Tunnel rock mass damage prediction method and system based on micro-seismic data

Abstract

The invention provides a tunnel rock mass damage prediction method and system based on micro-seismic data, and relates to the technical field of tunnel engineering safety monitoring, and the method comprises the steps: collecting micro-seismic monitoring data in a tunnel construction process; performing data preprocessing on the micro-seismic monitoring data to obtain a normalized micro-seismic data sequence; based on the normalized micro-seismic data sequence, micro-seismic characteristic parameters related to rock mass damage are extracted; performing multi-parameter fusion analysis on the micro-seismic characteristic parameters, and forming a comprehensive damage index based on a preset analysis rule set; based on the spatial distribution characteristics of the comprehensive damage indexes, three core monitoring positions are determined in the monitoring area, and the three core monitoring positions are a current construction tunnel face center area, a vault stress concentration area and a side wall stability key area respectively; and constructing a spatial analysis unit for representing the rock mass damage evolution trend based on the three core monitoring positions. The damage assessment accuracy and the construction guidance practicability can be improved.

Description

Technical Field

[0001] This invention relates to the field of tunnel engineering safety monitoring technology, and in particular to a method and system for predicting tunnel rock mass damage based on microseismic data. Background Technology

[0002] With the advancement of various large-scale development projects across the country, especially the continuous expansion of the scale of deep-buried high-stress tunnel construction, rock bursts, collapses, and other disasters induced by rock mass damage have become the core threat to construction safety.

[0003] However, facing various threats to construction safety, existing technologies generally have several shortcomings: First, data processing accuracy is insufficient. In scenarios such as TBM construction, mechanical noise and microseismic signals are intertwined, and the effective signal ratio is often less than 1 / 3. Moreover, signal distortion caused by differences in propagation paths is not effectively corrected. Second, feature analysis has a single dimension. For example, it relies heavily on isolated parameters such as event frequency and energy, and has not formed a comprehensive evaluation index that integrates time-frequency domain features, making it difficult to reflect the essential characteristics of damage. Furthermore, spatial analysis lacks systematicity. For example, traditional methods do not establish dedicated monitoring units for key areas such as the working face and arch, and ignore the impact of spatial variation on damage assessment, resulting in low early warning accuracy. In addition, decision-making coordination is also poor. Monitoring results are mostly qualitative descriptions and cannot be directly converted into quantitative suggestions for adjusting support parameters and optimizing blasting schemes.

[0004] These deficiencies are particularly prominent under complex geological conditions with high geostress. Although existing technologies have made some improvements to protective measures, some practical technical deficiencies, such as the lack of damage correction mechanisms, decision transformation faults, and insufficient feature fusion analysis, still exist. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for predicting tunnel rock mass damage based on microseismic data, which can realize real-time monitoring, grade determination, spatial visualization and construction decision guidance of rock mass damage during construction, thereby improving the accuracy of damage assessment and the practicality of construction guidance.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a tunnel rock mass damage prediction method based on microseismic data, the method comprising: Collect microseismic monitoring data during tunnel construction; preprocess the microseismic monitoring data to obtain a standardized microseismic data sequence; Based on standardized microseismic data sequences, microseismic characteristic parameters related to rock mass damage are extracted; multi-parameter fusion analysis is performed on the microseismic characteristic parameters, and a comprehensive damage index is formed based on a preset set of analysis rules. Based on the spatial distribution characteristics of comprehensive damage indicators, three core monitoring locations were determined within the monitoring area: the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for sidewall stability. Based on these three core monitoring locations, a spatial analysis unit was constructed to characterize the evolution trend of rock mass damage. Based on spatial analysis units, a minimum circumscribed triangle is constructed to establish a quantitative description of rock mass structure stability. This involves calculating the structural morphology parameters of the minimum circumscribed triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, a spatial variation correction coefficient is formed. The comprehensive damage index is then spatially modified according to the spatial variation correction coefficient to obtain the optimized comprehensive damage index. The optimized comprehensive damage index is compared with the preset damage threshold range; the damage level of the tunnel rock mass is determined based on the comparison results. Based on the determined damage level, a spatial distribution map of tunnel rock mass damage is generated; Based on the damage severity level and the spatial distribution map of tunnel rock mass damage, decision-making recommendations are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

[0007] Furthermore, microseismic monitoring data was collected during tunnel construction; the microseismic monitoring data was preprocessed to obtain a standardized microseismic data sequence, including: By deploying an array of microseismic sensors within the surrounding rock of the tunnel, waveform signals of microseismic events induced by construction disturbances are continuously collected. The collected microseismic event waveform signals are denoised to filter out environmental noise interference caused by construction machinery vibration, resulting in denoised waveform signals. Based on a preset amplitude threshold, effective micro-vibration signal segments are identified and extracted from the denoised waveform signal. The extracted microseismic signal segments are subjected to time-domain normalization to eliminate signal intensity deviations caused by differences in propagation paths, resulting in normalized microseismic signal segments. The normalized microseismic signal segments are reorganized and arranged according to the time series to form a standardized microseismic data sequence.

[0008] Furthermore, based on the standardized microseismic data sequence, microseismic characteristic parameters related to rock mass damage are extracted; multi-parameter fusion analysis is performed on the microseismic characteristic parameters, and a comprehensive damage index is formed based on a preset set of analysis rules, including: Based on the normalized microseismic data sequence, the time-frequency domain characteristic parameters of each microseismic event are calculated; Key feature parameters related to rock mass damage are extracted from time-frequency domain feature parameters, including event energy, dominant frequency characteristics, and duration; The key feature parameters are subjected to multi-parameter weighted fusion processing to obtain preliminary damage assessment values; Spatial cluster analysis is performed on the preliminary damage assessment values ​​to identify areas of concentrated damage based on a pre-defined set of analysis rules. Based on the distribution characteristics of the identified concentrated damage areas, a comprehensive damage index is calculated.

[0009] Furthermore, based on the spatial distribution characteristics of comprehensive damage indicators, three core monitoring locations were identified within the monitoring area: the central area of ​​the current construction face, the stress concentration zone at the crown, and the critical area for sidewall stability. Based on these three core monitoring locations, spatial analysis units were constructed to characterize the evolution trend of rock mass damage, including: Based on the spatial distribution characteristics of comprehensive damage indicators, key areas where damage is concentrated are identified. Based on the distribution characteristics of the key areas where damage is concentrated, three core monitoring locations were determined. These three core monitoring locations correspond to the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewalls, respectively. Using three core monitoring locations as reference points, the perpendicular bisectors between adjacent reference points are calculated, and the monitoring area is divided into three non-overlapping spatial partitions. Based on three non-overlapping spatial partitions, the spatial influence range corresponding to the core monitoring location is determined. Based on the geometric characteristics of the spatial influence range, a spatial analysis unit is constructed to characterize the trend of rock mass damage evolution.

[0010] Furthermore, based on spatial analysis units, a minimum circumscribed triangle is constructed to establish a quantitative description of rock mass structural stability. This involves calculating the structural morphological parameters of the minimum circumscribed triangle to obtain the side length ratios and interior angle distribution characteristics. Based on these structural morphological parameters, a spatial variation correction coefficient is generated. This coefficient is then used to spatially correct the comprehensive damage index, resulting in an optimized comprehensive damage index, including: Based on the spatial influence range of the spatial analysis unit, the outer boundary points of the spatial distribution of rock mass damage are determined; By connecting the outermost boundary points, the smallest enclosing region is constructed, forming the smallest circumscribed triangle of the spatial distribution of rock mass damage; Calculate the structural morphological parameters of the minimum circumscribed triangle, including the proportional relationship of the lengths of each side, the distribution of the interior angles, and the ratio of area to perimeter. Based on structural morphological parameters, spatial variation correction coefficients are obtained through multi-parameter fusion calculations. Based on the spatial variation correction coefficient, the spatial distribution characteristics of the comprehensive damage index are calibrated to obtain the optimized comprehensive damage index.

[0011] Furthermore, the optimized comprehensive damage index is compared with the preset damage threshold range; based on the comparison results, the damage level of the tunnel rock mass is determined, including: The optimized comprehensive damage index is compared and analyzed with the preset multi-level damage threshold range to obtain the comparison analysis value. Based on the comparative analysis values, a preliminary assessment of the rock mass damage level was determined; Spatial consistency verification is performed on the preliminary grade determination, and spatial consistency verification result data is obtained by combining the geometric characteristic parameters of the spatial analysis unit; Based on the spatial consistency verification results, the final tunnel rock mass damage level and its confidence level assessment are formed.

