Goaf collapse evaluation method based on multi-source collaborative calibration

Abstract

The invention relates to the technical field of goaf geological disaster prevention and control, in particular to a goaf collapse evaluation method based on multi-source collaborative calibration, which comprises the following steps: acquiring geophysical prospecting data, drilling data and roadway water outlet point data of a to-be-detected mining area, and performing collaborative calibration and cross validation on the geophysical prospecting data, the drilling data and the roadway water outlet point data to obtain a goaf collapse evaluation result; static space parameters and hydrogeological parameters of the goaf are obtained; inputting the static space parameters into a geological background quantitative model to obtain geological background indexes; meanwhile, based on the hydrogeological parameters, obtaining an aging stability coefficient of the to-be-measured mining area; acquiring real-time multi-source dynamic monitoring data of a to-be-monitored mining area, normalizing the multi-source dynamic monitoring data, and performing weighted fusion to generate a dynamic monitoring comprehensive index; and coupling the geological background comprehensive index, the aging stability coefficient and the dynamic monitoring comprehensive index to obtain the collapse probability of the goaf, and performing graded early warning according to the collapse probability. The method can accurately simulate the collapse risk of the stopping old mining area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of goaf geological disaster prevention, and in particular relates to a goaf collapse evaluation method based on multi-source collaborative calibration. BACKGROUND

[0002] After the underground coal mine stops mining, the roof of the goaf is prone to instability due to residual settlement, water quality weakening (groundwater) latent corrosion, rock mass corrosion and other factors, which induces ground collapse and threatens the safety of residents and ecological restoration in the mining area. The current goaf ground collapse evaluation model has the following key problems:

[0003] (1) Insufficient spatial positioning accuracy: existing models mostly rely on geophysical prospecting (transient electromagnetic, ground penetrating radar) to delineate the goaf once, but lack direct calibration and verification of drilling mechanical parameters, resulting in significant deviation in goaf boundary and roof and floor lithology judgment, with a deviation often >2m in the middle and deep exploration scene, making it difficult to accurately lock the risk target area;

[0004] (2) Missing hydrological signals: ignoring the hydrological indication of the mine roadway water outlet - the mine roadway water outlet is a direct "window" for goaf water filling state and water quality weakening degree, and its water quality parameters (such as pH, TDS, COD) can reflect the degree of rock mass corrosion and microbial corrosion, but the existing model does not include it in the calibration system;

[0005] (3) Single dynamic monitoring: mostly relying on InSAR settlement monitoring, without combining the "residual settlement + gravity anomaly (underground mass loss + surface micro-strain)" and other multi-dimensional dynamic signals of the stopped mining area, making it difficult to capture the gradual process of the goaf roof from "stable - damaged - unstable";

[0006] (4) Incomplete consideration of stop mining effect: the correction of coal pillar weathering and residual settlement attenuation in the stopped mining area only relies on empirical formula, without combining the coal pillar distribution determined by geophysical prospecting and the weathering degree measured by drilling, resulting in deviation in the quantitative risk of time effect.

[0007] Therefore, there is an urgent need for a goaf collapse evaluation method based on multi-source collaborative calibration. SUMMARY

[0008] (I) Technical problems to be solved

[0009] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present application provides a goaf collapse evaluation method based on multi-source collaborative calibration, which solves the technical problems of spatial positioning ambiguity, missing hydrological signals, single dynamic monitoring and inaccurate quantitative stop mining effect in the prior art goaf ground collapse evaluation model.

[0010] (II) Technical scheme

[0011] In order to achieve the above-mentioned purposes, the main technical scheme adopted by the present application includes:

[0012] The embodiment of the present application provides a goaf collapse evaluation method based on multi-source collaborative calibration, comprising:

[0013] S100, geophysical data, drilling data and roadway water outlet point data of a to-be-tested mining area are acquired, and the geophysical data, drilling data and roadway water outlet point data are collaboratively calibrated and cross-validated to acquire static spatial parameters and hydrogeological parameters of a goaf;

[0014] S200, the static spatial parameters are input into a geological background quantification model to acquire a geological background index; meanwhile, based on the hydrogeological parameters, a time-effect stability coefficient of the to-be-tested mining area is acquired;

[0015] S300, real-time multi-source dynamic monitoring data of the to-be-tested mining area are acquired, the multi-source dynamic monitoring data are fused after normalization and weighting to generate a dynamic monitoring comprehensive index;

[0016] S400, the geological background comprehensive index, the time-effect stability coefficient and the dynamic monitoring comprehensive index are coupled to acquire a goaf collapse probability, and hierarchical early warning is performed according to the collapse probability.

[0017] Optionally, in some embodiments of the present application, the S100 comprises:

[0018] S110, geophysical data covering a to-be-tested mining area are acquired, and preliminary boundaries of a goaf, roof-to-floor buried depth, lithology layering information and coal pillar distribution morphology are acquired based on the geophysical data;

[0019] S120, based on drilling of the to-be-tested mining area, measured rock mechanics parameters, overburden physical parameters, coal pillar weathering degree parameters and core logging data are acquired, the preliminary boundaries, the roof-to-floor buried depth and the lithology layering information are calibrated and corrected to acquire corrected goaf boundaries, roof-to-floor buried depth and lithology layering information, and corresponding rock mechanics parameters, overburden physical parameters and coal pillar weathering degree parameters are synchronously acquired;

[0020] The corrected goaf boundaries, roof-to-floor buried depth and lithology layering information and the synchronously acquired rock mechanics parameters, overburden physical parameters and coal pillar weathering degree parameters jointly constitute the static spatial parameters of the goaf;

[0021] S130, based on water quality monitoring data collected by a roadway water outlet point of the to-be-tested mining area, the water filling state and the dissolution degree corresponding to the corrected goaf boundaries, roof-to-floor buried depth and lithology layering information are verified to acquire verified water filling state and dissolution degree as the hydrogeological parameters of the goaf.

[0022] Optionally, in some embodiments of the present application, the S110 specifically comprises:

[0023] S111, collecting apparent resistivity data by using transient electromagnetic method, collecting reflection wave data by using ground penetrating radar, and collecting resistivity stratification data by using high-density electrical method;

[0024] S112, identifying and delineating a region with apparent resistivity lower than a preset threshold as a low-resistance abnormal region based on the apparent resistivity data, determining the roof-to-floor buried depth and shallow fissure distribution of the goaf based on the reflection wave data, and dividing lithological interface and delineating a dissolution abnormal region based on the resistivity stratification data;

[0025] S113, comprehensively obtaining the preliminary boundary of the goaf, the roof-to-floor buried depth, the lithological stratification information, and the coal pillar distribution pattern based on the low-resistance abnormal region, the roof-to-floor buried depth and fissure distribution, the lithological interface, and the dissolution abnormal region information.

[0026] Optionally, in some embodiments of the present application, the S120 specifically comprises:

[0027] comparing the preliminary boundary of the goaf, the roof-to-floor buried depth, the lithological stratification information, and the coal pillar distribution pattern with the core logging data actually measured by drilling;

[0028] when the spatial position deviation of the preliminary boundary from the actual boundary in the core logging data exceeds a preset threshold, adjusting the parameters of the transient electromagnetic method, the ground penetrating radar, or the high-density electrical method for iterative correction until the boundary deviation meets the accuracy requirement.

[0029] Optionally, in some embodiments of the present application, in the S130, the water quality monitoring data comprises conductivity and total dissolved solids;

[0030] the S130 comprises:

[0031] S131, obtaining the conductivity and total dissolved solids of the water outlet points of the roadway in the mine area to be measured, spatially correlating and matching the spatial positions of the water outlet points of the roadway with the corrected boundary of the goaf to obtain the goaf region with the water outlet points of the roadway;

[0032] S132, judging whether the conductivity of the water outlet point of the roadway in the goaf region with the water outlet point of the roadway is greater than a first threshold value and whether the water outlet point is located in the low-resistance abnormal region delineated in the S112; if both conditions are met, the region is determined to be in a strong water filling state;

[0033] S133, when the goaf region with the water outlet point of the roadway is in a strong water filling state, judging whether the total dissolved solids TDS of the water outlet point of the roadway in the goaf region with the water outlet point of the roadway is greater than a second threshold value; if so, the region is determined to be in a high dissolution degree;

[0034] S134, aggregate all the water filling state and the erosion degree determination result of the goaf area where the roadway water outlet exists to form the hydrogeological parameter of the goaf.

[0035] Optionally, in some embodiments of the present application, the S200, the geological background index comprises:

[0036] S210, obtaining a rock mass quality index based on the rock mechanics parameter in the static space parameter, and obtaining a overburden quality index based on the overburden physical parameter in the static space parameter;

[0037] S220, weighted sum of the rock mass quality index and the overburden quality index to obtain the geological background index.

[0038] Optionally, in some embodiments of the present application, the S210, the rock mechanics parameter is input into the following formula to obtain the rock mass quality index:

[0039] ;

[0040] Wherein, RQI rock is the rock mass quality index, R C is the compressive strength, c is the cohesion, φ is the internal friction angle, A r is the acid resistance, μ is the Poisson's ratio, K d is the PH corrosion coefficient, IEI new is the ion erosion index, Mb new is the microbial corrosion factor, EH f is the oxidation-reduction correction term, ρ r is the rock mass density, Rtsat is the saturated tensile strength, Rtdry is the dry tensile strength, and all the rock mechanics parameters in the formula are normalized parameters.

[0041] Optionally, in some embodiments of the present application, the S210, the overburden physical parameter is input into the following formula to obtain the overburden quality index:

[0042] ;

[0043] Wherein, RQI soil is the overburden quality parameter, ω is the water content, ωa is the water absorption, ωL is the liquid limit, ωp is the plastic limit, ωn is the natural moisture content, δ L is the surface crack indication function, L is the crack length, when the crack length is greater than 2m, δ L is 1, otherwise 0, and all the overburden physical parameters in the formula are normalized parameters except L and δ L .

[0044] Optionally, in some embodiments of the present application, in the S200, the time-dependent stability coefficient of the mine area to be tested is obtained by:

[0045] S230, based on the stop-mining time of the mine area to be tested, obtaining a basic residual subsidence decay factor, and correcting the basic residual subsidence decay factor according to whether the condition of "at least two continuous water outlets existing around the goaf" is met in the hydrogeological parameters, to obtain a residual subsidence decay factor;

[0046] S240, based on the coal pillar distribution form and the coal pillar weathering degree parameter in the static spatial parameter, obtaining a coal pillar strength reduction coefficient;

[0047] S250, obtaining a time-dependent stability coefficient according to the participation subsidence decay factor and the coal pillar strength reduction coefficient.

[0048] Optionally, in some embodiments of the present application, in the S300, the multi-source dynamic monitoring data includes:

[0049] Surface subsidence rate, rock mass micro-strain data, gravity anomaly data, surface temperature difference data, crack length, and water quality time series data.

[0050] (Three) beneficial effects

[0051] The beneficial effects of the present application are: a goaf collapse evaluation method based on multi-source collaborative calibration according to an embodiment of the present application, which uses multi-source data of geophysical prospecting, drilling, and roadway water outlets for collaborative calibration and cross-validation to determine static and hydrogeological parameters, and innovatively integrates a geological background quantification model representing long-term geological background, a stability coefficient reflecting time-dependent changes in hydrological conditions, and a dynamic comprehensive index based on real-time monitoring. Compared with the prior art, it can significantly improve the accuracy and reliability of goaf space and hydrological property detection, realize full-chain, multi-dimensional coupling evaluation from static geological conditions, dynamic time-dependent evolution to real-time state monitoring, achieve the technical effects of breaking through the limitations of single methods and greatly improving the foresight and accuracy of collapse risk early warning, and provide more scientific and comprehensive decision support for mine safety control. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 It is a flowchart of a goaf collapse evaluation method based on multi-source collaborative calibration according to an embodiment of the present application;

[0053] Figure 2 It is a flowchart of obtaining static spatial parameters and hydrogeological parameters of a goaf according to an embodiment of the present application;

[0054] Figure 3 It is a flowchart of obtaining geological background indicators and time-dependent stability coefficients according to an embodiment of the present application;

[0055] Figure 4 A geophysical-drilling-roadway cooperative calibration schematic diagram according to an embodiment of the present application;

[0056] Figure 5 A process schematic diagram for obtaining a geological background index in a geological background quantification model according to an embodiment of the present application;

[0057] Figure 6 A process schematic diagram for generating a dynamic monitoring comprehensive index by source dynamic monitoring according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to better explain the present application, so as to be understood, the present application is described in detail in the specific implementation mode, combined with the accompanying drawings.

[0059] In the related art, the existing goaf collapse evaluation method mainly has the following limitations:

[0060] Single geophysical data delineation method: this method mainly uses transient electromagnetic, ground penetrating radar and other means to detect and delineate the goaf space at one time, and its technical problem is that: lacking direct calibration and verification of drilling, roadway and other measured data, leading to large deviation in the explanation of goaf boundary, roof and floor thickness and lithology, insufficient spatial positioning accuracy, and difficult to form a reliable static geological model.

[0061] Surface deformation (such as InSAR) monitoring and early warning method: this method mainly uses InSAR technology to monitor the ground subsidence. Its technical problem is that the monitoring signal is single, and cannot fuse multi-dimensional dynamic signals such as underground hydrology, micro-strain and gravity anomaly, and is not associated with the actual hydrogeological state (such as water filling and corrosion) of the underground goaf, and it is difficult to fully capture and early warn the roof instability gradual change process driven by “water weakening” and “residual subsidence”.

[0062] Time-effect evaluation method based on empirical formula: this method mainly uses empirical formula to correct and evaluate the weathering and subsidence attenuation of coal pillars after stopping mining. Its technical problem is that the correction parameters lack measured basis, and are not combined with the coal pillar distribution delineated by geophysical prospecting, the weathering degree revealed by drilling, and the water-rock interaction intensity reflected by the roadway water outlet, leading to inaccurate quantification of the time-effect risk specific to the stopped mining area.

[0063] Therefore, the application provides a goaf collapse evaluation method based on multi-source collaborative calibration, aiming to solve the core technical problems of "low static space precision, missing hydrological dynamic signal, and inaccurate time risk quantification".

[0064] In order to better understand the above technical solutions, the exemplary embodiments of the application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the application can be more clearly, thoroughly understood, and the scope of the application can be fully conveyed to those skilled in the art.

[0065] Figure 1 For the flowchart of the goaf collapse evaluation method based on multi-source collaborative calibration in Embodiment 1 of the application, as shown in Figure 1 The goaf collapse evaluation method based on multi-source collaborative calibration comprises:

[0066] Step S100, obtaining geophysical prospecting data, drilling data and roadway water outlet point data of the mine area to be measured, and collaboratively calibrating and cross- verifying the geophysical prospecting data, the drilling data and the roadway water outlet point data to obtain static space parameters and hydrogeological parameters of the goaf;

[0067] Referring to Figure 4 , step S100 comprises:

[0068] Step S110, obtaining geophysical prospecting data covering the mine area to be measured, and obtaining preliminary boundary, roof-to-floor depth, lithology layering information and coal pillar distribution pattern of the goaf based on the geophysical prospecting data;

[0069] Step S110 specifically comprises:

[0070] Step S111, collecting apparent resistivity data by using transient electromagnetic method, collecting reflection wave data by using ground penetrating radar, and collecting resistivity layering data by using high-density electrical method;

[0071] Specifically, when collecting geophysical data, the following three methods are sequentially collected:

[0072] (1) Ground Penetrating Radar (GPR)

[0073] Select 250MHz antenna, scanning speed 5m / min; record the reflection wave reflection axis displacement, determine the goaf roof buried depth and shallow fracture distribution; roof buried depth measurement error ≤0.8m, adaptive layer level is ultra-shallow (0~10m), relying on centimeter level resolution, accurate identification of shallow soil hole, emptying and crack, matching shallow hidden danger survey demand.

[0074] (2) High-density electrical method (EIT)

[0075] Adopt Wenner device, electrode distance 5m. Core function is to collect resistivity stratified data, divide "soil layer-bedrock layer-gob" interface, delineate dissolution abnormal area (ρ s <15Ω・m). Key advantage: strong vertical stratification ability, can clearly depict the electrical structure of the stratum and the boundary of the gob.

[0076] Adaptive level: medium and shallow layer (10~60m), adaptive medium and shallow gob fine detection.

[0077] (3) Transient electromagnetic method (TEM)

[0078] Device parameters: adopt dipole device, collection depth 0~100m, apparent resistivity (ρ s ) resolution 0.1Ω・m.

[0079] Core function: delineate low-resistivity abnormal area (ρ s <20Ω・m), identify suspected water-filled gob.

[0080] Key advantage: large detection depth, high sensitivity to low-resistivity bodies (water-filled gob).

[0081] Adaptive level: deep (60~150m), solve the problem of deep hidden danger detection.

[0082] Step S112, based on the apparent resistivity data, identify and delineate the area with apparent resistivity lower than the preset threshold as a low-resistivity abnormal area; based on the reflection wave data, determine the gob roof and floor buried depth and shallow fracture distribution; based on the resistivity stratified data, divide the lithology interface and delineate the dissolution abnormal area;

[0083] Among them, the preset threshold of apparent resistivity is 20Ω·m, which is determined according to the hydrogeological investigation data of similar mining areas and GB / T50218-2014 "Engineering rock mass classification standard", and lower than the preset threshold usually indicates the presence of water filling, fracture development or argillaceous filling.

[0084] By identifying the abnormal features of the broken, interrupted, and other characteristics of the reflected wave events, the depth of the goaf roof (and floor) can be accurately determined, with an interpretation error of ≤0.8 meters. At the same time, in the shallow (0-20 meters) profile, by analyzing the reflection of the disorder, diffraction, and other phenomena, the distribution of the shallow fissure or soft area can be circled.

[0085] Based on the resistivity stratification data, the vertical interfaces of the soil layer, bedrock layer, and goaf disturbance area can be divided. At the same time, the abnormal area with significantly lower resistivity (<15 Ω·m) in the bedrock is circled. Such areas often indicate dissolution development or filling in soluble rock formations and are key indicators for assessing water-induced weakening risks.

[0086] Step S113, comprehensively integrating the low-resistance abnormal area, roof and floor depth, and fissure distribution, lithology interface, and dissolution abnormal area information, obtaining the preliminary boundary of the goaf, the roof and floor depth, the lithology stratification information, and the coal pillar distribution form.

[0087] Among them, step S113 specifically includes:

[0088] Obtaining the preliminary boundary of the goaf: integrating the low-resistance abnormal area circled by the transient electromagnetic method, the roof and floor reflection interface determined by the ground penetrating radar, and the lithology interface mutation zone identified by the high-density electrical method. The abnormal boundaries determined by the three are analyzed in space, and the isolated and contradictory abnormal points are removed, and the regions that are continuous in space and mutually confirmed by multiple methods are retained, and finally the three-dimensional preliminary external contour and internal boundary of the goaf are outlined.

[0089] Determining the depth of the roof and floor: taking the depth of the roof and floor reflection interface accurately interpreted by the ground penetrating radar as the main basis. At the same time, the "bedrock-disturbed area" interface divided by the high-density electrical method is used for depth calibration, and the vertical range of the transient electromagnetic low-resistance abnormal body is verified. Through data fusion, the average depth and undulating form of the roof and floor of the goaf at each place are finally determined, and the depth interpretation comprehensive error can be controlled within the preset range.

[0090] Extracting lithology stratification information: based on the resistivity stratification data provided by the high-density electrical method, combined with known geological data, the correspondence between resistivity and lithology is established. From this, the main lithology interface (such as soil cover layer, complete bedrock, coal seam, mudstone aquifuge, etc.) is divided in three-dimensional space, forming a preliminary three-dimensional lithology model of the study area.

[0091] Infer the distribution pattern of coal pillars: Within the delineated goaf area, identify the relatively high-resistance or relatively stable continuous blocks that are not classified into the "low-resistance abnormal area" or "disturbed area". These blocks are often distributed in strips or islands within the goaf area. Combined with mine exploitation data (such as roadway layout, coal pillar design), the distribution pattern of residual coal pillars or unsampled areas can be reasonably inferred, providing spatial basis for evaluating their long-term stability.

[0092] Further, to facilitate subsequent quantitative evaluation and risk management, after obtaining the above preliminary spatial parameters, the study area needs to be divided into standardized evaluation units. This embodiment follows the principles of "spatial continuity, clear boundaries, and internal homogeneity" and uses a fixed grid of 100m x 100m to systematically divide the study area.

[0093] For example, as shown in Figure 4 If the total area is 65.73 km², approximately 6573 evaluation units are generated.

[0094] The specific division of the unit boundary is as follows: The 41 low-resistance abnormal areas, 7 high-density electrical method abnormal areas, and 10 ground penetrating radar abnormal areas delineated by comprehensive geophysical interpretation are used as core control elements, and the grid boundaries are strictly fitted to the actual boundaries of the goaf area (positioning deviation ≤1.5 meters) that has been accurately determined. Through manual adjustment, the boundaries of each 100m x 100m grid are aligned with the above abnormal area boundaries or goaf area boundaries as much as possible, avoiding the inclusion of both "abnormal areas" and "normal areas" in a single evaluation unit or the crossing of multiple regions with different geological characteristics, thereby ensuring high consistency in geological abnormal characteristics within each grid, laying a reliable spatial foundation for subsequent parameter assignment and risk calculation.

[0095] In summary, through the technical path of "multi-source data fusion" and "fine-grained unit division", the spatial structure of complex goaf areas in old mines is accurately described, and the traditional vague and qualitative geological understanding is transformed into quantitative and positioned spatial parameters, providing accurate input conditions for subsequent risk quantification evaluation.

[0096] Further, based on the drilling data, the above geophysical data is further corrected, as shown in the following step S120:

[0097] Step S120, based on the drilling data of the mine area to be tested, the measured rock mechanics parameters, overburden physical parameters, coal pillar weathering degree parameters, and core logging data are used to calibrate and correct the preliminary boundaries, roof and floor depths, and lithology layering information, obtain the corrected goaf boundaries, roof and floor depths, and lithology layering information, and simultaneously obtain the corresponding rock mechanics parameters, overburden physical parameters, and coal pillar weathering degree parameters;

[0098] The modified goaf boundary, roof and floor buried depth, and lithology layering information, together with the simultaneously obtained rock mechanics parameters, overburden physical parameters, and coal pillar weathering degree parameters, constitute the static spatial parameters of the goaf.

[0099] Step S120 specifically includes:

[0100] The preliminary boundary of the goaf, roof and floor buried depth, lithology layering information, and coal pillar distribution pattern are compared with the core logging data actually measured by drilling;

[0101] When the deviation of the preliminary boundary from the actual boundary spatial position in the core logging data exceeds a preset threshold, the parameters of the transient electromagnetic method, ground penetrating radar, or high-density electrical method are adjusted for iterative correction until the boundary deviation meets the accuracy requirement.

[0102] Specifically, at least two drillings penetrating the goaf floor by more than 10 meters in depth are arranged in the suspected high-risk area delineated by geophysical prospecting, and core logging and system testing are carried out. The core logging accurately records the roof and floor lithology, goaf filling and water-bearing state, coal pillar position, and weathering characteristics; laboratory testing obtains key mechanical and hydrological parameters of the rock, such as compressive strength, tensile strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, permeability coefficient, and acid resistance, as well as the physical properties of the overburden and the coal pillar weathering degree parameters.

[0103] The core logging data actually measured by drilling are taken as the benchmark, and are finely compared and calibrated with the preliminary boundary, roof and floor buried depth, lithology layering, and coal pillar distribution pattern interpreted by geophysical prospecting. When the spatial position deviation between the geophysical interpretation results and the actual drilling conditions exceeds a preset threshold (e.g., 1.5 meters), the geophysical interpretation parameters are iteratively corrected. For example, the wave velocity of the ground penetrating radar or the resistivity threshold of the transient electromagnetic method is adjusted, and the data are reprocessed and interpreted until the spatial coincidence rate of the two reaches more than 90%. Through this process, the modified, high-precision goaf boundary, roof and floor buried depth, and lithology layering information are obtained.

[0104] Finally, these corrected accurate spatial information, together with the rock mechanics parameters, overburden physical parameters, and coal pillar weathering degree parameters with clear spatial positions simultaneously obtained from the corresponding drilling core testing, constitute a complete and reliable set of goaf static spatial parameters. This parameter set provides an accurate geological model and reliable mechanical property input for subsequent risk quantitative evaluation.

[0105] Further, the roadway water outlet verification is carried out again, specifically as follows:

[0106] Step S130, based on the water quality monitoring data collected by the roadway water outlet in the mine area to be tested, the water filling state and the dissolution degree corresponding to the corrected goaf boundary, the roof and floor buried depth and the lithology layering information are verified, and the verified and determined water filling state and dissolution degree are obtained as the hydrogeological parameters of the goaf.

[0107] In step S130, the water quality monitoring data includes conductivity and total dissolved solids;

[0108] Step S130 includes:

[0109] Step S131, the conductivity and total dissolved solids of the roadway water outlet in the mine area to be tested are obtained, the spatial positions of each roadway water outlet are spatially associated and matched with the corrected goaf boundary, and the goaf area where the roadway water outlet exists is obtained.

[0110] Among them, the roadway water outlet of the mine area to be tested is more than or equal to 10 water quality monitoring points arranged at the outlet of the main mining roadway or the key water collection point, ensuring that its spatial distribution covers different goaf partitions. Water samples are collected every 5 days, and a series of indicators covering basic hydrology, pollution and dissolution indicators are monitored, including but not limited to: pH, temperature, conductivity, dissolved oxygen, total dissolved solids, turbidity, total organic carbon, chemical oxygen demand, salinity and oxidation-reduction potential.

[0111] Step S132, determine whether the conductivity of the roadway water outlet in the goaf area where the roadway water outlet exists is greater than the first threshold value and the water outlet is located in the low-resistance anomaly area circled in step S112; if both conditions are met, the area is determined to be in a strong water filling state;

[0112] Step S133, when the goaf area where the roadway water outlet exists is in a strong water filling state, determine whether the total dissolved solids TDS of the roadway water outlet in the goaf area where the roadway water outlet exists is greater than the second threshold value; if satisfied, it is determined that the area has a high dissolution degree;

[0113] Step S134, summarize the water filling state and dissolution degree determination results of all goaf areas where the roadway water outlet exists to form the hydrogeological parameters of the goaf.

[0114] In the specific implementation process, taking a certain stop-mining old mine area A mining area as an example, the specific implementation and determination process of step S130 is as follows:

[0115] At the outlets of the main drainage roadway and branch roadway in the A mining area, a total of 10 water quality monitoring points (numbered W1-W10) were arranged, covering the presumed eastern water-rich area, the western relatively dry area and the central transition area. Monitoring started on January 1, 2023, and water samples were collected every 5 days, continuously monitoring indicators such as pH, conductivity EC, total dissolved solids TDS, etc.

[0116] The coordinates of the monitoring points W1-W10 are superimposed on the three-dimensional geological model of the A mining area after drilling correction. The results show that the monitoring points W1, W2, W3 and W7 are located in the eastern goaf delineated by the model, among which W1, W2 and W3 are located in the south of the area, and W7 is located in the north; W4, W5 and W6 are located in the western goaf; W8, W9 and W10 are located in the central goaf. At the same time, the previous transient electromagnetic interpretation delineated three main low-resistance anomaly areas (numbered LZ-1, LZ-2 and LZ-3). Spatial analysis shows that the monitoring points W1, W2 and W3 are located in the LZ-1 anomaly area, and the monitoring point W7 is located in the LZ-3 anomaly area, and the remaining monitoring points are not located in any low-resistance anomaly area.

[0117] Then, the monitoring data of the first quarter of 2023 is analyzed. It is found that the monitoring points W1, W2 and W3 located in the south of the eastern goaf have a continuous fluctuation in the range of 1200-1800 μS / cm in the conductivity monitoring value, all of which are stably higher than the first preset threshold value (1000 μS / cm). At the same time, these three monitoring points are all located in the transient electromagnetic low-resistance anomaly area LZ-1. According to the judgment logic of step S132, both the conditions of high conductivity and being located in the low-resistance anomaly area are met, so it is determined that the "eastern goaf south area" represented by the monitoring points W1, W2 and W3 is in a strong water filling state. While the monitoring points W4, W5 and W6 located in the same goaf but not in the low-resistance anomaly area, or the conductivity of which does not continuously exceed the threshold value (such as the conductivity of W7 point is in the range of 800-950 μS / cm), or although located in the low-resistance area but the conductivity is not high, in this case, they are not determined to be in a strong water filling state.

[0118] Further analysis of the TDS data of the eastern goaf south area (corresponding to the monitoring points W1, W2 and W3) which has been determined to be in a "strong water filling state". The data shows that the TDS values of W1, W2 and W3 points are 1850 mg / L, 2100 mg / L and 1950 mg / L respectively during the monitoring period, all of which are stably higher than the second preset threshold value (1500 mg / L). According to the judgment logic of step S133, on the basis of having been determined to be in a strong water filling state, the TDS exceeds the standard condition is met, so it is determined that the area is in a "high dissolution degree".

[0119] Based on the above determination, the hydrogeological parameters of A mining area are finally formed as follows:

[0120] Eastern goaf south area: water filling state = strong water filling; dissolution degree = high dissolution. (Based on the data of W1, W2 and W3 points, verified by LZ-1 anomaly area)

[0121] Eastern goaf north area: water filling state = weak water filling / not determined to be in a strong water filling state; dissolution degree = needs further evaluation / low dissolution. (Based on the data of W7 point, the conductivity does not continuously exceed the threshold value)

[0122] West and central mined-out area: water filling state = determined according to conductivity and position, in this case, not up to the strong water filling standard; dissolution degree = determined according to TDS.

[0123] Through this example, step S130 successfully integrates and cross- verifies the discrete point water quality data (high EC and high TDS of W1-W3 points) with the planar geophysical anomaly (LZ-1 low resistance area) and the three-dimensional geological model (eastern mined-out area). Not only does it confirm that the LZ-1 low resistance area inferred by geophysical prospecting is a strong water filling area from the perspective of hydrogeochemistry, but more importantly, it further identifies that this strong water filling area has the special risk attribute of high dissolution. This provides direct, quantitative and spatially explicit input parameters for the subsequent step of assessing the risk of strength attenuation (water-induced weakening) of the rock mass in this area due to long-term dissolution, significantly improving the relevance and accuracy of risk assessment.

[0124] Further, the mined-out area collapse evaluation method based on multi-source collaborative calibration of the application, after obtaining the static spatial parameters and hydrogeological parameters of the mined-out area, executes the following steps:

[0125] Step S200, input the static spatial parameters into the geological background quantification model to obtain the geological background index; at the same time, based on the hydrogeological parameters, obtain the time-dependent stability coefficient of the mine area to be measured;

[0126] Referring to Figure 5 , in step S200, obtaining the geological background index includes:

[0127] Step S210, obtaining rock mass quality index based on rock mechanics parameters in static spatial parameters, and obtaining overburden quality index based on overburden physical parameters in static spatial parameters;

[0128] In step S210, input the rock mechanics parameters into the following formula to obtain the rock mass quality index:

[0129] ;

[0130] Wherein, RQI rock is the rock mass quality index (value range 0-1, the larger the value, the better the rock mass quality), R C is the compressive strength, c is the cohesion, and φ is the internal friction angle, R C , c and φ are all positive parameters (the larger the value, the better the rock mass quality);

[0131] A r is the acid resistance, ρ r is the rock mass density, EH f is the oxidation-reduction correction term, Rtsat is the saturated tensile strength, and Rtdry is the dry tensile strength, all of which are positive parameters;

[0132] μ is Poisson's ratio, K d PH is the PH dissolution coefficient, IEI new IEI is the ion erosion index, Mb new Mb is the microbial corrosion factor, all of which are negative parameters (the larger the value, the worse the rock mass quality).

[0133] The above rock mechanics parameters are normalized parameters. Specifically, the normalization formula of the positive parameters is:

[0134] ;

[0135] P i is the measured value of the parameter, P i,min is the minimum value of the parameter in the same mining area (or the theoretical lower limit value determined according to GB / T 50218-2014 “Engineering Rock Mass Classification Standard”), and P i,max is the maximum value of the parameter in the same mining area (or the theoretical upper limit value determined according to the national standard).

[0136] Similarly, the normalization formula of the negative parameters is:

[0137] ;

[0138] P i is the measured value of the parameter, P i,min is the minimum value of the parameter in the same mining area (or the theoretical lower limit value determined according to GB / T 50218-2014 “Engineering Rock Mass Classification Standard”), and P i,max is the maximum value of the parameter in the same mining area (or the theoretical upper limit value determined according to the national standard).

[0139] Through the formula, the original parameters with different dimensions and different value ranges are uniformly mapped to the interval [0, 1], realizing parameter normalization processing and ensuring that each index can be used for weighted summation calculation.

[0140] In step S210, the overburden physical parameters are input into the following formula to obtain the overburden quality index:

[0141] ;

[0142] RQI soil is the overburden quality parameter, with a value range of 0-1, and the larger the value, the stronger the bearing capacity and deformation resistance of the overburden, and the lower the collapse risk. ω is the water content, ωa is the water absorption rate, ωL is the liquid limit, ωp is the plastic limit, ωn is the natural moisture content, δ L is the surface crack indicator function, and L is the crack length. When the crack length is > 2m, δ L is 1, otherwise it is 0, and in the formula, L and δL All the physical parameters of the overburden are normalized parameters, with a value range of 0-1.

[0143] Further, the liquid index : ≤0.5, the soil has no risk of plastic deformation, and >1.0, plastic flow failure is prone to occur; if the surface crack L is monitored to be >2m, it indicates that the overburden has been damaged, and the RQI soil is additionally reduced by 10%; the higher the overburden quality index value, the stronger the overburden collapse resistance (for example, when the RQI soil >0.8, the overburden is stable).

[0144] Step S220, the rock mass quality index and the overburden quality index are weighted and summed to obtain the geological background index. The weight of the rock mass quality index is 0.3, and the weight of the overburden quality index is 0.7.

[0145] Further, in step S200, the time-dependent stability coefficient of the mine area to be measured is obtained, including:

[0146] Step S230, based on the stop-mining time of the mine area to be measured (mapped to the [0, 1] interval after normalization processing), a basic residual settlement attenuation factor is obtained, and the basic residual settlement attenuation factor is modified according to whether the condition of "at least two continuous water outlets existing around the goaf" is met in the hydrogeological parameters, to obtain a normalized residual settlement attenuation factor (value range [0, 1]);

[0147] Step S240, based on the coal pillar distribution form (normalized by spatial characteristics) and the coal pillar weathering degree parameter (mapped to the [0, 1] interval after normalization processing) in the static spatial parameter, a normalized coal pillar strength reduction coefficient (value range [0, 1]) is obtained;

[0148] Step S250, the time-dependent stability coefficient is obtained according to the participation settlement attenuation factor and the coal pillar strength reduction coefficient.

[0149] Specifically, the calculation formula of the residual settlement attenuation factor is:

[0150] ηt=e -0.05t ×δwater;

[0151] Wherein, t is the stop-mining time, δwater is the roadway water outlet correction coefficient (0.9 when there are ≥2 continuous water outlets around the goaf, otherwise 1.0), and ηt is the residual settlement attenuation factor.

[0152] The calculation formula of the coal pillar strength reduction coefficient is:

[0153] βcoal=1-0.4δ W / H＜3 -0.2γ-0.1δwater;

[0154] wherein βcoal is a coal mine strength reduction factor, δ W / H <3 is a coal column width-height ratio indicating function defined by geophysical prospecting (1 when W / H < 3), and γ is a measured coal column compressive strength loss rate.

[0155] Therefore, the formula of the time-effect stability coefficient is:

[0156] T S = ηt x βcoal.

[0157] wherein T S is the time-effect stability coefficient, ηt is a residual settlement decay factor, and βcoal is a coal mine strength reduction factor.

[0158] For example, taking an old mine area with a 20-year suspension of mining as an example, assuming that the monitoring data shows that there are 3 water outlets with continuous water flow in the past 3 months. The calculation process of the time-effect stability coefficient is specifically explained. The coefficient aims to quantify the stability change caused by long-term settlement decay and coal pillar weathering after suspension of mining, and is a key parameter for evaluating the unique risks of old mine areas:

[0159] First, according to the suspension time t of 20 years, when there are ≥2 continuous water outlets around the goaf, take 0.9, and obtain the residual settlement decay factor as 0.33.

[0160] Assuming that the width-height ratio W / H of a certain key coal pillar is determined to be 2.0 through geophysical prospecting data, and the drilling core test shows that the current saturated uniaxial compressive strength of the coal pillar is 7.0 MPa, while the strength of the same fresh coal sample is 10.0 MPa, and the coal pillar strength reduction factor formula is obtained as 0.54 by substituting the above formula, which means that the current comprehensive bearing capacity of the coal pillar is only 54% of the ideal perfect state.

[0161] Finally, multiply the residual settlement decay factor and the coal pillar strength reduction factor to obtain the time-effect stability coefficient as 0.18, which is used as a key input parameter in the subsequent steps, coupled with dynamic monitoring data and geological background indicators, and finally used to calculate the collapse probability.

[0162] Step S200 generates a quantitative time-dependent stability coefficient by fusing and calculating multiple source information such as abandonment time, roadway water outlet point, coal pillar shape and weathering degree, and innovatively converts the elusive time and long-term water-rock interaction factor into quantifiable and comparable engineering parameters, thereby realizing accurate quantitative evaluation of the special risks of old mining areas. This method overcomes the limitations of traditional empirical formulas that ignore hydrological conditions and actual conditions of coal pillars, and converts the abstract concept of time-dependent into specific numerical values, providing a crucial long-term evolution background correction for subsequent dynamic risk analysis, and significantly enhancing the explanation ability and early warning foresight of the entire evaluation system for the special risk mechanism of old mining areas.

[0163] Step S300, acquiring real-time multi-source dynamic monitoring data of the to-be-tested mining area, weighting and fusing the normalized multi-source dynamic monitoring data to generate a dynamic monitoring comprehensive index;

[0164] Referring to Figure 6 In step S300, the multi-source dynamic monitoring data includes:

[0165] surface subsidence rate, rock mass micro-strain data, gravity anomaly data, surface temperature difference data, crack length and water quality time series data.

[0166] Therefore, the calculation formula of the surface dynamic monitoring comprehensive index is:

[0167] ;

[0168] wherein, Vs 归一化 is the normalized surface subsidence rate, ε 归一化 is the normalized rock mass micro-strain data, is the normalized gravity anomaly data, is the normalized surface temperature difference data, L is the crack length, and the water quality index mean 归一化 is obtained according to the water quality time series data.

[0169] In the specific implementation process, the surface subsidence rate is monitored by InSAR in the whole area, which is a direct indicator for judging whether the roof residual subsidence is active or not. When it is greater than 5mm / y, it usually indicates that the roof instability process is accelerating. Its weight in the dynamic monitoring comprehensive index is 0.2.

[0170] The rock mass micro-strain data is monitored by local optical fiber in the seismic monitoring of geophysical high-risk areas. Significant increase of micro-strain is a precursor of development, expansion and even penetration of internal cracks of rock mass. When it is >800με, it usually means that the rock mass structure has entered the accelerated damage stage. Its weight is 0.2.

[0171] The gravity anomaly data is obtained for the key goaf, and the core is the gravity anomaly change. The mass loss of goaf and the migration of underground water will cause measurable gravity anomaly. The gravity anomaly change <-50 μGal is often regarded as an important signal of significant loss of underground mass, and its weight is 0.15.

[0172] The surface temperature difference and crack length of some areas are obtained by infrared unmanned aerial vehicle. The surface temperature anomaly and the rapid expansion of cracks are the near indicators of surface subsidence. The weight of this item is 0.2.

[0173] Ten fixed monitoring points are arranged in the key roadway, and sampling analysis is performed every 5 days. The monitoring parameters include pH value, electrical conductivity EC, total dissolved solids TDS, chemical oxygen demand COD and the like as water quality time series data. These water quality time series data are chemical windows reflecting the corrosion, dissolution and microbial corrosion strength of groundwater on surrounding rock. For example, the combination of pH <5 (acidification enhancement) and TDS >2000 mg / L (dissolved matter increase) strongly indicates that the water-induced rock mass weakening effect is intensifying. The weight of this item is 0.25, which emphasizes the importance of hydrochemical process in long-term risk evolution.

[0174] The specific multi-source dynamic monitoring data is shown in Table 1:

[0175] Table 1 Multi-source dynamic monitoring data table

[0176]

[0177] After obtaining the above related parameters, a unified evaluation time window is set, such as every month. In the window, various monitoring data are aligned to the same time reference by interpolation, taking the latest value or the average value in the period.

[0178] In the unified time window, each parameter is processed according to the principle that "the lower the risk, the higher the normalized value", and is mapped to the interval [0, 1]. For example, for the gravity anomaly change obtained every month, the normalized value is set to 1.0 when it is greater than or equal to -20 μGal, and 0.2 when it is less than -50 μGal, and linear interpolation is performed therebetween.

[0179] Further, the normalized values of various parameters in the same time window are multiplied by their corresponding fixed weights and summed to obtain the dynamic monitoring comprehensive index value of the time window. The lower the dynamic monitoring comprehensive index value, the higher the comprehensive dynamic risk. The grading threshold can be set, such as DMI <0.4 for high risk. According to the set window (such as every month), the rolling calculation is performed, and the DMI time series reflecting the change of risk with time can be generated.

[0180] In the present application, step S300 integrates the monitoring data obtained from five different time and space frequencies, including InSAR, local fiber vibration, gravity, infrared unmanned aerial vehicle and water quality at the roadway water outlet, and designs a normalization rule and weight distribution mechanism based on "the lower the risk, the higher the value" to fuse them into a unified dynamic monitoring comprehensive index. The beneficial effect of this approach is that it effectively overcomes the limitations of single monitoring means in coverage, monitoring frequency and risk dimension, and realizes the synchronous capture and quantitative integration of multi-dimensional and cross-scale dynamic risk signals of the goaf from "macroscopic subsidence-microscopic strain-physical field anomaly-surface features-water-rock chemical action". It not only converts asynchronous and heterogeneous monitoring data into continuous and comparable risk time series indicators, but also highlights the key signals with high frequency and high sensitivity through weighted fusion, thereby significantly improving the early identification ability and warning timeliness of the system for the gradual change process of the goaf roof from "stable" to "damage" and even "unstable" state.

[0181] Step S400, coupling the geological background comprehensive index, time-effect stability coefficient and dynamic monitoring comprehensive index to obtain the goaf collapse probability, and grading warning according to the collapse probability.

[0182] Specifically, the calculation of the goaf collapse probability is integrated through a coupling model. The basic form is:

[0183] Pc=1-0.3RQI total -0.2T s -0.3DMI-0.2λ geo-water ;

[0184] Wherein, DMI is the dynamic monitoring comprehensive index, and Ts is the time-effect stability coefficient. The physical meaning of the formula is that when the dynamic risk is higher (DMI is lower) or the long-term stability is worse (Ts is lower), the basic contribution value to the collapse probability is greater, RQI total RQI is the geological background comprehensive index, and λgeo-water is the geological-hydrological coupling coefficient (0.2 when the fault passes through the goaf and the water outlet pH<5, and 0.8 when there is no fault and the water quality is stable).

[0185] In the specific implementation process, low risk (Pc<0.3), medium risk (0.3≤Pc<0.6), and high risk (Pc≥0.6) correspond to trigger blue (intensive monitoring), yellow (preparation of preplan), and red (emergency grouting + personnel evacuation) warning, as shown in Table 2:

[0186] Table 2 Collapse probability warning level table

[0187]

[0188] Further, the goaf collapse evaluation method based on multi-source collaborative calibration of the application is specially designed for the special risk evolution stage faced by old mining areas with more than 10 years of stop mining.

[0189] The "residual settlement attenuation, coal pillar weathering, and long-term water quality erosion" in the embodiment is designed for the unique risks of old mining areas with more than 10 years of stop mining. The goaf boundary of the old mining area is blurred after long-term collapse and compaction of the filling material, and needs to be calibrated by "geophysical prospecting-drilling-hydrology" (deviation ≤1.5 m). The goaf boundary of the new mining area is clear, and single geophysical prospecting can meet the accuracy. The long-term infiltration of groundwater in the old mining area has a significant erosion effect, and the water quality parameters of the water outlet point of the roadway can reflect the long-term erosion degree. The water quality impact in the new mining area has not yet accumulated, and the hydrological signal is limited. The coal pillar weathering and residual settlement attenuation of the old mining area need to be corrected in combination with the measured data. The coal pillar of the new mining area has not been significantly weathered, and the residual settlement has not entered the attenuation period, so there is no need for complex time-effect correction.

[0190] Therefore, the embodiment of the application is a systematic solution to the three core problems of the old mining area, i.e., "blurred boundary, chemical potential erosion, and long-term attenuation". For the new mining area, the boundary is clear, the hydration effect is weak, and the time-effect is not obvious. Some complex environments of the embodiment of the application can be simplified to realize the collapse evaluation of the new mining area.

[0191] In another embodiment of the application, a computer device is also included, which comprises a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to realize the steps of the goaf collapse evaluation method based on multi-source collaborative calibration.

[0192] The goaf collapse evaluation method based on multi-source collaborative calibration of the application effectively solves the key problems of the existing methods, such as insufficient spatial positioning accuracy, missing hydrological signals, single dynamic monitoring, and incomplete consideration of time-effect, and significantly improves the accuracy, comprehensiveness, dynamic early warning capability, and time-effect risk quantification reliability of the goaf collapse risk evaluation, thereby providing scientific decision support for precise prevention and control and safety management of the mining area.

[0193] In the description of the application, it should be understood that the terms "first", "second" are used only for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second" can be explicitly or implicitly included one or more of the features. In the description of the application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified and limited.

[0194] In this application, unless otherwise expressly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrated; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0195] In this application, unless otherwise expressly specified and limited, the first feature is "on" or "under" the second feature, which can be direct contact between the first and second features, or indirect contact between the first and second features through intermediate medium. Moreover, the first feature is "above", "above" and "above" the second feature, which can be directly above or obliquely above the first feature, or only indicates that the first feature is higher than the second feature in horizontal height. The first feature is "below", "below" and "below" the second feature, which can be directly below or obliquely below the first feature, or only indicates that the first feature is lower than the second feature in horizontal height.

[0196] In the description of the application, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the application. In this specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples, without contradiction.

[0197] Although the embodiments of the application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be construed as limiting the application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the application.

Claims

1. A method for evaluating goaf collapse based on multi-source collaborative calibration, characterized in that, include: S100. Obtain geophysical data, borehole data, and roadway water outlet data of the mining area to be tested, and perform collaborative calibration and cross-verification of the geophysical data, borehole data, and roadway water outlet data to obtain the static spatial parameters and hydrogeological parameters of the goaf. S200. Input the static spatial parameters into the geological background quantification model to obtain the geological background index; at the same time, based on the hydrogeological parameters, obtain the time stability coefficient of the mining area to be tested. S300: Acquire real-time multi-source dynamic monitoring data of the mining area to be monitored, and then normalize and weight the multi-source dynamic monitoring data to generate a dynamic monitoring comprehensive index. S400. The geological background comprehensive index, the time-dependent stability coefficient, and the dynamic monitoring comprehensive index are coupled to obtain the probability of subsidence in the goaf area, and a graded early warning is given based on the subsidence probability.

2. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 1, characterized in that, S100 includes: S110. Obtain geophysical data covering the mining area to be tested, and based on the geophysical data, obtain the preliminary boundary of the goaf, the burial depth of the roof and floor, lithological stratification information, and the distribution pattern of the coal pillars. S120. Based on the measured rock mechanics parameters, overburden physical parameters, coal pillar weathering degree parameters and core logging data obtained from the boreholes in the mining area to be tested, the preliminary boundary, roof and floor burial depth and lithological stratification information are calibrated and corrected, and the corrected goaf boundary, roof and floor burial depth and lithological stratification information are obtained, and the corresponding rock mechanics parameters, overburden physical parameters and coal pillar weathering degree parameters are obtained simultaneously. The revised goaf boundary, roof and floor burial depth, and lithological stratification information, together with the synchronously obtained rock mechanics parameters, overburden physical parameters, and coal pillar weathering degree parameters, constitute the static spatial parameters of the goaf. S130. Based on the water quality monitoring data collected from the water outlet points of the mine roadway to be tested, the water filling state and degree of dissolution corresponding to the corrected goaf boundary, roof and floor burial depth and lithological stratification information are verified, and the verified water filling state and degree of dissolution are obtained as hydrogeological parameters of the goaf.

3. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 2, characterized in that, S110 specifically includes: S111. Transient electromagnetic method is used to collect apparent resistivity data, ground penetrating radar is used to collect reflected wave data, and high-density electrical resistivity method is used to collect resistivity layered data. S112. Based on the apparent resistivity data, identify and delineate areas with apparent resistivity below a preset threshold as low-resistivity anomaly zones; based on the reflected wave data, determine the burial depth of the top and bottom plates of the goaf and the distribution of shallow fractures; based on the resistivity stratification data, delineate lithological interfaces and delineate dissolution anomaly zones. S113. Based on the information on the low resistivity anomaly zone, the burial depth of the roof and floor and the distribution of fractures, the lithological interface and the dissolution anomaly zone, obtain the preliminary boundary of the goaf, the burial depth of the roof and floor, the lithological stratification information and the distribution pattern of the coal pillar.

4. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 3, characterized in that, S120 specifically includes: The preliminary boundary of the goaf, the burial depth of the roof and floor, the lithological stratification information, and the distribution pattern of the coal pillars are compared with the core logging data measured in the boreholes. When the spatial deviation between the preliminary boundary and the actual boundary in the core logging data exceeds a preset threshold, the parameters of the transient electromagnetic method, ground penetrating radar, or high-density electrical resistivity tomography are adjusted for iterative correction until the boundary deviation meets the accuracy requirements.

5. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 3, characterized in that, In step S130, the water quality monitoring data includes conductivity and total dissolved solids; S130 includes: S131. Obtain the electrical conductivity and total dissolved solids of the water outlet points in the mine roadway to be tested, and perform spatial correlation matching between the spatial location of each water outlet point and the corrected goaf boundary to obtain the goaf area with water outlet points. S132. Determine whether the conductivity of the water outlet in the goaf area with the water outlet is greater than the first threshold and whether the water outlet is located in the low-resistivity abnormal area defined in S112; if both conditions are met, then the area is determined to be in a state of strong water filling. S133. When the goaf area with the roadway water outlet is in a strongly water-filled state, determine whether the total dissolved solids (TDS) of the roadway water outlet in the goaf area is greater than the second threshold; if it is satisfied, then the area is determined to have a high degree of dissolution. S134. Summarize the results of the water filling status and erosion degree determination of all goaf areas with water outlets in the roadways to form the hydrogeological parameters of the goaf area.

6. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 1, characterized in that, In S200, obtaining geological background indicators includes: S210. Obtain the rock mass quality index based on the rock mechanics parameters in the static spatial parameters, and obtain the overburden quality index based on the overburden physical parameters in the static spatial parameters. S220. The rock mass quality index and the overburden quality index are weighted and summed to obtain the geological background index.

7. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 6, characterized in that, In step S210, the rock mechanical parameters are input into the following formula to obtain the rock mass quality index: ； Among them, RQI rock R is the rock mass quality index. C Where c is the compressive strength, φ is the cohesive force, and A is the internal friction angle. r For acid resistance, μ is Poisson's ratio, and K is... d IEI is the pH dissolution coefficient. new The ion erosion index, Mb new EH is a microbial corrosive agent. f For redox correction terms, ρ r Rtsat is the rock mass density, Rtdry is the saturated tensile strength, and Rtdry is the dry tensile strength. All rock mechanics parameters in the formula are normalized parameters.

8. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 6, characterized in that, In step S210, the physical parameters of the cover layer are input into the following formula to obtain the cover layer quality index: ； Among them, RQI soil Here are the quality parameters for the capping layer: ω is the moisture content, ωa is the water absorption rate, ωL is the liquid limit, ωp is the plastic limit, ωn is the natural moisture content, and δ... L Let δ be the surface crack indicator function, where L is the crack length. When the crack length is greater than 2m, δ L The value is 1 if it is not 0 otherwise, and the formula excludes L and δ. L All physical parameters of the outer cover layer are normalized parameters.

9. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 1, characterized in that, In step S200, obtaining the time stability coefficient of the mining area to be tested includes: S230. Based on the shutdown time of the mining area to be tested, obtain the basic residual settlement attenuation factor, and correct the basic residual settlement attenuation factor according to whether the hydrogeological parameters meet the condition of "at least two continuous water outlets around the goaf" to obtain the residual settlement attenuation factor. S240. Based on the coal pillar distribution pattern and coal pillar weathering degree parameters in the static spatial parameters, obtain the coal pillar strength reduction coefficient; S250. Based on the aforementioned settling attenuation factor and coal pillar strength reduction coefficient, obtain the time-dependent stability coefficient.

10. The method for evaluating goaf collapse based on multi-source collaborative calibration according to claim 1, characterized in that, In S300, the multi-source dynamic monitoring data includes: Surface subsidence rate, rock mass microstrain data, gravity anomaly data, surface temperature difference data, crack length, and water quality time series data.

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