Sand shale settlement monitoring system and method

Abstract

The invention relates to the technical field of geological condition monitoring, in particular to a sand shale settlement monitoring system and method.The sand shale settlement monitoring system comprises an FLAC3D simulation assembly, a monitoring module, a data fusion module, an early warning module and a comparison module, various monitoring data of a sand shale stratum are collected in real time through the monitoring module, and the timeliness and accuracy of the data are ensured. And the data fusion module performs fusion processing on various monitoring data, so that the representativeness and effectiveness of the data are improved, and a future settlement trend and potential risks are predicted by using an FLAC3D simulation component. The early warning module timely notifies a related person in charge, and the comparison module compares and verifies a prediction result with actual monitoring data, evaluates prediction accuracy and optimizes model parameters. By integrating a plurality of monitoring means, a data fusion technology and a numerical simulation analysis method, real-time monitoring, accurate prediction and timely early warning of sand shale stratum settlement are realized, and the comprehensiveness and accuracy of prediction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological condition monitoring, and in particular to a sandstone and mudstone settlement monitoring system and method. Background Art

[0002] Sandstone refers to a mixed rock layer composed of sandstone and mudstone. Sandstone settlement monitoring is to conduct real-time monitoring and early warning of the settlement of sandstone and mudstone formations under engineering construction or natural conditions to ensure engineering safety and the stability of the geological environment. Traditional monitoring methods are mainly manual monitoring and prediction based on experience, which not only has a long cycle, but also has limited measured data.

[0003] The existing announcement number CN205558913U discloses a monitoring system for ground deformation during shield construction, including a microprocessor, multiple monitoring terminals, a FLAC3D simulation component, an alarm and a display. The multiple monitoring terminals are connected to the microprocessor, and the FLAC3D simulation component, the display and the alarm are connected to the microprocessor. Multiple monitoring terminals are combined to form a monitoring surface, and the deformation of the monitoring surface is analyzed to predict the ground deformation. The FLAC3D simulation component can amplify the vertical height information data of the monitoring terminal during modeling, and achieve the purpose of predicting settlement in advance through linear or nonlinear amplification methods, saving manpower and making predictions more accurate.

[0004] However, although the FLAC3D simulation component can predict formation deformation through modeling and data analysis, the precision and accuracy of its prediction results are highly dependent on the accuracy of the input data and the rationality of the model. If the model parameters are not set properly or there are errors in the input data, the prediction results may be inaccurate. Summary of the invention

[0005] The purpose of the present invention is to provide a sand and mudstone settlement monitoring system and method, which solves the problem that the FLAC3D simulation component is highly dependent on the accuracy of input data and the rationality of the model. If the model parameters are improperly set or there are errors in the input data, the prediction results will be inaccurate.

[0006] To achieve the above object, the present invention provides a sand and mudstone settlement monitoring system, including a FLAC3D simulation component, a monitoring module, a data fusion module, an early warning module and a comparison module, wherein the data fusion module is connected to the monitoring module, the FLAC3D simulation component is connected to the data fusion module, the early warning module is connected to the FLAC3D simulation component and the data fusion module respectively, and the comparison file is connected to the data fusion module and the FLAC3D simulation component respectively;

[0007] The monitoring module is used to collect various monitoring data of the sandstone and mudstone formations in real time and upload them to the data fusion module;

[0008] The data fusion module is used to pre-process the received monitoring data and fuse multiple monitoring data, and upload the processed data to the FLAC3D simulation component and the comparison module;

[0009] The FLAC3D simulation component is used to perform numerical simulation and analysis of sandstone and mudstone formation deformation based on the processed data, and to predict future settlement trends and potential risks of sandstone and mudstone formations;

[0010] The early warning module is used to set an early warning threshold according to the prediction result of the FLAC3D simulation component and the real-time monitoring data of the data fusion module, and automatically trigger an early warning mechanism when the monitoring data or the prediction result exceeds the early warning threshold;

[0011] The comparison module is used to compare and verify the prediction results of the FLAC3D simulation component with subsequent actual monitoring data, and to evaluate the accuracy and reliability of the FLAC3D simulation component, discover possible problems and errors, and perform further calibration and optimization.

[0012] Wherein, the monitoring module includes a surface settlement monitoring unit and an underground layer settlement monitoring unit, and the surface settlement monitoring unit and the underground layer settlement monitoring unit are respectively connected to the data fusion module;

[0013] The surface settlement monitoring unit is used to perform regular or continuous surface settlement measurements using a GNSS surface displacement monitor to obtain time series data of surface settlement;

[0014] The underground stratified settlement monitoring unit is used to install stratified settlement monitors at different depths underground to monitor the settlement of each layer of sandstone and mudstone.

[0015] Wherein, the monitoring module further comprises an infrasound monitoring unit, and the infrasound monitoring unit is connected to the data fusion module;

[0016] The infrasound monitoring unit is used to perform real-time monitoring of underground rock fracture geological activities using an infrasound monitoring system.

[0017] Wherein, the data fusion module includes a preprocessing unit and a fusion unit, the preprocessing unit is respectively connected to the surface settlement monitoring unit, the underground layered settlement monitoring unit and the infrasound monitoring unit, and the fusion unit is connected to the preprocessing unit;

[0018] The preprocessing unit is used to perform data cleaning, filtering, denoising, filling and standardization on the received monitoring data;

[0019] The fusion unit is used to fuse the pre-processed multiple monitoring data.

[0020] Wherein, the early warning module comprises an editing unit and a notification unit, the editing unit is connected to the FLAC3D simulation component and the fusion unit respectively, and the notification unit is connected to the editing unit;

[0021] The editing unit is used to obtain user instructions, edit the warning threshold, and monitor the prediction results of the FLAC3D simulation component and the real-time monitoring data of the fusion unit;

[0022] The notification unit is used to automatically trigger the warning mechanism when the monitoring data or prediction results exceed the warning threshold, and notify the relevant responsible persons via SMS, email or display screen.

[0023] Wherein, the comparison module includes an extraction unit and an analysis unit, the extraction unit is connected to the FLAC3D simulation component, and the analysis unit is connected to the extraction unit and the fusion unit respectively;

[0024] The extraction unit is used to extract the corresponding prediction results from the FLAC3D simulation component and organize the prediction results into a format that is convenient for comparison;

[0025] The analysis unit is used to directly compare the actual monitoring data with the prediction results, calculate the difference and relative error between the two, and analyze the consistency of the prediction results and the actual monitoring data in terms of change trends, evaluate the accuracy of the prediction based on the comparison results, and determine the size and distribution range of the prediction error.

[0026] A sandstone sedimentation monitoring method comprises the following steps:

[0027] Real-time collection of various monitoring data of sandstone and mudstone formations;

[0028] Pre-process the monitoring data and integrate various monitoring data;

[0029] Based on the processed data, numerical simulation and analysis of sandstone and mudstone formation deformation are carried out to predict the future subsidence trend and potential risks of sandstone and mudstone formations;

[0030] According to the prediction results and real-time monitoring data, the warning threshold is set. When the monitoring data or prediction results exceed the warning threshold, the warning mechanism is automatically triggered;

[0031] The prediction results are compared and verified with the subsequent actual monitoring data, and the accuracy and reliability of the FLAC3D simulation components are evaluated to identify possible problems and errors and conduct further calibration and optimization.

[0032] A sand-mudstone settlement monitoring system and method of the present invention collects various monitoring data of sand-mudstone formations in real time through the monitoring module to ensure the timeliness and accuracy of the data. The data fusion module fuses multiple monitoring data to improve the representativeness and effectiveness of the data and provide reliable data support for subsequent analysis and prediction. The FLAC3D simulation component is used to perform numerical simulation and analysis of sand-mudstone formation deformation to predict future settlement trends and potential risks. When the monitoring data or prediction results exceed the warning threshold, the early warning module promptly notifies the relevant responsible persons to ensure timely response and processing. The comparison module compares and verifies the prediction results with the actual monitoring data, evaluates the prediction accuracy and optimizes the model parameters. By integrating multiple monitoring means, data fusion technology and numerical simulation analysis methods, real-time monitoring, accurate prediction and timely warning of sand-mudstone formation settlement are achieved, providing a strong guarantee for engineering construction and geological environment safety, and improving the comprehensiveness and accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0034] Figure 1 It is a principle block diagram of the sand and mudstone settlement monitoring system according to the first embodiment of the present invention.

[0035] Figure 2 It is a principle block diagram of a fusion unit according to the first embodiment of the present invention.

