not fall within the first allowable value exceeds a first control value; and a message output unit that, based on the determination result of the second determination unit, outputs a message indicating that the geological model should be modified if the first control value is exceeded.

[0007] Furthermore, one aspect of the present invention is a permeability coefficient parameter identification assistance method performed by a computer, which acquires observation data including at least measurement results of the amount of groundwater seeping in at the tunnel face, acquires field condition setting information including at least the location of the tunnel face indicating the excavation section, takes in the observation data, the field condition setting information, and hydraulic parameters stored in a hydraulic parameter storage unit that stores hydraulic parameters including the permeability coefficient, and, in accordance with the performance of a 3D seepage flow analysis based on a geological model and an analysis model near the tunnel, determines whether the difference between the amount of groundwater seeping obtained from the 3D seepage flow analysis and the observation data at the location of the tunnel face where the groundwater seeping was obtained falls within a first allowable value based on the observation data, determines whether the period during which the amount of groundwater seeping is not within the first allowable value exceeds a first control value based on the determined result, and outputs a message indicating that the geological model should be modified if the first control value is exceeded based on the determined result. [Effects of the Invention]

[0008] As explained above, this invention makes it possible to determine whether or not it is appropriate to modify the hydrogeological analysis model. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic block diagram showing the configuration of a groundwater level prediction system S according to one embodiment of the present invention. [Figure 2] This is a flowchart illustrating the operation of the hydraulic conductivity parameter identification support system in the groundwater field prediction system S. [Modes for carrying out the invention]

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

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

[0012] The observation data acquisition unit 11 acquires observation data that includes at least the measurement results of the amount of groundwater seeping out at the tunnel face. Observation equipment is installed at the site where tunnel excavation work is being carried out. The observation equipment performs observations according to the observation items at the observation location and outputs observation data as a result of the observations. Tunnel excavation work is carried out daily. As this excavation work is carried out, the observation data measured on that day is output from the observation device as data that includes date and time data indicating the date and time of observation, on a daily basis (or every few hours). The observation data acquisition unit 11 acquires the observation data that includes date and time data each time observation data is output from the observation device. The observation data includes, for example, the amount of groundwater seeping into the tunnel near the tunnel face, the amount of groundwater seeping into the tunnel in the already excavated section, the groundwater level at the observation well, and precipitation. One of the observational data points is the amount of groundwater seeping into the tunnel, which can be measured only near the tunnel face or the total amount of groundwater seeping into the already excavated section. The observational data acquisition unit 11 only needs to acquire at least one of the two, the amount of groundwater seeping only near the tunnel face or the total amount of groundwater seeping into the already excavated section, depending on the measurement conditions at the site, and may acquire both.

[0013] The observation data completion unit 12 determines whether there are missing values or outliers in the observation data. If it is determined that there are missing values or outliers, the missing values and outliers are extracted, and correction processes such as data removal or data completion using the previously obtained observation data are performed on these data.

[0014] The site condition acquisition unit 13 acquires site condition setting information including at least the position of the face indicating the excavation section.

[0015] The display unit 14 receives the result of the three-dimensional seepage flow analysis output from the massively parallel computer 3 through the communication function and displays it on the display screen.

[0016] 〈Configuration of Cloud Server 2〉 The cloud server 2 is communicably connected to the field terminal 1 and is also communicably connected to the massively parallel computer 3 via a network. The cloud server 2 is constituted by, for example, at least one server device constructed on the Internet. The cloud server 2 has a communication function for communicating with the field terminal 1 and for communicating 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 flow analysis. The analysis data includes, for example, observation data, site condition setting information, hydraulic parameters, geological models, analysis models, analysis conditions, etc.

[0018] The observation data is the observation data acquired by the observation data acquisition unit 11. The site condition setting information is the site condition setting information acquired by the site condition acquisition unit 13. Hydraulic parameters include permeability coefficient, rainfall infiltration rate (retention capacity), specific storage coefficient, water characteristic curve, specific permeability coefficient, porosity, etc. Among these hydraulic parameters, the permeability coefficient, rainfall infiltration rate (retention capacity), and specific storage coefficient can be the parameters to be identified. Here, as an example, the case of identifying the permeability coefficient among these parameters will be described. The permeability coefficient is a coefficient that represents the degree of ease of water passing through soil or rock. The rainfall infiltration rate (retention capacity) is an index that represents the ability of rainfall to infiltrate underground. The specific storage coefficient represents the amount of water discharged or inhaled from a soil mass per unit volume when the groundwater level fluctuates in the ground. It means that the greater the specific storage coefficient of the ground, the greater the amount of groundwater discharged or inhaled accompanying the water level fluctuation. The water characteristic curve is data representing water retention. The specific permeability coefficient is the ratio of the unsaturated permeability coefficient to the saturated permeability coefficient and is introduced into the analysis based on its relationship with the volumetric water content. The relationship between the above-mentioned water characteristic curve and specific permeability coefficient and the volumetric porosity is the unsaturated infiltration characteristic required as input data for saturated-unsaturated infiltration flow analysis. The porosity represents the degree of voids and cracks between particles existing in the rock mass. The volumetric water content is the ratio of the volume of water to the volume of the substance.

