A method for establishing a roof pre-splitting blasting effect evaluation system based on microseismic monitoring

Abstract

This application relates to the field of rockburst monitoring technology and discloses a method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring. This method first collects blasting data including static parameters and dynamic monitoring data and classifies them by location. Utilizing the principle of energy conservation, an evaluation model is constructed by combining the total charge and microseismic energy. The blasting effectiveness index is calculated, and a preliminary evaluation level is determined. Surrounding rock stress, drill cuttings volume, and support resistance data are introduced. By constructing multi-dimensional verification rules including stress falsification, drill cuttings rejection, and support upgrade logic, the preliminary level is corrected to generate a final evaluation level. A database including geological, technological, and effect domains is established. Based on the final level, correlation analysis and sensitivity identification are performed, and target technical parameters guiding subsequent construction are output. This invention, through a combination of theoretical calculation and multi-source physical empirical verification, effectively overcomes the limitations of a single index and achieves accurate determination of blasting effects and adaptive dynamic optimization of parameters.

Description

Technical Field

[0001] This invention relates to the field of rockburst monitoring technology, specifically to a method for establishing an evaluation system for the pre-splitting blasting effect of roof based on microseismic monitoring. Background Technology

[0002] Currently, for rockbursts induced by hard roofs, deep-hole pre-splitting blasting of the roof is the most important local pressure relief method to cut off the suspended roof structure and release accumulated elastic energy.

[0003] However, significant technical bottlenecks remain in evaluating the effectiveness of roof pre-splitting blasting in practical engineering applications. Existing evaluation methods often rely on single monitoring tools, such as judging blasting effectiveness solely based on energy events captured by microseismic monitoring systems. However, in deep and complex geological environments, the high energy generated by explosive detonations often includes a large amount of ineffective wave energy not used for fracturing the rock mass. This energy attenuates and propagates over long distances in hard rock layers, easily leading to microseismic monitoring systems receiving high-energy signals and misjudging the blasting effect as good—a phenomenon known as "false positives." Furthermore, single borehole stress gauges or support resistance monitoring can only reflect the stress state at local points, making it difficult to comprehensively characterize the overall fracture penetration and stress relief effect of the blasted area. Due to the lack of an evaluation mechanism that deeply integrates and logically verifies multi-source heterogeneous data such as microseismic, stress, and drill cuttings data, on-site technicians struggle to accurately grasp the true fracturing state of the roof, resulting in the failure to promptly eliminate high-stress hazards.

[0004] Furthermore, current blasting operation management generally lacks a data-driven closed-loop optimization mechanism. The prevention of rockbursts in deep mining in the Ordos region is still in the exploratory stage. The design of blasting parameters (such as charge quantity and hole spacing) often relies on manual experience or simple on-site analogies, lacking quantitative feedback models for specific geological conditions (such as different lithologies and water-bearing capacities). When blasting results are unsatisfactory, it is difficult to accurately guide the adjustment of parameters in the next round through quantitative analysis of historical data, resulting in a waste of engineering resources or a lack of safety protection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for establishing an evaluation system for roof pre-splitting blasting effects based on microseismic monitoring. This method solves the problems of distorted effect evaluation caused by a single monitoring method and the lack of quantitative feedback and closed-loop optimization in blasting parameter design during roof pre-splitting blasting operations in deep rockburst mines.

[0006] To achieve the above objectives, the present invention provides a method for establishing an evaluation system for the pre-splitting blasting effect of the roof based on microseismic monitoring. This method is implemented through the following technical process: First, the data was systematically collected and classified. Monitoring data during the pre-splitting blasting operation of the mine roof was collected, covering both static parameter data and dynamic monitoring data. Static parameter data defined the boundary conditions of the blasting operation, including geological environmental parameters such as roof lithology, rock layer thickness, and water-bearing level, as well as blasting technical parameters such as borehole length, charge amount, and drilling angle. Dynamic monitoring data recorded the real-time response of the rock mass to the blasting, including micro-vibration waveforms, changes in surrounding rock stress, drill cuttings volume, and hydraulic support resistance. The method indexed and categorized the massive heterogeneous data based on the construction site to construct a standardized basic dataset.

[0007] Secondly, a blasting effectiveness index evaluation model based on energy conversion is constructed. This step, based on the principles of energy conservation and conversion, aims to quantify the efficiency of converting the chemical energy released by the explosive into the mechanical work driving rock fragmentation. The method involves obtaining the total charge and theoretical specific energy of a single detonation, and calculating the residual energy after the explosive propagation. This residual energy is defined as the energy benchmark that is not used to fracture the rock mass after the explosion, but is dissipated as useless vibrations. Simultaneously, the vibration energy induced by the blast is inverted using a microseismic monitoring system, and a rock mass property coefficient is introduced to correct for differences in wave velocity attenuation in different media. By calculating the logarithmic difference between the corrected vibration response value and the benchmark noise value, and then comparing it with the logarithm of the total charge, the blasting effectiveness index is obtained. This index reflects the energy utilization rate mechanistically, forming the theoretical basis of the evaluation system.

[0008] Next, a preliminary evaluation level is determined based on preset standards. A numerical range standard is set, comprising four levels: invalid, valid, good, and excellent. When the index is extremely low, it is judged as invalid, indicating that energy is mainly dissipated or escaped; when the index is in the low range, it is judged as valid, indicating that near-field initial fractures have been induced; when the index is in the middle range, it is judged as good, indicating that the fractures are extending deeper and connecting; when the index is high, it is judged as excellent, indicating that macroscopic fracturing or collapse has been induced.

[0009] Subsequently, multi-source heterogeneous data is introduced for logical correction. To address potential signal artifacts or vibrations in non-target strata during microseismic energy monitoring, this method utilizes stress, drill cuttings, and support data to construct a multi-dimensional physical empirical verification logic, generating the final evaluation level.

