CN119476677A - A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage - Google Patents
A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage Download PDFInfo
- Publication number
- CN119476677A CN119476677A CN202411321017.7A CN202411321017A CN119476677A CN 119476677 A CN119476677 A CN 119476677A CN 202411321017 A CN202411321017 A CN 202411321017A CN 119476677 A CN119476677 A CN 119476677A
- Authority
- CN
- China
- Prior art keywords
- shallow
- risk
- gas
- induced
- fuzzy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Entrepreneurship & Innovation (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Primary Health Care (AREA)
- Computing Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Fuzzy Systems (AREA)
- Biomedical Technology (AREA)
- Automation & Control Theory (AREA)
- Algebra (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Molecular Biology (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Computational Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
Abstract
本发明涉及路面塌陷风险综合识别与评估技术领域，具体地说是一种沼气浅层气泄漏诱发路面塌陷风险综合识别与评估方法，通过综合了浅层气诱发路面塌陷的各个识别指标与方法，引入故障树与T‑S模糊数概念以表达事件间的逻辑关系，构造浅层气泄漏引发路面塌陷故障树采用T‑S模糊故障树评估各因素的敏感性与重要程度，全面统计浅层气泄漏灾害的环境信息，归纳出准确的浅层气泄漏的风险识别指标与风险评估。
The present invention relates to the technical field of comprehensive identification and assessment of road collapse risk, and specifically to a comprehensive identification and assessment method for road collapse risk induced by shallow gas leakage of methane. The method integrates various identification indicators and methods for road collapse induced by shallow gas, introduces the concepts of fault tree and T-S fuzzy number to express the logical relationship between events, constructs a fault tree for road collapse induced by shallow gas leakage, uses T-S fuzzy fault tree to assess the sensitivity and importance of various factors, comprehensively counts environmental information of shallow gas leakage disasters, and summarizes accurate risk identification indicators and risk assessment for shallow gas leakage.
Description
Technical Field
The invention relates to the technical field of comprehensive recognition and evaluation of pavement collapse risks, in particular to a comprehensive recognition and evaluation method of pavement collapse risks induced by biogas shallow gas leakage.
Background
In engineering construction, shallow air is a potential geological disaster, and geotechnical engineering problems caused by the shallow air often occur in engineering construction of soft soil areas, and the shallow air is a large cause of pavement collapse, and in addition, as the main components of the shallow air are usually combustible gases, fire and even explosion accidents can be caused.
From the viewpoint of quantitative level, the safety and reliability evaluation method of shallow gas leakage can be divided into qualitative analysis, quantitative analysis and semi-qualitative and semi-quantitative analysis, wherein the qualitative analysis method is mainly used for evaluating the risk level of disaster factors based on experience. The classification of the risk level of the disaster factor generally comprises 5 grades of 'very high', 'medium', 'low' and 'very low', the qualitative method has the most direct engineering guiding significance, the safety of a certain item can be simply and rapidly judged and decided, certain errors exist, the quantitative analysis is based on the event case statistics of the road surface collapse caused by shallow air leakage, the road surface collapse factors are analyzed in multiple aspects and multiple angles, the weights of different factors are specifically analyzed, and the quantitative analysis is generally difficult to realize due to the lack of sufficient information quantity support.
Therefore, the invention of a comprehensive recognition and evaluation method for the risk of subsidence of the pavement induced by the leakage of methane shallow layer gas is urgently needed.
Disclosure of Invention
Aiming at the problems, the invention provides the technical scheme that the comprehensive recognition and evaluation method for the collapse risk of the pavement induced by the leakage of methane shallow layer gas comprises the following steps of;
s1, determining the burial depth, the position and the distribution range of shallow gas;
S2, determining the shallow gas leakage rate;
S3, determining the air pressure of the shallow air;
S4, determining reserves of shallow gas;
s5, determining the causative components of shallow air;
s6, introducing a T-S fuzzy model, describing the fault probability of each component in the form of a T-S fuzzy fault tree, more comprehensively considering the fault state, and decomposing and quantifying the complex problem and the uncertainty problem by establishing a T-S fuzzy gate so as to accurately identify and evaluate the pavement collapse risk.
