A reservoir geological disaster identification and evaluation method adaptive to deep-cut valley terrain

Abstract

This invention discloses a method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography. The core steps include: comprehensive remote identification of geological hazards, using multi-source remote sensing data to remotely identify geological hazards in the reservoir area and determine their type, spatial distribution, and activity; analysis of geological hazard development characteristics, analyzing the spatial distribution characteristics of geological hazards and identifying the main influencing factors affecting their development; and geological hazard susceptibility assessment, selecting evaluation indicators and using an information volume model to assess the susceptibility of geological hazards in the reservoir area, generating a susceptibility assessment map. This invention overcomes the problem of shadow overlap in deep valleys by fusing multi-source remote sensing with InSAR ascending and descending orbits, achieving accurate identification of hidden dangers with full coverage. Combined with quantitative evaluation using an information volume model, it forms an efficient technical closed loop, significantly reducing fieldwork and improving the timeliness and reliability of the assessment.

Description

Technical Field

[0001] This invention belongs to the field of geological disaster prevention and control technology, specifically relating to a method for identifying and evaluating geological disasters in reservoir areas that is adapted to deep valley topography. Background Technology

[0002] Currently, geological hazard risk assessment and prevention in deep river valley reservoir areas of western China largely employs a combined "air-space-ground" remote sensing interpretation and ground survey approach. This includes using InSAR, GNSS, and UAV aerial surveys for hazard identification and deformation monitoring; employing the limit equilibrium method or analytic hierarchy process (AHP) for stability analysis and risk zoning; and combining these with disaster mitigation measures such as anti-slide piles, anchor cables, drainage, and slope reduction. This type of technology has been widely applied in large reservoir areas of Southwest China.

[0003] However, due to the vast area of ​​the reservoir, the large number and scattered distribution of potential geological hazards, and the fact that many of these hazards are located in steep, remote areas that are difficult for personnel and equipment to access, existing technologies typically have the following three levels of shortcomings: First, in terms of data acquisition and hazard identification: traditional ground surveys and fixed-point monitoring have limited coverage and data updates are lagging behind, resulting in a high rate of missed and false diagnoses of high-altitude hidden hazards.

[0004] Second, regarding the disaster-causing mechanism and risk assessment: the description of the disaster-causing mechanism of multiple coupled fields such as reservoir water rise and fall, rainfall, earthquake, and rock unloading is not in-depth enough, and the risk assessment is mostly empirical and static, making it difficult to achieve dynamic and accurate quantification.

[0005] Third, in terms of prevention and control systems and emergency response: the system is mainly based on passive handling, single measures, and post-disaster management, lacking an intelligent monitoring, early warning, rapid assessment, and collaborative prevention and control system adapted to large, deep river valley reservoirs and remote high-risk disaster sites. The overall level of refinement, real-time monitoring, and intelligence is insufficient to meet the requirements for the long-term safe operation and full life-cycle management of giant reservoirs.

[0006] Therefore, there is an urgent need for a method that can overcome the above-mentioned shortcomings and achieve efficient, accurate, and dynamic identification and evaluation of geological hazards in deep river valley reservoir areas. Summary of the Invention

[0007] To address the aforementioned problems in the prior art, this invention provides a method for identifying and evaluating geological hazards in reservoir areas adapted to deep river valley topography, comprising the following steps: Step S1, Comprehensive Remote Identification of Geological Hazards: Based on multi-source remote sensing data, remote identification of geological hazards in the reservoir area is carried out to determine the type, spatial distribution, and activity of geological hazards; The remote identification of geological hazards in step S1 further includes: Step S11, High-resolution optical image survey: Use high-resolution optical remote sensing images to conduct preliminary interpretation and survey of geological hazards in the reservoir area, identify the morphology, boundaries and scale of geological hazards, obtain the types of geological hazards and determine the planar location and distribution range of geological hazards; Step S12, InSAR Detection of Active Geological Hazards: The InSAR technique of synthetic aperture radar interferometry is used to detect active geological hazards in the reservoir area. The detection includes: acquiring multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data, performing in-orbit and out-of-orbit synthetic aperture radar interferometry processing respectively, and fusing the in-orbit and out-of-orbit observation results to overcome the shadowing caused by the deep valley topography, generating a deformation observation map covering both sides of the reservoir area, and combining the planar location and distribution range of the geological hazards to obtain the accurate spatial distribution and determine the activity of the geological hazards. Step S13, Detailed drone aerial survey: Conduct detailed drone aerial surveys of the near-dam and reservoir bank sections and key geological hazard points to obtain corrected data to verify the morphology, boundaries, and scale of geological hazards, and output the final determined types, spatial distribution, and activity of geological hazards; Step S2, Geological Disaster Development Characteristics Analysis: Analyze the types, spatial distribution, and activity characteristics of the geological disasters identified in Step S1, and identify the main influencing factors affecting the development of geological disasters; Step S3, Geological Disaster Susceptibility Assessment: Based on the analysis results of Step S2, evaluation indicators are selected, the information content provided by each evaluation indicator to geological disasters is calculated using the information content model, and the information content of each evaluation indicator is summed. Based on the total information content value, the geological disaster susceptibility level of the reservoir area is classified, and a susceptibility assessment map is generated. The specific method for fusing the ascending orbit observation results and descending orbit observation results in step S12 is as follows: using the descending orbit observation map that can observe the deformation of the left bank as the main image, and the ascending orbit observation map that can observe the deformation of the right bank as the auxiliary image, the strongly deformed patches in the auxiliary image are overlaid on the main image to form a deformation observation map covering both banks of the reservoir area.

[0008] In a further preferred embodiment, step S12 includes a data preprocessing step before performing the synthetic aperture radar interferometry processing for the ascent and descent orbits: removing the terrain phase using external digital surface model data and performing image registration on the synthetic aperture radar data; wherein the registration accuracy needs to reach the sub-pixel level.

[0009] In a further preferred embodiment, the high-resolution optical remote sensing image survey in step S11 specifically includes: S111. Establish remote sensing interpretation markers for different types of geological hazards; S112. Preliminary interpretation based on Google Earth imagery; S113. A fine interpretation is performed using SPOT-7 orthophotos with a resolution of 1.5m and SPOT-7 digital surface models with a resolution of 5m as data sources.

[0010] In a further preferred embodiment, the multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data in step S12 includes at least: L-band PALSAR-1 and PALSAR-2 data, and C-band Sentinel-1 ascent and descent data.

[0011] In a further preferred embodiment of the technical solution, in step S1, based on the InSAR exploration results, active geological hazards are classified into three categories according to their annual deformation rate: Highly active type: Overall annual deformation rate ≥ 6cm; Medium activity type: Overall annual deformation rate 2-6 cm; Weakly active type: Overall annual deformation rate ≤2cm.

[0012] In a further preferred embodiment, the main influencing factors affecting the development of geological disasters identified in step S2 include topographic factors, geological factors, and external dynamic factors; The topographical factors include: topographic slope, surface elevation, slope aspect, and slope morphology. The geological factors include: stratigraphic lithology and its combination characteristics, and distance from the fault; The external driving factors include: rainfall, freeze-thaw cycles, and human engineering activities.

[0013] In a further preferred technical solution, the evaluation indicators selected in step S3 include: elevation, slope, aspect, slope morphology, engineering geological rock group, and distance from fault; wherein, the grading intervals of each evaluation indicator are pre-set according to the geological disaster development patterns of the study area.

[0014] A further preferred technical solution is that the specific method for evaluating the geological hazard susceptibility of the reservoir area using an information volume model in step S3 is as follows: multiple evaluation factors. The amount of information is calculated using the following formula:

[0015] in, Combination of factors The amount of information provided about geological disasters As factors The probability of geological disasters occurring under combined conditions The probability of a geological disaster occurring; Under the condition that the factors are independent, the above equation can be further rewritten according to conditional probability as follows:

[0016] In the formula, As factors The amount of information provided regarding geological disasters; The specific method for assessing geological hazard susceptibility using a grid cell division approach is as follows: multiple assessment factors The amount of information is calculated using the following formula:

[0017] in, The number of units used to divide a geological disaster area. This represents the number of units where geological disasters have already occurred. Multiple evaluation factors The number of units, for The number of units containing geological hazards; Using area ratio to calculate information content, the above formula can be further rewritten as:

[0018] In the formula, The total area of ​​the unit within the geological disaster area; This represents the sum of the areas of units where geological disasters have already occurred. Multiple evaluation factors The total area of ​​the units; factor The sum of the areas of the units in which geological disasters occur.

[0019] In a further preferred embodiment, the susceptibility evaluation map generated in step S3 divides the susceptibility to geological disasters into four levels based on the information content value: stable, basically stable, poorly stable, and unstable. Among them: information content <-1 indicates stability; -1 ≤ information content < 0 indicates basic stability; 0 ≤ information content < 1 indicates poor stability; Information content ≥ 1 indicates instability.

[0020] This invention also provides a reservoir area geological hazard identification and evaluation system adapted to deep valley topography, which applies the reservoir area geological hazard identification and evaluation method adapted to deep valley topography described in any of the foregoing claims, including: The optical image survey module is used to acquire and process high-resolution optical remote sensing images to conduct preliminary interpretation and survey of geological hazards in the reservoir area. The InSAR detection module is used to acquire multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data. It performs synthetic aperture radar interferometry processing for both ascending and descending orbits, and fuses the ascending and descending orbit observation results to generate a deformation observation map covering both banks of the reservoir area to identify active geological hazards. Specifically, the fusion involves using a descending orbit observation map that can observe deformation on the left bank as the main image and an ascending orbit observation map that can observe deformation on the right bank as the auxiliary image, and then overlaying the strongly deformed patches from the auxiliary image onto the main image. The drone survey module is used to conduct drone aerial surveys of the near-dam and reservoir bank sections and key geological hazard points. The developmental characteristics analysis module is used to analyze the spatial distribution characteristics and main influencing factors of geological disasters. The susceptibility assessment module is used to assess the susceptibility of geological disasters in the reservoir area based on the information content model and the assessment indicators output by the developmental characteristic analysis module, and to generate a susceptibility assessment map.

[0021] Compared with existing technologies, the present invention provides a method for identifying and evaluating geological hazards in reservoir areas that is adapted to deep river valley topography. The beneficial effects of this method are: 1. In terms of hazard identification and data acquisition, this invention establishes a three-tiered integrated remote identification system—high-resolution optical image survey, multi-source multi-angle InSAR exploration, and UAV aerial photography detailed investigation—achieving full coverage and multi-scale collaborative identification of geological hazards in deep river valley reservoir areas. Specifically, addressing the shadow overlap problem caused by the deep river valley terrain, it innovatively employs InSAR ascending and descending orbit data fusion processing, effectively overcoming the blind spots of single-orbit observation and significantly reducing the missed and false detection rates of high-altitude hidden hazards. Simultaneously, this method greatly reduces the scope and locations requiring on-site reconnaissance, lowering the intensity and cost of fieldwork and improving the timeliness of hazard identification.

[0022] 2. At the level of disaster-causing mechanisms and risk assessment, this invention, through a systematic analysis of the development characteristics of geological hazards, identifies the comprehensive influence of multiple factors such as topography, geological structure, and external forces. Based on an information volume model, it selects evaluation indicators (elevation, slope, aspect, slope morphology, engineering geological rock group, and distance from fault) specifically optimized for deep valley areas, achieving a quantitative and refined evaluation of the susceptibility to geological hazards in reservoir areas. Compared with existing empirical and static evaluation methods, the evaluation results of this invention have higher objectivity and repeatability, and can provide dynamically updated risk data for the long-term safe operation of reservoirs.

[0023] 3. At the level of prevention and control systems and emergency response, this invention forms a complete technical closed loop from hazard identification to feature analysis to susceptibility assessment. Data at each step mutually support each other, and results are progressively enhanced, avoiding the problems of single data sources, fragmented work patterns, and strong subjectivity in evaluation results found in traditional methods. This method is applicable to the investigation and risk assessment of slope hazards such as landslides, collapses, and deformable bodies in the basins and reservoir areas of various conventional hydropower stations. The technical process is standardized and highly operable, providing a scientific basis for early warning, key investigations, and collaborative prevention and control of geological hazards in reservoir areas. It has good engineering application value and promising prospects for widespread application. Attached Figure Description

[0024] Figure 1 This is a diagram illustrating the core steps of the method of the present invention; Figure 2 This is a detailed flowchart of the method of the present invention. Detailed Implementation

[0025] The following will clearly and completely describe the concept, specific steps, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be particularly noted that the described embodiments are merely some embodiments of the present invention, and not all embodiments. Other embodiments obtained by other personnel skilled in the art based on the embodiments of the present invention without making creative contributions are all within the protection scope of the present invention.

[0026] Example 1 This embodiment uses a reservoir area of ​​a large hydropower station in Southwest China (hereinafter referred to as "Guxue Reservoir Area") as the application object to describe in detail the specific implementation process of the present invention. Guxue Reservoir Area is located on the southeastern edge of the Qinghai-Tibet Plateau and belongs to the typical deep-cut river valley landform in the west. The river valleys are deep, the mountains are high and the slopes are steep, and there are many geological disaster hazard points that are scattered and located in high, steep and remote areas that are difficult for personnel and equipment to reach.

[0027] Combination Figure 1 The core steps of the method of this invention are shown in the diagram. Figure 2 The detailed flowchart of the method of this invention, provided in this embodiment, is a method for identifying and evaluating geological hazards in reservoir areas adapted to deep river valley topography, including the following steps: Step S1, comprehensive remote identification of geological hazards; Step S2, analysis of the development characteristics of geological hazards in the study area; Step S3, evaluation of the susceptibility of geological hazards in the study area. Each step is described in detail below.

[0028] S1. Comprehensive Remote Identification of Geological Hazards: Based on multi-source remote sensing data, remote identification of geological hazards in the reservoir area is conducted to determine the type, spatial distribution, and activity of geological hazards. This includes sub-steps S11 to S13.

[0029] S11, High-Resolution Optical Image Survey This sub-step utilizes high-resolution optical remote sensing imagery to conduct preliminary interpretation and survey of geological hazards in the reservoir area, identifying the morphology, boundaries, and scale of geological hazards, obtaining the types of geological hazards, and determining their planar location and distribution range. The specific process is as follows: (1) Establish remote sensing interpretation markers: Based on the full collection and familiarity with the geological data of the study area, remote sensing interpretation markers such as topography, image tone and texture characteristics, and water system characteristics are established through field surveys for different types of geological disasters.

[0030] (2) Preliminary interpretation: Using Google Earth imagery as the main data source, based on familiarity with the geological data of the study area and the establishment of remote sensing interpretation markers, geological hazards and their types are identified on the remote sensing images, the boundaries of geological hazards are determined, and preliminary interpretation maps are compiled.

[0031] (3) Refined interpretation: Based on remote sensing interpretation using Google Earth as the data source, SPOT-7 orthophotos with a resolution of 1.5m and digital surface models with a resolution of 5m made from SPOT-7 are used as data sources to cooperate with and verify Google Earth images to carry out refined interpretation of geological hazards.

[0032] (4) Field verification and improvement of interpretation markers: Conduct field verification of the preliminary and refined interpretation results. Based on the preliminary interpretation results and traffic conditions, conduct on-site verification of geological hazards with convenient transportation, and further improve the interpretation markers.

[0033] (5) Detailed interpretation: Detailed interpretation of geological disaster types, determination of geological disaster boundaries and scale, and analysis of geological disaster development patterns by analyzing the geological environment conditions such as topography, strata lithology, and geological structure where the geological disaster is located.

[0034] The core output of optical image surveys is the type and basic spatial distribution of geological hazards. By interpreting high-resolution optical images, the morphology, boundaries, and scale of different types of hazards such as landslides, collapses, and deformable bodies can be directly identified. The basic spatial distribution represents the planar location and distribution range of geological hazards. Through high-resolution optical image interpretation, a total of 1,121 geological hazards were interpreted in the study area, including 1,042 landslides, 41 deformable bodies, and 38 collapse deposits.

[0035] S12, InSAR Detection of Active Geological Hazards This sub-step employs synthetic aperture radar interferometry (SAR) technology to investigate active geological hazards in the reservoir area. Due to the deep valleys and steep terrain of the reservoir area, single-track InSAR images suffer from severe shadowing—ascending orbit data primarily observes deformation on the right bank, while descending orbit data primarily observes deformation on the left bank. To overcome the shadowing problem caused by the deep valley terrain, this embodiment uses multi-band, multi-angle, and multi-resolution spaceborne SAR data to perform SAR interferometry processing for both ascending and descending orbits. The ascending and descending orbit observation results are then fused to overcome the shadowing caused by the deep valley terrain, generating a deformation observation map covering both banks of the reservoir area. Combined with the planar location and distribution range of the geological hazards, a precise spatial distribution is obtained, and the activity of the geological hazards is determined.

[0036] (1) Data source selection This embodiment fully utilizes existing archived and programmed mainstream SAR data, primarily from the ESA Sentinel-1 and Japan PALSAR-2, supplemented by Japan PALSAR-1, employing SAR data from the ascent and descent observations of three spaceborne synthetic aperture radar sensors. Specifically, it includes: L-band PALSAR-1 SAR data, with an ascent incident angle of 38.75° and a resolution of 18m after multi-look synthesis; L-band PALSAR-2 SAR data, with an ascent incident angle of 32.47° and a resolution of 15m after multi-look synthesis; C-band Sentinel-1 SAR data with a resolution of 14–28 m, including ascending orbit 99 (incident angle 32.35°), ascending orbit 172 (incident angle 44.39°), and descending orbit 33 (incident angle 43.19°).

[0037] (2) Data preprocessing Before performing interferometric calculations, the generated single-view complex data is first registered with external DEM data, and the registration accuracy needs to reach the sub-pixel level. In this embodiment, external digital surface model data (using DSM data with a resolution of 5m acquired by SPOT satellite) is used to remove terrain phase to ensure the quality of InSAR calculations, and image registration is performed on the synthetic aperture radar data.

[0038] Taking PALSAR-1 data as an example, the prepared DEM data was transferred from the geographic coordinate system to the radar coordinate system for calculation. 820 registration points were selected from 1024 registration points. The registration accuracies in the Range and Azimuth directions were 0.3651 and 0.3637, respectively, which are within a reasonable range. One SAR image was selected as the primary reference image, and registration operations were performed on the remaining images. The registration errors for each data pair are shown in Table 1. The registration errors in the Range and Azimuth directions for each data pair are all less than 0.2, meeting the requirements for interferometric calculation.

[0039] Table 1 Matching results based on SAR data pairs

[0040] Taking Sentinel-1 data as an example, its imaging mode consists of multiple subbands stitched together. To ensure that no phase jump occurs during inter-subband interferometry processing, SLC data registration accuracy is required to be high. For ascending orbit data, in DEM correction registration, 910 registration points were selected from 1024 registration points, with Range and Azimuth registration errors of 0.0881 and 0.0895, respectively. For descending orbit data, 508 registration points were selected from 1024 registration points, with Range and Azimuth registration errors of 0.1242 and 0.1057, respectively. Using the built-in automated calculation program of GAMMA software, high-precision registered images were obtained. The Range direction image registration error was less than 0.1, and the Azimuth direction image registration error was less than 0.00005, meeting the interferometry requirements.

[0041] (3) InSAR observation and ascent / descending orbit fusion processing This embodiment uses multi-source, multi-angle, multi-band, and multi-resolution SAR data to perform two-phase data difference and multi-phase data integration processing, and obtains a total of 324 DInSAR deformation observation maps and 5 multi-phase data integrated deformation maps.

[0042] To overcome the shadowing problem caused by the deep valley topography, this embodiment fuses the InSAR results from both ascending and descending orbits. Specifically, the fusion method involves using a descending orbit observation image (capable of observing deformation on the left bank) as the main image and an ascending orbit observation image (capable of observing deformation on the right bank) as the auxiliary image. Strongly deformed patches from the auxiliary image are then overlaid on the main image to form the fused deformation map of both banks. The fused image takes into account the deformation on both the left and right banks, resulting in a better overall effect and meeting the InSAR interpretation requirements for most of the reservoir area.

[0043] (4) Interpretation of active geological hazards Based on InSAR observations, a systematic InSAR comprehensive interpretation of the reservoir area was performed. According to the morphology, deformation intensity, and geomorphological features of the InSAR observation patches, the interpreted active geological hazards were classified into three categories based on their annual deformation rate: Intensely active type: InSAR deformation patches are relatively complete, with most areas or the whole area deformed, the landform is relatively clear, and the overall annual deformation rate is ≥6cm. Moderately active type: It has InSAR deformation patches, local deformation, indistinct landform, and an overall annual deformation rate of 2-6 cm. Weakly active type: InSAR deformation patches are blurry and scattered, no overall deformation has occurred, the landform is unclear, and the average annual deformation rate is ≤2cm.

[0044] The core output of InSAR exploration is the "activity" (deformation rate and amplitude) and more precise spatial distribution of active geological hazards, enabling exploration in areas that optical imagery cannot reach, such as slowly deforming hazard areas that are difficult to detect on optical images. Through ascending and descending orbit fusion, the overlapping shadows of deep valleys are overcome, allowing simultaneous coverage of both the left and right banks to obtain more complete deformation patterns. InSAR exploration has identified 51 active geological hazards in the reservoir area, including 19 highly active, 12 moderately active, and 20 weakly active hazards.

[0045] S13, Detailed Inspection of Drone Aerial Photography This sub-step comprehensively analyzes the above identification results, conducts detailed drone aerial surveys of the near-dam reservoir bank section and key geological hazard hazard points, obtains corrected data to verify the morphology, boundaries, and scale of geological hazards, and outputs the final determined types, spatial distribution, and activity of geological hazards. The detailed drone aerial survey provides high-precision, three-dimensional verification and refinement of key areas (near-dam reservoir bank section and hazard points discovered in S11 / S12), acquiring centimeter-level orthophotos and three-dimensional models, accurately verifying the hazard boundaries identified in S11, identifying details such as internal cracks and local collapses, and analyzing slope morphology from a three-dimensional perspective, providing the most refined data for type determination and spatial distribution correction. Through high-resolution optical image interpretation, a total of 1121 geological hazards were interpreted in the study area; through InSAR exploration, 51 active geological hazards in the reservoir area were identified; and through drone aerial photography, detailed image data of geological hazards near the dam reservoir bank section were obtained, realizing a detailed drone aerial survey of the near-dam reservoir bank section and key geological hazard hazard hazard points. The above multi-source identification results corroborate and complement each other, forming a comprehensive understanding of geological hazards in the reservoir area.

[0046] S2. Analysis of Geological Hazard Development Characteristics; this includes analyzing the spatial distribution characteristics of the geological hazards identified in step S1, and identifying the main influencing factors affecting the development of geological hazards. Specifically: S21. Spatial Distribution Characteristics Analysis of Geological Disasters Analysis shows that the geological hazards in the study area have obvious spatial unevenness: geological hazards are mainly distributed along both sides of the valleys; the distribution of geological hazards on both banks of a certain river has obvious differences, with dense geological hazards on the left bank and less developed geological hazards on the right bank.

[0047] S22. Analysis of the main influencing factors on the development of geological disasters Geological hazards are closely related to geological environmental conditions such as topography, strata lithology, and geological structure, and are intrinsic factors in their formation and occurrence. Seismic activity and rainfall play a positive role in promoting the formation and occurrence of geological hazards. Based on the characteristics of the study area, this embodiment classifies the influencing factors of geological hazards into three categories: Topographical factors: including slope, surface elevation, slope aspect, slope morphology, etc. Geological factors: mainly include stratigraphic lithology and its combination characteristics, and distance from faults; External driving factors: mainly include rainfall, freeze-thaw cycles, and human engineering activities.

[0048] Geological hazards account for 7.54% of the study area, of which landslides account for 6.57%, collapse deposits for 0.33%, and deformable bodies for 0.64%, which are taken as the average values ​​for the study area. In this embodiment, approximately 10% above and below the average value is used as a reference limit: when a certain range of an influencing factor is greater than 110% of the average value, geological hazards are considered to be significantly developed; when it is less than 90% of the average value, geological hazards are considered to be underdeveloped.

[0049] S3. Geological Hazard Susceptibility Assessment: Based on the analysis results of step S2, evaluation indicators are selected, and the information content provided by each evaluation indicator to geological hazards is calculated using an information content model. The information content of each evaluation indicator is then summed, and the geological hazard susceptibility level of the reservoir area is classified according to the total information content value, generating a susceptibility assessment map. Details are as follows: S31, Evaluation Indicators The evaluation indicators selected in this embodiment include: elevation, slope, aspect, slope morphology, engineering geological rock group, and distance from fault. The grading intervals for the above evaluation indicators are pre-set based on the geological hazard development patterns of the study area.

[0050] S32, Evaluation Process This embodiment uses GIS technology to assess the susceptibility of geological disasters. The workflow includes: (1) Raw data processing: using GIS to establish a comprehensive spatial database, including a geological disaster layer and various factor layers affecting the susceptibility of geological disasters. (2) Factor analysis: through statistical analysis of geological disasters and individual factors, the relative weights of each factor are determined. (3) Geological disaster susceptibility assessment model: using an information volume model for susceptibility assessment. (4) Susceptibility assessment: based on the model parameters, using the influencing factor layers to perform model calculations, obtain the susceptibility assessment results, and determine the susceptibility level.

[0051] S33, Evaluation Methods This embodiment uses an information content model to assess the susceptibility to geological disasters. Geological disasters are influenced by a variety of factors. From the perspective of information content assessment, the occurrence of geological disasters is related to the quantity and quality of information obtained during the assessment process, which is measured by information content.

[0052] The specific method for assessing the geological hazard susceptibility of the reservoir area using the information content model in step S3 is as follows: multiple evaluation factors. The amount of information is calculated using the following formula:

[0053] in, Combination of factors The amount of information provided about geological disasters As factors The probability of geological disasters occurring under combined conditions The probability of a geological disaster occurring; Under the condition that the factors are independent, the above equation can be further rewritten according to conditional probability as follows:

[0054] In the formula, As factors The amount of information provided regarding geological disasters; The specific method for assessing geological hazard susceptibility using a grid cell division approach is as follows: multiple assessment factors The amount of information is calculated using the following formula:

[0055] in, The number of units used to divide a geological disaster area. This represents the number of units where geological disasters have already occurred. Multiple evaluation factors The number of units, for The number of units containing geological hazards; Using area ratio to calculate information content, the above formula can be further rewritten as:

[0056] In the formula, The total area of ​​the unit within the geological disaster area; This represents the sum of the areas of units where geological disasters have already occurred. Multiple evaluation factors The total area of ​​the units; factor The sum of the areas of the units in which geological disasters occur.

[0057] S34, Risk Assessment Based on the reclassification and layer operations of each factor layer, the information content of each factor for different geological hazard types was obtained. Table 2 lists the calculated information content results of each factor for landslide hazards and geological hazards.

[0058] Table 2. Calculation results of information content of various factors in landslide disasters and geological hazards.

[0059] Then, based on the susceptibility evaluation map generated by S3, layers are overlaid to obtain the susceptibility of different types of geological disasters. In this embodiment, the susceptibility of geological disasters is divided into four levels according to the information content value: information content < -1 is stable; -1 ≤ information content < 0 is basically stable; 0 ≤ information content < 1 is poorly stable; and information content ≥ 1 is unstable.

[0060] Based on the landslide susceptibility assessment results, areas classified as "stable" account for 42.66% of the total study area, "basically stable" for 15.64%, "poorly stable" for 23.65%, and "unstable" for 18.05%. Overlay analysis of existing landslides with the landslide susceptibility assessment map reveals that landslides comprise 1.62% of the stable area, 5.48% of the basically stable area, 8.49% of the poorly stable area, and 16.69% of the unstable area. Therefore, the landslide susceptibility assessment results effectively reflect the spatial distribution characteristics of landslide hazards in the study area.

[0061] According to the geological hazard susceptibility assessment results, areas classified as "stable" account for 36.43% of the total study area, "basically stable" for 19.52%, "poorly stable" for 30.07%, and "unstable" for 13.98%. Overlay analysis of existing geological hazards with the geological hazard susceptibility assessment results reveals that geological hazards account for 1.93% of the stable area, 6.60% of the basically stable area, 10.48% of the poorly stable area, and 17.14% of the unstable area. The geological hazard susceptibility assessment results effectively reflect the spatial distribution characteristics of geological hazards in the study area.

[0062] The present invention has the following beneficial effects: 1. In terms of hazard identification and data acquisition, this invention establishes a three-tiered integrated remote identification system—high-resolution optical image survey, multi-source multi-angle InSAR exploration, and UAV aerial photography detailed investigation—achieving full coverage and multi-scale collaborative identification of geological hazards in deep river valley reservoir areas. Specifically, addressing the shadow overlap problem caused by the deep river valley terrain, it innovatively employs InSAR ascending and descending orbit data fusion processing, effectively overcoming the blind spots of single-orbit observation and significantly reducing the missed and false detection rates of high-altitude hidden hazards. Simultaneously, this method greatly reduces the scope and locations requiring on-site reconnaissance, lowering the intensity and cost of fieldwork and improving the timeliness of hazard identification.

[0063] 2. At the level of disaster-causing mechanisms and risk assessment, this invention, through a systematic analysis of the development characteristics of geological hazards, identifies the comprehensive influence of multiple factors such as topography, geological structure, and external forces. Based on an information volume model, it selects evaluation indicators (elevation, slope, aspect, slope morphology, engineering geological rock group, and distance from fault) specifically optimized for deep valley areas, achieving a quantitative and refined evaluation of the susceptibility to geological hazards in reservoir areas. Compared with existing empirical and static evaluation methods, the evaluation results of this invention have higher objectivity and repeatability, and can provide dynamically updated risk data for the long-term safe operation of reservoirs.

[0064] 3. At the level of prevention and control systems and emergency response, this invention forms a complete technical closed loop from hazard identification to feature analysis to susceptibility assessment. Data at each step mutually support each other, and results are progressively enhanced, avoiding the problems of single data sources, fragmented work patterns, and strong subjectivity in evaluation results found in traditional methods. This method is applicable to the investigation and risk assessment of slope hazards such as landslides, collapses, and deformable bodies in the basins and reservoir areas of various conventional hydropower stations. The technical process is standardized and highly operable, providing a scientific basis for early warning, key investigations, and collaborative prevention and control of geological hazards in reservoir areas. It has good engineering application value and promising prospects for widespread application.

[0065] To verify the beneficial effects of this invention, the applicant conducted an on-site inspection of the identification results of the ancient studies database area. The results showed that: In terms of hazard identification, among the 51 active geological hazards identified through InSAR ascending and descending orbit fusion, 46 were verified on-site to show obvious signs of deformation, achieving an identification accuracy rate of over 90%. In contrast, comparative experiments using single-orbit InSAR data showed an identification rate of only about 65%, with significant observation blind spots. This verifies the significant advantages of this invention in overcoming the shadowing effect of deep valley terrain and reducing the rate of missed or false diagnoses. Known geological hazard points are located in "poor stability" and "unstable" areas, verifying the effectiveness and reliability of the evaluation index system specifically designed for deep valleys used in this invention. Furthermore, it significantly reduces operating costs and improves work efficiency.

[0066] Example 2 This embodiment provides a reservoir area geological hazard identification and evaluation system adapted to deep valley topography, applying the reservoir area geological hazard identification and evaluation method adapted to deep valley topography from Embodiment 1 above, including: Optical image survey module: Acquires and processes high-resolution optical remote sensing images to conduct preliminary interpretation and survey of geological hazards in the reservoir area; InSAR detection module: acquires multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data, performs synthetic aperture radar interferometry processing for both ascending and descending orbits, and fuses the ascending and descending orbit observation results to generate a deformation observation map covering both banks of the reservoir area to identify active geological hazards; specifically, the fusion involves using a descending orbit observation map that can observe deformation on the left bank as the main image and an ascending orbit observation map that can observe deformation on the right bank as the auxiliary image, and overlaying the strongly deformed patches in the auxiliary image onto the main image; UAV detailed survey module; conducts detailed aerial surveys of the near-dam and reservoir bank sections and key geological hazard points using UAVs; Developmental characteristics analysis module; analyzes the spatial distribution characteristics and main influencing factors of geological disasters; Susceptibility assessment module: Based on the information content model and combined with the evaluation indicators output by the developmental characteristic analysis module, the susceptibility of geological disasters in the reservoir area is assessed, and a susceptibility assessment map is generated.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for identifying and evaluating geological hazards in reservoir areas adapted to deep river valley topography, characterized in that, Includes the following steps: Step S1, Comprehensive Remote Identification of Geological Hazards: Based on multi-source remote sensing data, remote identification of geological hazards in the reservoir area is carried out to determine the type, spatial distribution, and activity of geological hazards; The remote identification of geological hazards in step S1 further includes: Step S11, High-resolution optical image survey: Use high-resolution optical remote sensing images to conduct preliminary interpretation and survey of geological hazards in the reservoir area, identify the morphology, boundaries and scale of geological hazards, obtain the types of geological hazards and determine the planar location and distribution range of geological hazards; Step S12, InSAR Detection of Active Geological Hazards: The InSAR technique of synthetic aperture radar interferometry is used to detect active geological hazards in the reservoir area. The detection includes: acquiring multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data, performing in-orbit and out-of-orbit synthetic aperture radar interferometry processing respectively, and fusing the in-orbit and out-of-orbit observation results to overcome the shadowing caused by the deep valley topography, generating a deformation observation map covering both sides of the reservoir area, and combining the planar location and distribution range of the geological hazards to obtain the accurate spatial distribution and determine the activity of the geological hazards. Step S13, Detailed drone aerial survey: Conduct detailed drone aerial surveys of the near-dam and reservoir bank sections and key geological hazard points to obtain corrected data to verify the morphology, boundaries, and scale of geological hazards, and output the final determined types, spatial distribution, and activity of geological hazards; Step S2, Geological Disaster Development Characteristics Analysis: Analyze the types, spatial distribution, and activity characteristics of the geological disasters identified in Step S1, and identify the main influencing factors affecting the development of geological disasters; Step S3, Geological Disaster Susceptibility Assessment: Based on the analysis results of Step S2, evaluation indicators are selected, the information content provided by each evaluation indicator to geological disasters is calculated using the information content model, and the information content of each evaluation indicator is summed. Based on the total information content value, the geological disaster susceptibility level of the reservoir area is classified, and a susceptibility assessment map is generated. The specific method for fusing the ascending orbit observation results and descending orbit observation results in step S12 is as follows: using the descending orbit observation map that can observe the deformation of the left bank as the main image, and the ascending orbit observation map that can observe the deformation of the right bank as the auxiliary image, the strongly deformed patches in the auxiliary image are overlaid on the main image to form a deformation observation map covering both banks of the reservoir area.

2. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 1, characterized in that, In step S12, before performing the synthetic aperture radar interferometry processing for the ascent and descent orbits, a data preprocessing step is also included: removing the terrain phase using external digital surface model data and performing image registration on the synthetic aperture radar data; wherein the registration accuracy needs to reach the sub-pixel level.

3. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 1, characterized in that, The high-resolution optical remote sensing image survey in step S11 specifically includes: S111. Establish remote sensing interpretation markers for different types of geological hazards; S112. Preliminary interpretation based on Google Earth imagery; S113. A fine interpretation is performed using SPOT-7 orthophotos with a resolution of 1.5m and SPOT-7 digital surface models with a resolution of 5m as data sources.

4. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 2, characterized in that, The multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data in step S12 includes at least: L-band PALSAR-1 and PALSAR-2 data, and C-band Sentinel-1 ascent and descent data.

5. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 1, characterized in that, In step S1, based on the InSAR exploration results, active geological hazards are classified into three categories according to their annual deformation rate: Highly active type: Overall annual deformation rate ≥ 6cm; Medium activity type: Overall annual deformation rate 2-6 cm; Weakly active type: Overall annual deformation rate ≤2cm.

6. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 1, characterized in that, The main influencing factors affecting the development of geological disasters identified in step S2 include topographic factors, geological factors, and external dynamic factors; The topographical factors include: topographic slope, surface elevation, slope aspect, and slope morphology. The geological factors include: stratigraphic lithology and its combination characteristics, and distance from the fault; The external driving factors include: rainfall, freeze-thaw cycles, and human engineering activities.

7. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 1, characterized in that, The evaluation indicators selected in step S3 include: elevation, slope, aspect, slope morphology, engineering geological rock group, and distance from fault; among them, the grading intervals of each evaluation indicator are pre-set according to the geological disaster development law of the study area.

8. The method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography as described in claim 7, characterized in that, The specific method for assessing the geological hazard susceptibility of the reservoir area using an information content model in step S3 is as follows: multiple evaluation factors. The amount of information is calculated using the following formula: in, Combination of factors The amount of information provided about geological disasters As factors The probability of geological disasters occurring under combined conditions The probability of a geological disaster occurring; Under the condition that the factors are independent, the above equation can be further rewritten according to conditional probability as follows: In the formula, As factors The amount of information provided regarding geological disasters; The specific method for assessing geological hazard susceptibility using a grid cell division approach is as follows: multiple assessment factors The amount of information is calculated using the following formula: in, The number of units used to divide a geological disaster area. This represents the number of units where geological disasters have already occurred. Multiple evaluation factors The number of units, for The number of units containing geological hazards; Using area ratio to calculate information content, the above formula can be further rewritten as: In the formula, The total area of ​​the unit within the geological disaster area; This represents the sum of the areas of units where geological disasters have already occurred. Multiple evaluation factors The total area of ​​the units; factor The sum of the areas of the units in which geological disasters occur.

9. A method for identifying and evaluating geological hazards in reservoir areas adapted to deep valley topography, as described in claim 8, is characterized in that... In the susceptibility evaluation map generated in step S3, the susceptibility to geological disasters is divided into four levels according to the information content value: stable, basically stable, poorly stable and unstable. Among them: information content <-1 indicates stability; -1 ≤ information content < 0 indicates basic stability; 0 ≤ information content < 1 indicates poor stability; Information content ≥ 1 indicates instability.

10. A reservoir area geological hazard identification and evaluation system adapted to deep valley topography, employing the reservoir area geological hazard identification and evaluation method adapted to deep valley topography as described in any one of claims 1 to 9, characterized in that, include: The optical image survey module is used to acquire and process high-resolution optical remote sensing images to conduct preliminary interpretation and survey of geological hazards in the reservoir area. The InSAR detection module is used to acquire multi-band, multi-angle, and multi-resolution spaceborne synthetic aperture radar data. It performs synthetic aperture radar interferometry processing for both ascending and descending orbits, and fuses the ascending and descending orbit observation results to generate a deformation observation map covering both banks of the reservoir area to identify active geological hazards. Specifically, the fusion involves using a descending orbit observation map that can observe deformation on the left bank as the main image and an ascending orbit observation map that can observe deformation on the right bank as the auxiliary image, and then overlaying the strongly deformed patches from the auxiliary image onto the main image. The drone survey module is used to conduct drone aerial surveys of the near-dam and reservoir bank sections and key geological hazard points. The developmental characteristics analysis module is used to analyze the spatial distribution characteristics and main influencing factors of geological disasters. The susceptibility assessment module is used to assess the susceptibility of geological disasters in the reservoir area based on the information content model and the assessment indicators output by the developmental characteristic analysis module, and to generate a susceptibility assessment map.

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