[0019] The geological model is a three-dimensional geological model that represents the geological structure of the ground where the tunnel to be constructed will be excavated. The geological model is created, for example, using the results of a geological survey of the ground where the tunnel to be constructed will be excavated. The three-dimensional geological model includes models representing multiple geological layers, and each element corresponding to a geological layer is assigned a physical property value (for example, a permeability coefficient) based on the infiltration characteristics of the bedrock (ground). Here, the constituent materials that make up the geological layer have a permeability coefficient according to the type of material. It is known that there is a typical range of values ​​for such permeability coefficients for each type of constituent material. The groundwater field prediction system S assigns permeability coefficients to the elements corresponding to each geological layer in the three-dimensional geological model based on the typical range of values ​​for permeability coefficients, based on the infiltration characteristics of the bedrock (ground) input as a reproduction condition. By appropriately assigning these permeability coefficients, the amount of groundwater seepage can be determined with high accuracy. The permeability coefficient is a parameter to be identified, and by repeatedly performing predictive analysis, the parameter is modified on the large-scale parallel computer 3 and updated to approach a more appropriate value. An analytical model is a model used to reproduce observational results obtained as observational data. For example, an analytical model may be one to which an analytical mesh is assigned to a geological model, and permeability coefficients are assigned to each rock mass in the geological model. Analysis conditions are the various conditions given for performing the analysis.

[0020] The data management unit 22 acquires observation data and field condition setting information from the field terminal 1 and writes it to the analysis data storage unit 21. When observation data is obtained from the field terminal 1, the data management unit 22 writes the observation data, along with the date and time the observation data was observed, to the analysis data storage unit 21. Furthermore, the data management unit 22 takes in various types of information from external sources and writes them to the analysis data storage unit 21. The hydraulic parameter setting unit 23 writes the hydraulic parameters to the analysis data storage unit 21.

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

[0022] The analysis data storage unit 21 and the prediction result storage unit 24 each use a storage medium, such as an HDD (Hard Disk Drive), flash memory, or EEPROM (Electrically Erasable Programmable Read Only Memory). It is composed of Memory, RAM (Random Access read / write Memory), ROM (Read Only Memory), or any combination of these storage media. The analysis data storage unit 21 and the prediction result storage unit 24 can use, for example, non-volatile memory.

[0023] <Configuration of Large-Scale Parallel Computer 3> The large-scale parallel computer 3 has communication capabilities to communicate with the cloud server 2. The large-scale parallel computer 3 includes 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 first determination unit 35, a second determination unit 36, a message output unit 37, and a data setting unit 38.

[0024] The data acquisition unit 31 acquires various data from the cloud server 2 that are used to perform three-dimensional seepage flow analysis on the large-scale parallel computer 3. For example, the data acquisition unit 31 acquires data indicating the position of the excavation face, which represents the excavation section.

[0025] The 3D seepage flow analysis unit 32 retrieves observation data, field condition setting information, and hydraulic parameters from the cloud server 2, and performs 3D seepage flow analysis based on the geological model and analysis model of the vicinity of the tunnel. Existing systems (software) commonly used for 3D seepage flow analysis can be applied.

[0026] The data assimilation processing unit 33 performs data assimilation processing by using the observation data obtained by the observation data acquisition unit 11 and the estimated values ​​(prediction results) to obtain analytical values ​​that reflect the observation data. When observation data is obtained, the data assimilation processing unit 33 performs data assimilation processing according to a sequential data assimilation method. The data assimilation processing unit 33 performs data assimilation processing, outputs the results of the 3D seepage flow analysis, updates the parameters to be identified (e.g., permeability coefficient, rainfall infiltration rate (recharge rate), specific storage coefficient, etc.), and writes the updated parameters to the hydraulic parameter storage unit. The data assimilation processing unit 33 modifies at least one of the geological model or the analytical model if the difference between the predicted results obtained from the 3D seepage flow analysis and the observed data is large to a certain extent, and if the difference between the predicted results and the hydraulic parameters is large to a certain extent, it modifies at least one of them to a certain extent. The data assimilation processing unit 33 primarily identifies the permeability coefficient as the parameter to reproduce observational data through data assimilation, but it also has the function to identify multiple parameters such as rainfall infiltration rate (recharge rate) and specific storage coefficient, which are also related to the amount of groundwater seeping into the mine. The data assimilation processing unit 33 performs data assimilation by using an ensemble Kalman filter to sequentially update time (prediction by simulation) and update observations (model modification) using the parameters of the ensemble members.