Preferably, the specific steps in S1 include:
S1.1, arranging a parallel electric method testing system according to the difference between a rich gas area in a shallow soil layer and a surrounding medium power supply point field source, wherein the acquisition mode of the parallel electric method testing system is divided into an AM method and an ABM method;
S1.2, adopting a parallel electric method instrument to collect potential difference delta U MN f between two points of a measuring electrode M, N for multiple times, and selecting a group with better data for analysis;
S1.3, integrating the acquired potential difference data, calculating the resistivity rho of the underground geologic body, analyzing by software, inverting the resistivity, outputting ground electric information, and mapping by software.
Preferably, the specific steps in S2 include:
s2.1, arranging an acoustic wave acquisition device at a place where shallow gas is likely to leak, acquiring acoustic wave signals through a multi-channel acoustic sensor, and adopting the frequency spectrum area in a wide frequency band range as a detection index;
S2.2, after wavelet transformation, obtaining wavelet coefficients of each scale from the observed signal, so as to distinguish useful signals with higher wavelet coefficients and noise signals with lower wavelet coefficients, performing threshold denoising processing by adaptively selecting proper threshold functions, repeatedly adjusting the threshold until all noise signals are removed, obtaining estimated values of the wavelet coefficients of each scale of the useful signals, and finally performing inverse transformation to reconstruct signals to obtain useful acoustic signals;
s2.3, analyzing the waveform of the acoustic signal by using an acoustic solver, and calculating the flow velocity characteristics of the leaked gas.
Preferably, the specific step in S3 includes:
S3.1, monitoring and capturing the sonic wave shape of the leakage gas by using a sonic wave sensor;
S3.2, reducing noise generated by other fluctuation in the fluid by using a wavelet analysis method or an Empirical Mode Decomposition (EMD) algorithm;
And S3.3, analyzing the denoised acoustic signals, performing leakage identification by adopting an acoustic-pressure coupling method or various pattern identification algorithms, and calculating shallow air pressure by utilizing lighthill equation characteristics and a finite element method.
Preferably, the specific step in S4 includes:
S4.1, selecting an acquisition mode AM method or an ABM method according to the actual condition of a shallow soil layer field, and arranging a parallel electrical method test system to generate a stable current field in an underground space;
S4.2, carrying out multiple data acquisition by using a parallel electric method instrument, and selecting data with better effect for data processing;
and S4.3, carrying out resistivity inversion according to the acquired data, and comprehensively analyzing shallow gas reserves of the underground space.
Preferably, in S5, the cause component of the superficial gas is analyzed and identified based on the composition and isotope change measurement data of the superficial gas.
Preferably, in S6, the probability of blurring is represented by a trapezoidal membership function.
Preferably, the membership function F is expressed as F= (FO, sl, ml, sr, mr), where F0 is the center of the fuzzy number support set, sl and sr are the left and right support radii, ml and mr are the left and right fuzzy regions. The degree of failure of the fuzzy number F described by the trapezoidal membership function is expressed as:
the invention has the technical effects and advantages that:
1. According to the invention, through integrating various identification indexes and methods of shallow air induced pavement collapse, a fault tree and T-S fuzzy number concept is introduced to express a logic relationship between events, the shallow air leakage induced pavement collapse fault tree is constructed, the sensitivity and importance degree of each factor are evaluated by adopting the T-S fuzzy fault tree, the environmental information of shallow air leakage disasters is comprehensively counted, and accurate risk identification indexes and risk evaluation of shallow air leakage are induced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention, the objects and other advantages of which are obtained by the structure as set forth hereinafter, as well as the drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings may be obtained according to these drawings without the need for inventive effort for a person skilled in the art.
FIG. 1 is a graph showing the factors that may cause the collapse of the road surface due to the leakage of shallow air in the present invention;
FIG. 2 is a schematic diagram of a trapezoidal membership function in the present invention;
FIG. 3 is a schematic diagram of a fault tree of the road surface subsidence caused by shallow air leakage in the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Embodiment one:
1-3, a comprehensive recognition and evaluation method for risk of collapse of a pavement induced by leakage of methane shallow gas comprises the following steps of;
s1, determining the burial depth, the position and the distribution range of shallow gas;
S2, determining the shallow gas leakage rate;
S3, determining the air pressure of the shallow air;
S4, determining reserves of shallow gas;
s5, determining the causative components of shallow air;
s6, introducing a T-S fuzzy model, describing the fault probability of each component in the form of a T-S fuzzy fault tree, more comprehensively considering the fault state, and decomposing and quantifying the complex problem and the uncertainty problem by establishing a T-S fuzzy gate so as to accurately identify and evaluate the pavement collapse risk.
The specific steps in S1 include:
S1.1, arranging a parallel electric method testing system according to the difference between a rich gas area in a shallow soil layer and a surrounding medium power supply point field source, wherein the acquisition mode of the parallel electric method testing system is divided into an AM method and an ABM method;
S1.2, adopting a parallel electric method instrument to collect potential difference delta U MN f between two points of a measuring electrode M, N for multiple times, and selecting a group with better data for analysis;
S1.3, integrating the acquired potential difference data, calculating the resistivity rho of the underground geologic body, analyzing by software, inverting the resistivity, outputting ground electric information, and mapping by software.
The specific steps in S2 include:
s2.1, arranging an acoustic wave acquisition device at a place where shallow gas is likely to leak, acquiring acoustic wave signals through a multi-channel acoustic sensor, and adopting the frequency spectrum area in a wide frequency band range as a detection index;
S2.2, after wavelet transformation, obtaining wavelet coefficients of each scale from the observed signal, so as to distinguish useful signals with higher wavelet coefficients and noise signals with lower wavelet coefficients, performing threshold denoising processing by adaptively selecting proper threshold functions, repeatedly adjusting the threshold until all noise signals are removed, obtaining estimated values of the wavelet coefficients of each scale of the useful signals, and finally performing inverse transformation to reconstruct signals to obtain useful acoustic signals;
s2.3, analyzing the waveform of the acoustic signal by using an acoustic solver, and calculating the flow velocity characteristics of the leaked gas.
The specific steps in S3 include:
S3.1, monitoring and capturing the sonic wave shape of the leakage gas by using a sonic wave sensor;
S3.2, reducing noise generated by other fluctuation in the fluid by using a wavelet analysis method or an Empirical Mode Decomposition (EMD) algorithm;
And S3.3, analyzing the denoised acoustic signals, performing leakage identification by adopting an acoustic-pressure coupling method or various pattern identification algorithms, and calculating shallow air pressure by utilizing lighthill equation characteristics and a finite element method.
The specific steps in S4 include:
S4.1, selecting an acquisition mode AM method or an ABM method according to the actual condition of a shallow soil layer field, and arranging a parallel electrical method test system to generate a stable current field in an underground space;
S4.2, carrying out multiple data acquisition by using a parallel electric method instrument, and selecting data with better effect for data processing;
and S4.3, carrying out resistivity inversion according to the acquired data, and comprehensively analyzing shallow gas reserves of the underground space.
In S5, the cause component of the shallow gas is analyzed and identified based on the isotope change measurement data of the shallow gas.
In S6, the probability of blurring is represented by a trapezoidal membership function.
Membership function F is represented as f= (F0, sl, ml, sr, mr), where FO is the center of the fuzzy number support set, sl and sr are the left and right support radii, ml and mr are the left and right fuzzy regions. The degree of failure of the fuzzy number F described by the trapezoidal membership function is expressed as:
The method comprises the steps of establishing a proper fault tree, determining that a top event is shallow gas leakage to induce pavement collapse, then determining a direct factor for inducing the top event, giving out a specific logic gate according to a logic relation, repeating the steps until a bottom event, and when the fault tree for inducing the pavement collapse is established, making the following assumption that only shallow gas leakage is considered, wherein the shallow gas leakage is the cause of pavement collapse;
for the road surface subsidence fault tree caused by the shallow air leakage established above, the fault probability and the fault degree are described by using fuzzy numbers in consideration of the fault probability and the logic relationship among the events, and the logic relationship among the events is described by using a T-S fuzzy model. According to the road surface collapse fault tree caused by shallow air leakage, M= (A1, A2, A5), wherein A1, A2, A5 respectively represent each bottom event in the fault tree, M1, M2, M4 respectively correspond to shallow air storage state mutation, underground holes are generated, shallow air leakage is caused, road surface collapse is respectively output by a T-S fuzzy gate 1-4, the common fault degree of A1, A2, A5 and M1, M2, M3 is (0,0.5,1), the membership function is selected as slr=sr=O.1, ml=mr=0.3, the common fault degree of M4 is (0, 1), the membership function is selected as slr=sr=0.25, ml=mr=0.5, and the detailed shallow damage T-S fuzzy gate rule under the action of expansive soil is formulated as follows according to experience;
The related disaster causing factor sensitivity analysis is displayed by the importance degree of each bottom event of the fault tree, when the bottom events have faults with different degrees, the fault probability of the events at the upper part of the system is changed along with the faults, so that the importance degree of the bottom events is measured by the magnitude of the fault probability change of the top events, and the related disaster causing factor sensitivity is analyzed;
according to a road surface subsidence T-S fuzzy fault tree and a fault gate caused by shallow air leakage, carrying out importance analysis on each bottom event A1, A2, and A5, changing the fault probability of a certain bottom event under the condition that other conditions are unchanged, calculating the generated change degree of the fault probability of the top event, further calculating the sensitivity of each related disaster causing factor, and assuming that the fault state of other bottom events is 0.2 and the fault state of a target bottom event is changed step by taking 0.1 as a gradient;
as the fault state of the bottom event increases, the fault probability of the top event increases, but the influence degree of different bottom events on the top event is different, namely the corresponding disaster causing factor sensitivity is different, and each disaster causing factor sensitivity is analyzed as follows:
The influence degree of the cause, the composition and the empirical distribution mode of the bottom event A5 is the greatest, the burial depth, the position and the distribution range of the bottom event A1 are the same, the influence degree of the leakage rate of the bottom event A2, the air pressure of the bottom event A3 and the reserve of the bottom event A4 are the same,
The following relation can be obtained according to the importance thereof:
A5>A1>A2=A3=A4。
Although the invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that modifications may be made to the technical solutions described in the foregoing embodiments or equivalents may be substituted for some of the technical features thereof, and these modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention in essence.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411321017.7A CN119476677A (en) | 2024-09-20 | 2024-09-20 | A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411321017.7A CN119476677A (en) | 2024-09-20 | 2024-09-20 | A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN119476677A true CN119476677A (en) | 2025-02-18 |
Family
ID=94578458
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411321017.7A Pending CN119476677A (en) | 2024-09-20 | 2024-09-20 | A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119476677A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119691951A (en) * | 2025-02-25 | 2025-03-25 | 杭州市勘测设计研究院有限公司 | A method for factorizing characteristics of sensitive elements of road hazards based on numerical mapping |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110988239A (en) * | 2019-12-12 | 2020-04-10 | 中国石油大学(华东) | Comprehensive identification method and system for origin and source of shallow natural gas |
| CN113868979A (en) * | 2021-09-29 | 2021-12-31 | 中国石油化工股份有限公司 | Shallow gas drilling risk determination method and device and server |
| CN114675330A (en) * | 2022-02-28 | 2022-06-28 | 中国海洋大学 | An in-situ monitoring system and method for the dynamic change of bubble-type shallow gas in combination with the well and the ground |
| CN116341296A (en) * | 2023-05-30 | 2023-06-27 | 交通运输部公路科学研究所 | Road collapse probability assessment method |
| CN117610273A (en) * | 2023-11-27 | 2024-02-27 | 中国科学院西安光学精密机械研究所 | Equipment failure rate analysis method based on interval type II T-S fuzzy fault tree |
| CN118111384A (en) * | 2024-02-02 | 2024-05-31 | 中国矿业大学 | A system and method for detecting and identifying urban ground subsidence or collapse |
-
2024
- 2024-09-20 CN CN202411321017.7A patent/CN119476677A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110988239A (en) * | 2019-12-12 | 2020-04-10 | 中国石油大学(华东) | Comprehensive identification method and system for origin and source of shallow natural gas |
| CN113868979A (en) * | 2021-09-29 | 2021-12-31 | 中国石油化工股份有限公司 | Shallow gas drilling risk determination method and device and server |
| CN114675330A (en) * | 2022-02-28 | 2022-06-28 | 中国海洋大学 | An in-situ monitoring system and method for the dynamic change of bubble-type shallow gas in combination with the well and the ground |
| CN116341296A (en) * | 2023-05-30 | 2023-06-27 | 交通运输部公路科学研究所 | Road collapse probability assessment method |
| CN117610273A (en) * | 2023-11-27 | 2024-02-27 | 中国科学院西安光学精密机械研究所 | Equipment failure rate analysis method based on interval type II T-S fuzzy fault tree |
| CN118111384A (en) * | 2024-02-02 | 2024-05-31 | 中国矿业大学 | A system and method for detecting and identifying urban ground subsidence or collapse |
Non-Patent Citations (4)
| Title |
|---|
| LU, JF: "Multihazard Risk Model for Reliability Analysis of Expansive Soil Landslide Based on T-S Fuzzy Logic", 《ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING》, vol. 8, no. 2, 1 June 2022 (2022-06-01), pages 1 - 4 * |
| 姚成玉;赵静一;: "基于T-S模型的液压系统模糊故障树分析方法研究", 中国机械工程, no. 16, 25 August 2009 (2009-08-25), pages 1913 - 1917 * |
| 张立宁: "《城市建筑火灾防控理论与方法》", 30 June 2023, 知识产权出版社, pages: 41 - 42 * |
| 陈庆;张洪沙;崔永昆;: "上海周边海域潜在的浅层气灾害及其防治措施", 上海地质, no. 01, 15 March 2008 (2008-03-15), pages 8 - 11 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119691951A (en) * | 2025-02-25 | 2025-03-25 | 杭州市勘测设计研究院有限公司 | A method for factorizing characteristics of sensitive elements of road hazards based on numerical mapping |
| CN119691951B (en) * | 2025-02-25 | 2025-06-06 | 杭州市勘测设计研究院有限公司 | Road hidden danger sensitive element characteristic factorization method based on numerical mapping |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Langbein | Estimating rate uncertainty with maximum likelihood: differences between power-law and flicker–random-walk models | |
| Goulet et al. | Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks | |
| US20110246161A1 (en) | Method for integrated inversion determination of rock and fluid properties of earth formations | |
| CN119476677A (en) | A comprehensive identification and assessment method for the risk of road collapse induced by shallow biogas leakage | |
| CN119204462B (en) | Intelligent mineral resource investigation evaluation system | |
| CN114880940A (en) | Compaction quality detection method and device based on multi-domain analysis and artificial neural network | |
| CN119649549A (en) | A geological disaster monitoring and early warning method and system based on digital twin technology | |
| CN119313152A (en) | AI-based risk assessment method for engineering survey | |
| CN119985264A (en) | A method for dynamic monitoring of soil seepage field on slope of power transmission tower | |
| Sadeghi et al. | Uncertainty quantification | |
| Teegavarapu | Trends and changes in hydroclimatic variables: Links to climate variability and change | |
| Zhu et al. | Fractal and multifractal characterization of stochastic fracture networks and real outcrops | |
| Giraud et al. | Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application | |
| CN120387663A (en) | Geological hazard risk assessment platform and method based on big data | |
| Feng et al. | Estimation of reservoir fracture properties from seismic data using markov chain monte carlo methods | |
| CN118169772B (en) | Submarine heavy sand ore body positioning method based on multivariate information | |
| Abi Nader et al. | Machine learning prediction of groundwater heights from passive seismic wavefield | |
| CN117371267B (en) | A quantitative prediction method for water inrush in mining areas based on ground transient electromagnetic | |
| CN118569748A (en) | A dynamic early warning system for underground pipe network modeling and power grid infrastructure projects | |
| Zhang et al. | Automatic Identification of Diverse Tunnel Threats With Machine Learning–Based Distributed Acoustic Sensing | |
| CN111221043B (en) | A working parameter optimization method of passive source electric field method | |
| Ni et al. | Sequential hydraulic tests for transient and highly permeable unconfined aquifer systems–model development and field-scale implementation | |
| CN120370409B (en) | Hydrocarbon source rock thickness prediction method | |
| CN120013023B (en) | A method for predicting surface displacement along traffic routes under the influence of high and steep slopes | |
| US20250085184A1 (en) | Probability-based pipeline leak monitoring tool |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |