A method and system for geological safety evaluation based on aviation gravity and magnetic data

Abstract

The application discloses a kind of geological safety evaluation method and system based on aerial gravity and magnetic data, it is related to geological safety evaluation technical field, including: using the block partition survey network planning algorithm to collect aerial gravity and magnetic data, remove aerial gravity and magnetic data noise using Kalman filter, FIR filter, nonlinear filter, low-pass filter, and using data fusion algorithm improves data accuracy and integrity. The accuracy of geological structure identification is improved by using deep learning and improved frequency domain power spectrum algorithm. The evaluation index weight is determined by analytic hierarchy process and principal component analysis method, and a comprehensive aerial gravity and magnetic geological safety evaluation index system is established. The block division of geological safety stability is carried out by using fuzzy mathematics evaluation method, which provides accurate geological safety guidance for water diversion project, reduces construction risk, and ensures project safety and long-term reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological safety evaluation, more particularly to a geological safety evaluation method and system based on airborne gravity and magnetic data. BACKGROUND

[0002] With the development of social economy and the progress of science and technology, it is increasingly urgent to carry out cross-basin water diversion project construction in high-altitude rugged mountainous areas. Whether it is the site selection, route selection, or subsequent project construction, the geological safety evaluation of the water diversion project area is an important basis. As an important infrastructure construction project, the route of the water diversion project often crosses complex geological areas, and the stability of the geological conditions plays a decisive role in the safety and long-term operation reliability of the project.

[0003] Traditional geological safety evaluation methods rely on ground geological survey, drilling exploration and other means, which have problems such as low efficiency, high cost, limited coverage, etc., and it is difficult to fully and accurately grasp the distribution of deep geological structures and concealed geological bodies in the project area. The traditional geological safety evaluation method is difficult to carry out in high-altitude rugged mountainous areas, and is limited by the evaluation method technology, and the analysis and evaluation ability of deep geological structures is limited. In recent years, airborne geophysical instrument equipment has been improved in miniaturization and light weight, domestic airborne gravity systems have been put into use, and the development of airborne gravity and magnetic data processing and interpretation technology. Airborne gravity and magnetic exploration technology has gradually become one of the important means of engineering geological exploration with the advantages of wide coverage and high work efficiency. Using airborne gravity and magnetic methods to study the geological safety problems of water diversion projects has the characteristics of rapid efficiency, economic and environmental protection, wide coverage, and less restriction by topography and geomorphology, making it possible to carry out airborne geophysical geological safety evaluation of water diversion project areas in high-altitude deep-cut rugged mountainous areas. However, the existing geological safety evaluation method based on airborne gravity and magnetic data has deficiencies in the refinement degree of data processing, the depth of multi-source information fusion, and the scientificity and systematicness of the evaluation index system. For example, the accuracy control of multi-block data merging in the data preprocessing process is not enough, which leads to errors in subsequent analysis; in key links such as geological structure inference and geothermal gradient calculation, the method is single and lacks the optimization of multiple technical means; the evaluation index system is difficult to fully reflect the complex influencing factors of geological safety, leading to inaccurate division of geological safety stability blocks.

[0004] Therefore, how to propose a geological safety evaluation method and system based on airborne gravity and magnetic data to solve the current situation of limited analysis and evaluation ability of deep geological structures and improve the accuracy and reliability of the evaluation is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] In view of this, the present application provides a kind of geological safety evaluation method and system based on aerial gravity and magnetic data, solve the current situation of limited analysis and evaluation capability to deep geological structure, improve the accuracy and reliability of evaluation, realize according to the result of aerial gravity and magnetic geological safety evaluation, reasonable adjustment is carried out to water diversion engineering line, dam site etc., and reasonable measures are taken to protect the geological safety risk of water diversion engineering area, to achieve the above-mentioned purposes, the present application adopts the following technical solutions:

[0006] A kind of geological safety evaluation method based on aerial gravity and magnetic data, comprising:

[0007] According to the aerial gravity and magnetic data acquisition of water diversion engineering line;

[0008] According to the geological safety evaluation factor acquisition of collected aerial gravity and magnetic data;

[0009] According to the geological safety evaluation factor acquisition of constructed aerial gravity and magnetic geological safety evaluation index system;

[0010] The water diversion engineering area is divided into several grids, according to the geological safety evaluation index system constructed, the evaluation index data of each grid is collected, fuzzy comprehensive evaluation model is constructed by using fuzzy mathematics evaluation method, and the geological safety condition of each grid is comprehensively judged.

[0011] Optionally, the aerial gravity and magnetic data acquisition according to water diversion engineering line includes:

[0012] According to the different direction of water diversion engineering line, the engineering area is divided into multiple blocks;

[0013] Comprehensively consider the complexity of geological conditions, engineering construction demand factor, adopt the block measurement network planning algorithm, based on the geological prior information of engineering area, engineering direction, adjust the measurement network block, determine the cutting line, the length of measuring line, direction and spacing, and plan the measurement network for each block.

[0014] Optionally, the aerial gravity and magnetic data acquisition includes: using aerial gravity and magnetic measurement equipment, according to the planned measurement network, aerial gravity and magnetic data acquisition is carried out, and the original aerial gravity and magnetic data of each block is acquired, in the process of data acquisition, flight height, speed, heading flight parameters are recorded in real time, and consistency verification is carried out.

[0015] Optionally, it also includes that Kalman filter (hardware implementation) + FIR filtering method is used to remove noise pretreatment to the collected original aerial gravity data, optimal estimation is carried out according to the dynamic change characteristics of data, nonlinear filtering method and low-pass (smoothing) filter are used to remove noise pretreatment to the collected original aerial magnetic data;

[0016] The pre-processed data is horizontally adjusted by establishing regional gravity and magnetic reference surfaces to unify the data of different blocks to the same horizontal reference;

[0017] In the multi-block data merging process, a data fusion algorithm is introduced, based on spatial distance weighting and data correlation analysis, to smoothly transition the data of adjacent blocks, eliminate the data differences at the block boundaries, and obtain the airborne geophysical basic data.

[0018] Optionally, the construction of the aerial gravity and magnetic geologic safety evaluation index system according to the obtained geologic safety evaluation elements comprises:

[0019] Based on the aerial geophysical basic data, aerial gravity and magnetic contour maps, profile maps and conversion processed maps are generated, and the maps are identified to obtain the anomaly characteristics of the aerial gravity and magnetic field;

[0020] Regional faults are divided using various potential field conversion processing methods and regional geology and remote sensing data;

[0021] The amplitude and gradient change information of the gravity and magnetic anomalies are comprehensively utilized, combined with geostatistical methods, to establish a concealed rock mass boundary identification model for delineating the concealed rock mass;

[0022] The depth of the Curie surface is calculated using a frequency domain power spectrum algorithm, and the Curie isothermic surface is divided, and a geothermal gradient conversion model is established according to the relationship between the depth of the Curie isothermic surface and the geothermal gradient, to convert the depth of the Curie surface into the value of the geothermal gradient;

[0023] Through aerial gravity and magnetic long profile inversion and three-dimensional inversion quantitative methods, the long profile structure, three-dimensional morphology and physical property parameters of deep geological structures are obtained.

[0024] Optionally, the regional fault division using various potential field conversion processing methods and regional geology and remote sensing data comprises: using autocorrelation filtering, notch analysis methods combined with regional geology and remote sensing data to divide the regional faults; the autocorrelation filtering enhances the weak anomaly information of the gravity and magnetic field, and highlights the anomaly characteristics caused by the fault zone; the notch analysis identifies the position and trend of the fault through the shape analysis of the gravity and magnetic anomaly curve; a deep learning algorithm is introduced to construct a fault identification model based on the anomaly characteristics of the aerial gravity and magnetic field, which automatically identifies the fault structure under complex geological conditions by learning and training the gravity and magnetic data of the known fault region.

[0025] Optionally, the construction of the aerial gravity and magnetic geologic safety evaluation index system according to the obtained geologic safety evaluation elements comprises:

[0026] The gravity and magnetic anomaly strength, anomaly gradient and anomaly shape quantitative indexes are extracted according to the aerial gravity and magnetic field characteristics;

[0027] Obtaining the fracture structure parameters and the rock mass distribution characteristics of the inferred explanation;

[0028] In combination with the quantitative inversion results, such as the depth of the crust, the geothermal gradient value, and the physical property parameters of the deep geological structure, the geological, seismic, and geological disaster data are comprehensively considered.

[0029] The aerial gravity-magnetic geological safety evaluation index system is jointly constructed.

[0030] Optionally, the weight of each evaluation index is determined by combining the analytic hierarchy process and the principal component analysis method, the hierarchical structure of the evaluation index is constructed by the analytic hierarchy process, and the mutual relationship between the indexes is determined; the indexes are processed by dimension reduction by the principal component analysis method, and the main influencing factors are extracted.

[0031] Optionally, the comprehensive evaluation of the geological safety condition of each grid comprises:

[0032] The diversion project area is divided into a plurality of grids, and the size of the grid is set according to the geological complexity degree of the project area and the evaluation accuracy requirement;

[0033] According to the constructed aerial gravity-magnetic geological safety evaluation index system, the data of each evaluation index in each grid is collected, the fuzzy comprehensive evaluation model is constructed by using the fuzzy mathematics evaluation method, and the geological safety condition of each grid is comprehensively evaluated;

[0034] According to the geological safety evaluation index value of the grid obtained by the comprehensive evaluation, in combination with the preset geological safety stability grade division standard, the diversion project area is divided into different geological safety stability blocks.

[0035] Optionally, a geological safety evaluation system based on aerial gravity-magnetic data comprises:

[0036] The acquisition module is used for acquiring aerial gravity-magnetic data according to the diversion project line;

[0037] The geological safety evaluation element acquisition module is used for acquiring geological safety evaluation elements according to the acquired aerial gravity-magnetic data;

[0038] The evaluation index system construction module is used for constructing an aerial gravity-magnetic geological safety evaluation index system according to the acquired geological safety evaluation elements;

[0039] The comprehensive evaluation module is used for dividing the diversion project area into a plurality of grids, collecting the data of each evaluation index in each grid according to the constructed geological safety evaluation index system, constructing a fuzzy comprehensive evaluation model by using the fuzzy mathematics evaluation method, and comprehensively evaluating the geological safety condition of each grid.

[0040] Compared with the prior art, the method and system for evaluating geological safety based on aerial gravity and magnetic data have the following beneficial effects:

[0041] The application provides a method for evaluating geological safety based on aerial gravity and magnetic data, which comprises the following steps: collecting aerial gravity and magnetic data according to a water diversion engineering route; obtaining geological safety evaluation elements according to the collected aerial gravity and magnetic data; constructing an aerial gravity and magnetic geological safety evaluation index system according to the obtained geological safety evaluation elements; dividing the water diversion engineering area into a plurality of grids; collecting evaluation index data in each grid according to the constructed geological safety evaluation index system; and constructing a fuzzy comprehensive evaluation model by using a fuzzy mathematics evaluation method, and comprehensively evaluating the geological safety condition of each grid. (1) In the aerial gravity and magnetic data collection link, a block partition survey network planning algorithm is used, the survey network density can be flexibly adjusted according to the geological conditions, the data quality is ensured, the collection efficiency is improved, and the cost is reduced. In the data preprocessing process, Kalman filtering (hardware implementation), FIR filtering, nonlinear filtering and low-pass (smoothing) filtering are applied to aerial gravity and magnetic noise removal, and an advanced data fusion algorithm is introduced for multi-block data merging, so that the accuracy and integrity of the data are effectively improved, and a reliable data basis is provided for subsequent geological safety evaluation. (2) A plurality of advanced data processing technologies and algorithms are comprehensively used, a deep learning algorithm is used for fracture identification, and an improved frequency domain power spectrum algorithm is used for calculating the depth of the bedrock, so that the limitations of traditional methods are broken through, the identification and analysis accuracy of the stratum information such as geological structure and concealed rock mass are improved, and the geological safety evaluation elements can be more comprehensively and accurately obtained. (3) The evaluation index weight is determined by combining the analytic hierarchy process and the principal component analysis method, the aerial gravity and magnetic geological safety evaluation index system comprehensively considers the aerial gravity and magnetic data characteristics, geological structure, earthquake and geological disasters and other factors, and can more scientifically and accurately reflect the geological safety condition of the water diversion engineering area. (4) The fuzzy mathematics evaluation method is used for geological safety stability block division, the uncertainty and fuzziness of the geological conditions can be fully considered, the division result is more in line with the actual geological conditions, precise and reliable geological safety guidance is provided for the planning, design and construction of the water diversion engineering, the engineering construction risk is effectively reduced, and the safety and long-term operation reliability of the engineering are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are only embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0043] Figure 1 A flow chart of a geological safety evaluation method based on aerial gravity and magnetic data is provided.

[0044] Figure 2 A structural framework diagram of a geological safety evaluation system based on aerial gravity and magnetic data is provided. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0046] The embodiments of the present application disclose a geological safety evaluation method based on aerial gravity and magnetic data, as shown in the figure, comprising: Figure 1

[0047] Aerial gravity and magnetic data are collected according to the water diversion engineering route;

[0048] Geological safety evaluation factors are obtained according to the collected aerial gravity and magnetic data;

[0049] An aerial gravity and magnetic geological safety evaluation index system is constructed according to the obtained geological safety evaluation factors;

[0050] The water diversion engineering area is divided into a plurality of grids, according to the constructed geological safety evaluation index system, each grid is collected for each evaluation index data, a fuzzy comprehensive evaluation model is constructed by using a fuzzy mathematics evaluation method, and the geological safety condition of each grid is comprehensively evaluated.

[0051] Further, the aerial gravity and magnetic data collection according to the water diversion engineering route comprises:

[0052] According to different directions of the water diversion engineering route, the engineering area is divided into a plurality of blocks;

[0053] The geological condition complexity degree and the engineering construction demand factor are comprehensively considered, a block-by-block survey network planning is adopted, the survey network block is adjusted based on the geological prior information of the engineering area and the engineering direction, the survey network of each block is planned, including the spacing, direction and length of the survey line and the cutting line of the survey network.

[0054] Further, the aerial gravity and magnetic data collection comprises: using an aerial gravity and magnetic measurement device, collecting aerial gravity and magnetic data according to the planned survey network, obtaining original aerial gravity and magnetic data of each block, recording flight height, airspeed, heading flight parameters in real time during data collection, and verifying the consistency of data collection. Further, the aerial gravity and magnetic data collection comprises: using an aerial gravity and magnetic measurement device, collecting aerial gravity and magnetic data according to the planned survey network, obtaining original aerial gravity and magnetic data of each block, recording flight height, airspeed, heading flight parameters in real time during data collection, and verifying the consistency of data collection.

[0055] Further, the method further comprises: removing noise from the collected original gravity and magnetic data by using Kalman filtering (hardware implementation) + FIR filtering method, performing optimal estimation according to dynamic change characteristics of the data, and removing noise from the collected original gravity and magnetic data by using a nonlinear filtering method and a low-pass (smoothing) filter;

[0056] The preprocessed data is adjusted horizontally, and the data of different blocks is unified to the same horizontal datum by establishing regional gravity and magnetic reference surfaces.

[0057] In the process of merging multi-block data, a data fusion algorithm is introduced, and based on spatial distance weighting and data correlation analysis, the data of adjacent blocks is smoothly transitioned to eliminate the data difference of the block boundary, and the airborne geophysical basic data is obtained.

[0058] Further, the method further comprises: constructing an airborne gravity and magnetic geological safety evaluation index system according to the obtained geological safety evaluation elements.

[0059] Based on the airborne geophysical basic data, an airborne gravity and magnetic contour plane map, a profile plane map and a conversion processing map are generated, and the anomaly characteristics of the airborne gravity and magnetic field are obtained by identifying the map.

[0060] Regional fractures are divided by using a variety of potential field conversion processing methods and regional geology and remote sensing data.

[0061] The amplitude and gradient change information of the gravity and magnetic anomalies are comprehensively utilized, and a hidden rock mass boundary identification model is established by combining a geological statistical method to delineate the hidden rock mass.

[0062] The depth of the Curie surface is calculated by using a frequency domain power spectrum algorithm, the Curie isotherm is divided, and a geothermal gradient conversion model is established according to the relationship between the depth of the Curie isotherm and the geothermal gradient to convert the depth of the Curie surface into the value of the geothermal gradient.

[0063] The long profile structure, three-dimensional morphology and physical property parameters of deep geological structures are obtained by using the quantitative methods of airborne gravity and magnetic long profile inversion and three-dimensional inversion.

[0064] Further, the method further comprises: dividing regional fractures by using autocorrelation filtering, notch analysis method combined with regional geology and remote sensing data; the autocorrelation filtering enhances the weak anomaly information of gravity and magnetic anomalies and highlights the anomaly characteristics caused by the fracture zone; the notch analysis identifies the position and trend of the fracture by analyzing the shape of the gravity and magnetic anomaly curve; a fracture identification model based on the anomaly characteristics of the airborne gravity and magnetic field is constructed by introducing a deep learning algorithm, and the fracture identification model automatically identifies the fracture structure under complex geological conditions by learning and training the gravity and magnetic data of the known fracture region.

[0065] Further, the constructing the aviation gravity and magnetic geological safety evaluation index system according to the obtained geological safety evaluation elements comprises:

[0066] According to the aviation gravity and magnetic field characteristics, the gravity and magnetic anomaly strength, anomaly gradient and anomaly shape quantitative indexes are extracted;

[0067] The inferred and interpreted fracture structure parameters and rock mass distribution characteristics are obtained;

[0068] The quantitative inversion results, such as the depth of the Moho surface, the geothermal gradient value and the physical property parameters of the deep geological structure, are combined, and the geological, seismic and geological disaster data are comprehensively considered;

[0069] The aviation gravity and magnetic geological safety evaluation index system is jointly constructed.

[0070] Further, the weight of each evaluation index is determined by combining the analytic hierarchy process and the principal component analysis method, the hierarchical structure of the evaluation index is constructed by the analytic hierarchy process, and the mutual relationship between the indexes is determined; the indexes are processed by dimension reduction by the principal component analysis method, and the main influencing factors are extracted.

[0071] Further, the comprehensive evaluation of the geological safety condition of each grid comprises:

[0072] The diversion project area is divided into a plurality of grids, and the grid size is set according to the geological complexity degree of the project area and the evaluation accuracy requirement;

[0073] According to the constructed aviation gravity and magnetic geological safety evaluation index system, the evaluation index data of each grid is collected, the fuzzy mathematics evaluation method is used, the fuzzy comprehensive evaluation model is constructed, and the geological safety condition of each grid is comprehensively evaluated;

[0074] According to the comprehensive evaluation, the geological safety evaluation index value of the grid is obtained, the preset geological safety stability grade division standard is combined, the diversion project area is divided into different geological safety stability blocks.

[0075] In the specific embodiment, a geological safety evaluation system based on aviation gravity and magnetic data, as shown in Figure 2 , comprises:

[0076] The acquisition module is used for acquiring the aviation gravity and magnetic data according to the diversion project line;

[0077] The geological safety evaluation element acquisition module is used for acquiring the geological safety evaluation elements according to the acquired aviation gravity and magnetic data;

[0078] The evaluation index system construction module is used for constructing the aviation gravity and magnetic geological safety evaluation index system according to the obtained geological safety evaluation elements;

[0079] The comprehensive evaluation module is used for dividing the water diversion project area into a plurality of grids, collecting each evaluation index data in each grid according to the constructed geological safety evaluation index system, constructing a fuzzy comprehensive evaluation model by using a fuzzy mathematics evaluation method, and comprehensively evaluating the geological safety condition of each grid.

[0080] In the specific embodiment, a geological safety evaluation method based on aerial gravity and magnetic data is implemented as follows:

[0081] S1: aerial gravity and magnetic data acquisition:

[0082] According to different directions of the water diversion project line, the project area is divided into a plurality of blocks. Considering the complexity of the geological conditions, the engineering construction requirements and other factors, a block-based survey network planning algorithm is used to plan a survey network for each block. The block-based survey network planning is based on the geological prior information and engineering setting of the project area, such as the known complexity of the geological conditions, the structural trend, the engineering line direction and the like, and dynamically adjusts the interval, direction and length of the survey lines and cutting lines to improve the collection efficiency and effect under the premise of ensuring the data quality.

[0083] Specifically, the survey line interval dynamic adjustment formula is:

[0084]

[0085] Wherein, Δθ i = |θ str - θ eng |, the angle between the structure and the engineering direction, when Δθ i > 90°, take 180°- Δθ i , R i is the geological complexity index of the i-th block, D base is the survey line reference interval, θ str is the structural trend azimuth angle, and θ eng is the engineering line direction azimuth angle. The higher the geological complexity index R i , the smaller the survey line interval D i , and the data precision can be improved by densifying the survey lines. When the angle Δθ i between the structural trend and the engineering direction tends to 0° (the survey line is parallel to the structural direction), the interval can be appropriately enlarged (cos Δθ i → 1) to improve the collection efficiency.

[0086] The survey line length adaptive formula is:

[0087]

[0088] L lineFor the reference length of the survey line, λ is the efficiency optimization coefficient, λ is dynamically valued according to whether there is a key exploration target (such as a fault intersection zone) in the block, and λ = 1 in the ordinary area and λ = 1.2 (prolonging the survey line to cover the abnormal area) in the key area. In the complex block (R i →1), the length of the survey line is increased by 33%, ensuring complete imaging of the geological body, the efficiency coefficient λ balances the accuracy and the construction period, and redundancy acquisition is avoided.

[0089] Cutting line direction optimization model:

[0090] θ cut = γ str + 90° ± δ

[0091] δ is a dynamic adjustment angle (0° ≤ δ ≤ 15°), which is calculated in real time according to the curvature of the engineering line: where ΔS is the line segment offset, L seg is the engineering sub-length. The cutting line is perpendicular to the structure trend (θ cut = θ str + 90°) by default, ensuring that it is transverse to the geological interface. When the engineering line has a curve (such as a pipe bend), the cutting line direction is fine-tuned through δ to ensure that the survey network is coupled with the engineering layout.

[0092] In the specific implementation, the specific execution process includes:

[0093] Block division: according to the geological stratification (such as Quaternary overburden, bedrock surface) and engineering zoning (such as tunnel section, bridge section), the study area is divided into n blocks {B1, B2, …, B n}.

[0094] Parameter initialization: read the block R i , θ str and engineering parameters θ eng , call industry benchmark values D base , L line .

[0095] Survey network calculation: for each block B i , D i , L i and θ cut are calculated in turn to generate the survey line coordinate matrix X i = [x ij , y ij ], j is the survey line number.

[0096] Interactive verification: import the survey network scheme into the GIS platform, superimpose the geological risk area (such as landslide point) and the engineering sensitive point (such as pile foundation position), and manually adjust the local survey line density or direction.

[0097] Key area encryption: In the key position such as fault zone, karst area, additional encryption measuring line is added, forming the mixed measuring network of "main measuring line + local encryption".

[0098] Using high-precision airborne gravity and magnetic measurement equipment, according to the planned measuring network, the original airborne gravity and magnetic data of each block are collected. In the data collection process, the flight height, speed, direction and other flight parameters are recorded in real time to ensure the accuracy and consistency of data collection.

[0099] The collected original airborne gravity and magnetic data are preprocessed, including noise removal and abnormal data rejection. The Kalman filter (hardware implementation) + FIR filter method is used to remove noise from the airborne gravity and magnetic data, and the optimal estimation is made according to the dynamic characteristics of the data. The nonlinear filtering method and low-pass (smoothing) filter are used to remove noise from the collected airborne magnetic data. The above preprocessing methods can well remove noise and retain effective data characteristics.

[0100] The preprocessed data are adjusted horizontally, and the regional gravity and magnetic reference surface are established to unify the data of different blocks to the same horizontal reference.

[0101] In the process of merging multi-block data, a data fusion algorithm is introduced. Based on spatial distance weighting and data correlation analysis, the data of adjacent blocks are smoothly transitioned, the data difference at the block boundary is eliminated, and complete and accurate airborne geophysical basic data are obtained, which provides reliable data support for subsequent geological safety evaluation.

[0102] The specific steps of the above technical solution include:

[0103] S1: Establishing regional gravity and magnetic reference surface

[0104] Calculating the average observation value of the block: for each block i, the average observation value is calculated according to the original observation value O ij of all the measuring points of the block. Where N i is the number of measuring points of the block i.

[0105] Determining the theoretical value of the regional reference surface: by collecting the known geological structure, standard measuring point data, etc. in the region, combined with the experience of geologists, the theoretical value R of the regional gravity and magnetic reference surface is determined.

[0106] Data horizontal adjustment: the original observation value O ij of each measuring point of each block is substituted into the formula

[0107] to obtain the corrected data C ij , and the unification of the data of different blocks to the same horizontal reference is completed.

[0108] S2: Calculate the data fusion weights

[0109] Calculate spatial distance weights: For each measuring point k in the overlapping area of ​​adjacent blocks, calculate its distance d to the center of blocks m and n respectively. mk d nk And according to the formula Calculate the spatial distance weights.

[0110] Calculate data correlation weights: Select a certain range of data near measurement point k, and calculate the covariance Cov of data in blocks m and n. mn and their respective variances Var m Var n Through formula

[0111] Obtain the data relevance weights.

[0112] Determine the final fusion weights: Based on experiments or experience, set the weight coefficient α, and then perform a weighted average of the spatial distance weights and the data correlation weights to obtain the final weights w″ used for data fusion. mk w″ nk ,

[0113]

[0114] S3: Data Fusion and Smooth Transition

[0115] Data fusion calculation: For each measurement point k in the overlapping area of ​​adjacent blocks, the corrected value C is calculated. mk C nk With the final fusion weight w″ mk w″ nk Substitute into formula F k =w″ mk ·C mk +w″ nk ·C nk Calculate the fused value F k .

[0116] Smoothing transition processing: For the data after merging multiple blocks, methods such as moving average filtering and Gaussian filtering are used to smooth the data in the fused area and surrounding areas, further eliminating data differences and obtaining complete and accurate airborne geophysical basic data.

[0117] S2: Obtaining Geological Safety Assessment Elements:

[0118] 1) Based on the processed airborne gravity and magnetic data, generate airborne gravity and magnetic contour maps, profile maps, and converted processed maps. Use image enhancement and feature extraction techniques to process these maps, highlighting the abnormal features of the airborne gravity and magnetic field. Through analyzing the strength, shape, distribution, and other characteristics of the airborne gravity and magnetic field, preliminarily identify potential geological structure anomaly areas.

[0119] 2) Use autocorrelation filtering, notch analysis, and other potential field conversion techniques, combined with regional geology, remote sensing, and other data, to divide regional faults. Autocorrelation filtering can enhance the correlation of gravity and magnetic anomalies, highlighting the abnormal features caused by fault zones; notch analysis identifies the location and trend of faults by analyzing the shape of gravity and magnetic anomaly curves. At the same time, introduce deep learning algorithms to build a fault recognition model based on gravity and magnetic anomaly features. This model can automatically identify fault structures under complex geological conditions by learning and training a large amount of gravity and magnetic data from known fault areas, improving the accuracy and efficiency of fault division. In delineating concealed rock bodies, comprehensively use information such as the amplitude and gradient changes of gravity and magnetic anomalies, combined with geostatistical methods, to establish a concealed rock body boundary recognition model, achieving accurate delineation of concealed rock bodies.

[0120] 3) Use frequency domain power spectrum algorithm to calculate the Curie depth. Through frequency domain analysis of airborne gravity and magnetic data, extract frequency components related to the Curie depth, and then calculate the Curie depth. To improve the calculation accuracy, introduce regularization constraints to eliminate the interference of data noise and non-Curie depth factors. Then, according to the theoretical relationship between the Curie isotherm depth and the geothermal gradient, establish a geothermal gradient conversion model to convert the Curie depth into geothermal gradient values, providing data support for evaluating the geothermal conditions of the project area.

[0121] Specifically, for a horizontally infinite magnetic layer, its magnetic anomaly in the frequency domain power spectrum density (PSD) is expressed as:

[0122] PSD(k) = A·e -2kZt ·(1-e -kΔZ ) 2 ;

[0123] where: is the wave number, Z t is the depth of the top boundary of the magnetic body, ΔZ is the thickness of the magnetic body, Z b = Z t + ΔZ is the depth of the bottom boundary of the magnetic body (Curie depth), and A is a constant related to the magnetization intensity.

[0124] The traditional method is greatly affected by noise and non-Curie depth factors. The improved algorithm introduces regularization constraints to construct the objective function:

[0125] min{‖ln(PSD obs (k))-ln(PSD model (k, Z t , Z b ))‖2 2 +α·R(Z t , Z b )};

[0126] where PSD obs (k) is the observed power spectrum, PSD model (k, Z t , Z b ) is the theoretical model power spectrum, and α is the regularization parameter. R(Z t , Z b ) is the regularization term, and R(Z t , Z b ) is the total variation (TV) regularization.

[0127] The optimization problem is solved using an iterative algorithm, the damped least squares method:

[0128]

[0129] where J is the Jacobian matrix, residual is the residual between the observation and the model, and the regularization parameter is selected by generalized cross-validation (GCV).

[0130] By introducing regularization constraints, the improved algorithm can effectively suppress noise interference and improve the accuracy and stability of the tomographic calculation, especially for complex geological environments and low signal-to-noise ratio data.

[0131] 4) Through quantitative calculation methods such as airborne gravity and magnetic long profile inversion and three-dimensional inversion, the deep geological structure of the key area of the diversion project is studied. In the long profile inversion, the inversion strategy based on the particle swarm optimization algorithm is used, combined with geological constraints, to optimize the inversion model parameters and improve the accuracy of the inversion results. In the three-dimensional inversion, a three-dimensional inversion model based on the finite element method is constructed, fully considering the three-dimensional spatial distribution characteristics and physical property differences of geological bodies, and through three-dimensional fitting of airborne gravity and magnetic data, the three-dimensional shape and physical property parameters of deep geological structures are obtained, providing a basis for a comprehensive understanding of the deep geological structure of the project area.

[0132] In the specific embodiment, in the long profile inversion, the objective function based on the particle swarm optimization algorithm (PSO) is used to measure the difference between the inversion model and the observation data, combined with the geological constraints, the objective function is constructed as follows:

[0133]

[0134] where X is the inversion model parameter vector; n is the number of observation data points; d obs,i is the ith observation data; d cal,i (X) is the ith theoretical data calculated based on the parameters X; w i is the weight of the ith data point, used to highlight important data; λ is the regularization parameter, used to balance the observation data fitting term and the geological constraint term; m is the number of geological constraint conditions; g j (X) is the jth geological constraint function.

[0135] In three-dimensional inversion, based on the finite element method (FEM), the geological body is discretized into a finite number of elements, and the physical field distribution of the geological body is described by solving partial differential equations. Let the physical property parameter distribution of the three-dimensional inversion model be ρ(x, y, z), and the observed airborne gravity and magnetic data be D obs , the theoretical calculation data be D cal (ρ), then the objective function of three-dimensional inversion is:

[0136]

[0137] where l is the number of observation points of the airborne gravity and magnetic data; μ is the smoothing factor, used to make the physical property parameter distribution more consistent with the actual geological conditions; Ω is the spatial region of the three-dimensional geological body; is the smoothing term of the physical property parameter.

[0138] Integrating the inversion strategy based on the particle swarm optimization algorithm and the three-dimensional inversion model based on the finite element method, the final joint objective function is: O(X, ρ) = αF(X) + (1-α)J(ρ);

[0139] where α is the weight coefficient, used to balance the contributions of long profile inversion and three-dimensional inversion. By adjusting α, the inversion result can be optimized according to the actual geological requirements and data characteristics.

[0140] In the specific embodiment, the coupling calculation of quantitative calculation methods such as airborne gravity and magnetic long profile inversion and three-dimensional inversion is performed to improve the accuracy of information, and the specific steps include:

[0141] 1) Long profile inversion initialization

[0142] Initialize the particle swarm, set the number of particles N, the dimension of the particle (i.e. the number of inversion model parameters), the maximum number of iterations T max , learning factors c1 and c2, inertia weight ω, etc. Each particle represents a set of inversion model parameters.

[0143] According to the geological constraint conditions, randomly generate an initial position for each particle in the particle swarm, to ensure that the initial parameters are within a reasonable geological range.

[0144] 2) Long profile inversion iterative optimization

[0145] For each particle, the corresponding theoretical data d is calculated according to the current position, and substituted into the objective function F(X) to calculate the fitness value. cal (X), and substituted into the objective function F(X) to calculate the fitness value.

[0146] Update the velocity and position of the particle:

[0147]

[0148] where, and are the velocity and position of the i-th particle at the t-th iteration; p id is the historical optimal position of the i-th particle; g d is the global optimal position of the entire particle swarm; r 1id and r 2id are two randomly generated numbers in the interval [0, 1].

[0149] Check if the maximum number of iterations T max is reached or the convergence condition (such as the fitness value change being less than a certain threshold) is met, if so, stop the iteration and obtain the optimal model parameters of the long profile inversion.

[0150] 3) Three-dimensional inversion model construction

[0151] According to the geological structure and range of the study area, the three-dimensional geological body is discretized into a finite number of elements using the finite element method, and a three-dimensional finite element grid model is established. Assign initial physical property parameter values to each element, which can be set by referring to the long profile inversion results or geological data.

[0152] 4) Three-dimensional inversion calculation

[0153] Based on the established three-dimensional finite element model, the theoretical values D cal (ρ) of the airborne gravity and magnetic data are calculated according to the physical property parameter distribution, and the distribution of the physical field is obtained by solving the partial differential equation.

[0154] Substitute the theoretical values D cal (ρ) and the observed values D obs into the objective function J(ρ) to calculate the objective function value.

[0155] Optimize the objective function J(ρ) using the conjugate gradient method to update the physical property parameters ρ until the objective function value converges.

[0156] 5) Joint inversion optimization

[0157] The optimal model parameter X obtained by long profile inversion and the physical property parameter p obtained by three-dimensional inversion are substituted into the joint objective function O(X, p) to calculate the value of the joint objective function.

[0158] Meanwhile, the long profile inversion parameter X and the three-dimensional inversion physical property parameter p are cooperatively optimized, and the particle swarm optimization algorithm is continuously used to adjust the parameters X and p so that the joint objective function O(X, p) reaches the minimum.

[0159] When the joint objective function converges or reaches the preset stop condition, the final inversion result is obtained, including the three-dimensional shape of deep geological structure, physical property parameters and other information.

[0160] S3: Construction of air gravity and magnetic geological safety evaluation index system:

[0161] Based on the characteristics of air gravity and magnetic field, multiple quantitative indexes including gravity and magnetic anomaly intensity, anomaly gradient, anomaly shape, etc. are extracted. The inferred and interpreted fracture structure parameters such as fracture length, fracture density, angle between fracture strike and engineering line, etc. and rock mass distribution characteristics such as rock mass type, rock mass integrity coefficient, etc. are included in the evaluation index system.

[0162] Combined with the quantitative inversion results such as crustal depth, geothermal gradient value, physical property parameters of deep geological structure, etc., other data such as geology, earthquake, geological disasters, etc. are comprehensively considered. Among them, the geological data include stratigraphic lithology, geological age, etc.; the seismic data cover seismic activity frequency, magnitude, etc.; the geological disaster data include the distribution range, occurrence probability, etc. of landslides, debris flows and other disasters. Through the combination of analytic hierarchy process and principal component analysis, the weights of each evaluation index are determined, and a comprehensive and scientific air gravity and magnetic geological safety evaluation index system is established. The analytic hierarchy process is used to build the hierarchical structure of the evaluation indexes and clarify the mutual relationship between the indexes; the principal component analysis is used to reduce the dimension of multiple indexes, extract the main influencing factors, and improve the rationality and effectiveness of the index system.

[0163] S4: Division of different geological safety and stability blocks:

[0164] The water diversion project area is divided into several grids, and the grid size is reasonably set according to the geological complexity of the project area and the evaluation accuracy requirements.

[0165] According to the established geological safety evaluation index system, the data of each evaluation index in each grid is collected. The fuzzy comprehensive evaluation model is constructed by using fuzzy mathematical evaluation method to comprehensively evaluate the geological safety condition of each grid. The fuzzy comprehensive evaluation model maps the actual value of each evaluation index to the fuzzy membership degree through the establishment of fuzzy relationship matrix, and then calculates the weighted value of each grid according to the index weight to obtain the geological safety evaluation index value of each grid.

[0166] According to the geological safety evaluation index value of the grid, combined with the set geological safety stability grade division standard, the water diversion engineering area is divided into different geological safety stability blocks, such as stable area, relatively stable area, relatively unstable area and unstable area, which provides intuitive and clear geological safety guidance for engineering planning and construction.

[0167] In specific embodiments, a method for geological safety evaluation based on aerial gravity and magnetic data, which is used for geological safety evaluation of a certain water diversion project, specifically includes:

[0168] Aerial gravity and magnetic data acquisition: The geological conditions of a certain large-scale water diversion project are complex, and the route direction changes greatly. According to the changes of the route direction of the project, combined with the geological conditions, the project area is divided into 3 blocks, all of which take the project route as the center line, along the project route, the measuring lines are arranged, and the cutting lines are arranged perpendicular to the route, the measuring line spacing is 1km, 17 measuring lines are arranged in each block, the cutting lines are arranged at intervals of 10km, and the overlapping area between blocks is ensured to be 2km. High-precision aerial gravity and magnetic measurement equipment is used for data acquisition to obtain the original data of each block. The original data is preprocessed, first, Kalman filtering (hardware implementation), FIR filtering, nonlinear filtering, low-pass (smoothing) filtering and other methods are used to remove noise, then the regional gravity and magnetic reference surface is established for horizontal adjustment, finally, the data fusion algorithm is used to complete the merging of multi-block data, and the complete aerial geophysical basic data is obtained.

[0169] Geological safety evaluation factor acquisition: Based on the aerial geophysical basic data, aerial gravity and magnetic contour maps and other drawings are generated, and image enhancement and feature extraction processing are performed. 18 regional faults are divided by using autocorrelation filtering, notch analysis and deep learning fracture identification model, and 9 concealed rock masses are delineated. The improved frequency domain power spectrum algorithm is used to calculate the depth of the crust, and the geothermal gradient value is obtained through the geothermal gradient conversion model. The detailed information of the deep geological structure in the key area is obtained by using the aerial gravity and magnetic long profile inversion and three-dimensional inversion method, including the stratum distribution, rock mass physical property parameters and the like.

[0170] Construction of aerial gravity and magnetic geological safety evaluation index system: 6 aerial gravity and magnetic field characteristic indexes, 4 fault structure parameter indexes and 3 rock mass distribution characteristic indexes are extracted, combined with the quantitative inversion results and geological, seismic, geological disaster and other data, the weights of each index are determined by using the analytic hierarchy process and principal component analysis method, and an aerial gravity and magnetic geological safety evaluation index system containing 15 indexes is constructed. The constructed geological safety evaluation index system comprehensively reflects the geological safety condition of the project area.

[0171] Different geological safety stability block division: the diversion project area is divided into 200*200 grid, the evaluation index data of each grid is collected, the fuzzy comprehensive evaluation model is constructed by using fuzzy mathematics evaluation method, and the geological safety evaluation index value of each grid is calculated. According to the set division standard, the engineering area is divided into stable area (accounting for 30%), relatively stable area (accounting for 40%), relatively unstable area (accounting for 20%) and unstable area (accounting for 10%), which provides clear geological safety guidance for engineering planning and construction. The geological safety stability block divided by the fuzzy mathematics evaluation method provides a scientific basis for the site selection and design of the project, avoids the construction of the project in the dangerous area, and effectively reduces the engineering risk.

[0172] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0173] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for geological safety evaluation based on airborne gravity and magnetic data, characterized in that, The method comprises the following steps: Airborne gravity and magnetic data are collected according to the route of the water diversion project; In addition, the collected original airborne gravity and magnetic data are preprocessed by removing noise by using Kalman filtering combined with FIR filtering, and optimal estimation is performed according to the dynamic change characteristics of the data, and the collected original airborne magnetic data are preprocessed by removing noise by using nonlinear filtering combined with low-pass filtering; The preprocessed data are adjusted horizontally, and the data of different blocks are unified to the same horizontal datum by establishing regional gravity and magnetic datum surfaces; In the process of merging multi-block data, a data fusion algorithm is introduced, and based on spatial distance weighting and data correlation analysis, the data of adjacent blocks are smoothly transitioned to eliminate the data difference at the block boundary, thereby obtaining airborne geophysical basic data; Geological safety evaluation factors are obtained according to the collected airborne gravity and magnetic data; An index system for airborne gravity and magnetic geological safety evaluation is constructed according to the obtained geological safety evaluation factors; The water diversion project area is divided into a plurality of grids, and according to the constructed index system for geological safety evaluation, the evaluation index data in each grid are collected, a fuzzy comprehensive evaluation model is constructed by using fuzzy mathematics evaluation method, and the geological safety condition of each grid is comprehensively evaluated.

2. The method according to claim 1, wherein, The method comprises the following steps: According to different directions of the water diversion project route, the project area is divided into a plurality of blocks; Considering the complexity of geological conditions and the factors of engineering construction requirements, a block-by-block survey network planning algorithm is adopted, and based on the geological prior information and the direction of the project area, the survey network blocks are adjusted and the survey network is planned respectively.

3. The method of claim 2, wherein the method further comprises: The method comprises the following steps:

4. The method of claim 1, wherein the method further comprises: The airborne gravity and magnetic data are collected by using airborne gravity and magnetic measurement equipment according to the planned survey network, the original airborne gravity and magnetic data of each block are obtained, and in the data collection process, the flight height, speed and heading flight parameters are recorded in real time for consistency verification of the data collection. The method comprises the following steps: Based on the airborne geophysical basic data, the contour maps of airborne gravity and magnetic fields, the profile maps and the conversion processed maps are generated, and the abnormal characteristics of the airborne gravity and magnetic fields are obtained by identifying the maps; Regional fractures are divided by using a plurality of potential field conversion processing methods and regional geology and remote sensing data; The amplitude and gradient change information of the gravity and magnetic anomalies are comprehensively utilized, and a hidden rock mass boundary identification model is established by combining with the geological statistical method, so as to delineate the hidden rock mass; The depth of the Curie surface is calculated by using the frequency domain power spectrum algorithm, the Curie isotherm is divided, and a geothermal gradient conversion model is established according to the relationship between the depth of the Curie isotherm and the geothermal gradient, so as to convert the depth of the Curie surface into the value of the geothermal gradient; The long profile structure, three-dimensional shape and physical property parameters of deep geological structure are obtained by using the quantitative methods of airborne gravity and magnetic long profile inversion and three-dimensional inversion.

5. The method according to claim 4, wherein, The regional fault division using the plurality of potential field conversion processing methods and regional geology and remote sensing data comprises: using autocorrelation filtering, notch analysis method combined with regional geology and remote sensing data, performing regional fault division; autocorrelation filtering enhances gravity and magnetic weak anomaly information, and highlights the abnormal characteristics caused by the fault zone; notch analysis identifies the position and trend of the fault through the shape analysis of the gravity and magnetic anomaly curve; a deep learning algorithm is introduced to construct a fault identification model based on the gravity and magnetic anomaly characteristics of the aerial field, and the fault identification model automatically identifies the fault structure under complex geological conditions through learning and training of the gravity and magnetic data of the known fault region.

6. The method of claim 1, wherein the method further comprises: The aerial gravity and magnetic geological safety evaluation index system is constructed according to the obtained geological safety evaluation elements, which comprises: According to the aerial gravity and magnetic field characteristics, the gravity and magnetic anomaly strength, anomaly gradient and anomaly shape quantitative index are extracted; The inferred fault structure parameters and rock mass distribution characteristics are obtained; Combined with the quantitative inversion results, the depth, geothermal gradient value and physical property parameters of deep geological structure are considered comprehensively in combination with geological, seismic and geological disaster data; The aerial gravity and magnetic geological safety evaluation index system is constructed.

7. The method according to claim 6, wherein, Further comprising: the weights of each evaluation index are determined by combining the analytic hierarchy process and the principal component analysis method; the hierarchical structure of the evaluation index is constructed by the analytic hierarchy process, and the mutual relationship between the indexes is determined; the indexes are reduced in dimension by the principal component analysis method, and the influencing factors are extracted.

8. The method of claim 1, wherein the method further comprises: The comprehensive evaluation of the geological safety condition of each grid comprises: The water diversion project area is divided into a plurality of grids, and the grid size is set according to the geological complexity of the project area and the evaluation accuracy requirement; According to the constructed aerial gravity and magnetic geological safety evaluation index system, the evaluation index data in each grid is collected, the fuzzy mathematics evaluation method is used to construct a fuzzy comprehensive evaluation model, and the geological safety condition of each grid is comprehensively evaluated; According to the geological safety evaluation index value of the grid obtained by the comprehensive evaluation, in combination with the preset geological safety stability grade division standard, the water diversion project area is divided into different geological safety stability blocks.

9. A geological safety evaluation system based on airborne gravity and magnetic data, characterized in that, It comprises: The acquisition module is used for collecting aerial gravity and magnetic data according to the water diversion project line; Further comprising: the collected original aerial gravity and magnetic data are preprocessed by removing noise by using Kalman filtering combined with FIR filtering, and the optimal estimation is performed according to the dynamic change characteristics of the data; the collected original aerial gravity and magnetic data are preprocessed by removing noise by using nonlinear filtering combined with low-pass filtering; The preprocessed data are horizontally adjusted by establishing regional gravity and magnetic reference surfaces to unify the data of different blocks to the same horizontal reference surface; In the process of merging the data of multiple blocks, a data fusion algorithm is introduced, the data of adjacent blocks are smoothly transitioned based on the spatial distance weighting and data correlation analysis, the data difference of the block boundary is eliminated, and the aerial geophysical basic data are obtained; The geological safety evaluation element acquisition module is used for acquiring the geological safety evaluation elements according to the collected aerial gravity and magnetic data; The evaluation index system construction module is used for constructing the aerial gravity and magnetic geological safety evaluation index system according to the obtained geological safety evaluation elements; The comprehensive evaluation module is used for dividing the waterway engineering area into a plurality of grids, collecting each evaluation index data in each grid according to the constructed geological safety evaluation index system, constructing a fuzzy comprehensive evaluation model by using a fuzzy mathematics evaluation method, and comprehensively evaluating the geological safety condition of each grid.

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