[0036] Figure 3 It is a principle block diagram of a comparison module according to the second embodiment of the present invention.

[0037] Figure 4 4 is a flow chart of the steps of the sandstone and mudstone settlement monitoring method according to the third embodiment of the present invention.

[0038] In the figure: 101-FLAC3D simulation component, 102-monitoring module, 103-data fusion module, 104-early warning module, 105-comparison module, 106-surface subsidence monitoring unit, 107-underground stratified subsidence monitoring unit, 108-infrasound monitoring unit, 109-preprocessing unit, 110-fusion unit, 111-editing unit, 112-notification unit, 113-data fusion subunit, 114-feature fusion subunit, 115-decision fusion subunit, 201-extraction unit, 202-analysis unit, 203-optimization unit. DETAILED DESCRIPTION

[0039] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0040] The first embodiment of the present application is:

[0041] See also Figure 1 and Figure 2 ,in, Figure 1 It is a principle block diagram of the sand and mudstone settlement monitoring system according to the first embodiment of the present invention. Figure 2 It is a principle block diagram of the fusion unit 110 of the first embodiment of the present invention. The present invention provides a sandstone and mudstone settlement monitoring system: including a FLAC3D simulation component 101, a monitoring module 102, a data fusion module 103, an early warning module 104 and a comparison module 105, wherein the monitoring module 102 includes a surface settlement monitoring unit 106, an underground layered settlement monitoring unit 107, and an infrasound monitoring unit 108, wherein the data fusion module 103 includes a preprocessing unit 109 and a fusion unit 110, wherein the early warning module 104 includes an editing unit 111 and a notification unit 112, wherein the fusion unit 110 includes a data fusion subunit 113, a feature fusion subunit 114 and a decision fusion subunit 115, and the above-mentioned solution solves the problem that the FLAC3D simulation component 101 is highly dependent on the accuracy of the input data and the rationality of the model, and if the model parameters are improperly set or there are errors in the input data, the prediction results are inaccurate.

[0042] According to this specific implementation, the data fusion module 103 is connected to the monitoring module 102, the FLAC3D simulation component 101 is connected to the data fusion module 103, the early warning module 104 is connected to the FLAC3D simulation component 101 and the data fusion module 103 respectively, and the comparison file is connected to the data fusion module 103 and the FLAC3D simulation component 101 respectively;

[0043] The monitoring module 102 is used to collect various monitoring data of the sandstone and mudstone formations in real time and upload them to the data fusion module 103;

[0044] The data fusion module 103 is used to pre-process the received monitoring data and fuse various monitoring data, and upload the processed data to the FLAC3D simulation component 101 and the comparison module 105;

[0045] The FLAC3D simulation component 101 is used to perform numerical simulation and analysis of sandstone and mudstone formation deformation based on the processed data, and predict future settlement trends and potential risks of sandstone and mudstone formations;

[0046] The early warning module 104 is used to set an early warning threshold according to the prediction result of the FLAC3D simulation component 101 and the real-time monitoring data of the data fusion module 103, and automatically trigger an early warning mechanism when the monitoring data or the prediction result exceeds the early warning threshold;

[0047] The comparison module 105 is used to compare and verify the prediction results of the FLAC3D simulation component 101 with subsequent actual monitoring data, and evaluate the accuracy and reliability of the FLAC3D simulation component 101, discover possible problems and errors, and perform further calibration and optimization.

[0048] The monitoring module 102 collects various monitoring data of sandstone and mudstone formations in real time to ensure the timeliness and accuracy of the data. The data fusion module 103 fuses various monitoring data to improve the representativeness and effectiveness of the data and provide reliable data support for subsequent analysis and prediction. The FLAC3D simulation component 101 performs numerical simulation and analysis of the deformation of sandstone and mudstone formations to predict future settlement trends and potential risks. When the monitoring data or prediction results exceed the warning threshold, the early warning module 104 promptly notifies the relevant responsible persons to ensure timely response and processing. The comparison module 105 compares and verifies the prediction results with the actual monitoring data, evaluates the prediction accuracy and optimizes the model parameters.

[0049] Wherein, the surface subsidence monitoring unit 106 and the underground layered subsidence monitoring unit 107 are respectively connected to the data fusion module 103;

[0050] The surface settlement monitoring unit 106 is used to perform regular or continuous surface settlement measurements using a GNSS surface displacement monitor to obtain time series data of surface settlement;

[0051] The underground stratified settlement monitoring unit 107 is used to install stratified settlement monitoring instruments at different depths underground to monitor the settlement of each layer of sandstone and mudstone.

[0052] The infrasound monitoring unit 108 is connected to the data fusion module 103;

[0053] The infrasound monitoring unit 108 is used to monitor the underground rock fracture geological activities in real time using the infrasound monitoring system.

[0054] The surface settlement monitoring unit 106 uses a GNSS surface displacement monitor to perform all-weather, high-precision surface settlement measurements, and obtains time series data of surface settlement through regular or continuous observations; the underground stratified settlement monitoring unit 107 installs stratified settlement monitors at different depths underground to monitor the settlement of each layer of sandstone and mudstone, and understands the hierarchical distribution and change trend of settlement by comparing and analyzing settlement data at different depths; the infrasound monitoring unit 108 uses an infrasound monitoring system to monitor geological activities such as underground rock fractures in real time, and determines the changes and potential risks of underground rock formations by capturing and analyzing infrasound signals.

[0055] Secondly, the pre-processing unit 109 is connected to the surface settlement monitoring unit 106, the underground layer settlement monitoring unit 107 and the infrasound monitoring unit 108 respectively, and the fusion unit 110 is connected to the pre-processing unit 109;

[0056] The preprocessing unit 109 is used to perform data cleaning, filtering, denoising, filling and standardization on the received monitoring data;

[0057] The fusion unit 110 is used to fuse the pre-processed multiple monitoring data.

[0058] The preprocessing unit 109 removes invalid data from the received data: deletes invalid or erroneous data such as null values, abnormal values, and duplicate data; data format conversion: unifies the data format, such as converting the timestamp into a unified date and time format to ensure the consistency and comparability of the data; data range adjustment: scales the data to a specific range according to the analysis requirements for subsequent processing and analysis; time range screening: screens out data within a specific time range according to the analysis requirements to reduce unnecessary data processing; threshold screening: sets a reasonable threshold to screen out data that meets specific conditions. Attribute screening: screens out data with specific attributes according to the analysis objectives. Smoothing: For dynamic monitoring data, such as vibration, displacement, etc., smoothing is performed to eliminate random noise. Commonly used smoothing methods include five-point quadratic smoothing, five-point cubic smoothing, etc. Denoising: further removes noise components in the data through filtering, wavelet transform, etc. to improve the signal-to-noise ratio of the data. Interpolation filling: For missing data points, interpolation methods are used to fill them. Commonly used interpolation methods include linear interpolation, polynomial interpolation, etc. Interpolation filling can maintain the continuity and integrity of the data. Default value filling: In some cases, if there are few missing data points and they have little impact on the overall analysis, use default values, such as mean, median, etc., to fill in; Standardization: Scaling the data at a certain ratio so that it falls into a small specific interval (such as 0-1 or -1 to 1) helps eliminate the impact of different dimensions on data analysis; Normalization: Scaling the data to the [0, 1] interval helps improve the convergence speed and accuracy of the algorithm.

[0059] At the same time, the data fusion subunit 113 is connected to the preprocessing unit 109, the feature fusion subunit 114 is connected to the preprocessing unit 109, and the decision fusion subunit 115 is connected to the preprocessing unit 109;

[0060] The data fusion subunit 113 is used to align the data from different monitoring devices in time, space or logic, and directly integrate the aligned data into a unified data set;

[0061] The feature fusion subunit 114 is used to extract feature information that can reflect the deformation of sandstone and mudstone formations from each monitoring data, match and associate the feature information extracted by different monitoring methods to ensure that they can correspond to the same physical phenomenon or process, and fuse the matched feature information to form a comprehensive feature vector or feature set;

[0062] The decision fusion subunit 115 is used to make preliminary decision judgments according to each monitoring data and its characteristic information, fuse multiple preliminary decision results, and optimize the decision results according to historical data and expert knowledge.

[0063] The data fusion unit 110 first aligns the data from different monitoring devices in time, space or logic to ensure the consistency and comparability of the data, and directly integrates the aligned data into a unified data set for subsequent processing and analysis. During the integration process, the data is filtered to remove noise and outliers to improve the accuracy of the data. Data-level fusion retains the integrity and detailed information of the original data. It is suitable for scenarios with high real-time requirements and not very large data volumes. The feature fusion unit 110 extracts feature information that can reflect the deformation of sandstone and mudstone formations from each type of monitoring data, such as displacement, deformation rate, stress change, etc., matches and associates the feature information extracted by different monitoring methods to ensure that they can correspond to the same physical phenomenon or process, and fuses the matched feature information to form a comprehensive feature vector or feature set. Feature-level fusion can remove redundant information and improve the representativeness and effectiveness of data. It is suitable for scenarios with large data volumes and the need to extract key features for subsequent analysis. The decision fusion unit 110 makes preliminary decision judgments according to each monitoring data and its characteristic information, such as judging whether the stratum has subsided and the degree of subsidence, etc., and fuses multiple preliminary decision results, comprehensively considers the influence of various factors, and obtains the final decision result. According to historical data and expert knowledge, the decision result is optimized to improve the accuracy and reliability of the decision. Decision-level fusion can make full use of the advantages and complementarity of different monitoring methods. It is suitable for scenarios where it is necessary to comprehensively consider multiple factors for decision-making. Through data-level, feature-level or decision-level fusion, a more comprehensive and accurate comprehensive description of the deformation of sandstone and mudstone strata can be obtained. This comprehensive description not only contains detailed information of the original data, but also removes redundant information, improves the representativeness and effectiveness of the data through processing methods such as feature extraction and decision fusion, and comprehensively considers the influence of multiple factors. Such a comprehensive description can provide more powerful support and guarantee for engineering construction and geological environment safety.

[0064] In addition, the editing unit 111 is connected to the FLAC3D simulation component 101 and the fusion unit 110 respectively, and the notification unit 112 is connected to the editing unit 111;

[0065] The editing unit 111 is used to obtain user instructions, edit the warning threshold, and monitor the prediction results of the FLAC3D simulation component 101 and the real-time monitoring data of the fusion unit 110;

[0066] The notification unit 112 is used to automatically trigger the warning mechanism when the monitoring data or prediction results exceed the warning threshold, and notify the relevant responsible persons through SMS, email or display screen.

[0067] The user can use the editing unit 111 to set the warning threshold. When the monitoring data or prediction results exceed the warning threshold, the warning mechanism is automatically triggered. The notification unit 112 notifies the relevant responsible person through SMS, email or display screen to ensure timely response and processing.

[0068] Using a sand and mudstone settlement monitoring system of this embodiment, the monitoring module real-time monitoring and data analysis ensure the timeliness and accuracy of the data, provide a basis for timely warning and response, and obtain more comprehensive and multi-dimensional monitoring data through various means such as surface settlement, underground layered settlement and infrasound monitoring, so as to improve the accuracy of analysis. The data fusion module 103 improves the representativeness and effectiveness of the data, and provides more reliable data support for subsequent numerical simulation and prediction. The early warning module 104 sets the early warning threshold and automatically triggers the early warning mechanism, which can notify the relevant responsible persons at the first time to ensure timely response and handling of potential risks. The accuracy and reliability of the FLAC3D simulation component 101 are evaluated through the comparison module 105, possible problems and errors are found, and further calibration and optimization are performed to improve the prediction ability of the model. The user can conveniently set the early warning threshold through the editing unit 111, and adjust the monitoring parameters as needed, which improves the flexibility and user-friendliness of the system. A closed-loop comprehensive system of monitoring, analysis, early warning and optimization is formed, which provides strong support and guarantee for engineering construction and geological environment safety, and improves the comprehensiveness and accuracy of prediction.

[0069] The second embodiment of the present application is:

[0070] Based on the first embodiment, please refer to Figure 3 ,in, Figure 3 2 is a principle block diagram of the comparison module 105 of the second embodiment of the present invention. The comparison module 105 of this embodiment includes an extraction unit 201, an analysis unit 202 and an optimization unit 203.

[0071] According to this specific implementation, the extraction unit 201 is connected to the FLAC3D simulation component 101, and the analysis unit 202 is connected to the extraction unit 201 and the fusion unit 110 respectively;

[0072] The extraction unit 201 is used to extract the corresponding prediction results from the FLAC3D simulation component 101 and organize the prediction results into a format that is convenient for comparison;

[0073] The analysis unit 202 is used to directly compare the actual monitoring data with the prediction results, calculate the difference and relative error between the two, and analyze the consistency of the prediction results and the actual monitoring data in the change trend, evaluate the accuracy of the prediction based on the comparison results, and determine the size and distribution range of the prediction error.

[0074] Wherein, the optimization unit 203 is connected to the analysis unit 202 and the FLAC3D simulation component 101 respectively;

[0075] The optimization unit 203 is used to adjust the model parameters in the FLAC3D simulation component 101 according to the comparison and verification results.

[0076] Using a sand and mudstone settlement monitoring system of this embodiment, the extraction unit 201 extracts the corresponding prediction results from the FLAC3D simulation component 101, and organizes the prediction results into a format that is easy to compare, such as a table, a chart, etc., so as to intuitively display the difference between the predicted value and the actual value. The analysis unit 202 directly compares the actual monitoring data with the prediction results, and calculates statistical indicators such as the difference and relative error between the two. It also analyzes the consistency of the prediction results and the actual monitoring data in the change trend, such as whether they both show an upward, downward or fluctuating trend. The accuracy of the prediction is evaluated based on the comparison results, and the size and distribution range of the prediction error are determined. The optimization unit 203 adjusts the model parameters in the FLAC3D simulation component 101 based on the results of the comparison verification. If the prediction error is large, recalibrate the model parameters or improve the model structure. And formulate specific strategies for model optimization, such as using more advanced algorithms, increasing the density of monitoring points, and increasing the data sampling frequency, etc., to improve the accuracy of the prediction. The accuracy and reliability of the prediction results are ensured, providing a strong guarantee for engineering construction and geological environment safety.

[0077] The third embodiment of the present application is:

[0078] Based on the second embodiment, please refer to Figure 4 ,in, Figure 4 1 is a flow chart of the steps of the sand-shale sedimentation monitoring method of the third embodiment of the present invention. The sand-shale sedimentation monitoring method of this embodiment includes the following steps:

[0079] S301: Real-time collection of various monitoring data of sandstone and mudstone formations;

[0080] S302: pre-processing the monitoring data and fusing multiple monitoring data;

[0081] S303: numerically simulate and analyze the deformation of the sandstone-mudstone formation based on the processed data, and predict the future subsidence trend and potential risks of the sandstone-mudstone formation;

[0082] S304: setting an early warning threshold based on the prediction results and real-time monitoring data, and automatically triggering an early warning mechanism when the monitoring data or prediction results exceed the early warning threshold;

[0083] S305: Compare and verify the prediction results with subsequent actual monitoring data, evaluate the accuracy and reliability of the FLAC3D simulation component 101, discover possible problems and errors, and perform further calibration and optimization.

[0084] Specifically, the monitoring module 102 collects various monitoring data of sandstone and mudstone formations in real time to ensure the timeliness and accuracy of the data. The data fusion module 103 fuses a variety of monitoring data to improve the representativeness and effectiveness of the data and provide reliable data support for subsequent analysis and prediction. The FLAC3D simulation component 101 is used to perform numerical simulation and analysis of the deformation of sandstone and mudstone formations to predict future settlement trends and potential risks. When the monitoring data or prediction results exceed the warning threshold, the early warning module 104 promptly notifies the relevant responsible persons to ensure timely response and processing. The comparison module 105 compares and verifies the prediction results with the actual monitoring data, evaluates the prediction accuracy and optimizes the model parameters. By integrating a variety of monitoring means, data fusion technology and numerical simulation analysis methods, real-time monitoring, accurate prediction and timely early warning of sandstone and mudstone formation settlement are achieved, providing a strong guarantee for engineering construction and geological environment safety, and improving the comprehensiveness and accuracy of the prediction.

[0085] What is disclosed above is only one or more preferred embodiments of the present application, and cannot be used to limit the scope of rights of the present application. Ordinary technicians in this field can understand that all or part of the processes of implementing the above embodiments and equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A sandstone and mudstone settlement monitoring system, comprising a FLAC3D simulation component, characterized in that: It also includes a monitoring module, a data fusion module, an early warning module and a comparison module, wherein the data fusion module is connected to the monitoring module, the FLAC3D simulation component is connected to the data fusion module, the early warning module is connected to the FLAC3D simulation component and the data fusion module respectively, and the comparison file is connected to the data fusion module and the FLAC3D simulation component respectively; The monitoring module is used to collect various monitoring data of the sandstone and mudstone formations in real time and upload them to the data fusion module; The data fusion module is used to pre-process the received monitoring data and fuse multiple monitoring data, and upload the processed data to the FLAC3D simulation component and the comparison module; The FLAC3D simulation component is used to perform numerical simulation and analysis of sandstone and mudstone formation deformation based on the processed data, and to predict future settlement trends and potential risks of sandstone and mudstone formations; The early warning module is used to set an early warning threshold according to the prediction result of the FLAC3D simulation component and the real-time monitoring data of the data fusion module, and automatically trigger an early warning mechanism when the monitoring data or the prediction result exceeds the early warning threshold; The comparison module is used to compare and verify the prediction results of the FLAC3D simulation component with subsequent actual monitoring data, and to evaluate the accuracy and reliability of the FLAC3D simulation component, discover possible problems and errors, and perform further calibration and optimization.

2. The sandstone and mudstone settlement monitoring system according to claim 1, characterized in that: The monitoring module includes a surface settlement monitoring unit and an underground stratified settlement monitoring unit, and the surface settlement monitoring unit and the underground stratified settlement monitoring unit are respectively connected to the data fusion module; The surface settlement monitoring unit is used to perform regular or continuous surface settlement measurements using a GNSS surface displacement monitor to obtain time series data of surface settlement; The underground stratified settlement monitoring unit is used to install stratified settlement monitors at different depths underground to monitor the settlement of each layer of sandstone and mudstone.

3. The sandstone and mudstone settlement monitoring system according to claim 2, characterized in that: The monitoring module further comprises an infrasound monitoring unit, and the infrasound monitoring unit is connected to the data fusion module; The infrasound monitoring unit is used to perform real-time monitoring of underground rock fracture geological activities using an infrasound monitoring system.

4. The sandstone and mudstone settlement monitoring system according to claim 3, characterized in that: The data fusion module includes a preprocessing unit and a fusion unit, the preprocessing unit is respectively connected to the surface settlement monitoring unit, the underground layered settlement monitoring unit and the infrasound monitoring unit, and the fusion unit is connected to the preprocessing unit; The preprocessing unit is used to perform data cleaning, filtering, denoising, filling and standardization on the received monitoring data; The fusion unit is used to fuse the pre-processed multiple monitoring data.

5. The sandstone and mudstone settlement monitoring system according to claim 4, characterized in that: The early warning module includes an editing unit and a notification unit, wherein the editing unit is connected to the FLAC3D simulation component and the fusion unit respectively, and the notification unit is connected to the editing unit; The editing unit is used to obtain user instructions, edit the warning threshold, and monitor the prediction results of the FLAC3D simulation component and the real-time monitoring data of the fusion unit; The notification unit is used to automatically trigger the warning mechanism when the monitoring data or prediction results exceed the warning threshold, and notify the relevant responsible persons via SMS, email or display screen.

6. The sandstone and mudstone settlement monitoring system according to claim 4, characterized in that: The comparison module includes an extraction unit and an analysis unit, wherein the extraction unit is connected to the FLAC3D simulation component, and the analysis unit is connected to the extraction unit and the fusion unit respectively; The extraction unit is used to extract the corresponding prediction results from the FLAC3D simulation component and organize the prediction results into a format that is convenient for comparison; The analysis unit is used to directly compare the actual monitoring data with the prediction results, calculate the difference and relative error between the two, and analyze the consistency of the prediction results and the actual monitoring data in terms of change trends, evaluate the accuracy of the prediction based on the comparison results, and determine the size and distribution range of the prediction error.

7. A sand-shale settlement monitoring method, applicable to the sand-shale settlement monitoring system according to claim 1, characterized in that: The following steps are involved: Real-time collection of various monitoring data of sandstone and mudstone formations; Pre-process the monitoring data and integrate various monitoring data; Based on the processed data, numerical simulation and analysis of sandstone and mudstone formation deformation are carried out to predict the future subsidence trend and potential risks of sandstone and mudstone formations; According to the prediction results and real-time monitoring data, the warning threshold is set. When the monitoring data or prediction results exceed the warning threshold, the warning mechanism is automatically triggered; The prediction results are compared and verified with the subsequent actual monitoring data, and the accuracy and reliability of the FLAC3D simulation components are evaluated to identify possible problems and errors and conduct further calibration and optimization.

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