Microseismic Monitoring System Research Articles

Deep transfer learning for microseismic waveforms recognition across geological conditions in TBM tunnels

In deeply buried tunneling projects, geological conditions are often complex and varied. Microseismic monitoring systems are extensively deployed to enhance construction safety. However, when the current geological conditions differ from those present during the signal collection for model training, recognition accuracy tends to decline significantly. Therefore, improving the applicability and stability of microseismic waveform recognition models across varying geological conditions has emerged as a critical challenge. To address this issue, we first analyze the impact of lithological changes and the development of structural planes on the features of microseismic waveforms. Subsequently, we propose a category-domain-aligned transfer learning method that enables the transfer of recognition capabilities across geological conditions by facilitating similar feature extraction and the recognition of cross-geological fracture waveforms. In this model, feature separation modeling enhances the extraction of category features of waveforms under different geological conditions. A deep transfer learning mechanism distinguishes between unique and common features, allowing for the capture of essential features necessary for model parameter updates. Through comparative experiments and feature distribution alignment and visualization, we demonstrate that the accuracy of microseismic waveform recognition across geological conditions achieves 90 %. Additionally, the performance of our method is validated using microseismic signals collected from different sections of the construction site.

Optimization and Numerical Verification of Microseismic Monitoring Sensor Network in Underground Mining: A Case Study

A scientific and reasonable microseismic monitoring sensor network is crucial for the prevention and control of rockmass instability disasters. In this study, three feasible sensor network layout schemes for the microseismic monitoring of Sanshandao Gold Mine were proposed, comprehensively considering factors such as orebody orientation, tunnel and stope distributions, blasting excavation areas, construction difficulty, and maintenance costs. To evaluate and validate the monitoring effectiveness of the sensor networks, three layers of seismic sources were randomly generated within the network. Four levels of random errors were added to the calculated arrival time data, and the classical Geiger localization algorithm was used for locating validation. The distribution of localization errors within the monitoring area was analyzed. The results indicate that when the arrival time data are accurate or the error is between 0% and 2%, scheme 3 is considered the most suitable layout; when the error of the arrival time data is between 2% and 10%, scheme 2 is considered the optimal layout. These research results can provide important theoretical and technical guidance for the reasonable design of microseismic monitoring systems in similar mines or projects.

Microseismic Monitoring and Disaster Warning via Mining and Filling Processes of Residual Hazardous Ore Bodies

The thick ore bodies in the Xianglushan tungsten mine have been irregularly mined, forming a super large, connected irregular goaf group and tall, isolated irregular pillars inside. At the same time, there is a production capacity task of recovering residual and dangerous ore bodies. This poses the potential for serious ground-pressure disasters, such as roof caving, pillar collapse, and large-scale goaf collapse during mining. Based on the actual needs of the site, we established a microseismic monitoring system. After analyzing the mining and filling processes and their relationships, and, combined with the distribution characteristics of microseismic multiple parameters, we constructed a ground-pressure disaster warning mode and mechanism. We analyzed the stability of the goaf, further formed a warning system, and achieved disaster warning. In response to the current situation of the difficulty of early warning of ground pressure in the Xianglushan tungsten mine, continuous on-site monitoring of existing goaves, point pillars, and strip pillars, as well as analysis of stress changes during dynamic mining and filling processes, we explored scientific and reasonable early warning mechanisms and models, understanding the relationship between the changes in microseismic parameters during dynamic mining and filling processes and ground pressure, studying and improving the reliability of underground microseismic monitoring and early warning, and achieved the internal connection between building early warning systems and the prevention of ground-pressure disasters. The results indicate that the mining and filling process of the ore body is the main factor in maintaining a stable and balanced distribution of underground ground pressure in mining engineering. Microseismic monitoring can invert the evolution of ground pressure and form a feedback system with ground-pressure warning, achieving mine safety management.