[0027] The calculation program that implements the functions of the 3D seepage flow analysis unit 32 and the data assimilation processing unit 33 combines 3D seepage flow analysis with a sequential data assimilation method that has relatively low computational costs. Furthermore, this predictive analysis (calculation program) can be executed using a large-scale parallel computer.

[0028] The analysis result output unit 34 outputs the analysis results of the 3D seepage flow analysis. For example, the analysis result output unit 34 sends the analysis results to the cloud server 2, causing the cloud server 2 to write the prediction results to the prediction result storage unit 24.

[0029] The large-scale parallel computer 3 can perform 3D seepage flow analysis processing by the 3D seepage flow analysis unit 32 and data assimilation processing by the data assimilation processing unit 33.

[0030] The aforementioned groundwater prediction system S is a system that automatically performs a series of processes from acquiring observation data to outputting the results of prediction analysis, thereby enabling real-time predictions. Generally, predicting groundwater environments requires advanced knowledge and skills from analytical specialists (for example, technical evaluation of reproduction and predictive analysis results, methods for revising analysis conditions, etc.), and also takes a considerable amount of time (e.g., several days). The aforementioned groundwater field prediction system S combines three-dimensional seepage flow analysis with a sequential data assimilation method that has relatively low computational costs, and these calculations are performed by a large-scale parallel computer 3. This makes it possible to reproduce observational data and predict results such as mine seepage volume and groundwater level in a short time with high accuracy. As a result, predictive analysis can be performed without requiring advanced knowledge, skills, or judgment from analytical specialists.

[0031] The observation data acquired by the Groundwater Field Prediction System S can handle any data that can be processed by the calculation program of the Groundwater Field Prediction System S. Furthermore, since a check system is provided to compensate for outliers in the observation data, the maintenance and improvement of accuracy can be expected. Regarding the amount of groundwater seepage in the mine, which is one of the observational data points, the calculation program and system can handle either the amount of seepage only near the working face or the total amount of seepage in the already excavated section, depending on the measurement conditions at the site. Furthermore, in the groundwater field prediction system S, the parameter identified during prediction analysis is the permeability coefficient. However, it is also possible to identify multiple parameters, including not only the permeability coefficient but also rainfall infiltration rate (recharge rate) and specific storage coefficient, which are related to the amount of groundwater seeping into the mine.

[0032] The first determination unit 35 refers to the results of the three-dimensional seepage flow analysis and determines whether the difference between the amount of groundwater obtained from the three-dimensional seepage flow analysis and the observed data at the face where the groundwater amount was obtained falls within a first allowable value based on the observed data.

[0033] Based on the determination result of the first determination unit 35, the second determination unit 36 ​​determines whether the period during which the value is not within the first tolerance limit exceeds the first control value if the value is not within the first tolerance limit.

[0034] The message output unit 37 outputs a message indicating that the geological model should be modified if the first control value is exceeded, based on the determination result of the second determination unit 36. The message output unit 37 outputs the message to the large-scale parallel computer 3, which in turn allows the message to be output to the field terminal 1 via the cloud server 2. When the large-scale parallel computer 3 and the field terminal 1 are connected, the message output unit 37 may output messages to the field terminal 1 without going through the cloud server 2.

[0035] Furthermore, the message output unit 37, based on the determination result of the second determination unit 36, outputs a message indicating that the reproducibility of the spring water volume prediction results may increase as the amount of observation data increases, provided that the first control value is not exceeded.

[0036] Based on the determination result of the first determination unit 35, the data setting unit 38, if the values ​​are within the first allowable value, calculates the average value of the permeability coefficient used in the 3D seepage flow analysis if the period during which the values ​​are within the second allowable value exceeds the second control value, and sets the calculated average value of the permeability coefficient as the average value of the normal distribution in the initial distribution of the geological permeability coefficient in the geological model in which the 3D seepage flow analysis was performed, and sets the standard deviation of the normal noise to fix the set permeability coefficient.

[0037] Furthermore, the permeability coefficient parameter identification auxiliary device, which includes the first determination unit 35, the second determination unit 36, and the message output unit 37, 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 permeability coefficient parameter identification auxiliary device may be separated as different devices, and the large-scale parallel computer 3 and the permeability coefficient parameter identification auxiliary device may be configured to cooperate. The permeability coefficient parameter identification auxiliary device may also include a data setting unit 38. The permeability coefficient parameter identification auxiliary device may then execute the functions of the first determination unit 35, the second determination unit 36, the message output unit 37, and the data setting unit 38 in response to the 3D seepage flow analysis being performed.