[0012] Furthermore, based on the determined damage severity level, a spatial distribution map of tunnel rock mass damage is generated, including: Based on the damage severity level and its confidence level assessment, a mapping relationship between damage level and spatial coordinates is established; Based on the geometric characteristic parameters of the spatial analysis unit, spatial interpolation calculation is performed on the mapping relationship to obtain a continuous spatial distribution surface of rock mass damage; Based on the spatial distribution surface of rock mass damage, a spatial distribution map of tunnel rock mass damage containing multi-level damage indicators is constructed; Mark the damage level and confidence level information of key monitoring locations on the spatial distribution map of tunnel rock mass damage.

[0013] Furthermore, based on the damage severity level and the spatial distribution map of tunnel rock mass damage, decision-making recommendations are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes, including: Based on the degree of damage and its spatial distribution characteristics, identify the rock mass damage areas that require key treatment. Based on the distribution range and degree of damage of the identified rock mass damage areas, a corresponding support parameter adjustment plan is formulated; Based on the distribution characteristics of the rock mass damage area, the key control area for blasting scheme optimization is determined by combining the spatial distribution map of tunnel rock mass damage. By integrating the key control areas of the support parameter adjustment scheme and the blasting scheme optimization, and combining the confidence assessment of the damage level, decision recommendations including support priority and blasting control points are obtained.

[0014] Secondly, a tunnel rock mass damage prediction system based on microseismic data includes: The acquisition module is used to collect microseismic monitoring data during tunnel construction; the microseismic monitoring data is preprocessed to obtain a standardized microseismic data sequence. The analysis module is used to extract microseismic characteristic parameters related to rock mass damage based on standardized microseismic data sequences; perform multi-parameter fusion analysis on the microseismic characteristic parameters, and form a comprehensive damage index based on a preset set of analysis rules; The module is used to determine three core monitoring locations within the monitoring area based on the spatial distribution characteristics of comprehensive damage indicators. These three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewalls. Based on these three core monitoring locations, a spatial analysis unit is constructed to characterize the evolution trend of rock mass damage. The module is used to construct the minimum bounding triangle based on the spatial analysis unit to establish a quantitative description of the stability of the rock mass structure. Specifically, it calculates the structural morphology parameters of the minimum bounding triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, a spatial variation correction coefficient is formed. The spatial characteristics of the comprehensive damage index are corrected according to the spatial variation correction coefficient to obtain the optimized comprehensive damage index. The comparison module is used to compare the optimized comprehensive damage index with the preset damage threshold range; and to determine the damage level of the tunnel rock mass based on the comparison results. The summary module is used to generate a spatial distribution map of tunnel rock mass damage based on the determined damage level; based on the damage level and the spatial distribution map of tunnel rock mass damage, decision-making suggestions are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

[0015] Thirdly, a computing device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0016] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0017] The above-described solution of the present invention has at least the following beneficial effects: By employing noise reduction and time-domain normalization in microseismic data preprocessing, the problems of high mechanical noise interference, low effective signal ratio, and signal distortion caused by propagation path differences in existing construction scenarios are overcome, thereby improving the effectiveness and standardization of microseismic data. By using key time-frequency domain characteristic parameters, such as event energy, dominant frequency characteristics, and duration, and employing weighted fusion and spatial clustering analysis to construct comprehensive damage indicators, the problems of traditional techniques—such as single-dimensional feature analysis, reliance on isolated parameters, and inability to reflect the essential characteristics of damage—are overcome, thus obtaining core assessment criteria that accurately characterize the overall damage state of the rock mass. Furthermore, by constructing spatial analysis units based on three core monitoring locations—the center of the tunnel face, the stress concentration zone at the arch crown, and the key stability zone of the sidewalls—and combining them with the minimum circumscribed triangle... The method of forming spatial variation correction coefficients from structural morphology parameters overcomes the problems of lack of systematicity in existing spatial analysis, blind spots in key area monitoring, and the absence of damage correction mechanisms, thereby achieving precise spatial positioning and assessment optimization of rock mass damage. Because it employs multi-level damage threshold comparison plus spatial consistency verification to determine damage level, constructs a damage spatial distribution map with confidence level annotations, and combines damage characteristics to output quantitative support parameters and blasting scheme suggestions, it overcomes the problems of traditional monitoring results being largely qualitatively descriptive, having poor decision-making coherence, and having decision-making transition faults. This achieves visualized presentation of rock mass damage and direct guidance for construction decisions, comprehensively improving the accuracy of tunnel rock mass damage prediction and the practicality of construction guidance, effectively reducing the risk of construction disasters such as rock bursts and collapses. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a tunnel rock mass damage prediction method based on microseismic data provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of a tunnel rock mass damage prediction system based on microseismic data provided in an embodiment of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] like Figure 1 As shown, embodiments of the present invention propose a tunnel rock mass damage prediction method based on microseismic data, the method comprising the following steps: Step 1: Collect microseismic monitoring data during tunnel construction; perform data preprocessing on the microseismic monitoring data to obtain a standardized microseismic data sequence; Step 2: Based on the standardized microseismic data sequence, extract the microseismic characteristic parameters related to rock mass damage; perform multi-parameter fusion analysis on the microseismic characteristic parameters, and form a comprehensive damage index based on the preset analysis rule set; Step 3: Based on the spatial distribution characteristics of comprehensive damage indicators, three core monitoring locations are determined within the monitoring area. These three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for sidewall stability. Based on these three core monitoring locations, a spatial analysis unit is constructed to characterize the trend of rock mass damage evolution. Step 4: Based on the spatial analysis unit, construct the minimum circumscribed triangle to establish a quantitative description of the rock mass structure stability. That is, calculate the structural morphology parameters of the minimum circumscribed triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, form a spatial variation correction coefficient. According to the spatial variation correction coefficient, perform spatial feature correction on the comprehensive damage index to obtain the optimized comprehensive damage index. Step 5: Compare the optimized comprehensive damage index with the preset damage threshold range; determine the damage level of the tunnel rock mass based on the comparison results. Step 6: Based on the determined damage level, generate a spatial distribution map of tunnel rock mass damage; Step 7: Based on the damage level and the spatial distribution map of tunnel rock mass damage, decision-making suggestions are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

[0022] In this embodiment of the invention, by collecting and preprocessing microseismic monitoring data during tunnel construction, mechanical noise and signal propagation deviations can be filtered out to obtain a standardized microseismic data sequence. Based on this sequence, microseismic characteristic parameters related to rock mass damage are extracted and multi-parameter fusion analysis is carried out, which avoids the limitations of single-parameter evaluation and forms a comprehensive damage index that accurately reflects the rock mass damage state according to preset rules. The spatial distribution of the comprehensive damage index determines three core monitoring locations, and a spatial analysis unit is constructed to specifically cover key areas prone to tunnel problems and effectively characterize the evolution trend of rock mass damage. Based on the spatial analysis unit, a minimum circumscribed triangle is constructed, and structural morphology parameters are calculated to form a spatial variation correction coefficient, which can correct the impact of spatial differences on damage assessment and obtain an optimized comprehensive damage index. The optimized index is compared with a preset damage threshold to determine the damage level, which can clearly identify the degree of rock mass damage. A damage spatial distribution map is formed based on the damage level, which can intuitively present the spatial distribution of damage. Decision suggestions for adjusting support parameters and optimizing blasting schemes are given based on the damage level and distribution map, which can directly guide on-site construction and help ensure the safety of tunnel construction.

[0023] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1 involves continuously acquiring waveform signals of microseismic events induced by construction disturbance through a microseismic sensor array deployed within the tunnel surrounding rock. Specifically, this includes: firstly, based on the tunnel's cross-sectional dimensions and the range of influence of construction disturbance, deploying a microseismic sensor array within the tunnel surrounding rock at a circumferential interval of 3 to 5 meters and a radial depth of 2 to 4 meters to ensure that the sensors can cover the current construction face and the surrounding rock area at a certain distance behind it; then, activating the sensor array to continuously acquire waveform signals of microseismic events generated by microfractures in the rock mass during construction at a frequency of no less than 1000 sampling points per second. These continuously acquired raw waveform signals completely record the rock mass activity information induced by construction disturbance.

[0024] Step 1.2 involves denoising the acquired microseismic event waveform signal to filter out environmental noise interference from construction machinery vibration, resulting in a denoised waveform signal. Specifically, this includes processing the acquired microseismic event waveform signal containing noise from construction machinery such as TBM tunneling and blasting vibration using an adaptive wavelet threshold denoising method. First, the original waveform signal is decomposed into wavelet coefficients of a preset number of layers, for example, 8 layers. Then, based on the noise energy distribution of each layer of wavelet coefficients, the threshold is dynamically adjusted. Wavelet coefficients smaller than the threshold are zeroed out, retaining only those that reflect the characteristics of the microseismic signal. Finally, an inverse wavelet transform is performed to reconstruct the processed wavelet coefficients into a complete waveform, resulting in the denoised waveform signal. This step effectively filters out environmental noise interference with the microseismic signal, making the characteristics of the microseismic signal clearer.

[0025] Step 1.3: Based on a preset amplitude threshold, identify and extract valid microseismic signal segments from the denoised waveform signal. Specifically, this includes: based on the denoised waveform signal, first collect valid microseismic signal data from the past six months under the same geological conditions of the tunnel, analyze the maximum and minimum amplitude values ​​of these valid signals, and determine that the lower limit of the amplitude threshold is 80% of the minimum amplitude of the valid signal, with no upper limit. Then, use a sliding time window to scan the denoised waveform signal segment by segment. The window length is set to 1.5 times the typical duration of the microseismic event. When the amplitude of the signal within the window exceeds the set amplitude threshold, the signal segment corresponding to the window is marked as a candidate valid segment. Then, by comparing the similarity between the signal waveform of the candidate segment and the historical valid microseismic signal waveform, interference segments with inconsistent waveform characteristics are eliminated, and finally, valid microseismic signal segments are extracted.

[0026] Step 1.4 involves time-domain normalization of the extracted microseismic signal segments to eliminate signal intensity deviations caused by differences in propagation paths, resulting in normalized microseismic signal segments. Specifically, this includes: considering that the extracted effective microseismic signal segments may exhibit intensity deviations due to different propagation paths, a standard microseismic source with known energy is prepared and fixed in a borehole in the surrounding rock at a predetermined distance behind the tunnel face. The borehole depth is consistent with the sensor deployment depth, and all sensors simultaneously acquire signals. After removing abnormal sampling points, the average signal intensity of each acquisition is calculated as the standard signal intensity for the corresponding sensor. Next, the attenuation coefficient is determined, and the sensor closest to the standard source is selected as the benchmark, recording its distance and standard intensity. Then, the attenuation coefficient is recorded. The distance and standard intensity of the sensors are used to determine the attenuation coefficient for each non-reference sensor. The attenuation coefficient is obtained by dividing the reference intensity by the sensor intensity and then dividing by the ratio of the reference distance to the sensor distance. The average value is taken as the uniform attenuation coefficient for the tunnel surrounding rock. For each effective microseismic signal segment, the straight-line distance from the event location to each receiving sensor is calculated by combining the time difference of signal reception. Based on the reference distance, reference intensity, and attenuation coefficient, the theoretical signal intensity at that distance is calculated, i.e., the theoretical correction value. Finally, the ratio of the theoretical correction value to the actual intensity is calculated to obtain the intensity correction coefficient. The intensity of each sampling point of the signal is multiplied by this coefficient to make the signal intensity characterization of different propagation paths consistent, thus obtaining the normalized microseismic signal segment.

[0027] Step 1.5 involves reorganizing and arranging the normalized microseismic signal segments according to the time series to form a standardized microseismic data sequence. This includes: collecting all normalized microseismic signal segments; extracting the corresponding occurrence timestamp from the acquisition record of each segment; sorting all segments in order of timetamp from earliest to latest; connecting the sorted segments sequentially to form a continuous signal sequence; labeling the corresponding sensor number and spatial coordinates of the microseismic event at every interval in the sequence; and finally, performing an integrity check on the entire sequence and supplementing the timestamps corresponding to missing segments caused by temporary acquisition interruptions to form a standardized microseismic data sequence. The standardized microseismic data sequence completely preserves the temporal and spatial information of the microseismic events, and the data format is uniform.

[0028] In this embodiment of the invention, a microseismic sensor array deployed within the tunnel surrounding rock continuously acquires waveform signals of microseismic events induced by construction disturbances, ensuring the acquisition of complete and continuous raw microseismic data, providing a foundation for data processing. Noise reduction processing of the acquired waveform signals effectively filters out environmental noise interference generated by construction machinery vibrations, reducing noise interference with microseismic signal analysis. Extracting effective microseismic signal segments from the denoised signals based on a preset amplitude threshold accurately identifies useful signals related to rock mass damage and eliminates invalid signal components. Time-domain normalization of the extracted effective signal segments eliminates signal intensity deviations caused by differences in propagation paths, ensuring the comparability of microseismic signal intensities at different locations. Finally, the normalized signal segments are recombined according to a time sequence to form a standardized microseismic data sequence, resulting in a unified data structure and clear logic, providing a data foundation for extracting microseismic characteristic parameters and constructing comprehensive damage indicators.

[0029] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Based on the normalized microseismic data sequence, calculate the time-frequency domain characteristic parameters of each microseismic event. Specifically, this includes: extracting each independent microseismic event signal from the normalized microseismic data sequence; for each microseismic event signal, calculating its peak amplitude, rise time, and fall time in the time domain, where the rise time refers to the time required for the signal to rise from 10% to 90% of the peak amplitude, and the fall time refers to the time required for the signal to fall from 90% to 10% of the peak amplitude; in the frequency domain, converting the time-domain signal to a frequency-domain signal, calculating the power spectral density of the signal, and thus obtaining the signal's dominant frequency range, bandwidth, and power value corresponding to the peak frequency; integrating these time-domain and frequency-domain calculation results to form the complete time-frequency domain characteristic parameters of each microseismic event.

[0030] Step 2.2 involves extracting key characteristic parameters related to rock mass damage from the time-frequency domain feature parameters, including event energy, dominant frequency characteristics, and duration. Specifically, this includes: collecting damage research data on similar rock masses in the tunnel area, combining the correlation records of rock mass damage degree and microseismic parameters in past projects, determining the types of parameters closely related to rock mass damage, and finding that event energy, dominant frequency characteristics, and duration have the most significant impact on damage assessment. Event energy is calculated by time integration of the amplitude of the microseismic event signal, with the integration interval being the complete time period from the start to the end of the signal. The dominant frequency characteristic is selected from the frequency value corresponding to the maximum power spectral density in the frequency domain analysis, and the proportion of the power corresponding to the dominant frequency to the total power is recorded. The duration is defined as the time period during which the signal amplitude exceeds a preset minimum effective amplitude threshold, starting from the first time the signal reaches the threshold and ending the last time the signal falls below the threshold. Key characteristic parameters are extracted from the time-frequency domain feature parameters in this way.

[0031] Step 2.3 involves performing multi-parameter weighted fusion processing on the key characteristic parameters to obtain preliminary damage assessment values. Specifically, this includes: scoring the influence of each key characteristic parameter on the rock mass damage assessment, with event energy reflecting the damage scale most directly and receiving the highest weight score, followed by dominant frequency characteristics, and duration receiving a relatively lower weight score. After averaging the expert scores, the weight coefficients for event energy, dominant frequency characteristics, and duration are determined to be 0.5, 0.3, and 0.2, respectively. Next, the key characteristic parameters of each microseismic event are standardized, converting each parameter value to the range of 0 to 1. Then, each standardized key characteristic parameter is multiplied by its corresponding weight coefficient, and the three products are summed to obtain the preliminary damage assessment value for each microseismic event.

[0032] Step 2.4 involves spatial clustering analysis of the preliminary damage assessment values. Based on a pre-defined set of analysis rules, damage concentration areas are identified. Specifically, this includes: acquiring the spatial coordinates of each microseismic event within the tunnel, associating these spatial coordinates with the corresponding preliminary damage assessment values ​​to form a dataset containing location and assessment values; performing spatial clustering analysis on this dataset, i.e., setting the number of clusters to 5 to 8 based on the actual extent of the tunnel construction area and past damage distribution patterns to ensure coverage of areas where damage may occur; during the clustering process, using spatial distance and the similarity of preliminary damage assessment values ​​as the clustering basis, grouping microseismic events with similar spatial locations and small differences in assessment values ​​into the same category; then, based on the pre-defined set of analysis rules, judging each cluster category. If the average preliminary damage assessment value of a certain category exceeds the set damage warning threshold, and the number of microseismic events in that category exceeds 15% of the total number of events, then the spatial area corresponding to that category is determined as a damage concentration area. This method effectively locates key areas of rock mass damage.

[0033] Step 2.5: Based on the distribution characteristics of the identified damage concentration areas, calculate the comprehensive damage index. This includes: statistically analyzing the basic distribution characteristics of each damage concentration area, including the area's spatial range, the number density of microseismic events within the area (i.e., the number of microseismic events per unit area), and the average value of the preliminary damage assessment of all microseismic events within the area; then, calculate the comprehensive damage index based on these distribution characteristics. The calculation first quantifies the area, number density, and average preliminary assessment value according to preset standards to ensure they are at the same numerical level. Then, multiply the converted area by 0.3, the number density by 0.4, and the average preliminary assessment value by 0.3. Finally, add the three products to obtain the local damage contribution value corresponding to the damage concentration area. Add the local damage contribution values ​​of all damage concentration areas to the basic damage value of the tunnel as a whole that does not form damage concentration areas to obtain the comprehensive damage index reflecting the overall damage state of the tunnel rock mass.

[0034] In this embodiment of the invention, the time-frequency domain characteristic parameters of each microseismic event are calculated based on standardized microseismic data sequences, which can comprehensively capture the basic information related to rock mass damage in the microseismic signal. Key characteristic parameters such as event energy, dominant frequency characteristics, and duration are extracted from the time-frequency domain characteristic parameters, focusing on the core influencing factors of rock mass damage and eliminating interference from irrelevant parameters, making the characteristic analysis more aligned with damage assessment needs. Multi-parameter weighted fusion processing of the key characteristic parameters yields a preliminary damage assessment value, which can comprehensively consider the different degrees of influence of each parameter on the damage, avoiding the one-sidedness of single-parameter assessment and improving the rationality of the preliminary assessment results. Spatial cluster analysis of the preliminary damage assessment value is performed, and damage concentration areas are identified based on preset rules, which can accurately locate key areas of rock mass damage, solving the problem that traditional global analysis struggles to detect local damage clusters. Finally, a comprehensive damage index is calculated based on the distribution characteristics of the damage concentration areas, integrating local damage information with the overall distribution pattern, allowing the index to comprehensively reflect the overall damage state of the tunnel rock mass, providing a reliable core basis for the construction of spatial analysis units and damage level determination.

[0035] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Based on the spatial distribution characteristics of the comprehensive damage index, identify key areas where damage is concentrated. Specifically, this includes: first, associating the comprehensive damage index with the three-dimensional spatial coordinates of the tunnel to form a spatial distribution heat map of the comprehensive damage index. Different colors are used to mark the index values ​​in the heat map, with red areas representing areas with higher comprehensive damage indices and blue areas representing areas with lower indices; then, setting a threshold for the comprehensive damage index, which is determined by referring to the index values ​​when the rock mass is slightly damaged under the geological conditions of the tunnel location, and marking areas in the heat map where the index values ​​exceed the threshold as candidate areas; then, performing a continuity check on the candidate areas. If the area of ​​a candidate area exceeds 5 square meters and there are no obvious fracture intervals inside, then the area is identified as a key area where damage is concentrated.

[0036] Step 3.2: Based on the distribution characteristics of the key areas where damage is concentrated, three core monitoring locations are determined. These three core monitoring locations correspond to the central area of ​​the current construction face, the stress concentration area at the tunnel crown, and the critical area for sidewall stability, respectively. Specifically, this includes: First, analyzing the correspondence between each key area and the key parts of the tunnel construction based on the identified key areas where damage is concentrated; For key areas distributed near the current construction face, the geometric center of this area is taken as the core monitoring location of the central area of ​​the current construction face, which can directly reflect the real-time damage state of the rock mass at the face; For key areas distributed at the tunnel crown, combined with the design axis of the tunnel crown, the point within this area that is closest to the design axis of the crown and has the highest comprehensive damage index is selected as the core monitoring location of the stress concentration area at the crown, which can accurately capture the damage changes caused by the stress at the crown; For key areas distributed on both sides of the tunnel, based on the stability requirements of the sidewalls, the point at the midpoint along the height direction of the sidewalls within this area and with a high comprehensive damage index is selected as the core monitoring location of the critical area for sidewall stability. In this way, three core monitoring locations are determined, covering the key parts of the tunnel that are prone to damage.

[0037] Step 3.3: Using the three core monitoring locations as reference points, calculate the perpendicular bisectors between adjacent reference points to divide the monitoring area into three non-overlapping spatial partitions. Specifically, this includes: first, marking the three core monitoring locations in the tunnel's three-dimensional spatial coordinates, denoted as point A (center of the tunnel face), point B (stress concentration area at the arch crown), and point C (critical area for sidewall stability); connecting points A and B, measuring the length of line segment AB and finding its midpoint, then drawing a straight line perpendicular to line segment AB through this midpoint; this line is the perpendicular bisector of line segment AB; similarly, drawing the perpendicular bisectors of line segments BC and AC respectively; the three perpendicular bisectors intersect within the monitoring area, dividing the entire monitoring area into three non-overlapping spatial partitions: the partition centered on point A, the partition centered on point B, and the partition centered on point C. This division method ensures that each partition corresponds to a core monitoring location, avoiding analysis interference caused by overlapping monitoring areas.

[0038] Step 3.4: Based on three non-overlapping spatial partitions, determine the spatial influence range corresponding to the core monitoring location. Specifically, this includes: for the three non-overlapping spatial partitions, first select multiple sampling points within each partition, with the sampling points evenly distributed in each corner of the partition, and the number of sampling points in each partition not less than 20; measure the distance from each sampling point to the corresponding core monitoring location, and record the comprehensive damage index value of the sampling point; by analyzing the variation pattern of the comprehensive damage index of the sampling points, when the comprehensive damage index value of a certain sampling point drops to 50% of the index value of the core monitoring location, determine the location of the sampling point as the boundary point of the spatial influence range of the core monitoring location; connect all boundary points within the same partition to form a closed region, and this closed region is the spatial influence range corresponding to the core monitoring location.

[0039] Step 3.5: Based on the geometric characteristics of the spatial influence range, construct a spatial analysis unit characterizing the rock mass damage evolution trend. This specifically includes: extracting the geometric characteristics of the spatial influence range of the three core monitoring locations, including the shape of each influence range (e.g., circular, elliptical, or irregular polygon), area size, and boundary contour data; then combining this with the temporal variation data of comprehensive damage indicators within each influence range, such as the maximum, minimum, and rate of change of the indicators over the past 24 hours, integrating these geometric characteristics with the temporal variation data; establishing an independent monitoring data record table for each spatial influence range, updating the damage indicator data, geometric boundary changes, and indicator change trends within the influence range in real time. This independent monitoring unit, containing geometric characteristics, real-time data, and change trends, is identified as the spatial analysis unit characterizing the rock mass damage evolution trend.

[0040] In this embodiment of the invention, the key areas of concentrated damage distribution are identified based on the spatial distribution characteristics of comprehensive damage indicators, which can accurately pinpoint the key areas of rock mass damage. Based on the distribution characteristics of these key areas, three core monitoring locations are determined: the central area of ​​the current construction face, the stress concentration area at the crown, and the critical area for sidewall stability. This allows for targeted coverage of critical parts prone to damage during tunnel construction, overcoming the shortcomings of traditional methods that do not establish dedicated monitoring units for key areas. Using these three core monitoring locations as reference points, the perpendicular bisectors between adjacent reference points are calculated to divide the space into three non-overlapping zones, avoiding analytical confusion caused by overlapping monitoring areas and making the zone division clearer and more orderly. The spatial influence range corresponding to each core monitoring location is determined based on these non-overlapping spatial zones, clarifying the effective coverage area of ​​each core location and providing boundary criteria for accurately analyzing the damage status of different areas. A spatial analysis unit characterizing the evolution trend of rock mass damage is constructed based on the geometric characteristics of the spatial influence range, systematically integrating damage information from key areas to form a structured analytical framework, solving the problem of the lack of systematicity in traditional spatial analysis.

[0041] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Based on the spatial influence range of the spatial analysis unit, determine the outer boundary points of the spatial distribution of rock mass damage. This includes: retrieving the spatial influence range data of each spatial analysis unit, including the three-dimensional coordinate contour of the range and the distribution coordinates of all microseismic events within it; uniformly distributing sampling points within the spatial influence range at a density of one sampling point per square meter to ensure that the sampling points can cover the entire contour and internal area of ​​the influence range; calculating the straight-line distance from each sampling point to the geometric center of the spatial analysis unit, and simultaneously determining whether the sampling point is located at the edge of the influence range contour, i.e., there are no other sampling points within 0.5 meters around the sampling point; marking the sampling point farthest from the center and located at the edge of the contour as candidate boundary points, and then filtering the candidate boundary points to remove redundant points with a distance of less than 1 meter between them, finally determining 10-15 outer boundary points of the spatial distribution of rock mass damage that can completely delineate the outer contour of the damage area.

[0042] Step 4.2 involves connecting the outermost boundary points to construct the smallest enclosing region, forming the smallest circumscribed triangle of the rock mass damage spatial distribution. Specifically, this includes: sorting the outer boundary points according to their X, Y, and Z three-dimensional coordinates; extracting the boundary points with the largest and smallest coordinate values ​​along the tunnel axis, the horizontal direction of the vertical axis, and the vertical direction; initially selecting 8-10 key boundary points most likely to form the outer contour; constructing the smallest enclosing region by probing point by point; first, arbitrarily selecting three key boundary points to form an initial triangle and calculating its area; then, replacing the vertices of the initial triangle with other key boundary points in turn, recalculating the area of ​​the new triangle, and retaining the triangle with the smallest area that completely encloses all the outer boundary points; repeating this replacement process until no smaller enclosing triangle can be found, ultimately forming the smallest circumscribed triangle of the rock mass damage spatial distribution. This triangle reflects the spatial extent of the damage area with the most compact geometric shape.

[0043] Step 4.3: Calculate the structural morphological parameters of the minimum circumscribed triangle. These parameters include the proportional relationships of the side lengths, the distribution of interior angle measures, and the area-to-perimeter ratio. Specifically, this involves: obtaining the coordinates of the three vertices of the minimum circumscribed triangle; calculating the actual lengths of the three sides; sorting the three sides in descending order of length; calculating the ratio of the longest side to the shortest side and the ratio of the longest side to the side of moderate length to obtain the proportional relationships of the side lengths; then, using the law of cosines, calculating the degree measures of the three interior angles of the triangle using the lengths of the three sides; recording the specific values ​​of each interior angle measure and whether they fall within a reasonable range of 60° to 120° to form interior angle measure distribution data; finally, calculating the area of ​​the triangle by adding the lengths of the three sides to obtain the perimeter, and dividing the area by the perimeter to obtain the area-to-perimeter ratio. In this way, the structural morphological parameters of the minimum circumscribed triangle are calculated completely.

[0044] Step 4.4: Based on structural morphology parameters, spatial variation correction coefficients are obtained through multi-parameter fusion calculations. Specifically, this includes: determining the influence weights of each structural morphology parameter on spatial variation by combining the geological conditions of the tunnel project and past damage assessment experience: the side length ratio reflects the uniformity of damage distribution, with a weight set at 0.3; the interior angle distribution reflects the regularity of the damaged area's morphology, with a weight set at 0.4; and the area-to-perimeter ratio reflects the compactness of the damaged area, with a weight set at 0.3. Each structural morphology parameter is then standardized, converting the parameter values ​​to the range of 0 to 1. For example, the closer the side length ratio is to 1, the more uniform the distribution, and the closer the standardized value is to 1; the closer the interior angle is to 90°, the more regular the morphology, and the closer the standardized value is to 1; and the larger the area-to-perimeter ratio, the more compact the area, and the closer the standardized value is to 1. Each standardized parameter is multiplied by its corresponding weight, and the three products are summed to obtain the spatial variation correction coefficient for each spatial analysis unit. This coefficient quantifies the degree of influence of spatial variation on damage assessment.

[0045] Step 4.5: Based on the spatial variation correction coefficient, the comprehensive damage index is calibrated according to its spatial distribution characteristics to obtain the optimized comprehensive damage index. Specifically, this includes: obtaining the original comprehensive damage index value for each spatial analysis unit and the corresponding spatial variation correction coefficient; determining the relationship between the correction coefficient and 1: if the correction coefficient is greater than 1, it indicates that the original comprehensive damage index may underestimate the actual damage level due to spatial variation factors such as irregular damage area morphology and uneven distribution; if the correction coefficient is less than 1, it indicates that the original index may overestimate the actual damage level; multiplying the original comprehensive damage index value by the corresponding spatial variation correction coefficient to obtain the calibrated damage index value; checking the reasonableness of the calibrated value; if the value exceeds the reasonable range of rock mass damage under the geological conditions (this reasonable range is set with reference to past engineering data), the correction coefficient value is adjusted appropriately until the calibrated value is within a reasonable range. Finally, the optimized comprehensive damage index for each spatial analysis unit is obtained. This index eliminates the assessment bias caused by spatial variation and better reflects the actual damage state of the rock mass.

[0046] In this embodiment of the invention, the outer boundary points of the spatial distribution of rock mass damage are determined based on the spatial influence range of the spatial analysis unit. This accurately locks the outer contour of the damaged area, avoiding analysis deviations caused by ambiguous boundaries, and providing clear and accurate basic points for constructing the minimum enclosing region. By connecting the outermost boundary points to form the minimum circumscribed triangle of the rock mass damage spatial distribution, the dispersed damage area can be transformed into a regular geometric shape, making the originally difficult-to-quantify spatial distribution of damage easier to analyze, and providing an intuitive geometric carrier for assessing the stability of the rock mass structure. The structural relationships such as the ratio of the lengths of the sides, the distribution of the interior angles, and the area-to-perimeter ratio of the minimum circumscribed triangle are calculated. The spatial parameters can extract spatial feature information of the damaged area from the geometric dimension, accurately capture the spatial variation differences in different areas, and provide specific data support for quantifying the impact of spatial variation on damage assessment. Based on these structural morphological parameters, multi-parameter fusion calculations are performed to obtain the spatial variation correction coefficient, which fills the gap in the damage correction mechanism in traditional technology. It can specifically quantify the assessment error caused by spatial variation and solve the problem that traditional methods ignore the influence of spatial variation. Finally, the spatial distribution characteristics of the comprehensive damage index are calibrated using the spatial variation correction coefficient, which can effectively eliminate the index deviation caused by spatial variation and obtain an optimized comprehensive damage index that is more in line with the actual rock mass damage state.

[0047] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1 involves comparing the optimized comprehensive damage index with the preset multi-level damage threshold intervals to obtain the comparison analysis value. Specifically, this includes: combining the rock mechanics parameters of the tunnel area, damage case data of similar tunnel projects, and indoor rock damage simulation test results, setting three levels of damage threshold intervals: 0.2 to 0.4 for mild damage, 0.4 to 0.6 for moderate damage, and 0.6 to 0.8 for severe damage. For each spatial analysis unit, the optimized comprehensive damage index is compared with these three threshold intervals one by one to determine which interval the index value falls into. At the same time, the distance between the index value and the upper and lower boundaries of the interval is calculated. For example, when the index value is 0.5, it falls into the moderate damage interval, with a distance of 0.1 from the lower boundary of 0.4 and a distance of 0.1 from the upper boundary of 0.6. The combination of the interval and the distance from the boundary is used as the comparison analysis value of the spatial analysis unit.

[0048] Step 5.2, based on the comparative analysis values, determines the preliminary level of rock mass damage. Specifically, this includes: establishing a correspondence rule between the comparative analysis values ​​and the damage level; if the comparative analysis value shows that the index falls within the light damage range, the rock mass damage level of the spatial analysis unit is preliminarily determined to be light damage; if it falls within the moderate damage range, it is preliminarily determined to be moderate damage; if it falls within the severe damage range, it is preliminarily determined to be severe damage; at the same time, the cases where the index value is close to the boundary of the range are marked. For example, if the index value is 0.39, then 0.39 is close to the upper boundary of the light range (0.4); or 0.61, then 0.61 is close to the lower boundary of the severe range (0.6). In the preliminary level determination result, it is noted that the index value is close to the adjacent level, ensuring that the preliminary determination result is both clear and reflects the critical state of the value.

[0049] Step 5.3 involves spatial consistency verification of the preliminary damage level assessment. This is achieved by combining the geometric characteristic parameters of the spatial analysis units to obtain spatial consistency verification result data. Specifically, this includes: retrieving the geometric characteristic parameters of each spatial analysis unit, including the unit's spatial coordinate range, the overlap length with adjacent units, and the unit area; checking whether the preliminary damage levels of adjacent spatial analysis units are consistent or exhibit a reasonable transition, for example, units adjacent to a slightly damaged unit should be slightly or moderately damaged. If a slightly damaged unit is adjacent to a severely damaged unit, it is marked as a level anomaly; simultaneously, the total area of ​​the spatial analysis units corresponding to each damage level is calculated as a percentage of the total area of ​​the monitored area. If the area percentage of a certain level exceeds 60%, but there are individual adjacent units with excessively large level differences, it is also marked as a spatial distribution anomaly; and integrating the data on the number of anomalous level units, the boundary lengths of anomalous adjacent units, and the area percentages of each level to form the spatial consistency verification result data.

[0050] Step 5.4: Based on the spatial consistency verification results, the final tunnel rock mass damage level and its confidence assessment are formed. This includes: analyzing the spatial consistency verification results; if the proportion of anomalous units to the total number of units is less than 10%, the spatial consistency of the preliminary level determination is considered good, and the preliminary level determination is directly adopted; if the proportion of anomalous units exceeds 10%, the optimized comprehensive damage index of the anomalous units is re-verified, and the level is adjusted in conjunction with their geometric characteristic parameters, such as whether they are located in the stress concentration arch area. For example, if an anomalous unit in the arch area is initially determined to be slightly damaged but adjacent units are mostly moderately damaged, it can be adjusted to moderate damage based on the arch stress characteristics; then, the confidence of the final level determination result is calculated. The confidence calculation is based on the proportion of units that passed the spatial consistency verification to the total number of units. For example, if the proportion of units that passed the verification is 92%, the confidence is initially set at 92%. Then, the confidence is fine-tuned based on the adjusted number of units, with the adjustment range not exceeding 5%. Finally, the tunnel rock mass damage level and its corresponding confidence assessment for each spatial analysis unit are formed, making the damage level results more reliable.

[0051] In this embodiment of the invention, the optimized comprehensive damage index is compared and analyzed with a preset multi-level damage threshold range to obtain a comparison analysis value. This quantitative comparison avoids the ambiguity of the qualitative description of traditional monitoring results and provides a clear numerical basis for damage level determination. Based on the comparison analysis value, a preliminary level of rock mass damage is determined, which can quickly pinpoint the approximate extent of rock mass damage. The preliminary level determination is combined with the geometric feature parameters of the spatial analysis unit for spatial consistency verification, which can eliminate level misjudgments caused by local spatial variations and solve the problems of traditional methods ignoring spatial correlation and low early warning accuracy, resulting in more spatially reasonable verification result data. Based on the spatial consistency verification result data, the final tunnel rock mass damage level and its confidence assessment are formed, which not only fills the gap of traditional technology lacking quantitative reliability of results, but also makes the damage level results more convincing.

[0052] In a preferred embodiment of the present invention, step 6 above may include: Step 6.1: Based on the damage severity level and its confidence level assessment, establish a mapping relationship between damage level and spatial coordinates. This specifically includes: retrieving the damage severity level of each spatial analysis unit, such as mild, moderate, and severe, and its corresponding confidence level assessment value; simultaneously obtaining the geometric center coordinates of each spatial analysis unit, which are obtained by calculating the average of the coordinates of all vertices of the unit boundary contour, including the X coordinate along the tunnel axis, the Y coordinate in the horizontal direction perpendicular to the axis, and the Z coordinate in the vertical direction; and associating the geometric center coordinates of each spatial analysis unit with the corresponding damage severity level and confidence level value one by one. For example, coordinates (X1, Y1, Z1) correspond to mild damage with a confidence level of 90%; coordinates (X2, Y2, Z2) correspond to moderate damage with a confidence level of 85%, forming an association table with spatial coordinates as the index and damage information as the content. This is how the mapping relationship between damage level and spatial coordinates is established.

[0053] Step 6.2: Based on the geometric characteristic parameters of the spatial analysis units, perform spatial interpolation calculations on the mapping relationship to obtain a continuous spatial distribution surface of rock mass damage. Specifically, this includes: first, determining the range of spatial interpolation based on the mapping relationship between damage level and spatial coordinates. This range covers the entire tunnel monitoring area and extends 10 meters beyond the boundary of the monitoring area to avoid interpolation distortion in edge areas; then, retrieving the geometric characteristic parameters of each spatial analysis unit, including the unit's area size and boundary coordinates, to determine the spatial distance and distribution density between different units. For areas with denser unit distribution, such as near the tunnel face, set... A smaller interpolation grid spacing is used, while a larger grid spacing is set for sparsely distributed areas. The discrete data in the mapping relationship are calculated, and the quantized values ​​corresponding to the damage levels of adjacent spatial analysis units, such as assigning 1 to mild damage, 2 to moderate damage, and 3 to severe damage, are weighted with the confidence level. By fitting the spatial variation trend of adjacent data points, the quantized value of the damage level corresponding to each interpolation grid node is calculated. The quantized values ​​of all grid nodes are connected to form a continuous spatial distribution surface of rock mass damage. This surface fills the gaps in damage data between discrete analysis units, allowing the damage distribution to present a more complete spatial pattern.

[0054] Step 6.3: Based on the spatial distribution surface of rock mass damage, construct a spatial distribution map of tunnel rock mass damage containing multi-level damage indicators. Specifically, this includes: establishing a simplified 3D template of the tunnel based on the spatial distribution surface of rock mass damage from the previous step. The template includes the outlines of key structures such as the tunnel's arch, sidewalls, and face, and the coordinate system of the template is consistent with the coordinate system of the spatial analysis unit; setting multi-level damage indicators according to the damage level: light green for areas with minor damage, orange-yellow for areas with moderate damage, and dark red for areas with severe damage. A legend is also set on the side of the map, indicating the damage level and corresponding quantitative value range for each color; superimposing a continuous spatial distribution surface of damage on the template, ensuring the color filling of the surface precisely matches the template structure; adding coordinate scale lines and units to the map, and marking the starting mileage of the tunnel axis and the location of key sections. This method constructs a spatial distribution map of tunnel rock mass damage containing multi-level damage indicators.

[0055] Step 6.4: Mark the damage level and confidence level information of key monitoring locations on the spatial distribution map of tunnel rock mass damage. This includes: extracting the precise spatial coordinates of each of the three core monitoring locations and locating the corresponding locations on the spatial distribution map of tunnel rock mass damage; marking each core monitoring location with different special symbols: the center area of ​​the tunnel face is marked with a black triangle, the stress concentration area of ​​the arch is marked with a blue circle, and the key area for sidewall stability is marked with a red square. The symbol size is set to 1.5 times the legend size to ensure it is easily visible and identifiable on the map; next to each special symbol, mark the corresponding damage level and confidence level information. For example, marking the center of the tunnel face next to a black triangle indicates minor damage with a confidence level of 92%, and marking the stress concentration area of ​​the arch next to a blue circle indicates moderate damage with a confidence level of 88%. This marking allows construction personnel to quickly focus on the damage status and reliability of key areas.

[0056] In this embodiment of the invention, a mapping relationship between damage level and spatial coordinates is established based on the damage severity level and its confidence level assessment. This enables precise association between abstract damage levels and specific spatial locations of the tunnel, solving the problem of traditional monitoring results lacking clear spatial correspondence and difficulty in locating damage locations. Spatial interpolation calculations are performed on the mapping relationship based on the geometric characteristic parameters of the spatial analysis units, filling the gaps in damage data between different analysis units and transforming discrete damage information into a continuous spatial distribution surface of rock mass damage. This avoids fragmented damage distribution and facilitates a direct understanding of the overall damage trend of the tunnel. Based on this surface, a spatial distribution map of tunnel rock mass damage containing multi-level damage markers is constructed. This clearly distinguishes light, moderate, and severe damage areas through different markers, addressing the deficiency of traditional qualitative descriptions in intuitively presenting damage differences. Marking the damage level and confidence level information of key monitoring locations on the distribution map allows for rapid focus on the damage status and reliability of key areas such as the center of the tunnel face and the stress concentration zone at the arch crown, providing an intuitive and accurate spatial reference for construction decisions and comprehensively improving the visualization and practical value of damage information.

[0057] In a preferred embodiment of the present invention, step 7 above may include: Step 7.1: Based on the damage severity level and its spatial distribution characteristics, identify the rock mass damage areas requiring priority treatment. Specifically, this includes: based on the spatial distribution map of tunnel rock mass damage and combined with the damage severity level of each area, setting criteria for determining priority treatment areas; for severely damaged areas, regardless of their location, they are all included in the priority treatment scope; for moderately damaged areas, if they are located in the stress concentration area of ​​the arch crown, the critical area of ​​sidewall stability, or other weak parts of the tunnel structure, or if their area exceeds 10 square meters, they are also included in the priority treatment scope; for lightly damaged areas, they are only included in the priority treatment scope if they are adjacent to severely damaged areas and the confidence level assessment exceeds 90%; by delineating areas that meet the above criteria on the damage spatial distribution map, clarifying the boundary coordinates and area size of each priority area, a list of rock mass damage areas requiring priority treatment is formed.

[0058] Step 7.2: Based on the distribution range and degree of damage of the identified rock mass damage areas, formulate corresponding support parameter adjustment schemes. Specifically, this includes: Based on key treatment areas, first, statistically analyze the damage level and distribution range parameters of each area, including the maximum radial depth of the area, the extension length along the tunnel axis, and the tunnel structural parts involved, such as the arch, sidewall, or face; For severely damaged areas, if located at the arch, increase the anchor length in the original support scheme by 0.5 meters, reduce the anchor spacing to 1.5 meters, and increase the shotcrete thickness by 5 centimeters; if located at the sidewall, adopt a denser anchor arrangement, adding 2 anchors per square meter, and adding a grid arch frame; For moderately damaged areas, adjust the parameters according to their area size. When the area exceeds 15 square meters, increase the anchor length by 0.3 meters and increase the shotcrete thickness by 3 centimeters; for smaller areas, only appropriately reduce the anchor spacing; Associate these parameter adjustments with the location information of the corresponding areas to form a regional support parameter adjustment scheme.

[0059] Step 7.3: Based on the distribution characteristics of the rock mass damage area, and by combining the spatial distribution map of tunnel rock mass damage, determine the key control areas for blasting scheme optimization. Specifically, this includes: overlaying the spatial distribution map of tunnel rock mass damage with the tunnel blasting operation design range to determine the spatial relationship between the blasting operation area and the rock mass damage area; marking blasting zones less than 5 meters from the edge of severely damaged areas as primary control areas, requiring strict limitation of blasting vibration velocity; marking blasting zones 3 to 5 meters from the edge of moderately damaged areas as secondary control areas, requiring appropriate reduction of blasting intensity; and, based on the distribution characteristics of the damage area, if the damage area is continuously distributed along the tunnel axis, shortening the blasting cycle advance of the corresponding section by 20%; and if the damage area is concentrated on the left or right side of the tunnel face, adjusting the charge structure of the boreholes on that side to reduce the charge per borehole. In this way, the key control areas for blasting scheme optimization and their corresponding control requirements are clarified.

[0060] Step 7.4 integrates the key control areas of the support parameter adjustment plan and the blasting plan optimization. By combining the confidence assessment of the damage severity level, decision recommendations including support priority and blasting control points are obtained. Specifically, this includes: spatially matching the support parameter adjustment plan with the key blasting control areas; if an area requires both support parameter adjustment and is within the blasting control area, support operations should be prioritized before blasting optimization; referring to the confidence assessment of the damage severity level, for severely damaged areas with a confidence level exceeding 90%, the support priority is set to the highest, requiring 24-hour... The support adjustment was completed within 48 hours; for moderately damaged areas with a confidence level of 80% to 90%, the support priority was set to medium, and the adjustment was completed within 48 hours; regarding the key points of blasting control, the first-level control area clearly required the use of pre-splitting blasting technology, with a single charge not exceeding 50 kg and vibration velocity controlled within 1.5 cm / s; the second-level control area used smooth blasting, with a single charge not exceeding 80 kg; the support priority, specific parameter adjustment values, blasting control indicators, etc., were integrated to form a clear decision-making recommendation, which directly guided the on-site construction personnel to adjust the support parameters and optimize the blasting plan.

[0061] In this embodiment of the invention, based on the degree of damage and its spatial distribution characteristics, the rock mass damage areas requiring key treatment are identified, accurately pinpointing high-risk areas that need priority attention during construction and avoiding blind investment of resources. According to the distribution range and degree of damage of the key damage areas, corresponding support parameter adjustment schemes are formed, overcoming the limitations of traditional one-size-fits-all support parameters and allowing parameters such as anchor bolt length and anchor cable spacing to fit the actual damage needs of different areas, achieving quantitative adaptation of support parameters. Based on the distribution characteristics of rock mass damage areas and combined with the spatial distribution map of tunnel rock mass damage, the key control areas for blasting scheme optimization are determined, accurately locating areas where blasting intensity needs to be reduced or the charge structure adjusted, reducing secondary disturbance to the already damaged rock mass. Integrating the support parameter adjustment scheme with the key blasting control areas, and combining the confidence assessment of the degree of damage, clarifies the support priority and blasting control points, solving the problems of poor decision-making consistency and ambiguous implementation priorities in traditional monitoring results, making decision recommendations more systematic and operable.

[0062] like Figure 2 As shown, embodiments of the present invention also provide a tunnel rock mass damage prediction system based on microseismic data, comprising: The acquisition module is used to collect microseismic monitoring data during tunnel construction; the microseismic monitoring data is preprocessed to obtain a standardized microseismic data sequence. The analysis module is used to extract microseismic characteristic parameters related to rock mass damage based on standardized microseismic data sequences; perform multi-parameter fusion analysis on the microseismic characteristic parameters, and form a comprehensive damage index based on a preset set of analysis rules; The module is used to determine three core monitoring locations within the monitoring area based on the spatial distribution characteristics of comprehensive damage indicators. These three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewalls. Based on these three core monitoring locations, a spatial analysis unit is constructed to characterize the evolution trend of rock mass damage. The module is used to construct the minimum bounding triangle based on the spatial analysis unit to establish a quantitative description of the stability of the rock mass structure. Specifically, it calculates the structural morphology parameters of the minimum bounding triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, a spatial variation correction coefficient is formed. The spatial characteristics of the comprehensive damage index are corrected according to the spatial variation correction coefficient to obtain the optimized comprehensive damage index. The comparison module is used to compare the optimized comprehensive damage index with the preset damage threshold range; and to determine the damage level of the tunnel rock mass based on the comparison results. The summary module is used to generate a spatial distribution map of tunnel rock mass damage based on the determined damage level; based on the damage level and the spatial distribution map of tunnel rock mass damage, decision-making suggestions are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

[0063] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting tunnel rock mass damage based on microseismic data, characterized in that, The method includes: Step 1: Collect microseismic monitoring data during tunnel construction; perform data preprocessing on the microseismic monitoring data to obtain a standardized microseismic data sequence; Step 2: Based on the standardized microseismic data sequence, extract the microseismic characteristic parameters related to rock mass damage; perform multi-parameter fusion analysis on the microseismic characteristic parameters, and form a comprehensive damage index based on the preset analysis rule set; Step 3: Based on the spatial distribution characteristics of comprehensive damage indicators, three core monitoring locations are determined within the monitoring area. These three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for sidewall stability. Based on these three core monitoring locations, a spatial analysis unit is constructed to characterize the trend of rock mass damage evolution. Step 4: Based on the spatial analysis unit, construct the minimum circumscribed triangle to establish a quantitative description of the rock mass structure stability. That is, calculate the structural morphology parameters of the minimum circumscribed triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, form a spatial variation correction coefficient. According to the spatial variation correction coefficient, perform spatial feature correction on the comprehensive damage index to obtain the optimized comprehensive damage index. Step 5: Compare the optimized comprehensive damage index with the preset damage threshold range; determine the damage level of the tunnel rock mass based on the comparison results. Step 6: Based on the determined damage level, generate a spatial distribution map of tunnel rock mass damage; Step 7: Based on the damage level and the spatial distribution map of tunnel rock mass damage, decision-making suggestions are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

2. The tunnel rock mass damage prediction method based on microseismic data according to claim 1, characterized in that, Collect microseismic monitoring data during tunnel construction; The microseismic monitoring data were preprocessed to obtain a normalized microseismic data sequence, including: By deploying an array of microseismic sensors within the surrounding rock of the tunnel, waveform signals of microseismic events induced by construction disturbances are continuously collected. The collected microseismic event waveform signals are denoised to filter out environmental noise interference caused by construction machinery vibration, resulting in denoised waveform signals. Based on a preset amplitude threshold, effective micro-vibration signal segments are identified and extracted from the denoised waveform signal. The extracted microseismic signal segments are subjected to time-domain normalization to eliminate signal intensity deviations caused by differences in propagation paths, resulting in normalized microseismic signal segments. The normalized microseismic signal segments are reorganized and arranged according to the time series to form a standardized microseismic data sequence.

3. The tunnel rock mass damage prediction method based on microseismic data according to claim 2, characterized in that, Based on the normalized microseismic data sequence, microseismic characteristic parameters related to rock mass damage are extracted; Multi-parameter fusion analysis of microseismic characteristic parameters is performed, and a comprehensive damage index is formed based on a preset set of analysis rules, including: Based on the normalized microseismic data sequence, the time-frequency domain characteristic parameters of each microseismic event are calculated; Key feature parameters related to rock mass damage are extracted from time-frequency domain feature parameters, including event energy, dominant frequency characteristics, and duration; The key feature parameters are subjected to multi-parameter weighted fusion processing to obtain preliminary damage assessment values; Spatial cluster analysis is performed on the preliminary damage assessment values ​​to identify areas of concentrated damage based on a pre-defined set of analysis rules. Based on the distribution characteristics of the identified concentrated damage areas, a comprehensive damage index is calculated.

4. The tunnel rock mass damage prediction method based on microseismic data according to claim 3, characterized in that, Based on the spatial distribution characteristics of comprehensive damage indicators, three core monitoring locations were determined within the monitoring area. These three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewalls. A spatial analysis unit for characterizing the evolution trend of rock mass damage was constructed based on three core monitoring locations, including: Based on the spatial distribution characteristics of comprehensive damage indicators, key areas where damage is concentrated are identified. Based on the distribution characteristics of the key areas where damage is concentrated, three core monitoring locations were determined. These three core monitoring locations correspond to the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewalls, respectively. Using three core monitoring locations as reference points, the perpendicular bisectors between adjacent reference points are calculated, and the monitoring area is divided into three non-overlapping spatial partitions. Based on three non-overlapping spatial partitions, the spatial influence range corresponding to the core monitoring location is determined. Based on the geometric characteristics of the spatial influence range, a spatial analysis unit is constructed to characterize the trend of rock mass damage evolution.

5. The tunnel rock mass damage prediction method based on microseismic data according to claim 4, characterized in that, Based on spatial analysis units, a minimum circumscribed triangle is constructed to establish a quantitative description of rock mass structure stability, that is, to calculate the structural morphological parameters of the minimum circumscribed triangle in order to obtain the side length ratio and interior angle distribution characteristics. Spatial variation correction coefficients are generated based on structural morphology parameters. These coefficients are then used to spatially correct the comprehensive damage index, resulting in an optimized comprehensive damage index, including: Based on the spatial influence range of the spatial analysis unit, the outer boundary points of the spatial distribution of rock mass damage are determined; By connecting the outermost boundary points, the smallest enclosing region is constructed, forming the smallest circumscribed triangle of the spatial distribution of rock mass damage; Calculate the structural morphological parameters of the minimum circumscribed triangle, including the proportional relationship of the lengths of each side, the distribution of the interior angles, and the ratio of area to perimeter. Based on structural morphological parameters, spatial variation correction coefficients are obtained through multi-parameter fusion calculations. Based on the spatial variation correction coefficient, the spatial distribution characteristics of the comprehensive damage index are calibrated to obtain the optimized comprehensive damage index.

6. The tunnel rock mass damage prediction method based on microseismic data according to claim 5, characterized in that, The optimized comprehensive damage index is compared with the preset damage threshold range; based on the comparison results, the damage level of the tunnel rock mass is determined, including: The optimized comprehensive damage index is compared and analyzed with the preset multi-level damage threshold range to obtain the comparison analysis value. Based on the comparative analysis values, a preliminary assessment of the rock mass damage level was determined; Spatial consistency verification is performed on the preliminary grade determination, and spatial consistency verification result data is obtained by combining the geometric characteristic parameters of the spatial analysis unit; Based on the spatial consistency verification results, the final tunnel rock mass damage level and its confidence level assessment are formed.

7. The tunnel rock mass damage prediction method based on microseismic data according to claim 6, characterized in that, Based on the determined damage level, a spatial distribution map of tunnel rock mass damage is generated, including: Based on the damage severity level and its confidence level assessment, a mapping relationship between damage level and spatial coordinates is established; Based on the geometric characteristic parameters of the spatial analysis unit, spatial interpolation calculation is performed on the mapping relationship to obtain a continuous spatial distribution surface of rock mass damage; Based on the spatial distribution surface of rock mass damage, a spatial distribution map of tunnel rock mass damage containing multi-level damage indicators is constructed; Mark the damage level and confidence level information of key monitoring locations on the spatial distribution map of tunnel rock mass damage.

8. The tunnel rock mass damage prediction method based on microseismic data according to claim 7, characterized in that, Based on the damage severity level and the spatial distribution map of tunnel rock mass damage, decision-making recommendations are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes, including: Based on the degree of damage and its spatial distribution characteristics, identify the rock mass damage areas that require key treatment. Based on the distribution range and degree of damage of the identified rock mass damage areas, a corresponding support parameter adjustment plan is formulated; Based on the distribution characteristics of the rock mass damage area, the key control area for blasting scheme optimization is determined by combining the spatial distribution map of tunnel rock mass damage. By integrating the key control areas of the support parameter adjustment scheme and the blasting scheme optimization, and combining the confidence assessment of the damage level, decision recommendations including support priority and blasting control points are obtained.

9. A tunnel rock mass damage prediction system based on microseismic data, wherein the system implements the method as described in any one of claims 1 to 8, characterized in that, include: The acquisition module is used to collect microseismic monitoring data during tunnel construction. The microseismic monitoring data is preprocessed to obtain a standardized microseismic data sequence; The analysis module is used to extract microseismic characteristic parameters related to rock mass damage based on normalized microseismic data sequences; Multi-parameter fusion analysis of microseismic characteristic parameters is performed, and a comprehensive damage index is formed based on a preset set of analysis rules. The module is used to determine three core monitoring locations within the monitoring area based on the spatial distribution characteristics of comprehensive damage indicators. The three core monitoring locations are the central area of ​​the current construction face, the stress concentration area of ​​the arch crown, and the critical area for the stability of the sidewall. A spatial analysis unit for characterizing the evolution trend of rock mass damage was constructed based on three core monitoring locations; The module is used to construct the minimum bounding triangle based on the spatial analysis unit to establish a quantitative description of the stability of the rock mass structure, that is, to calculate the structural morphological parameters of the minimum bounding triangle to obtain the side length ratio and interior angle distribution characteristics. Based on the structural morphology parameters, a spatial variation correction coefficient is formed. The spatial characteristics of the comprehensive damage index are corrected according to the spatial variation correction coefficient to obtain the optimized comprehensive damage index. The comparison module is used to compare the optimized comprehensive damage index with the preset damage threshold range; and to determine the damage level of the tunnel rock mass based on the comparison results. The summary module is used to generate a spatial distribution map of tunnel rock mass damage based on the determined damage level. Based on the damage severity level and the spatial distribution map of tunnel rock mass damage, decision-making recommendations are obtained to guide the adjustment of tunnel support parameters and the optimization of blasting schemes.

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