A Real-Time Inverted Velocity Model for Fault Detection in Deep-Buried Hard Rock Tunnels Based on a Microseismic Monitoring System

Microseismic monitoring is an effective and widely used technology for dynamic fault disaster early warning and prevention in deep-buried hard rock tunnels. However, the insufficient understanding of the distribution of native faults poses a major challenge to yielding precise early warnings of disasters using an MS (Microseismic Monitoring System). Velocity field inversion is a reliable means to reflect fault information, and there is an urgent need to establish a real-time velocity field inversion method during tunnel excavation. In this paper, a method based on an MS is proposed to achieve the inversion of the velocity field in the monitoring area using microseismic event and excavation blasting data. The velocity field inversion method integrates the reflected wave ray-tracing method based on PSO (Particle Swarm Optimization) theory and FWI (Full-Waveform Inversion) theory. The accuracy of the proposed velocity inversion method was verified by various classic numerical simulation cases. In numerical simulations, the robustness of our method is evident in its ability to identify anomalous structural surfaces and velocity discontinuities ahead of the tunnel face.

Characterizing large deformation of soft rock tunnel using microseismic monitoring and numerical simulation

Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock. The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel. An in situ microseismic (MS) monitoring system is established in the plateau soft tock tunnel. This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation, which agree well with field convergence deformation results. The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results. The results demonstrate that: (1) Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section. As excavation continues, the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding; (2) Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass, vertical tension fractures (account for 45%) exist in deep rockmass, which play a crucial role in controlling the macroscopic failure of surrounding rock; and (3) Based on the radiated MS energy, a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock. The model depicted varying degrees of damage, and three high damage zones were identified. Generally, the depth of high damage zone ranged from 4 m to 12 m. This study may be a valuable reference for the warning and controlling of large deformations in similar projects.

A method for assessing the operational status of a microseismic monitoring system using energy distribution changes of observed data

Abstract The operational status of geophones plays a pivotal role in ensuring the accuracy and reliability of microseismic monitoring systems. However, conventional techniques used to evaluate the operational status of geophones require human intervention or significant time delays. To address this issue, we propose a method for online monitoring of geophone status using observed data obtained from a microseismic system. First, the energy features of the preprocessed observation data are extracted via wavelet packet decomposition. Subsequently, the distribution parameters of energy features are obtained through log-logistic distribution fitting. These parameters are then applied to a change-point detection model, enabling the online monitoring of seismic geophones. In addition, we select a long short-term memory network to classify the operational status of the geophones, which is trained using the obtained energy distribution data and the time-frequency characteristics of the observed data. The experimental results indicate that the model achieves an accuracy of 98.33%, surpassing the 89.58% accuracy of the support vector machine. The proposed method not only contributes to online monitoring and precise determination of the operating status of detectors, but also has enormous application potential in other fields that require monitoring and evaluating the operating status of instruments.

An ICEEMDAN-WPD based denoising method for MS signals and its engineering application

ABSTRACT The complex installation environments for microseismic (MS) monitoring systems generate significant noise in MS signals, hindering accurate event localization. This study proposes a noise reduction method combining improved complete ensemble empirical mode decomposition with adaptive noise and wavelet packet decomposition (ICEEMDAN-WPD). The signal is initially processed with ICEEMDAN to obtain intrinsic modal functions (IMFs), which are components ordered from high to low frequencies. The correlation between the original signal and each IMF is evaluated and low-correlation IMFs are eliminated for initial noise reduction. Additionally, the WPD method further decomposes and filters the highly correlated IMFs for secondary noise reduction, enhancing the noise reduction effect. Ultimately, the signal formed by reconstructing the secondary noise reduction IMF is the complete ICEEMDAN-WPD method filtered signal. The simulation results indicate that the ICEEMDAN-WPD method enhances the signal-to-noise ratio (SNR) and preserves the original signal characteristics to the greatest extent, demonstrating a significant noise reduction capability. In particular, the application of this method to the noise reduction process of MS signals from Shuangjiangkou hydropower station has led to a significant improvement in the accuracy of subsequent MS events localization. The research results can provide reference for similar signal noise reduction processing.