[0010] Specifically, stress monitoring data is used to perform falsification correction: if the initial evaluation is high-level, but the measured vertical stress reduction rate of the surrounding rock does not reach the preset effective stress relief threshold, it indicates that the high-energy vibration has not caused substantial stress transfer. The system will adjust the evaluation level down and eliminate false positive results.

[0011] The veto correction is implemented using drill cuttings monitoring data: it is used as the safety baseline. If the amount of drill cuttings in the area after blasting still exceeds the critical value of impact hazard, or if dynamic phenomena such as stuck drill or drill suction occur during construction, the evaluation result will be forcibly determined to be invalid, regardless of how high the micro-vibration energy is, in order to ensure impact safety.

[0012] Upgrade corrections are performed using support monitoring data: If the hydraulic support working resistance is found to show a characteristic waveform of a surge followed by a rapid drop, and the steady-state resistance decreases, it provides direct evidence of macroscopic instability and fracture of the roof. Even if the micro-seismic calculation results are average, the evaluation level will be adjusted upward to confirm the pressure relief effect.

[0013] Finally, a data-driven parameter closed-loop optimization model is established. The final evaluation level, after multi-source correction, is used as the output response and correlated with geological environment parameters (input conditions) and blasting technical parameters (control variables) to construct a blasting effect evaluation database. Through sensitivity analysis of the database, parameter thresholds leading to blasting ineffectiveness and coupling influence coefficients under specific geological conditions are identified. When faced with a new operational task, the system matches historical best cases based on current geological conditions and recommends technical parameters; if the current operational effect is unsatisfactory, targeted drilling or charge structure adjustment instructions are output based on the analysis results, realizing dynamic iteration and precise control of blasting design.

[0014] This invention provides a method for establishing an evaluation system for the pre-splitting blasting effect of roof slab based on microseismic monitoring. It has the following beneficial effects: 1. This invention establishes a blasting efficiency index evaluation model, which initially quantifies the blasting efficiency from the perspective of energy conversion mechanism. Furthermore, it introduces multi-source heterogeneous data such as surrounding rock stress, drill cuttings volume, and hydraulic support resistance as correction basis, and constructs a multi-dimensional logical verification rule that includes falsification, rejection, and upgrading. This mechanism effectively eliminates false high-energy misjudgments caused by wave velocity model deviation or far-field vibration interference from single microseismic monitoring, and also avoids missed detections due to signal attenuation, ensuring that the final evaluation results can truly reflect the stress transfer state and macroscopic fracture degree of the roof strata.

[0015] 2. This invention establishes a mandatory rejection correction logic based on drill cuttings monitoring data. It uses excessive drill cuttings or dynamic phenomena such as drill bit suction and stuck drills during construction as hard indicators for judging blasting failure. Regardless of the energy index calculated by microseismic inversion, once on-site physical exploration shows that the high stress state has not been relieved, the system immediately and forcibly judges it as invalid. This avoids the safety risks that may be caused by relying solely on numerical calculations, ensures that high stress danger areas are identified and remedied in a timely manner, and enhances the safety and practicality of the evaluation system on the engineering site.

[0016] 3. This invention establishes a standardized database that includes a geological condition domain, an engineering control domain, and an effect feedback domain. It transforms historical evaluation results into data assets that guide subsequent construction. By using sensitivity analysis to identify parameter failure thresholds and coupling influence coefficients under specific geological conditions, the system can automatically match optimal borehole spacing, charge structure, and other parameter schemes based on dynamic changes in geological conditions. This data-driven closed-loop feedback mechanism solves the problem of insufficient targeting in traditional empirical design methods, enabling roof pre-splitting engineering to be continuously iterated and optimized as the working face advances, thereby continuously improving the fracturing effect. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the index calculation logic flow of the present invention; Figure 3 This is a schematic diagram of the closed-loop optimization feedback mechanism of the present invention. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See attached document Figure 1 This invention provides a method for establishing an evaluation system for the pre-splitting blasting effect of the roof based on microseismic monitoring. The method includes the following steps: First, roof pre-splitting blasting monitoring data was collected. This data includes static parameter data and dynamic monitoring data. The data was categorized and organized according to the construction location, which includes the working face return airway, working face transport airway, and cut-in holes. Static parameter data includes blasting technical parameters and geological environment parameters. Blasting technical parameters include hole sealing length, single-hole charge, number of detonating holes, borehole azimuth, borehole inclination, and hole depth. Geological environment parameters include roof lithology, stratum thickness, and roof water-bearing capacity. Dynamic monitoring data includes waveform data from the microseismic monitoring system, stress data from borehole stress gauges, coal dust volume data from drill cuttings method construction, and working resistance data from hydraulic supports.

[0020] Next, a blasting effectiveness index evaluation model is established. This model, based on the mine seismic energy method, quantifies the efficiency of converting the energy input from explosive detonation into elastic energy released through rock fracturing. Specifically, this step includes: determining the total charge amount for a single hole or single initiation in deep-hole blasting of the roof, and obtaining the theoretical energy released per unit mass of explosive. The residual energy propagating from the explosive detonation is calculated; this residual energy represents the energy dissipated or remaining in the form of waves that did not participate in rock fracturing. Simultaneously, the vibration energy measured by a microseismic monitoring system after blasting is obtained, and a rock mass property coefficient is introduced to correct the energy propagation characteristics of different lithologies. Based on the vibration energy corrected by the rock mass property coefficient, the residual energy propagating from the explosive detonation, and the total charge amount, the blasting effectiveness index is calculated.

[0021] Subsequently, a preliminary evaluation level of the blasting effect is determined. A pre-defined blasting effectiveness judgment standard is established, comprising multiple corresponding evaluation level ranges. The calculated blasting effectiveness index is compared with the blasting effectiveness judgment standard. When the blasting effectiveness index is less than or equal to the first threshold, it is judged as invalid; when the blasting effectiveness index is greater than the first threshold and less than or equal to the second threshold, it is judged as effective; when the blasting effectiveness index is greater than the second threshold and less than or equal to the third threshold, it is judged as good; and when the blasting effectiveness index is greater than the third threshold, it is judged as excellent.

[0022] Subsequently, the preliminary evaluation level is revised based on the corrected data to generate the final evaluation level. The corrected data comes from stress monitoring data, drill cuttings monitoring data, and support monitoring data. The correction process follows this logic: First, the stress reduction rate before and after blasting is calculated based on the stress monitoring data. If the preliminary evaluation level is good or excellent, and the stress reduction rate is less than the preset stress change threshold, the preliminary evaluation level is downgraded to generate the final evaluation level. Second, the amount of drill cuttings per unit depth in the blasting-affected area is obtained based on the drill cuttings monitoring data. If the preliminary evaluation level is valid or above, and the amount of drill cuttings per unit depth is greater than the preset impact hazard threshold, or if drill bit suction or stuck force phenomena are detected, the preliminary evaluation level is revised to invalid. Finally, the working resistance change trend of the hydraulic support is analyzed based on the support monitoring data. If the working resistance of the support shows a surge followed by a decline within a preset time window, and the average resistance after the decline is less than the average resistance before blasting, the preliminary evaluation result of the valid level is upgraded to good.

[0023] Finally, a parameter optimization model is established based on the final evaluation level. A correlation analysis is performed between the final evaluation level and blasting technical parameters and geological environment parameters to establish a blasting effect evaluation database. Based on the correlation patterns in the blasting effect evaluation database, corresponding target blasting technical parameters are output for different geological environment parameters to guide subsequent roof pre-splitting blasting construction.

[0024] In constructing an evaluation system for the effectiveness of roof pre-splitting blasting, data acquisition and classification are the physical foundation for achieving accurate evaluation. This process mainly involves the acquisition, cleaning, time synchronization, and structured storage of multi-source heterogeneous data from underground mines.

[0025] First, comprehensive basic data acquisition is performed. This step is completed through collaboration between IoT sensing devices deployed underground and a surface-based manual reporting system. The collected data is divided into two main categories: static parameter data and dynamic monitoring data.

[0026] For static parameter data, it is mainly obtained by reviewing geological exploration reports and retrieving on-site construction logs. Specifically, the acquisition of geological environmental parameters includes extracting the lithological characteristics of the roof strata in the blasting area (such as coarse sandstone, fine sandstone, or mudstone), the uniaxial compressive strength of the strata, the thickness of the strata, and the water-bearing level of the roof obtained through hydrological observation wells. The acquisition of blasting technical parameters is based on the actual on-site construction records, extracting the physical properties of each blasting borehole, including the sealing length, the charge per borehole (converted to standard explosive mass), the number of boreholes detonated in a single operation, the spatial orientation of the borehole (azimuth and dip angle), and the borehole depth.

[0027] For dynamic monitoring data, real-time capture is mainly achieved through underground online monitoring systems. Specifically, this includes: using microseismic monitoring sensor arrays installed in the mining roadway and floor to continuously record vibration waveforms during and after blasting operations within a preset time window, and analyzing the three-dimensional spatial coordinates, occurrence time, and radiated energy of the vibration events; using borehole stress gauges buried deep in the coal or rock mass to collect data on vertical stress changes in the surrounding rock of the blasting area, with a sampling frequency sufficient to capture the stress disturbances at the moment of blasting; using the electro-hydraulic control system of the hydraulic support to record the working resistance data sequence of the support columns; and using on-site manual monitoring of drill cuttings to record the weight of coal dust discharged at specific borehole depths.

[0028] Next, the collected raw data undergoes preprocessing and time alignment. Due to significant differences in time scale between static and dynamic data (blasting parameters are discrete point data, while monitoring data is continuous stream data), a data synchronization mechanism needs to be established using the blast initiation time as the reference timestamp. Specifically, the initiation time of each blasting operation is identified, and this time is used as the zero point to extract the cumulative energy value within a specific time period after blasting from the microseismic monitoring data. Simultaneously, the change sequences of stress monitoring data and support resistance data before and after blasting are extracted. For microseismic waveform data, a bandpass filter is used to remove background noise interference from downhole electromechanical equipment, ensuring that the resolved energy value accurately reflects the rock mass fracture strength.

[0029] Finally, a multi-dimensional classification index database was established. The cleaned and synchronized data was structured and stored according to its physical spatial attributes. The primary classification dimension was the construction location, dividing the data into three independent datasets: the working face return airway area, the working face transport airway area, and the cut-in area. This eliminated baseline interference from different roadway surrounding rock support conditions and stress environments on the evaluation results. Under each regional dataset, sub-indexes based on geological conditions were further established, such as distinguishing between hard roof areas and composite roof areas, or between water-rich areas and water-free areas. This hierarchical classification storage method ensures that the parameters used in subsequent calculations of the blasting effectiveness index have clear physical correspondences, avoiding confusion and misuse of data under different geological backgrounds. Through these steps, a standardized historical blasting effect evaluation dataset was formed, providing validated data support for subsequent model parameter inversion and pattern summarization.

[0030] When establishing the blasting effectiveness index model, the energy flow mechanism during roof pre-splitting blasting is first clarified based on the principle of energy conservation and transformation in physics. This principle posits that not all of the total chemical energy released by the explosive explosion is converted into effective work for fracturing the roof rock strata. In actual deep-hole blasting operations, the total energy is distributed into two main parts: A portion of the energy is directly used to overcome the tensile strength of the rock, causing the rock mass to generate a fracture network and releasing the elastic energy accumulated inside the rock layer. This portion of energy is the effective energy for achieving the purpose of pressure relief. Another portion of the energy is converted into heat energy, air shock waves, and elastic vibration waves that do not cause substantial damage to the rock mass. This portion of energy is dissipated energy or residual energy for the depressurization of the roof.

[0031] To accurately evaluate the blasting effect, the principle model constructed in this embodiment aims to eliminate ineffective dissipated energy and quantify the proportion of effective energy in the total input energy. The vibration energy received by the microseismic monitoring system ( In reality, it is a mixed quantity, encompassing both the strain energy released from rock mass fracturing and the direct shock waves generated by the explosive explosion. Therefore, it cannot be determined solely based on... To evaluate the blasting effect based on the magnitude of the explosion, a normalized comparison mechanism for input and output must be introduced.

[0032] This principle model first defines the residual energy of the explosive propagation ( Residual energy is defined as the basic energy level generated by an explosive explosion in an ideal elastic medium, which, without causing rock fracture, propagates outward only in the form of waves. This value is positively correlated with the charge amount and is controlled by the energy propagation attenuation law. By calculating this residual energy, a baseline for removing background noise is provided for microseismic monitoring data. Only when the measured microseismic energy is significantly higher than this baseline is it considered that the blasting successfully induced the release of additional elastic energy in the rock mass.

[0033] Furthermore, this principle model introduces rock mass property coefficients ( The rock mass property coefficient serves as a medium correction factor. Because top strata of different geological origins (such as fine sandstone, coarse sandstone, or mudstone) have different wave impedances and energy attenuation characteristics, the transmission efficiency of the same explosion energy varies in different lithologies. Hard, dense rocks are more conducive to the propagation of high-frequency energy, while soft, fractured rocks have a strong absorption and attenuation effect on energy. The role of the rock mass property coefficient is to weight and correct the measured microseismic energy, eliminate the nonlinear distortion of monitoring results caused by the physical properties of the medium, and ensure the comparability of evaluation results at different construction sites.

[0034] Ultimately, the blasting effectiveness index The construction principle is based on the logarithmic scale energy difference ratio. Since the explosive energy and microseismic energy typically span multiple orders of magnitude, using a logarithmic scale can transform nonlinear energy changes into a linear exponential index. The physical essence of this index is to measure the gain of the corrected microseismic response energy relative to the theoretical remaining energy, and to normalize this gain relative to the total charge. When the index is positive and the higher the value, it indicates that a unit mass of explosive induces more rock fracture propagation and elastic energy release, achieving efficient roof fracturing; conversely, if the index approaches zero or is negative, it indicates that the explosive energy is mainly dissipated in the form of ineffective seismic waves, failing to effectively damage the rock mass structure.

[0035] See attached document Figure 2 In determining the specific value of the blasting effectiveness index, this embodiment adopts a step-by-step calculation strategy. First, the baseline energy is determined, and then the normalized effectiveness index is calculated. The mathematical model and its parameters involved in this calculation process are defined as follows. First, the residual energy of the explosive explosion propagation is calculated. This physical quantity represents the portion of the energy released by the explosive explosion that is used only to generate elastic vibration waves and propagate to distant locations under the ideal assumption that no rock mass damage occurs. This energy value serves as the baseline noise level for determining whether the blasting is effective. The calculation formula is as follows: ; in, It represents the energy remaining after the explosive detonation, and its unit is joule (J). This represents the charge amount per hole or the total charge amount in a single initiation circuit for deep-hole blasting of the roof, expressed in kilograms (kg). This value is directly derived from the on-site charging records. This represents the theoretical energy released per unit mass of explosive upon detonation, also known as the specific energy or heat of explosion of the explosive, and is measured in joules per kilogram (J / kg). This value depends on the type of industrial explosive used; for example, for the secondary permissible emulsion explosives commonly used in coal mines, this value is taken from its standard calorific value parameter. The value 10 in the formula...-4 It is an empirical energy propagation attenuation coefficient (or wave energy conversion coefficient) that reflects the proportion of total chemical energy of explosives converted into residual elastic wave energy that can be monitored in the far field in deep rock media. This coefficient is derived from statistical regression of a large amount of field blasting test data and is used to correct the order of magnitude difference between the theoretical total energy and the far field wave energy.

[0036] Secondly, after obtaining the remaining energy baseline, the blasting effectiveness index is calculated. This index is a dimensionless relative indicator used to measure the gain of the actually monitored rock mass fracture energy relative to the remaining baseline energy, and it eliminates the influence of the charge quantity. The calculation formula is as follows: ; In this formula, This is the blasting effectiveness index. This represents the vibration energy actually measured by the microseismic monitoring system after the blast. This energy value is obtained by performing time-frequency analysis on the microseismic waveform and inversion of the source parameters, reflecting the total elastic wave radiation energy released at the moment of blasting and the subsequent rock strata fracture. This represents a base-10 logarithmic operation, used to compress energy data spanning multiple orders of magnitude to a linear scale for easier comparison. This refers to the rock mass property coefficient. This coefficient is a physical parameter used to correct for the influence of lithological differences on microseismic energy transmission. Because different rocks (such as sandstone, limestone, and mudstone) have different densities and elastic moduli, their absorption and attenuation effects on seismic waves vary significantly. In this embodiment, The value is not arbitrarily set, but determined based on the physical and mechanical properties of the roof rock in the blasting area. In practice, this can be achieved by conducting ultrasonic wave velocity tests on the target rock layer underground, or by conducting uniaxial compressive strength tests on rock specimens in the laboratory, establishing the relationship between rock strength or wave velocity and [other properties]. A mapping table between them. Generally speaking, the harder and denser the rock, the higher its energy conduction efficiency. The values ​​are adjusted accordingly to ensure that the calculations are accurate under different lithological conditions. The index is horizontally comparable. By introducing this coefficient, the formula can distinguish whether the high microseismic energy is due to a good blasting pressure relief effect or simply an artificially high level caused by the good conductivity of the rock.

[0037] The specific blasting effectiveness index is obtained through the aforementioned calculation model. After obtaining the numerical values, they need to be mapped to evaluation levels with clear engineering guidance significance. This embodiment establishes a preliminary judgment standard based on four threshold levels. This standard is formulated based on the statistical distribution characteristics of a large amount of field measured data, aiming to establish a quantitative correspondence between index values ​​and the degree of rock mass damage, thereby transforming abstract mathematical indicators into an intuitive judgment of the effectiveness of downhole anti-surge and pressure relief.

[0038] First, define the invalid evaluation level. When the calculated blasting effectiveness index meets the condition... At that point, the blasting was deemed invalid. From a physical perspective, this result means that the measured logarithmic value of the microseismic energy, after correction for lithology, is less than or equal to the theoretical logarithmic value of the remaining energy propagated by the explosive. This indicates that the chemical energy released by the explosive failed to be effectively converted into mechanical work to drive rock mass fracturing; most of the energy may have been dissipated as heat, or the high-pressure gas may have prematurely escaped due to sealing failure, failing to create a sustained quasi-static pressure wedging effect on the borehole wall. In engineering terms, this corresponds to the blasting failing to induce additional fracture propagation or elastic energy release within the rock mass, and the integrity and stress state of the rock strata remaining essentially unchanged.

[0039] Secondly, define the effective evaluation level. When the blasting effectiveness index meets the conditions... At this point, the blasting was deemed effective. Within this numerical range, the measured microseismic energy slightly exceeded the baseline of the residual energy of the explosive explosion, indicating that the blasting operation successfully induced a small amount of elastic energy release in the rock mass surrounding the borehole. Technically, this is characterized by the explosive explosion generating a radial initial fracture zone around the borehole, or causing localized rock fragmentation, but the development length of the fractures is limited, and a long-distance penetrating fracture network has not yet formed within the top strata. At this time, the blasting caused local disturbance to the integrity of the rock mass, playing a basic loosening role.

[0040] Next, define the "good" evaluation level. When the blasting effectiveness index meets the conditions... At that time, the blasting effect was judged to be good. This numerical level reflects a significant nonlinear gain in the elastic energy released by the rock mass relative to the energy input by the explosive. Physically, this means that the blast-induced fractures have broken through the limitations of the near-field fracture zone, extending into the deeper rock mass and possibly communicating with the fracture fields generated by adjacent blast holes (Cross-hole crack propagation), resulting in structural damage or interlayer delamination of the roof strata over a large area. This level corresponds to an ideal pre-fracture effect, which can effectively reduce the overall strength of the roof and destroy its ability to accumulate high stress.

[0041] Finally, define the excellent evaluation level. When the blasting effectiveness index meets the conditions... At this point, the blasting effect is judged to be excellent. The high index at this time indicates that the system has detected an energy response far exceeding the input level of the explosive. This is usually not directly generated by the explosive itself, but rather by the successful blasting inducing macroscopic fracturing, rotation, or local collapse of the roof strata, thereby releasing a huge amount of accumulated elastic energy in the surrounding rock. This state signifies that the integrity of the key strata has been successfully severed, and the deep high-stress field has been dramatically released and transferred, achieving the most ideal stress relief state in rockburst prevention.

[0042] After obtaining the preliminary evaluation level through microseismic energy inversion, this embodiment introduces near-field in-situ monitoring data to perform secondary verification and correction of the evaluation results in order to eliminate false high-energy or missed detection errors that may be caused by limitations in sensor array distribution or deviations in wave velocity models. This correction process is not a simple data superposition, but a logical discrimination based on the rock mechanical response mechanism. The multi-source correction module performs strict logical threshold judgments based on three dimensions: stress field, microstructural damage, and macroscopic mineral pressure manifestation.

[0043] First, a falsification correction logic based on borehole stress is executed. This logic primarily targets high-grade results that were initially rated as good or excellent. Borehole stress gauges are pre-embedded in the coal and rock mass of the blasting area. These devices continuously sense minute changes in the vertical stress within the surrounding rock. The correction mechanism calculates the ratio of the average stress value during the stabilization period before blasting to the average stress value during the stabilization period after blasting, i.e., the stress reduction rate.

[0044] From a rock mechanics perspective, if the blasting truly achieves the cutting off or weakening of the roof's suspended structure, it will inevitably lead to the transfer or release of the supporting pressure on the underlying coal seam, manifested as a significant decrease in stress gauge readings. If the initially calculated blasting effectiveness index ( The initial high energy level was rated as excellent, but the measured stress reduction rate was less than the preset effective stress relief threshold (e.g., 10%). This indicates that the high-energy signal received by the microseismic system may originate from far-field vibrations in non-target areas, or simply from the elastic oscillation of a strong shock wave generated by an explosive explosion within the unfractured rock mass, without causing substantial stress transfer. In this case, the system determines that the initial high rating is a false positive and adjusts the rating down one level according to logical rules, for example, from excellent to good, or from good to effective, to reflect the true stress state of the surrounding rock.

[0045] Secondly, a veto logic based on drill cuttings monitoring is implemented. This logic serves as a safety baseline for identifying residual high-stress zones following blasting failure. Regardless of the preliminary assessment results, small-diameter test boreholes must be drilled within the post-blasting impact area, and the amount of drill cuttings removed per unit hole depth (kg / m) must be recorded.

[0046] This correction logic is based on the following physical fact: if the blasting successfully creates a sufficient fracture network in the coal and rock mass, the overall strength and elastic energy accumulation capacity of the coal will be significantly reduced, and the amount of coal dust discharged during drilling should be within the normal range. Conversely, if, during the inspection process, the monitored value of drill cuttings still exceeds the critical value for impact hazard specified for the mine, or if obvious dynamic phenomena occur during construction, including drill bit suction (drill rod being sucked in by the pressure inside the hole), drill bit jamming (drill rod being seized by the deformed hole wall), or drill bit jacking inside the hole, this directly proves that the stress concentration in the area is still at a dangerous level, and the blasting failed to destroy the high-stress carrier. In this case, regardless of the energy of the microseismic inversion, the system will forcibly abolish the preliminary evaluation results, directly correct the final evaluation level to invalid, and trigger an instruction to immediately repair the hole or take other pressure relief measures.

[0047] Finally, an empirical upgrade logic based on support resistance is implemented. This logic utilizes the hydraulic support as a large-scale sensor to capture signals of macroscopic roof fracture. The monitoring focuses on the time-series characteristics of the hydraulic support's working resistance within a preset time window after blasting (e.g., 0 to 2 hours after blasting).

[0048] If the blasting successfully induces the initial or periodic fracture of the rigid roof, the broken rock fragments will instantly exert dynamic load impact on the support below during rotational instability. Subsequently, due to the shortening of the cantilever beam structure, the long-term load pressure on the support may stabilize or decrease. Therefore, if the system identifies a typical waveform in the support resistance—a sharp peak pulse followed by a rapid decline and then low-level stabilization—and the average working resistance after the decline is lower than the average level before the blasting, this provides direct physical evidence of substantial damage to the roof structure. In this scenario, even if microseismic monitoring does not calculate extremely high values ​​due to signal attenuation, If the initial assessment is only valid, the system will adjust the final evaluation level by one level (e.g., correct it to good) based on this macro-level empirical evidence, confirming that the blasting achieved the expected goal of destroying the integrity of the roof.

[0049] See attached document Figure 3 After obtaining the final evaluation level corrected by multi-source data, this embodiment constructs a data-driven feedback loop to transform the evaluation results of a single blast into a quantitative basis for guiding subsequent engineering design. This process aims to solve the technical problem of the lack of targeted blasting parameter design under deep and complex geological conditions, and to achieve dynamic iteration and precise control of blasting schemes through the accumulation and analysis of historical data.

[0050] First, a relational mapping database containing geological features, technical parameters, and evaluation results is constructed. The system stores each blasting operation as an independent data sample entry in the database. Each sample entry contains three information domains: the first dimension is the geological condition domain, recording the physical and mechanical parameters of the roof lithology, stratum thickness, and water-bearing level of the blasting location; the second dimension is the engineering control domain, recording the actual borehole sealing length, single-hole charge, number of detonation holes, borehole azimuth and dip angles; the third dimension is the effect feedback domain, recording the final evaluation level determined after microseismic inversion and multi-source correction. This structured storage method establishes a complete causal chain of input conditions (geology), control variables (technology), and output response (effect), providing a standardized sample set for subsequent data mining.

[0051] Secondly, a sensitivity analysis of key influencing factors based on big data is performed. The optimization module utilizes regression or correlation analysis algorithms to explore the contribution and sensitivity of various blasting technical parameters to blasting effectiveness under different geological backgrounds in the database. Specific analysis logic includes: identifying parameter failure thresholds. For example, the system extracts features from historical samples marked as invalid. If it finds that when the sealing length is less than a certain value (e.g., 3 meters), the blasting effectiveness index remains low regardless of the increase in charge, the system determines this value as the minimum sealing length critical value under the specific lithology of the mining area. Quantifying environmental coupling effects. For example, in water-rich areas of the roof, the system analyzes the correspondence between charge and blasting effectiveness index. If it finds that the unit explosive consumption required to achieve the same fracturing effect is significantly higher than in water-free areas, the system calculates a specific water-rich attenuation compensation coefficient to guide the incremental design of charge during aquifer blasting.

[0052] Finally, adaptive parameter optimization strategies for specific geological conditions are generated. Based on the patterns and thresholds derived from the above analysis, the system establishes a hierarchical and categorized blasting parameter design model to achieve precise guidance for each mine and even each blast face. When faced with a new blasting operation, the system automatically matches historical cases of good or excellent levels with similar geological conditions in the database and extracts their corresponding technical parameter combinations as recommended solutions when the geological exploration parameters of the current area are input. If the final evaluation result of the current blasting operation is not ideal (e.g., determined to be effective or ineffective), the system outputs specific adjustment instructions based on the correlation analysis results. For example, if the failure is determined to be due to stress not being transferred and the borehole angle being in the stress-locked zone, the system outputs an instruction to change the borehole inclination angle to cut off the key rock block; if the failure is determined to be due to excessive energy dissipation, the system outputs an instruction to increase the sealing length or adjust the charge structure. Through this cyclical feedback mechanism, as the working face advances and data accumulates, the accuracy of the parameter optimization model continuously improves, enabling the blasting design to adapt to the dynamic changes in geological conditions and ensuring that the roof pre-splitting effect is always maintained at the optimal level.

[0053] To further illustrate the specific application process and calculation details of the technical solution of this invention in actual engineering, the following detailed explanation is based on the actual geological conditions of a deep well working face in the Ordos mining area. This embodiment selects the return airway of the 22303 fully mechanized mining face as the test area. The roof above this area contains a layer of hard, fine sandstone approximately 16 meters thick, posing a high risk of rockburst. Deep-hole pre-splitting blasting is required to cut off the overhanging structure.

[0054] First, basic data collection and parameter setting were performed. Before blasting operations, core sampling through geological drilling and laboratory rock mechanics testing determined the average uniaxial compressive strength of the fine sandstone layer to be 85 MPa. Based on this lithological characteristic, rock mass property coefficients were set. The value is 1.1. The blasting operation was designed with a fan-shaped borehole layout of three holes. Actual on-site construction records show that the sealing length of each hole was 8 meters, and the charge per hole was 16 kg. The total charge of the detonation circuit for this operation was... A total of 48 kg. The theoretical specific energy of the selected Class II coal mine permissible emulsion explosive. The parameter is 4.2 × 10 6 J / kg.

[0055] Next, the blasting effectiveness index was calculated and preliminarily determined. After the blasting initiation, the downhole microseismic monitoring system detected significant vibration events. The system first calculated the theoretical remaining energy benchmark for this blasting based on the principle of energy conservation. Substituting the aforementioned parameters into the calculation logic, we get 4.2 × 10⁻⁶. 6 ×48×10 -4， Calculation The value was 20,160 joules. Subsequently, the microseismic system performed source localization and energy inversion on the received waveform signal, and after removing background noise, determined the vibration energy induced by this blast. 8.5×10 4 joule.

[0056] Based on the above data, substituting it into the blasting effectiveness index Calculation formula. First, take the logarithm of the energy value. Approximately 4.929, Approximately 4.304, total charge Right now Approximately 1.681. Calculate the numerator: 1.1 × 4.929 4.304 = 1.118.

[0057] The final calculation yielded the blasting effectiveness index. .

[0058] Based on the pre-set evaluation criteria, since 0.66 > 0.4, the initial assessment of the blasting effect is excellent. This result theoretically indicates that the blasting successfully induced the release of rock elastic energy far exceeding the explosive energy, and the roof should have been sufficiently fractured.

[0059] Subsequently, multi-source monitoring data was introduced for logical correction. Borehole stress gauges were pre-embedded in the coal seam within a 15-meter radius of the blasting center, and the system retrieved real-time monitoring data from these gauges before and after the blast. The data showed that the average vertical stress in this area before blasting was 18.5 MPa, and the average stress during the stable phase after blasting was 17.8 MPa. The system calculated the stress reduction rate: (18.5...) 17.8) / 18.5×100%≈3.8%(18.5 17.8) / 18.5×100%≈3.8%.

[0060] At this point, the stress correction logic is triggered: although the microseismic inversion... The index was extremely high (judged as excellent), but the actual reduction rate of surrounding rock stress was only 3.8%, far below the preset effective stress relief threshold of 10%. This contradictory phenomenon indicates that the high energy monitored by the microseismic system may mainly originate from the long-distance attenuation propagation of strong shock waves generated by explosive detonation in hard rock strata, or from fracture vibrations in distant non-critical layers, while the key load-bearing structure in the target area did not experience substantial stress transfer or damage. Therefore, the system implemented a downgrade correction strategy, determining that the preliminary results contained false positives, and downgrading the final evaluation level from excellent to good, or even, under a conservative strategy, to effective, indicating that the stress relief effect of on-site technicians had not reached the ideal optimal state.

[0061] Finally, parameter feedback optimization was implemented based on the final evaluation results. For the aforementioned condition of high microseismic energy but insignificant stress transfer, the system analyzed the historical database and determined that this phenomenon is usually attributed to either excessively large borehole spacing preventing fracture penetration or an unreasonable charge structure leading to excessive energy dissipation along the axial direction. Accordingly, the system generated optimized instructions for the next blasting cycle: it recommended reducing the borehole spacing from 5 meters to 4 meters and adjusting the single-hole charge structure from continuous charge to segmented charge to enhance the lateral fracturing effect on the borehole wall, thereby improving stress transfer efficiency in subsequent construction and ensuring the effectiveness of rockburst prevention.

Claims

1. A method for establishing an evaluation system for the effect of roof pre-splitting blasting based on microseismic monitoring, characterized in that, Includes the following steps: Collect roof pre-splitting blasting monitoring data, which includes static parameter data and dynamic monitoring data, and classify and organize the roof pre-splitting blasting monitoring data according to the construction site; Based on the principle of energy conservation and transformation, a blasting efficiency index evaluation model is constructed. By combining the total charge in the static parameter data and the micro-vibration energy in the dynamic monitoring data, the blasting efficiency index characterizing the effective efficiency of rock mass fracture is obtained. By comparing the blasting effectiveness index with the preset blasting effectiveness judgment standard, the preliminary evaluation level of the blasting effect is determined; Using stress monitoring data, drill cuttings monitoring data, and support monitoring data from the dynamic monitoring data as the basis for correction, the preliminary evaluation level is corrected through multi-dimensional logical verification rules to generate the final evaluation level; A parameter optimization model is established based on the correspondence between the final evaluation level and the static parameter data, and target blasting technical parameters are output to guide subsequent construction through correlation analysis.

2. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 1, characterized in that, In the step of collecting roof pre-splitting blasting monitoring data, which includes static parameter data and dynamic monitoring data: The static parameter data includes blasting technical parameters and geological environment parameters. The blasting technical parameters include sealing hole length, single hole charge, number of detonating holes, drilling azimuth, drilling inclination and hole depth. The geological environment parameters include roof lithology, rock layer thickness and roof water-bearing level. The dynamic monitoring data includes waveform data from the microseismic monitoring system, stress data from the borehole stress gauge, coal dust volume data from the drill cuttings method, and working resistance data from the hydraulic support.

3. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 1, characterized in that, The specific steps in constructing the blasting effectiveness index evaluation model and obtaining the blasting effectiveness index include: Determine the energy input terms of the model, obtain the total charge amount for a single hole or a single detonation in this blasting operation, and determine the theoretical energy released per unit mass of explosive used in the explosion. The energy dissipation baseline term for the model is defined as the remaining energy, which is generated by the explosion of explosives but dissipates in a wave-like form without causing rock mass fracture. The effective response terms of the model are constructed, the vibration energy obtained by inversion through the microseismic monitoring system after blasting is obtained, and rock mass property coefficients are introduced to correct the energy propagation and attenuation characteristics of different lithological media. A blasting effectiveness index evaluation model is established, and the blasting effectiveness index is calculated by establishing the quantitative mapping relationship between the effective response term, the energy dissipation benchmark term and the energy input term.

4. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 3, characterized in that, The blasting effectiveness index is calculated by combining the rock mass property coefficient, the vibration energy, the remaining energy, and the total charge amount, using the following arithmetic logic: The value of the remaining energy is obtained by multiplying the theoretical energy released per unit mass of the explosion, the total amount of explosive charge, and the preset energy propagation attenuation coefficient. The corrected vibration response value is obtained by taking the logarithm of the vibration energy with a base of 10 and multiplying it by the rock mass property coefficient. The baseline noise value is obtained by calculating the logarithm of the remaining energy with a base of 10. The blasting effectiveness index is obtained by calculating the difference between the corrected vibration response value and the reference noise value, and by calculating the ratio between the difference and the logarithm of the total charge amount as a base of ten.

5. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 1, characterized in that, In the step of determining the preliminary evaluation level of the blasting effect, a first threshold and a second threshold with a value greater than the first threshold are preset. The preset blasting effectiveness judgment standard is specifically set as follows: If the blasting effectiveness index is less than or equal to zero, the preliminary evaluation level is determined to be invalid, indicating that the explosive energy failed to effectively induce rock mass fracture. If the blasting effectiveness index is greater than zero and less than or equal to the first threshold, the preliminary evaluation level is determined to be a valid level, indicating that initial fractures were induced around the borehole. If the blasting effectiveness index is greater than the first threshold and less than or equal to the second threshold, the preliminary evaluation level is determined to be good, indicating that the fracture extends to the depth and achieves interlayer separation. If the blasting efficiency index is greater than the second threshold, the preliminary evaluation level is determined to be excellent, indicating that macroscopic fracture or collapse of the roof strata has been induced.

6. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 2, characterized in that, In the step of using stress monitoring data, drill cuttings monitoring data, and support monitoring data from the dynamic monitoring data as the basis for correction, the following falsification correction logic is executed using the stress monitoring data: The stress reduction rate was calculated by comparing the vertical stress of the surrounding rock before and after the blasting operation. When the preliminary evaluation level is determined to be good or excellent, if the calculated stress reduction rate is less than the preset effective stress relief threshold, it is determined that the high micro-seismic energy has not triggered substantial stress transfer. The preliminary evaluation level is then adjusted down one level to correct the result and generate the final evaluation level.

7. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 2, characterized in that, In the step of using stress monitoring data, drill cuttings monitoring data, and support monitoring data from the dynamic monitoring data as the basis for correction, the following rejection correction logic is executed using the drill cuttings monitoring data: Obtain the amount of drill cuttings per unit depth in the inspection borehole within the blasting impact area; Independent of the results of the preliminary evaluation level, if the amount of drill cuttings per unit depth is greater than the preset impact hazard threshold, or if dynamic phenomena such as drill bit suction, drill bit jamming, or drill bit jacking are detected during the inspection drilling process, it is determined that the high stress state has not been relieved by blasting, and the final evaluation level is forcibly adjusted to an invalid level for correction.

8. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 2, characterized in that, In the step of using stress monitoring data, drill cuttings monitoring data, and support monitoring data from the dynamic monitoring data as the basis for correction, the following upgrade correction logic is executed using the support monitoring data: Analyze the working resistance time-series curve of the hydraulic support within the preset time window after blasting; If the monitored working resistance shows a waveform characteristic of a surge followed by a rapid decline, and the average resistance after the decline is less than the average resistance before the blast, it is determined that the top plate structure has become unstable and fractured. If the preliminary evaluation level is a valid level, it is corrected by adjusting the preliminary evaluation level to a good level to generate the final evaluation level.

9. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 2, characterized in that, The steps in establishing a parameter optimization model include constructing a database for evaluating blasting effects: Each blasting operation is defined as an independent data sample entry, and each data sample entry is divided into a geological condition domain, an engineering control domain, and an effect feedback domain. The geological environment parameters are stored in the geological working condition domain, the blasting technology parameters are stored in the engineering control domain, and the final evaluation level, after being corrected by multi-dimensional logical verification rules, is stored in the effect feedback domain, thus establishing a mapping relationship between input conditions, control variables, and output responses.

10. The method for establishing an evaluation system for roof pre-splitting blasting effect based on microseismic monitoring according to claim 9, characterized in that, The method of outputting target blasting technical parameters to guide subsequent construction through correlation analysis is as follows: By performing sensitivity analysis on the blasting effect evaluation database, the parameter failure thresholds that lead to invalidity levels and the environmental coupling influence coefficients under different geological environments are identified. When a new blasting operation is carried out, based on the geological environment parameters of the current area, historical data samples evaluated as good or excellent are retrieved and matched in the blasting effect evaluation database, and the corresponding engineering control domain parameters are extracted as a recommended scheme. If the final evaluation level of the current blasting operation is not ideal, based on the results of the sensitivity analysis, adjustment instructions will be output for the borehole spacing, sealing length, or charge structure.

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