[0038] Next, we will explain the operation of the groundwater level prediction system S described above. Figure 2 is a flowchart illustrating the operation of the hydraulic conductivity parameter identification support system in the groundwater field prediction system S. By executing the flowchart shown in Figure 2, this embodiment can automatically determine whether the permeability coefficient parameter of the soil at the excavation site has been identified. The Groundwater Level Prediction System S can perform predictions in real time by automatically carrying out a series of predictive analyses, from acquiring observation data to saving and transferring various information such as data, analysis setting conditions, and model information, to data assimilation predictive analysis, and outputting and transferring analysis results.

[0039] The observation data acquisition unit 11 of the field terminal 1 acquires observation data from the observation device (step S1). When multiple observation items are measured by existing measuring devices, the observation data acquisition unit 11 automatically acquires the measurement results (for example, each time a measurement result is output from the measuring device). The data to be acquired includes, for example, the amount of groundwater seepage in the tunnel near the tunnel face, the amount of groundwater seepage in the already excavated section, the groundwater level in the observation well, and precipitation. Here, the observational data used for data assimilation can be any data that can be handled by the calculation program. Furthermore, regarding the amount of groundwater seepage in the tunnel, which is one of the observational data, the calculation program and prediction system can handle both the amount of seepage only near the tunnel face and the total amount of seepage in the already excavated section, allowing for adaptation to the site conditions.

[0040] The observation data completion unit 12 determines whether the acquired observation data contains missing values ​​or outliers. If missing values ​​or outliers are found, it extracts them and performs correction processing on them, such as removing the data or completing the data using previously obtained observation data. Missing values ​​indicate cases where observation data that should be obtained as a value is missing. Outliers are values ​​that deviate drastically from previously obtained data for the observation item. Observational data may contain missing values ​​or outliers. Therefore, using this data as is would affect the results of predictive analysis. The observational data interpolation unit 12 checks the acquired observational data to extract missing values ​​and outliers, and performs correction processing on this data, such as removing data or interpolating with previous values. This enables data assimilation that is not affected by outliers, and allows prediction while maintaining accuracy. In addition, the observational data interpolation unit 12 has a function to display an error if the allowable time for correction processing is exceeded.

[0041] The field condition acquisition unit 13 acquires field condition setting information input from the input device of the field terminal 1. The data representing the field condition setting information input from the field terminal 1 only needs to include at least the face location (excavated section). If this face location (excavated section) information is input, the three-dimensional seepage flow analysis described later can be performed, thereby obtaining prediction results for underground water seepage and groundwater level. In addition, the data representing the field condition setting information can include the number of days to be predicted, and in this case, it is possible to output prediction results up to the input number of days. Furthermore, by inputting precipitation information based on the weather forecast of the site area as a field condition, more accurate prediction results can be obtained.

[0042] The data management unit 22 writes observation data obtained from the field terminal 1, field condition setting information, and analysis conditions to the analysis data storage unit 21. The hydraulic parameter setting unit 23 writes hydraulic parameters (permeability coefficient, rainfall infiltration rate (recharge rate), specific storage coefficient, moisture characteristic curve, specific permeability coefficient, volumetric water content, porosity, etc.) to the analysis data storage unit 21. Here, the analysis data storage unit 21 pre-stores various information about the hydrogeological model and the analysis model.

[0043] <Step S100> The data acquisition unit 31 of the large-scale parallel computer 3 imports various data necessary for analysis by acquiring them from the cloud server 2. For example, the data acquisition unit 31 acquires data that includes data indicating the current drilling location and data indicating the geological conditions being drilled. The 3D seepage flow analysis unit 32 performs 3D seepage flow analysis using various data obtained from the cloud server 2. Here, 3D unsteady seepage flow analysis is performed. Once the 3D seepage flow analysis is performed, the data assimilation processing unit 33 performs data assimilation processing.

[0044] <Steps S101, S101a> Subsequently, the 3D seepage flow analysis unit 32 calculates the spring water volume (predicted spring water volume) based on the permeability coefficient updated by the data assimilation processing unit 33. When the three-dimensional seepage flow analysis unit 32 calculates the amount of spring water as an analysis result, the first determination unit 35 determines whether the calculated amount of spring water (predicted result) falls within an acceptable range based on the amount of spring water indicated by the observation data (for example, within ±50% of the observation data value).