The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention

The main frequency of microseismic signals has recently been identified as a dominant indicator for characterizing vibration events because it reflects the energy level of these events. Frequency information directly determines whether effective signals can be collected, which has a significant impact on the accuracy of predicting rock burst disasters. In this study, we adopted a characterizing method and developed a monitoring system for capturing rock failure events at various strata in an underground coal mine. Based on the rock break mechanism and energy release level, three types of rock failure events, namely, high roof breaking, low roof breaking, and coal fracture events, were evaluated separately using specific sensors and monitoring systems to optimize the monitoring accuracy and reduce the general cost. The captured vibration signals were processed and statistically analyzed to characterize the main frequency features for different rock failure events. It was found that the main frequency distribution ranges of low roof breaking, high roof breaking, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, these frequency ranges are proposed to monitor different vibration events to improve detection accuracy and reduce the test and analysis times. The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect both in the maximum amplitude and main frequency, which could be an indicator to distinguish failure locations in the vertical direction. This result can support the selection and optimization of the measurement range and main frequency parameters of microseismic monitoring systems. This study also discussed the distribution law of the maximum amplitude and main frequency of different events and the variation in test values with the measurement distance, which are of great significance in expanding the application of optimized microseismic monitoring systems for rock burst monitoring and prevention.

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained.

Deep Neural Network for Distinguishing Microseismic Signals and Blasting Vibration Signals Based on Deep Learning of Spectrum Features

ABSTRACT Similar waveforms and overlapping frequency ranges make distinguishing blasting vibrations and microseismic signals challenging, causing interference with coal mine microseismic monitoring systems. To address this problem, we propose a spectrum dataset (MSData) reflecting the spectrum features of both signal types and present a signal classification network (SCNet) combining CNNs and Transformers for signal classification. The network can learn multi-dimensional features of both signals from MSData and automatically and efficiently identify the two signal types. Experimental results yield F1-scores of 0.991 for microseismic signals and 0.993 for blasting vibration signals, meeting engineering application requirements.

Investigation of the microfracture and damage characteristics of dam during impoundment at Sanhekou hydropower station

Dam stability is one of the most important issues in hydraulic engineering. Microfractures and damage commonly occur during impoundment, which might lead to serious dam problems. In this study, based on the engineering background of the Sanhekou hydropower station, microseismic monitoring and numerical simulation were employed to systematically investigate the microfracture and damage characteristics of the dam body. First, the microseismic monitoring system was established to capture the microfractures inside the dam. The results indicated that the rise in water level elevation has a significant effect on the microfracture and damage characteristics of the dam body, especially during the early stage of impoundment. This can be reflected by the variation in the derived source parameters, i.e., the b value, daily energy release, daily apparent stress and daily apparent volume. In addition, the failure mode of the microfractures could be determined by using the ES/EP value of microseismic events and the moment tensor inversion method. The cracking orientation of the failure surfaces could also be determined by the moment tensor inversion method. Subsequently, numerical simulation was conducted where the initial damage of the dam was considered by integrating the microseismic monitoring data. The simulation results suggested that dam deformation under impoundment considering microseismic feedback agrees well with the real field measured results. The stress level of the dam toe was larger than that of the dam heel, and both the dam toe and dam heel were under compression before impoundment. However, with increasing water level elevation, the stress status of the dam heel area changes from compression to tension. The findings in this study will provide a better understanding of the damage and failure mechanism of dams during impoundment, which might be helpful for the design and support of dams in hydropower stations.

Integrating Microseismic Monitoring for Predicting Water Inrush Hazards in Coal Mines

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams.

Rockburst early warning based on microseism and critical point theory

During the tunnel construction, rockbursts occurred, posing a risk to safety. To mitigate this risk, a microseismic monitoring system was installed in the tunnel to provide early warning of rockbursts. After conducting research on the evolution of microseismic events in terms of number, moment magnitude, and energy within a specific time frame, an approach for rockbursts risk assessment based on microseismic moment magnitude was proposed. The critical point theory was utilized to quantitatively analyze the energy release process of the surrounding rock before intense rockbursts. This analysis revealed that the energy release of the surrounding rock before rockbursts can be divided into three stages: stable energy release stage, transitional energy release stage and accelerated energy release stage. The obtained process, derived from the critical point theory, was compared and analyzed alongside the evolution process of value of b, cumulative apparent volume, and energy index.

Application and Research of Microseismic Monitoring System and Hydraulic Fracturing Technology in Coal Mines

In order to improve the effectiveness of coal mine gas control and enhance the level of coal mine safety production, the application of a microseismic monitoring system and hydraulic fracturing technology in coal mines was studied. Applying hydraulic fracturing technology to coal mine gas treatment, firstly, the geological structure and gas concentration in the mining area are detected using the radio tunnel perspective method and infrared differential absorption method. Then, the relevant parameters of hydraulic fracturing are determined, and finally, hydraulic fracturing technology is implemented. Microseismic monitoring technology is used to monitor the cracks formed during hydraulic fracturing construction and evaluate the fracturing effect. The instantaneous energy envelope is obtained from the microseismic data of each detection channel after stacking and Hilbert transform static correction. A microseismic in-phase inversion positioning objective function based on travel time residuals is constructed, and under the constraints of polarization analysis, the optimal solution is obtained through search iteration to complete microseismic in-phase inversion positioning. Experimental results have shown that after applying this method to coal mine gas control, the gas concentration decreases below the execution standard, achieving good control effects. Under microseismic monitoring in coal mines, the hydraulic fracturing effect can be effectively and reasonably evaluated, and the safety production level of coal mines can be improved.

Study on stress distribution law of surrounding rock of roadway under the goaf and mechanism of pressure relief and impact reduction

To safely extract residual coal from a mine affected by rock bursts, the concept of “roadways under goafs” has been proposed as an effective layout. The pressure relief and shock reduction mechanism of roadways under goafs is elucidated through theoretical analysis, numerical simulation, and engineering verification. The stress model for goafs is established based on elastoplastic mechanics, followed by the construction of a force model for coal beneath the goafs. The stress composition of roof, floor and two sides of roadway under a goaf are analyzed. Using the FLAC-PFC coupling simulation method, the excavation process from top coal mining to bottom coal mining roadways of a No. 17 coal mine in Heilongjiang Province was simulated. The simulation results validate the spatiotemporal evolution of the stress and displacement in the surrounding rock mass of the roadway situated beneath goafs and determine the precise location of the roadway and length of its bottom coal face. A large diameter borehole of the roadway and presplitting blasting of the coal face roof are used to solve the local high stress danger area. Moreover, the roadway support mode of the “steel shed + spray arch support + monomer + π steel beam” is proposed. Finally, through the data of the field micro-seismic and stress monitoring system, the effectiveness of the pressure relief and shock reduction of roadways under goafs, the high efficiency of local danger relief of the roadways and working faces, and the practicability of the “steel shed + spray arch support + monomer + π steel beam” support of the roadways broken by surrounding rock are verified. A good basis for choosing a reasonable location for roadways and surrounding rock control methods for residual bottom coal mining and rock burst mining can be found in the research results.

Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes.

The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure monitoring and early warning. In this paper, the wavelet coefficient threshold denoising method in the time-frequency domain, STA/LTA method, AIC method, and skew and kurtosis method are studied, and the automatic P-phase-onset-time-picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized, the reliability of the P-phase-onset-time-picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity in the underground mine, which is of great significance to the data processing and analysis of microseismic monitoring.

Experimental Study on Calibration of Amplitude-Frequency Measurement Deviation for Microseismic Sensors in Coal Mines.

Microseismic monitoring systems (MMS) have become increasingly crucial in detecting tremors in coal mining. Microseismic sensors (MS), integral components of MMS, profoundly influence positioning accuracy and energy calculations. Hence, calibrating these sensors holds immense importance. To bridge the research gap in MS calibration, this study conducted a systematic investigation. The main conclusions are as follows: based on calibration tests on 102 old MS using the CS18VLF vibration table, it became evident that certain long-used MS in coal mines exhibited significant deviations in frequency and amplitude measurements, indicating sensor failure. Three important calibration indexes, frequency deviation, amplitude deviation, and amplitude linearity are proposed to assess the performance of MS. By comparing the index of old and new MS, critical threshold values were established to evaluate sensor effectiveness. A well-functioning MS exhibits an absolute frequency deviation below 5%, an absolute amplitude deviation within 55%, and amplitude linearity surpassing 0.95. In normal operations, the frequency deviation of MS is significantly smaller than the amplitude deviation. Simplified waveform analysis has unveiled a linear connection between amplitude deviation and localization results. An analysis of the Gutenberg-Richter microseismic energy calculation formula found that the microseismic energy calculation is influenced by both the localization result and amplitude deviation, making it challenging to pinpoint the exact impact of amplitude deviation on microseismic energy. Reliable MS, as well as a robust MS, serve as the fundamental cornerstone for acquiring dependable microseismic data and are essential prerequisites for subsequent microseismic data mining. The insights and findings presented here provide valuable guidance for future MS calibration endeavors and ultimately can guarantee the dependability of microseismic data.

Microseismic event waveform classification using CNN-based transfer learning models

The efficient processing of large amounts of data collected by the microseismic monitoring system (MMS), especially the rapid identification of microseismic events in explosions and noise, is essential for mine disaster prevention. Currently, this work is primarily performed by skilled technicians, which results in severe workloads and inefficiency. In this paper, CNN-based transfer learning combined with computer vision technology was used to achieve automatic recognition and classification of multi-channel microseismic signal waveforms. First, data collected by MMS was generated into 6-channel original waveforms based on events. After that, sample data sets of microseismic events, blasts, drillings, and noises were established through manual identification. These datasets were split into training sets and test sets according to a certain proportion, and transfer learning was performed on AlexNet, GoogLeNet, and ResNet50 pre-training network models, respectively. After training and tuning, optimal models were retained and compared with support vector machine classification. Results show that transfer learning models perform well on different test sets. Overall, GoogLeNet performed best, with a recognition accuracy of 99.8%. Finally, the possible effects of the number of training sets and the imbalance of different types of sample data on the accuracy and effectiveness of classification models were discussed.

The construction of mine water recycling performance evaluation index system under the Internet of Things environment

The utilization rate of water resources of mines in China is still relatively low. The evaluation of mine water recycling has practical guiding significance for the planning, positioning, development, and construction of groundwater in today’s society. This article constructs an evaluation system for mine water recycling based on the key performance index (KPI) via the Internet of Things and big data platforms. This system evaluates the recycling status of mine water. First, the micro-seismic monitoring system and the hydrological dynamic detection system are deployed in work. The installation and debugging methods are compared to meet the monitoring requirements. Second, the filtered clear water is used for equipment cooling and firefighting dust removal at the mining face through the constant pressure supply pump. The excess clear water is discharged to the surface. Finally, 16 indicators are screened from four dimensions to construct a key KPI mine water evaluation system for evaluation and optimization. The results demonstrate that the first mine water monitoring system runs well and is fully functional, achieving the expected goal. The utilization rate evaluation score has increased yearly, from 3.05 points in 2016 to 3.39 points in 2020. However, the per capita utilization rate score still needs improvement. It is essential to improve the rationality of development and utilization.

Characterizing Rockbursts and Analysis on Hilbert-Huang Transform Spectrum of Microseismic Events, Shuangjiangkou Hydropower Station, Based on Microseismic Monitoring

The Shuangjiangkou hydropower station in China has complex geological conditions with high in situ stress. During the tunnel excavation, rockbursts occurred frequently, which seriously affected construction progress. Microseismic (MS) monitoring technology was used to explore rock MS activities to predict rockbursts. The MS monitoring system can capture a large number of MS signals. Based on Hilbert–Huang transform (HHT) instantaneous frequency analysis technology, using MATLAB software (R2022a) to write a program to convert the MS waveform, the frequency and energy characteristics of MS signals at a certain time can be obtained. By analyzing the frequency and energy characteristics of every event, the microseism active areas can be determined, and then rockbursts can be predicted scientifically. This paper selected two different construction sites, which were the main powerhouse and the access tunnel in the main powerhouse, as the research background. Introducing HHT instantaneous time–frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. The results indicated that some parameter anomalies in the event spectrum can predict rockbursts. For complex tunnel construction conditions, the HHT time–frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful.