US-20260127331-A1 - KARST COLLAPSE SUSCEPTIBILITY EVALUATION ANALYSIS METHOD AND SYSTEM BASED ON ANALYTIC HIERARCHY PROCESS

US20260127331A1US 20260127331 A1US20260127331 A1US 20260127331A1US-20260127331-A1

Abstract

The invention belongs to the field of karst collapse analysis technology. The invention discloses a karst collapse susceptibility evaluation analysis method and system based on analytic hierarchy process, including: marking the measurement points in the karst area, collecting the karst data of the measurement points, standardizing the karst data, and obtaining the standard karst data; performing a geomagnetic division of the measurement points based on the geomagnetic intensity to obtain a hole point set; evaluating the biological activity of the hole point set by soil microbial data, and obtaining the microbial activity index; based on the atmospheric pressure, performing a pressure fluctuation analysis of the hole point set to obtain the pressure fluctuation index; based on the microbial activity index and pressure fluctuation index, constructing the collapse evaluation function. It effectively improves the accuracy of karst collapse analysis.

Inventors

Zhongfeng DUAN
Fulai LI
Fangyu DONG
Xiang Yu
Jianyang SONG

Assignees

CHINA UNIVERSITY OF PETROLEUM (EAST CHINA)

Dates

Publication Date: 20260507
Application Date: 20251104
Priority Date: 20250324

Claims (10)

1 . A karst collapse susceptibility evaluation analysis method based on an analytic hierarchy process, comprising: S 1 , marking a measurement point in a karst area, collecting karst data of the measurement point, standardizing the karst data, and obtaining the standard karst data; wherein the karst data include: geomagnetic intensity, soil microbial data, karst temperature, soil nitrogen and carbon content, atmospheric pressure, and point coordinates; S 2 , performing a geomagnetic division of the measurement point based on geomagnetic intensity to obtain a hole point set; S 3 , evaluating a biological activity of the hole point set by soil microbial data to obtain a microbial activity index, and performing a pressure fluctuation analysis of the hole point set based on the atmospheric pressure to obtain the pressure fluctuation index; S 4 , constructing a collapse evaluation function based on the microbial activity index and the pressure fluctuation index; wherein the method for obtaining the microbial activity index comprises: using a statistical analysis method to analyze soil microbial data of all measurement points, and obtaining a microbial category table, wherein the microbial category table comprises the microbial species and a corresponding total number of species; dividing a count of species of each microbial species in a hole point by the total number of species of the corresponding microbial species in the microbial category table as a species richness; based on the species richness, evaluating a biodiversity of each measurement point to obtain a biodiversity index, and recording a maximum value of the biodiversity index as a maximum activity index of the area; based on the maximum activity index of the area, evaluating a microbial activity of the hole point to obtain the microbial activity index; wherein obtaining the pressure fluctuation index comprises: using a hole point in the hole point set as a regional center, and presetting a distance scale; using the distance scale as a regional radius, the regional center as a center of a circle, and the regional radius as the circle radius to draw a circle to obtain a regional circle; using measurement points contained in the regional circle as pressure evaluation points, and the pressure evaluation points and pressure centers as fluctuation analysis points; for each fluctuation analysis point, using the fluctuation analysis point as a pressure center to draw a cross line, and using the four areas divided by the cross line as selection domains; using a distance measurement formula to calculate a distance between other fluctuation analysis points and a center of the cross line as an evaluation distance; selecting a fluctuation analysis point with a smallest evaluation distance in each selection domain as a pressure boundary point; using the pressure boundary points in the diagonal area as a boundary group, and using the fluctuation analysis point in the center of the cross line as a difference center; using a central difference algorithm to calculate a pressure fluctuation gradient of each group of boundary groups; using a mean value of the pressure fluctuation gradient as a fluctuation factor, and calculating a mean value of the fluctuation factor of all fluctuation analysis points as a pressure propagation operator; based on the pressure propagation operator, obtaining a pressure fluctuation index by evaluating the pressure fluctuation of the hole point.
2 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 1 , wherein the collection method of karst data comprises: presetting a measurement route and a measurement interval, and marking the measurement points in a measurement line based on the measurement interval; using an initial position of the measurement line as an initial measurement point; starting from the initial measurement point, marking a position of each measurement interval as the measurement point, and setting a geomagnetic detector, a microbial sensor, a soil micro sensor, a temperature sensor and a pressure sensor for each measurement point; and acquiring data for each measurement point in the same time series; determining the point coordinates based on the karst area, selecting a point in the karst area as a coordinate center point to construct a standard coordinate system; wherein a horizontal axis distance and a vertical axis distance of each measurement point from the coordinate center point constitute the point coordinates.
3 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 2 , wherein the method of geomagnetic division of the measurement point comprises: for karst data, using each type of data in the karst data as data to be converted; presetting a time scale, and calculating a historical mean of each data to be converted based on time scale parameters; using a recorded time point of the data to be converted as a cut-off point; selecting a data to be converted at the same measurement point in the time scale before the cut-off point to form a scale set, wherein a mean value of the data in the scale set is calculated as a historical mean; using a recorded time point of the data to be converted as a measurement time, and selecting the data to be converted at the same measurement time to form a time data set; using a mean value of the data in the time data set as a mean time, and using a standard deviation of the data in the time data set as a time standard deviation; based on the historical mean, time mean and time standard deviation, standardizing the data to be converted, the formula for standardizing the data to be converted is: Res = arcsin ⁢ h ⁡ ( data - μ σ ) × exp ⁡ ( - ❘ "\[LeftBracketingBar]" data - d ¯ ❘ "\[RightBracketingBar]" τ ) ; where Res denotes the standard data, data denotes the data to be converted, μ denotes the mean value of the time, σ denotes the time standard deviation, τ denotes the time scale, d denotes the historical mean, arcsinh( ) denotes an inverse hyperbolic sine function, and all standard data constitute the standard karst data.
4 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 3 , wherein the method of geomagnetic division of the measurement points comprises: based on the measurement route, selecting a previous measurement point of the current measurement point as a differential analysis point; using a finite difference method to analyze a geomagnetic intensity of the current measurement point by the differential analysis point, and obtaining a lateral variation factor; using the finite difference method to analyze the geomagnetic intensity of the current measurement point by the finite difference method, and obtaining a longitudinal variation factor; presetting a depth scale set, traversing the depth scale set, and using the traversed element as a scale factor; performing a magnetic field tensor analysis of each scale factor to obtain a geomagnetic depth tensor; presetting a geomagnetic span, wherein the geomagnetic span comprises a horizontal axis span and a vertical axis span; performing a geomagnetic anomaly evaluation of the measurement points based on the geomagnetic depth tensor to obtain a magnetic anomaly intensity index; using a spline analysis method to construct a magnetic anomaly fluctuation curve with a scale factor and a corresponding magnetic anomaly intensity index, and recording a maximum point and a minimum point of an extreme value of the magnetic anomaly fluctuation curve; using a difference between the maximum point and the minimum point of the extreme value as a hole evaluation index, and presetting a hole evaluation threshold; marking the measurement points with the hole evaluation index as greater than or equal to the hole evaluation threshold as the hole points; wherein the scale factor with the smallest value of the scale factor corresponds to the maximum point and wherein a minimum point is the start point of the hole, and the other is an end point of the hole; merging the data of the start point and the end point of the hole into the hole point, wherein all the hole points constitute the hole point set.
5 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 4 , wherein the formula for evaluating the geomagnetic anomaly of the measurement point is: MAI = ∫ y - b y + b ∫ x - a x + a ❘ "\[LeftBracketingBar]" ∇ B ❘ "\[RightBracketingBar]" ⁢ dx ⁢ dy × ( 1 + γcos ( wt ) ) ; where MAI denotes a magnetic anomaly intensity index, a denotes a horizontal axis span, b denotes a vertical axis span, x denotes a horizontal axis value of the point coordinate, y denotes a vertical axis value of the point coordinate, dx and dy denote integral operations, γ denotes a period adjustment coefficient, w denotes an angular frequency parameter, and t denotes a time scale.
6 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 5 , wherein the formula for evaluating the microbial activity of the hole points is: BAI = Act Mliv × θ ⁢ ln ( 1 + N C ) ; where BAI denotes a microbial activity index, Mliv denotes a maximum activity index, θ denotes an environmental regulation coefficient, N denotes a soil nitrogen content, and C denotes a soil carbon content.
7 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 6 , wherein the formula for biodiversity evaluation at each measurement point is as follows: Act = - sin ⁢ π ⁢ S S max ⁢ ∑ ( E i × ln ⁢ E i ) × exp ⁡ ( - δ ⁢ T ) ; where Act denotes a biodiversity index, π denotes a circumference rate, S denotes the microbial species, S max denotes the total number of microbial species in the microbial category table, P i denotes a species richness of the i-th microbial species in the hole point, δ denotes a temperature adjustment coefficient, and T denotes a karst temperature.
8 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 7 , wherein the formula for evaluating the pressure fluctuation of the hole point is: PI = ∫ low hig ( SZ × tan ⁢ h ⁡ ( Δ ⁢ P P 0 ) ) × exp ⁡ ( - ε ⁢ H ) ⁢ dH ; where PI denotes a pressure fluctuation index, SZ denotes a pressure fluctuation factor, P 0 denotes a standard atmospheric pressure, ΔP denotes a difference between the atmospheric pressure and the standard atmospheric pressure, ε denotes a fluctuation attenuation coefficient, low denotes a start point of the hole, hig denotes the end point of the hole, and H denotes a depth of the hole.
9 . The karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 8 , wherein the formula of the collapse evaluation function is as follows: Dang=w 1 ×BAI+w 2 × PI; Dang denotes a collapse index, w 1 denotes the weight of microbial activity, and w 2 denotes the weight of pressure fluctuation; microbial activity weight and pressure fluctuation weight meet the weight constraint conditions: w 1 +w 2 =1.
10 . A karst collapse susceptibility evaluation analysis system based on analytic hierarchy process, the system is used to implement the karst collapse susceptibility evaluation analysis method based on analytic hierarchy process according to claim 1 , wherein comprising: data acquisition module, configured to mark the measurement points in the karst area, collect the karst data of the measurement points, standardize the karst data, and obtain the standard karst data; point division module, configured to perform a geomagnetic division of measurement points based on geomagnetic intensity to obtain a hole point set; index evaluation module, configured to evaluate the biological activity of the hole point set by soil microbial data and obtain the microbial activity index; based on the atmospheric pressure, the pressure fluctuation analysis of the hole point set is carried out to obtain the pressure fluctuation index; evaluation and construction module, configured to construct the collapse evaluation function based on the microbial activity index and pressure fluctuation index.

Description

TECHNICAL FIELD The present disclosure relates to the field of karst collapse analysis technology, and specifically, the present disclosure relates to a karst collapse susceptibility evaluation analysis method and system based on analytic hierarchy process. BACKGROUND The patent with an application number of CN118114007A discloses a karst collapse geological monitoring information analysis system, including an information acquisition unit, a regional division unit, a normal analysis unit, an abnormal analysis unit, an information storage unit, an analysis and an early warning unit, and an information output unit. It involves the field of karst collapse information analysis technology, and solves the technical problem that there is no comprehensive past data for objective analysis, and the data obtained from a single aspect has analysis errors. By combining the acquired data with the past data, the error in the analysis process is further reduced. Meanwhile, different areas are divided according to the obtained data, and different methods are adopted to analyze the divided regional data. Secondly, data acquisition and comparison are carried out for abnormal areas to carry out timely early warning analysis of abnormal areas, which can remind relevant staff to respond. At the same time, various data are obtained for analysis, which can ensure the objectivity of the analysis results. In the field of karst collapse susceptibility evaluation, the current karst collapse evaluation methods usually ignore the relationship between the morphological characteristics of the underground karst pipeline network and geomagnetic anomalies. The existence and morphology of karst pipelines have a direct impact on karst development and collapse. Geomagnetic anomaly is one of the important precursors of karst collapse, since the karst pipelines and underground cavities will affect the distribution of underground magnetic fields and due to the lack of research on the correlation between underground karst pipeline network morphology and geomagnetic anomalies, it is impossible to accurately identify the distribution and changes of underground pipelines in the actual karst collapse evaluation process, resulting in inaccurate evaluation results. Thus, the occurrence of karst collapse cannot be effectively predicted; karst development is also affected by soil microbial community structure. Microorganisms can change the formation environment of karst by chemical reaction and mineral degradation. The existing evaluation methods fail to take into account the influence of microbial communities and ignore the role of this biological factor in karst development, which makes the evaluation model not comprehensive enough and underestimates the risk of karst collapse in some areas, especially in areas where microorganisms are active, which increases the error and inaccuracy of the evaluation results. The fluctuation of atmospheric pressure affects the stability of underground cavities, especially under seasonal or sudden climate change, the pressure fluctuation will have different degrees of influence on the underground structure. The current karst collapse evaluation methods lack the analysis of the coupling mechanism of atmospheric pressure fluctuation, which increases the evaluation error and leads to the failure of effective early warning before the disaster. In view of this, the invention proposes a karst collapse susceptibility evaluation analysis method and system based on analytic hierarchy process to solve the above problems. SUMMARY In order to overcome the above defects of the existing technology and achieve the above purposes, the present disclosure provides the following technical solutions: a karst collapse susceptibility evaluation analysis method based on analytic hierarchy process, including: S1, marking a measurement point in a karst area, collecting karst data of the measurement point, standardizing the karst data, and obtaining the standard karst data; the karst data include: geomagnetic intensity, soil microbial data, karst temperature, soil nitrogen and carbon content, atmospheric pressure, and point coordinates;S2, performing a geomagnetic division of the measurement point based on geomagnetic intensity to obtain a hole point set;S3, evaluating a biological activity of the hole point set by soil microbial data to obtain a microbial activity index, performing a pressure fluctuation analysis of the hole point set based on the atmospheric pressure to obtain the pressure fluctuation index;S4, constructing a collapse evaluation function based on microbial activity index and pressure fluctuation index. In some embodiments, the collection method of karst data includes: a measurement route and a measurement interval are preset, and the measurement points in the measurement line are marked based on the measurement interval; an initial position of the measurement line is used as an initial measurement point; starting from the initi