Groundwater field prediction system, groundwater field prediction method

Abstract

To predict groundwater environment in real time on the basis of observation data of a site while improving prediction accuracy.SOLUTION: A groundwater field prediction system is provided with a data assimilation processing part for acquiring observation data including at least a measurement result obtained by measuring an amount of sump water that occurs at the working face of a tunnel, site condition setting information including at least the position of a working face showing an excavation section, and a hydraulic parameter, correcting at least either a geological model or an analytic model in the case that a difference between a prediction result obtained by three-dimensional penetration flow analysis and the observation data is larger than a first reference value on the basis of the geological model and the analytic model near the tunnel or that a difference between the prediction result and the hydraulic parameter is larger than a second reference value, otherwise outputting a result of the three-dimensional penetration flow analysis, updating a parameter of an assimilation target, and writing and updating the updated parameter in a hydraulic parameter storage part.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a groundwater field prediction system and a groundwater field prediction method.

Background Art

[0002] In order to predict the amount of tunnel water inflow and the groundwater environment of the tunnel rock mass, groundwater analysis may be performed. In groundwater analysis, a hydrogeological model is created based on geological surveys and other data, and three-dimensional seepage flow analysis is performed using methods such as the finite element method. To carry out groundwater analysis, advanced knowledge and techniques of professional engineers are required, such as the creation of complex hydrogeological models, technical evaluation of reproduction analysis and prediction analysis, and review of analysis conditions. Moreover, a great deal of time and effort are required for the creation and analysis of the model itself. Therefore, unless there is a social demand for groundwater problems such as environmental protection, groundwater analysis and monitoring are not often carried out, and the amount of water inflow is assumed by empirical methods and the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in tunnel construction, since it is rare to fully grasp the geological conditions of the tunnel route before starting construction, information-based construction that performs appropriate construction based on on-site observations, measurements, evaluations, and predictions of the face, especially in the face, during construction becomes an important issue. As for groundwater problems related to construction, even if groundwater analysis is carried out to predict the water inflow volume, it does not always match the actual groundwater behavior, and there are cases where the prediction simulation cannot keep up with the progress of the construction. Also, even if inverse analysis is carried out by reflecting construction and measurement data such as the tunnel water inflow volume obtained on-site during tunnel construction in the model, it is often difficult to carry out inverse analysis due to the complex model. There is the SDA-SWING method in which the ensemble Kalman filter, which is one of the sequential data assimilation methods, is introduced into the SWING method, which is a groundwater analysis method that simplifies the analysis model, to obtain the permeability coefficient that reproduces the observed water inflow volume. With the SDA-SWING method, the water inflow volume can be predicted sequentially and quickly on-site, but since the model of groundwater flow is simplified, it is not possible to predict the exact water inflow volume and groundwater environment. Also, it is not assumed to be carried out in real time based on on-site observation data.

[0005] The present invention has been made in view of such circumstances, and its object is to provide a groundwater field prediction system and a groundwater field prediction method that can perform real-time groundwater environment prediction based on on-site observation data while improving prediction accuracy.

Means for Solving the Problems

[0006] In order to solve the above-described problems, one aspect of the present invention includes an observation data acquisition unit that acquires observation data including at least the measurement result of the water inflow volume generated at the face of the 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 hydrogeological parameter storage unit that stores hydrogeological parameters including the permeability coefficient, and takes in the observation data, the site condition setting information, and the hydrogeological parameters, and based on the geological model and the analysis model in the vicinity of the tunnel, a three-dimensional seepage flow analysis unit that performs three-dimensional seepage flow analysis, and the prediction result obtained from the result of the three-dimensional seepage flow analysis The water yield contained in and the observation data The water yield of when the difference from the first reference value is greater, or the prediction result The permeability coefficient used when determining the water yield contained in and the hydrogeological parameters The permeability coefficient which is When the difference from [it] is greater than the second reference value, at least one of the geological model and the analysis model is corrected. Output data indicating that it is necessary to When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is less than the first reference value, or when the difference between the prediction result and the hydrogeological parameter is less than the second reference value, the result of the three-dimensional seepage flow analysis is output, and the parameter to be identified The permeability coefficient which is is updated, and a data assimilation processing unit that writes and updates the updated parameter to the hydrogeological parameter storage unit. It is a groundwater field prediction system having

[0007] Also, one aspect of the present invention is a groundwater field prediction method executed by a computer, which acquires observation data including at least the measurement result of the amount of water gushing out occurring at the face of the tunnel, acquires the site condition setting information including at least the position of the face indicating the excavation section, stores the hydrogeological parameter storage unit that stores the hydrogeological parameters including the permeability coefficient, takes in the observation data, the site condition setting information, and the hydrogeological parameters, and based on the geological model and the analysis model in the vicinity of the tunnel, performs three-dimensional seepage flow analysis, and the prediction result obtained from the result of the three-dimensional seepage flow analysis The water yield contained in and the observation data The water yield of When the difference is greater than the first reference value, or the prediction result The permeability coefficient used when determining the water yield contained in and the hydrogeological parameter The permeability coefficient which is When the difference from [it] is greater than the second reference value, at least one of the geological model and the analysis model is corrected. Output data indicating that it is necessary to When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is less than the first reference value, or when the difference between the prediction result and the hydrogeological parameter is less than the second reference value, the result of the three-dimensional seepage flow analysis is output, and the parameter to be identified The permeability coefficient which is is updated, and it is a groundwater field prediction method that writes and updates the updated parameter to the storage unit.

Advantages of the Invention

[0008] As described above, according to the present invention, it is possible to perform real-time groundwater environment prediction based on on-site observation data while improving the prediction accuracy.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Mode for Carrying Out the Invention

[0010] Hereinafter, a groundwater field prediction system according to an embodiment of the present invention will 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 the on-site terminal 1〉 The on-site terminal 1 is communicably connected to the cloud server 2 via a network such as the Internet. The on-site terminal 1 is provided in the on-site office at the site where the 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 part of the on-site terminal 1, etc. In addition to these functions, the on-site terminal 1 has an observation data acquisition unit 11, an observation data complementation unit 13, an on-site condition acquisition unit 12, and a display unit 14.

[0012] The observation data acquisition unit 11 acquires observation data including at least the measurement results of the water inflow occurring at the face of the tunnel. At the site where tunnel excavation work is carried out, an observation device is provided. The observation device performs observations according to the observation items at the observation location and outputs the observation data, which is the result of the observations. The observation data output from the observation device is acquired by the observation data acquisition unit 11. The observation data is, for example, the amount of in-pit water inflow near the face, the amount of in-pit water inflow in the excavated section, the groundwater level in the observation hole, the precipitation, etc. The in-pit water inflow, which is one of the observation data, includes the water inflow only near the face, the total water inflow in the excavated section, etc. The observation data acquisition unit 11 only needs to acquire at least one of the water inflow only near the face and the total water inflow in the excavated section according to the measurement situation at the site, etc., and both may be acquired.

[0013] The site condition acquisition unit 12 acquires site condition setting information including at least the position of the face indicating the excavation section.

[0014] The observation data completion unit 13 determines whether there are missing values or abnormal values in the observation data. If it is determined that there are missing values or abnormal values, the missing values and abnormal values are extracted, and correction processes such as data removal or data completion using the previously obtained observation data are performed on these data.

[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 on-site terminal 1 and is also communicably connected to the massively parallel computer 3 via the network. The cloud server 2 has a communication function for communicating with the on-site terminal 1 and the massively parallel computer 3. In addition, the cloud server 2 has an analysis data storage unit 21, a data management unit 22, a hydrogeological 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 infiltration flow analysis. The analysis data includes, for example, observation data, in-situ condition setting information, hydrogeological parameters, geological models, analysis models, analysis conditions, and the like.