[0045] <Step S102, Step S102a> If the reproduced flow rate in the analysis results exceeds the permissible value (step S101a - outside the permissible value), the second determination unit 36 ​​calculates how many days ago the period in which the permissible value was exceeded began, and determines whether that period exceeds the control value b (for example, 5 days). For example, the second determination unit 36 ​​refers to the date and time data of the observation data used when it was determined that the permissible value was exceeded, and determines whether it is continuous over the period of control value b.

[0046] <Step S103> In the determination in step S102, if the second determination unit 36 ​​determines that the control value b is exceeded (step S102a - outside control value b), that is, if the recurrence values ​​for at least five consecutive days of observation data all exceed the acceptable value, the message output unit 37 outputs an error message indicating that "the hydrogeological model needs to be reviewed." In this step, S102, if the result exceeds the control value b, it is likely that the accuracy of the model is insufficient, such as the hydrogeological model used in the analysis being different from the actual geology. Therefore, continuing data assimilation in this manner is unlikely to accurately reproduce the amount of spring water, and future predictions are also unlikely to be accurate. As a result, the hydrogeological model needs to be revised. Therefore, the message output unit 37 stops the 3D seepage flow analysis by outputting an error message. Here, the message output unit 37 outputs an error message to the field terminal 1. This allows the user of the field terminal 1 to input a command to stop the 3D seepage flow analysis, thereby stopping the 3D seepage flow analysis processing on the large-scale parallel computer 3. Alternatively, the message output unit 37 stops the 3D seepage flow analysis by outputting an error to the 3D seepage flow analysis unit 32, and also outputs an error message to the field terminal 1. By outputting an error message and stopping the 3D seepage flow analysis in this way, it is possible to encourage a review of the hydrogeological model. In response to the display of such an error message, the operator of field terminal 1 can make corrections to the geological model or analysis model.

[0047] <Step S104> When the analysis is stopped, the 3D seepage flow analysis unit 32 stores the results by writing a log to the prediction result storage unit 24.

[0048] <Step S105> On the other hand, in the determination in step S102, if the control value b is not exceeded (step S102a-NO), the message output unit 37 outputs an error message indicating that "the hydrogeological model may need to be revised." Here, although there is a possibility that the hydrogeological model may need to be revised, the information from the observational data used for data assimilation is insufficient, and it is thought that the reproducibility of the flow rate in the analysis results may improve as the amount of observational data increases in the future. Therefore, by outputting this error message, the message output unit 37 can output that there is a possibility that the hydrogeological model may need to be revised. By referring to this message, the user of the field terminal 1 can understand that there is a possibility that the hydrogeological model may need to be revised, and that the reproducibility of the flow rate in the analysis results may improve as the amount of observational data increases in the future.

[0049] <Step S115> After an error is output in step S105, the 3D seepage flow analysis unit 32 does not stop the analysis. Instead, it stores the current analysis results as a log in the prediction result storage unit 24, and then proceeds to the next analysis step, thereby continuing the 3D seepage flow analysis. For example, when new observation data is acquired, the unit performs a 3D seepage flow analysis using that observation data.

[0050] <Step S110, Step S110a> If the reproduced flow rate from the analysis results does not exceed the allowable value (step S101a - within the allowable value), the data assimilation processing unit 33 calculates the ensemble standard deviation of the identified permeability coefficient of the geological structure being drilled. The second determination unit 36 ​​determines whether the standard deviation falls within an acceptable range. The acceptable range may be a predetermined value. For example, if the standard deviation is 0.1 or less, it can be said that the distribution of the ensemble is clustered around the mean, and therefore it is possible that identification can be performed with high accuracy. For this reason, the acceptable range can be set to, for example, 0.1. Alternatively, the acceptable range may be set to a value other than 0.1 depending on the site (the mountain to be excavated).

[0051] <Step S120> Based on the determination result of the second determination unit 36, the message output unit 37 outputs an error message indicating "the parameter identification accuracy may be insufficient" if the standard deviation exceeds the allowable value (step S110a - outside the allowable value).

[0052] <Step S115> Here, if the second determination unit 36 ​​determines that the standard deviation exceeds the allowable value, it is possible that the accuracy of identifying the permeability coefficient is low because there is a large variation in the permeability coefficients of each ensemble. However, as the amount of observational data increases in the future, the variation in the permeability coefficients of each ensemble may decrease. Therefore, although an error message is output from the message output unit 37, the 3D seepage flow analysis unit 32 does not stop the 3D seepage flow analysis. Instead, it stores the current analysis results as a log in the prediction result storage unit 24 and then proceeds to the next analysis step, thus continuing the 3D seepage flow analysis.

[0053] <Step S111, Step S111a> On the other hand, if the standard deviation is within the acceptable range (step S110a - within the acceptable range), the second determination unit 36 ​​further determines whether the period during which the standard deviation is within the acceptable range exceeds the control value a (for example, 7 days). For example, the second determination unit 36 ​​refers to the date and time data of the observation data used when it was determined that the standard deviation was within the acceptable range, and determines whether it has continued for the period of control value a.

[0054] <Step S120> If the period during which the standard deviation is within the acceptable range does not exceed the control value a (step S111a - within the control value), the message output unit 37 outputs an error indicating "there is a possibility that the parameter identification accuracy is insufficient," because although the identification accuracy of the permeability coefficient is high, there is a possibility that the variation in the permeability coefficients of each ensemble will increase again, leading to a decrease in the identification accuracy of the permeability coefficient.

[0055] <Step S115> If an error message is output in step S120, the 3D seepage flow analysis unit 32 does not stop the 3D seepage flow analysis. Instead, it stores the current analysis results as a log in the prediction result storage unit 24, and then proceeds to the next analysis step, thereby continuing the 3D seepage flow analysis.

[0056] <Step S112> On the other hand, if the second determination unit 36 ​​determines that the value exceeds the control value a (step S111a - outside the control value), it is determined that the permeability coefficient can be identified with high reliability because the high accuracy of identifying the permeability coefficient has continued for a certain period of time. Therefore, the data setting unit 45 calculates the average value from among the multiple permeability coefficients identified over the entire period (duration) corresponding to the control value a. Here, the average value of each permeability coefficient identified at multiple excavation locations that differ in the excavation direction is calculated.

[0057] <Step S113> When the data setting unit 45 calculates the average value of the identified permeability coefficients over the entire period according to the control value a, it sets this calculated average value of permeability coefficients as the permeability coefficient of the geology at the drilling site. In other words, it sets it as the permeability coefficient for the geology at the current drilling location among the geology in the geological model.

[0058] Here, when setting the permeability coefficient of the geological structure in the geological model of the drilling site, the data setting unit 45 generates multiple initial values ​​by applying fluctuations (perturbations) to the permeability coefficient obtained by data assimilation using the function of the ensemble Kalman filter. There are two types of distributions for these initial values ​​(initial distribution): a uniform distribution and a normal distribution. The data setting unit 45 uses a uniform distribution as the initial distribution when the hydraulic conductivity is unknown. In this case, boring may be performed at the excavation site as part of the geological survey. Based on the results of the boring, the permeability coefficient of the target geology may be obtained by conducting field permeability tests or laboratory permeability tests using boring cores. On the other hand, if such boring is not performed, the permeability coefficient of the target geology is unknown and is treated as unknown. When the permeability coefficient is unknown, the minimum and maximum values ​​of the uniform distribution can be determined based on general values ​​using literature values. In this case, the data assimilation processing unit 33 sets the minimum and maximum values ​​of the uniform distribution based on literature values ​​and also sets the uncertainty of the permeability coefficient, i.e., the standard deviation of normal noise. In other words, uncertainty is considered for each of the multiple initial values ​​(permeability coefficients), but the standard deviation is used as that uncertainty. This makes it possible to obtain predicted values ​​such that each of the multiple initial values ​​(permeability coefficients) varies within the range of the standard deviation. On the other hand, the data setting unit 45 uses a normal distribution as the initial distribution when the estimated permeability coefficient is known in advance. As mentioned above, when boring is performed as part of a geological survey at an excavation site, the permeability coefficient of the target geology can sometimes be obtained by performing field permeability tests or laboratory permeability tests using boring cores. In addition, multiple permeability coefficients can be obtained for the target geology, and the obtained permeability coefficients are input as the average value, and the variation (noise) is set by the standard deviation. In this case, the data assimilation processing unit 33 sets the mean and standard deviation of the normal distribution, and also sets the standard deviation of the normal noise in the case of a normal distribution, similar to the uniform distribution.

[0059] <Step S114> If the data setting unit 45 determines that the permeability coefficient of the geological structure at the excavation site can be identified, it uses a normal distribution as the initial distribution, sets the mean value of the permeability coefficient calculated in step S112 to the mean value of the normal distribution, and then reduces the standard deviation of the normal noise. This allows the 3D seepage flow analysis unit 32 to proceed with the analysis in subsequent steps with the permeability coefficient almost fixed (the ensemble variation reduced).

[0060] <Step S115> After step S114, the 3D seepage flow analysis unit 32 stores a log showing the current analysis results in the prediction result storage unit 24, and proceeds to the next analysis step without stopping the 3D seepage flow analysis.

[0061] When prediction results are output from the large-scale parallel computer 3, the prediction result storage unit 24 of the cloud server 2 stores the prediction results as a log. When the prediction results are stored in the prediction result storage unit 24, the field terminal 1 retrieves these prediction results. The display unit 14 displays the retrieved prediction results on its display screen. Based on the prediction results, the field terminal 1 can display data such as the amount of groundwater seeping into the mine, the total head distribution, and a water level drop diagram 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 field terminal 1 at the site where they are to be displayed.

[0062] According to the embodiment described above, there is no need for an analysis specialist to determine whether the permeability coefficient parameters have been identified, and the permeability coefficient parameters of the soil at the excavation site can be automatically identified according to certain criteria. Furthermore, even if the groundwater prediction system is automated, it is possible to determine when to review the geological model. Furthermore, even if the permeability coefficient parameters of the geological structure at the excavation site cannot be identified, it is possible to make a determination without requiring a specialized analysis engineer and to indicate that the accuracy of the hydrogeological analysis model is insufficient and that the model needs to be modified. Furthermore, since the tolerance and control values ​​within the system can be set to different values ​​as parameters, the parameters can be changed to suit the specific conditions of each tunnel site, making it highly versatile.

[0063] Furthermore, according to the above embodiment, while the permeability coefficient parameters of the geological formation during excavation are identified based on daily observation data, the identified permeability coefficient parameters of the geological formation can be fixed at a certain stage of excavation in order to predict future water seepage and identify the permeability coefficient parameters of the next geological formation to be excavated.

[0064] Furthermore, according to the embodiments described above, when predicting and analyzing the groundwater environment of the tunnel ground, the accuracy of reproduction and prediction analyses can be improved by assimilating actual sequential observation data (in-tunnel seepage volume, groundwater level, precipitation, etc.) with the results of 3D seepage flow analysis obtained from geological surveys.

[0065] Furthermore, the entire process, from acquiring observational data to outputting predictive analysis results, can be automated, enabling real-time predictions. Furthermore, by automating the process by combining 3D seepage flow analysis with data assimilation techniques, it eliminates the need for highly specialized knowledge and skills of analysis engineers for technical evaluation of reproduction and predictive analyses, as well as for reviewing analysis conditions. In addition, because 3D analysis is performed, it becomes possible to reproduce and predict the groundwater environment of the entire analysis area.

[0066] In the embodiment described above, the permissible values ​​and control values ​​can be set to arbitrary values ​​according to the conditions of each tunnel site. These settings may be entered by an analysis specialist using an input device such as a mouse or keyboard on the site terminal 1.

[0067] In the above-described embodiment, the field terminal 1, cloud server 2, and large-scale parallel computer 3 may each be implemented using computers. In that case, the program for implementing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into the computer system and executed. Here, "computer system" includes hardware such as the OS and peripheral devices. Furthermore, "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into the computer system. Moreover, "computer-readable recording medium" may also include those that dynamically hold programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or communication lines such as telephone lines, and those that hold programs for a certain period of time, such as volatile memory inside the computer system that acts as the server or client in that case. Furthermore, the above-mentioned program may be for implementing a part of the above-mentioned function, or it may be a program that can implement the above-mentioned function in combination with a program already recorded in the computer system, or it may be implemented using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0068] While embodiments of this invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the spirit of this invention. [Explanation of symbols]

[0069] 1…Field terminal, 2…Cloud server, 3…Large-scale parallel computer, 11…Observation data acquisition unit, 12…Observation data supplementation unit, 13…Field condition acquisition unit, 14…Display unit, 21…Analysis data storage unit, 22…Data management unit, 23…Hydraulic parameter setting unit, 24…Prediction result storage unit, 31…Data acquisition unit, 32…3D seepage flow analysis unit, 33…Data assimilation processing unit, 34…Analysis result output unit, 35…First determination unit, 36…Second determination unit, 37…Message output unit, 38…Data setting unit, 44…Message output unit, 45…Data setting unit

Claims

1. An observation data acquisition unit that acquires observation data including at least measurement results of the amount of groundwater seeping in at the tunnel face, A field condition acquisition unit that acquires field condition setting information including at least the location of the excavation face, A hydraulic parameter storage unit that stores hydraulic parameters including the permeability coefficient, A three-dimensional seepage flow analysis unit takes in the aforementioned observation data, the aforementioned field condition setting information, and hydraulic parameters, and performs a three-dimensional seepage flow analysis based on a geological model and an analysis model of the vicinity of the tunnel, This is a hydraulic conductivity parameter identification auxiliary device used in a groundwater field prediction system, A first determination unit determines whether the difference between the amount of spring water obtained from the three-dimensional seepage flow analysis and the observation data at the face where the spring water was obtained falls within a first allowable value based on the observation data, Based on the determination result of the first determination unit, if the value is not within the first tolerance value, a second determination unit determines whether the period during which the value is not within the first tolerance value exceeds the first control value. A message output unit outputs a message indicating that the geological model should be modified if the first control value is exceeded based on the determination result of the second determination unit. It has, The message output unit is: Based on the determination result of the second determination unit, if the value does not exceed the first control value, a message is output indicating that the reproducibility of the spring water volume prediction result may improve as the observation data increases. A device to assist in identifying hydraulic conductivity parameters.

2. An observation data acquisition unit that acquires observation data including at least measurement results of the amount of groundwater seeping in at the tunnel face, A field condition acquisition unit that acquires field condition setting information including at least the location of the excavation face, A hydraulic parameter storage unit that stores hydraulic parameters including the permeability coefficient, A three-dimensional seepage flow analysis unit takes in the aforementioned observation data, the aforementioned field condition setting information, and hydraulic parameters, and performs a three-dimensional seepage flow analysis based on a geological model and an analysis model of the vicinity of the tunnel, This is a hydraulic conductivity parameter identification auxiliary device used in a groundwater field prediction system, A first determination unit determines whether the difference between the amount of spring water obtained from the three-dimensional seepage flow analysis and the observation data at the face where the spring water was obtained falls within a first allowable value based on the observation data, Based on the determination result of the first determination unit, if the value is not within the first tolerance value, a second determination unit determines whether the period during which the value is not within the first tolerance value exceeds the first control value. A message output unit outputs a message indicating that the geological model should be modified if the first control value is exceeded based on the determination result of the second determination unit, Based on the determination result of the first determination unit, if the value falls within the first allowable value, and if the period during which the permeability coefficient used in the three-dimensional seepage flow analysis falls within the second allowable value exceeds the second control value, the average value of the permeability coefficient during the period corresponding to the second control value is calculated, and the calculated average value of the permeability coefficient is set as the average value of the normal distribution in the initial distribution of the geological permeability coefficient in the geological model in which the three-dimensional seepage flow analysis was performed, and the data setting unit sets the standard deviation of the normal noise to fix the set permeability coefficient. A device for identifying hydraulic conductivity parameters, which has the following features.

3. A computer-based method for assisting the identification of hydraulic conductivity parameters, Observational data was obtained that included at least measurement results of the amount of groundwater seeping in at the tunnel face. Obtain field condition setting information that includes at least the location of the excavation face, indicating the excavation section. The observation data, the field condition setting information, and the hydraulic parameters stored in the hydraulic parameter storage unit, which stores hydraulic parameters including the permeability coefficient, are taken in, and a three-dimensional seepage flow analysis is performed based on the geological model and analysis model of the vicinity of the tunnel. The difference between the amount of spring water obtained from the three-dimensional seepage flow analysis and the observational data at the face where the spring water amount was obtained is determined to be within a first allowable value based on the observational data. Based on the results determined above, if the value does not fall within the first tolerance value, it is determined whether the period during which the value does not fall within the first tolerance value exceeds the first control value. Based on the results determined above, if the first control value is exceeded, a message indicating that the geological model will be modified will be output. Outputting the aforementioned message means Based on the determined result, if the value does not exceed the first control value, the system includes outputting a message indicating that an increase in the observation data may improve the reproducibility of the spring water volume prediction result. A method to assist in identifying hydraulic conductivity parameters.

4. A computer-based method for assisting the identification of hydraulic conductivity parameters, Observational data was obtained that included at least measurement results of the amount of groundwater seeping in at the tunnel face. Obtain field condition setting information that includes at least the location of the excavation face, indicating the excavation section. The observation data, the field condition setting information, and the hydraulic parameters stored in the hydraulic parameter storage unit, which stores hydraulic parameters including the permeability coefficient, are taken in, and a three-dimensional seepage flow analysis is performed based on the geological model and analysis model of the vicinity of the tunnel. The difference between the amount of spring water obtained from the three-dimensional seepage flow analysis and the observational data at the face where the spring water amount was obtained is determined to be within a first allowable value based on the observational data. Based on the results determined above, if the value does not fall within the first tolerance value, it is determined whether the period during which the value does not fall within the first tolerance value exceeds the first control value. Based on the results determined above, if the first control value is exceeded, a message indicating that the geological model will be modified will be output. Based on the determined results, if the value falls within the first allowable value, and the period during which the permeability coefficient used in the three-dimensional seepage flow analysis falls within the second allowable value exceeds the second control value, the average value of the permeability coefficient during the period corresponding to the second control value is calculated, and the calculated average value of the permeability coefficient is set as the average value of the normal distribution in the initial distribution of the geological permeability coefficient in the geological model in which the three-dimensional seepage flow analysis was performed, and the standard deviation of the normal noise is set to fix the set permeability coefficient. A method to assist in identifying hydraulic conductivity parameters.

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