[0018] The observation data is the observation data acquired by the observation data acquisition unit 11. The in-situ condition setting information is the in-situ condition setting information acquired by the in-situ condition acquisition unit 12. The hydrogeological parameters include hydraulic conductivity, rainfall infiltration rate (retention capacity), specific storage coefficient, water retention curve, relative hydraulic conductivity, porosity, etc. Among these hydrogeological parameters, the hydraulic conductivity, rainfall infiltration rate (retention capacity), and specific storage coefficient can be the parameters to be identified. The hydraulic conductivity is a coefficient representing the degree of ease of water passage when water passes through soil. The rainfall infiltration rate (retention capacity) is an index representing the ability of rainfall to infiltrate underground. The specific storage coefficient represents the amount of water discharged or inhaled per unit volume of soil mass 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 retention curve is data representing water retention properties. The relative hydraulic conductivity is the ratio of the unsaturated hydraulic conductivity to the saturated hydraulic conductivity and is introduced into the analysis based on the relationship with the volumetric water content. The relationship between the above-mentioned water retention curve and relative hydraulic conductivity 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 representing the geological structure of the ground when excavating a tunnel to be constructed. The geological model is created, for example, using the results of geological surveys on the ground where the tunnel to be constructed is excavated. The three-dimensional geological model includes, for example, models representing a plurality of strata, and physical property values (e.g., physical property values such as hydraulic conductivity) corresponding to this stratum are assigned to the elements corresponding to each stratum based on the infiltration characteristics of the rock mass (ground). Here, the components constituting the stratum have a hydraulic conductivity corresponding to the type of the component. It is known that such hydraulic conductivities have a typical value range for each type of component. The groundwater field prediction system S assigns a hydraulic conductivity to the elements corresponding to each stratum in the three-dimensional geological model based on the typical value range of the hydraulic conductivity based on the infiltration characteristics of the rock mass (ground) input as the reproduction conditions. By appropriately assigning this hydraulic conductivity, the water inflow can be obtained with high accuracy. The hydraulic conductivity is a parameter to be identified, and by repeatedly performing prediction analysis, the parameter is corrected in the massively parallel computer 3 and updated to approach a more appropriate value. The analysis model is a model for reproducing the observation results obtained as observation data. For example, it is a model in which an analysis mesh is assigned to the geological model and a hydraulic conductivity 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 the observation data and the on-site condition setting information obtained from the on-site terminal 1 and writes them into the analysis data storage unit 21. In addition, the data management unit 22 takes in various information from the outside and writes it into the analysis data storage unit 21. The hydrogeological parameter setting unit 23 writes the hydrogeological parameters into the analysis data storage unit 21.

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

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

[0023] 〈Configuration of the large-scale parallel computer 3〉 The large-scale parallel computer 3 has a communication function for communicating with the cloud server 2. The large-scale parallel computer 3 includes a three-dimensional seepage flow analysis unit 31, a data assimilation processing unit 32, and an analysis result output unit 33.

[0024] The three-dimensional seepage flow analysis unit 31 takes in observation data, in-situ condition setting information, and hydraulic parameters, and performs three-dimensional seepage flow analysis based on a geological model and an analysis model in the vicinity of the tunnel. Existing systems (software) that are generally used can be applied to the three-dimensional seepage flow analysis.

[0025] When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is greater than the first reference value, or when the difference between the prediction result and the hydraulic parameters is greater than the second reference value, the data assimilation processing unit 32 corrects at least one of the geological model and the analysis model. When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is less than the first reference value, or when the difference between the prediction result and the hydraulic parameters is less than the second reference value, the data assimilation processing unit 32 outputs the result of the three-dimensional seepage flow analysis, updates the parameters to be identified (for example, permeability coefficient, rainfall infiltration rate (retention amount), specific storage coefficient, etc.), and writes the updated parameters to the hydraulic parameter storage unit for updating. The data assimilation processing unit 32 has a function that can identify multiple parameters such as the rainfall infiltration rate (recharge amount) and specific storage coefficient that are involved in the in-pit water inflow, although the parameter identified to reproduce the observed data by data assimilation is basically the hydraulic conductivity. When performing data assimilation processing, the data assimilation processing unit 32 performs data assimilation by sequentially repeating time update (prediction by simulation) and observation update (model correction) using parameters that are ensemble members by means of an ensemble Kalman filter.

[0026] The calculation program that realizes the three-dimensional seepage flow analysis unit 31 and the data assimilation processing unit 32 combines three-dimensional seepage flow analysis and a sequential data assimilation method with a relatively small computational cost. And this prediction analysis (calculation program) can be executed using a large-scale parallel computer.

[0027] The analysis result output unit 33 outputs the analysis results of the three-dimensional seepage flow analysis. For example, the analysis result output unit 33 transmits the analysis results to the cloud server 2 and causes the prediction result storage unit 24 of the cloud server 2 to write the prediction results.

[0028] The large-scale parallel computer 3 can perform three-dimensional seepage flow analysis processing by the three-dimensional seepage flow analysis unit 31 and data assimilation processing by the data assimilation processing unit 32.

[0029] In the above-described groundwater field prediction system S, it is a system that automatically performs a series of processes from the acquisition of observed data to the output of the results of prediction analysis, and thus, prediction can be performed in real time. Generally, predicting the groundwater environment requires advanced knowledge and skills of analytical experts (for example, technical evaluation of the results of reproduction analysis and prediction analysis, methods for reviewing analysis conditions, etc.), and furthermore, it takes a great deal of time (for example, several days). According to the above-mentioned groundwater field prediction system S, by combining three-dimensional seepage flow analysis with a sequential data assimilation method with relatively low computational cost, and having these operations performed by a large-scale parallel computer 3, it is possible to obtain accurate reproduction of observational data and prediction results such as in-pit water inflow and groundwater levels in a short time. As a result, it is possible to perform prediction analysis without the need for advanced knowledge, techniques, and judgments of analytical experts.

[0030] The observational data acquired by the groundwater field prediction system S can correspond to any data as long as it can be handled by the calculation program in the groundwater field prediction system S. In addition, since a check system for complementing abnormal values and the like of observational data is provided, it is expected to maintain and improve accuracy. Regarding the in-pit water inflow, which is one of the observational data, a calculation program and system are in place that can handle either the water inflow only near the face or the total water inflow in the already excavated section, depending on the measurement situation at the site. In addition, in the groundwater field prediction system S, the parameters to be identified during prediction analysis are the hydraulic conductivity, but it is also possible to identify multiple parameters such as the rainfall infiltration rate (recharge amount) and specific storage coefficient that are also involved in the in-pit water inflow, not just the hydraulic conductivity.

[0031] Next, the operation of the above-mentioned groundwater field prediction system S will be described. Figure 2 is a flowchart showing the flow of prediction analysis of the groundwater field prediction system S. The groundwater field prediction system S can perform predictions in real time by automatically performing a series of prediction analyses, from the acquisition of observational data to the storage and transfer of each piece of information such as data, analysis setting conditions, and model information, data assimilation prediction analysis, and output and transfer of analysis results.

[0032] The observation data acquisition unit 11 of the on-site terminal 1 acquires observation data from the observation device (step S1). When the measurement results of a plurality of observation items are measured by existing measuring devices or the like respectively, the observation data acquisition unit 11 automatically acquires the measurement results (for example, every time the measurement results are output from the measuring device). The data to be acquired is, for example, the amount of in-pit water inflow near the face, the amount of in-pit water inflow in the already excavated section, the groundwater level of the observation hole, the precipitation, etc. Here, the observation data used for data assimilation can correspond to any data as long as it can be handled by the calculation program. Also, regarding the amount of in-pit water inflow, which is one of the observation data, since the calculation program and the prediction system can handle either the water inflow only near the face or the total water inflow in the already excavated section, it is possible to make corresponding adjustments according to the on-site situation.

[0033] The observation data complementation unit 13 determines whether there are missing values or abnormal values in the acquired observation data. If it is determined that there are missing values or abnormal values, the missing values and abnormal values are extracted, and correction processes such as data removal or data complementation using the previously obtained observation data are performed on these data (step S2). A missing value indicates a case where the observation data that should be obtained as a value is missing. An abnormal value is a value that extremely deviates from the data obtained in the past for the observation data obtained this time in the observation item. The observation data may include missing values or abnormal values. Therefore, using these data as they are will affect the result of the prediction analysis. Thus, the observation data complementation unit 13 checks the acquired observation data to extract the missing values and abnormal values, and performs correction processes such as data removal or complementation with the previous value on these data. As a result, data assimilation that is not dragged down by abnormal values or the like becomes possible, and prediction can be performed while maintaining the accuracy. Also, when performing the correction process, the observation data complementation unit 13 has a function of displaying an error when the allowable time for the correction process is exceeded.

[0034] The in-situ condition acquisition unit 12 acquires the in-situ condition setting information input from the input device of the in-situ terminal 1 (step S3). The data representing the in-situ condition setting information input from the in-situ terminal 1 only needs to include at least the face position (excavated section). If the information on this face position (excavated section) is input, three-dimensional seepage flow analysis described later can be performed, and thereby prediction results such as the amount of in-pit water inflow and the groundwater level can be obtained. Also, the data representing the in-situ condition setting information can include the number of days to be predicted, such as up to how many days in the future. In this case, it is also possible to output the prediction results up to the input number of days. Further, as an in-situ condition, by inputting information on precipitation based on the weather forecast of the area of the site, more accurate prediction results can be obtained.

[0035] The data management unit 22 writes the observation data obtained from the in-situ terminal 1, the in-situ condition setting information, and the analysis conditions into the analysis data storage unit 21. The hydrogeological parameter setting unit 23 writes hydrogeological parameters (permeability coefficient, rainfall infiltration rate (retention amount), specific storage coefficient, moisture characteristic curve, specific permeability coefficient, volumetric water content, porosity, etc.) into the analysis data storage unit 21. Here, the analysis data storage unit 21 stores in advance various information on the hydrogeological model and the analysis model.

[0036] The large-scale parallel computer 3 imports various data necessary for analysis from the cloud server 2 (step S6). The three-dimensional seepage flow analysis unit 31 performs three-dimensional seepage flow analysis using the various data obtained from the cloud server 2 (step S7). Here, three-dimensional unsteady seepage flow analysis is performed. When the three-dimensional seepage flow analysis is performed, the data assimilation processing unit 32 performs data assimilation processing (step S8). In the data assimilation process, when the divergence between the observed data value and the analysis result of the three-dimensional infiltration flow analysis is large (step S9 - YES), or even when the divergence is small (step S9 - NO) but the deviation of the parameter estimation value becomes large (step S10 - YES), the data assimilation processing unit 32 determines that the initially created hydrogeological model and analysis model may be different. In this case, since it is necessary to correct the model to accurately reproduce the observation results, the data assimilation processing unit 32 interrupts the prediction analysis and outputs data indicating that model correction is required (step S11). This output can be displayed on the display unit 14 of the on-site terminal 1 via, for example, the cloud server 2. As a result, the operator of the on-site terminal 1 can correct the geological model, analysis model, etc.

[0037] On the other hand, when the divergence between the observed data value and the prediction result is small (step S9 - NO) and the deviation of the parameter estimation value is small (step S10 - NO), the data assimilation processing unit 32 corrects and updates the identified parameters (step S12) and outputs the prediction results such as the in-pit water inflow and groundwater level to the cloud server 2 (step S13). Also, the corrected and updated parameters, analysis conditions, etc. are carried over to the next analysis step (for example, step S7 to be executed next).

[0038] When the prediction result is output from the massively parallel computer 3, the prediction result storage unit 24 of the cloud server 2 stores the prediction result (step S14). When the prediction result is stored in the prediction result storage unit 24, the on-site terminal 1 acquires this 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 in-pit water inflow, total head distribution, water level decline diagram, etc. on the display unit 14. In this way, the prediction result is output from the massively parallel computer 3 so that it can be managed and shared on the cloud server 2. Therefore, it can be displayed on the on-site terminal 1 at the site where the prediction result needs to be displayed.

[0039] According to the embodiments described above, when predicting and analyzing the groundwater environment of tunnel rock masses, by assimilating actual sequential observation data (such as in-pit water inflow, groundwater level, precipitation, etc.) into the results of three-dimensional seepage flow analysis through geological surveys, the accuracy of reproduction analysis and prediction analysis can be improved.

[0040] In addition, a series of processes from the acquisition of observation data to the output of the results of prediction analysis can be automatically performed, enabling real-time prediction. In addition, by combining three-dimensional seepage flow analysis and data assimilation techniques for automation, it is not necessary to rely on the advanced knowledge and techniques of professional engineers, such as the technical evaluation of reproduction analysis and prediction analysis and the review of analysis conditions. Also, since three-dimensional analysis is performed, it is possible to reproduce and predict the groundwater environment field of the entire analysis area.

[0041] The observation data complementing unit 13 can remove missing values and outliers from the observation data and complement them using the previous values, thus preventing adverse effects on the prediction analysis by the data assimilation technique.

[0042] In addition, as the observation data of the in-pit water inflow in the groundwater field prediction system S, it is possible to handle two types of data: the in-pit water inflow near the face or the in-pit water inflow of the entire excavation section. Whichever in-pit water inflow is being observed at the site, prediction can be performed using the data corresponding to the observation data.

[0043] In the large-scale parallel computer 3, when performing data assimilation prediction analysis, the parameters to be identified are not limited to the permeability coefficient. It is also possible to identify multiple parameters such as the rainfall infiltration rate (recharge amount) and specific storage coefficient that also affect the in-pit water inflow of the prediction result. In addition, information on precipitation such as weather forecasts can be input from the on-site terminal 1 as on-site condition setting information. As a result, prediction analysis considering precipitation can be performed, enabling the output of highly accurate prediction results.

[0044] The on-site terminal 1, cloud server 2, and massively parallel computer 3 in the above-described embodiments may each be implemented by a computer. In that case, a program for realizing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be read into a computer system and executed to realize it. Here, the "computer system" shall include hardware such as an OS and peripheral devices. Also, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, magneto-optical disk, ROM, CD-ROM, etc., and a storage device such as a hard disk built into a computer system. Furthermore, the "computer-readable recording medium" refers to something that dynamically holds a program for a short time, like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, and also includes something that holds a program for a certain time, like a volatile memory inside a computer system that serves as a server or client in that case. Also, the above program may be for realizing a part of the aforementioned functions, and may further be realizable in combination with a program already recorded in the computer system for the aforementioned functions, and may also be realized using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0045] As described above, the embodiments of this invention have been detailed with reference to the drawings. However, the specific configuration is not limited to this embodiment, and designs and the like within the scope not departing from the gist of this invention are also included.

Explanation of Reference Numerals

[0046] 1... On-site terminal, 2... Cloud server, 3... Massively parallel computer, 11... Observation data acquisition unit, 12... On-site condition acquisition unit, 13... Observation data completion unit, 14... Display unit, 21... Analysis data storage unit, 22... Data management unit, 23... Hydraulic parameter setting unit, 24... Prediction result storage unit, 31... Dimension penetration flow analysis unit, 32... Data assimilation processing unit, 33... Analysis result output unit

Claims

1. An observation data acquisition unit that acquires observation data including at least measurement results of the amount of water inflow occurring at the face of the 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 hydrogeological parameter storage unit that stores hydrogeological parameters including the permeability coefficient; A three-dimensional seepage flow analysis unit that takes in the observation data, the site condition setting information, and the hydrogeological parameters, and performs three-dimensional seepage flow analysis based on the geological model and the analysis model in the vicinity of the tunnel; When the difference between the amount of water inflow included in the prediction result obtained from the result of the three-dimensional seepage flow analysis and the amount of water inflow of the observation data is greater than the first reference value, or when the difference between the permeability coefficient used to obtain the amount of water inflow included in the prediction result and the permeability coefficient that is the hydrogeological parameter is greater than the second reference value, outputs data indicating that it is necessary to correct at least one of the geological model and the analysis model; When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is less than the first reference value, or when the difference between the prediction result and the hydrogeological parameter is less than the second reference value, outputs the result of the three-dimensional seepage flow analysis, updates the permeability coefficient, which is the parameter to be identified, and writes and updates the updated parameter to the hydrogeological parameter storage unit; A data assimilation processing unit; A groundwater field prediction system having the above.

2. The groundwater field prediction system, has a field terminal provided at the field office at the site where the tunnel excavation work is carried out, The field terminal, has the site condition acquisition unit, a display unit that receives the output result of the three-dimensional seepage flow analysis and displays it on a display screen; and has the above. The groundwater field prediction system according to claim 1.

3. The three-dimensional seepage flow analysis process by the three-dimensional seepage flow analysis unit and the data assimilation process by the data assimilation processing unit are performed by a large-scale parallel computer. The groundwater field prediction system according to claim 1 or claim 2.

4. The geological model and the analysis model are stored in a cloud server. The groundwater field prediction system according to any one of claims 1 to 3.

5. A groundwater field prediction method executed by a computer, comprising: acquiring observation data including at least measurement results of the amount of water inflow occurring at the face of the tunnel; acquiring site condition setting information including at least the position of the face indicating the excavation section; Store the hydrogeological parameters including the permeability coefficient in the storage unit of the storage unit for hydrogeological parameters, Take in the observation data, the on-site condition setting information, and the hydrogeological parameters, and perform three-dimensional seepage flow analysis based on the geological model and the analysis model in the vicinity of the tunnel, When the difference between the water inflow volume included in the prediction result obtained from the result of the three-dimensional seepage flow analysis and the water inflow volume of the observation data is greater than the first reference value, or when the difference between the permeability coefficient used when obtaining the water inflow volume included in the prediction result and the permeability coefficient that is the hydrogeological parameter is greater than the second reference value, output data indicating that it is necessary to correct at least one of the geological model and the analysis model. When the difference between the prediction result obtained from the result of the three-dimensional seepage flow analysis and the observation data is less than the first reference value, or when the difference between the prediction result and the hydrogeological parameter is less than the second reference value, output the result of the three-dimensional seepage flow analysis, update the permeability coefficient which is the parameter to be identified, and write the updated parameter into the storage unit for updating Groundwater field prediction method.

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