Microseismic Monitoring Technology Research Articles

Promoting Sustainable Development of Coal Mines: CNN Model Optimization for Identification of Microseismic Signals Induced by Hydraulic Fracturing in Coal Seams

Borehole hydraulic fracturing in coal seams can prevent dynamic coal mine disasters and promote the sustainability of the mining industry, and microseismic signal recognition is a prerequisite and foundation for microseismic monitoring technology that evaluates the effectiveness of hydraulic fracturing. This study constructed ultra-lightweight CNN models specifically designed to identify microseismic waveforms induced by borehole hydraulic fracturing in coal seams, namely Ul-Inception28, Ul-ResNet12, Ul-MobileNet17, and Ul-TripleConv8. The three best-performing models were selected to create both a probability averaging ensemble CNN model and a voting ensemble CNN model. Additionally, an automatic threshold adjustment strategy for CNN identification was introduced. The relationships between feature map entropy, training data volume, and model performance were also analyzed. The results indicated that our in-house models surpassed the performance of the InceptionV3, ResNet50, and MobileNetV3 models from the TensorFlow Keras library. Notably, the voting ensemble CNN model achieved an improvement of at least 0.0452 in the F1 score compared to individual models. The automatic threshold adjustment strategy enhanced the identification threshold’s precision to 26 decimal places. However, a continuous zero-entropy value in the feature maps of various channels was found to detract from the model’s generalization performance. Moreover, the expanded training dataset, derived from thousands of waveforms, proved more compatible with CNN models comprising hundreds of thousands of parameters. The findings of this research significantly contribute to the prevention of dynamic coal mine disasters, potentially reducing casualties, economic losses, and promoting the sustainable progress of the coal mining industry.

Optimizing microseismic monitoring: a fusion of Gaussian–Cauchy and adaptive weight strategies

Abstract In mining mineral resources, it is vital to monitor the stability of the rock body in real time, reasonably regulate the area of ground pressure concentration, and guarantee the safety of personnel and equipment. The microseismic signals generated by monitoring the rupture of the rock body can effectively predict the rock body disaster, but the current microseismic monitoring technology is not ideal. In order to address the issue of microseismic monitoring in deep wells, this research suggests a machine learning-based model for predicting microseismic phenomena. First, this work presents the random spare, double adaptive weight, and Gaussian–Cauchy fusion strategies as additions to the multi-verse optimizer (MVO) and suggests an enhanced MVO algorithm (RDGMVO). Subsequently, the RDGMVO-Fuzzy K-Nearest Neighbours (RDGMVO-FKNN) microseismic prediction model is presented by combining it with the FKNN classifier. The experimental section compares 12 traditional and recently enhanced algorithms with RDGMVO, demonstrating the latter’s excellent benchmark optimization performance and remarkable improvement effect. Next, the FKNN comparison experiment, the classical classifier experiment, and the microseismic dataset feature selection experiment confirm the precision and stability of the RDGMVO-FKNN model for the microseismic prediction problem. According to the results, the RDGMVO-FKNN model has an accuracy above 89%, indicating that it is a reliable and accurate method for classifying and predicting microseismic occurrences. Code has been available at https://github.com/GuaipiXiao/RDGMVO.

MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio.

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.

Research and application of ‘three zones’ range within overlying strata in goaf of steep coal seam

The determination of the ‘three zones’ range within the overlying strata in goaf of paramount importance for effective gas extraction in the goaf and the prevention and control of gas levels exceeding limits in both the working face and the upper corner. Due to the influence of their dip angles, the existing formula used to calculate the breaking boundary angle of the overlying strata in the goaf of horizontal coal seams is no longer suitable for steep coal seams. In response to this issue, the movement law of the overlying strata during the mining of steep coal seams was analyzed and a formula for calculating the breaking boundary angle of the overlying strata in the goaf of steep coal seams was derived. The theoretical calculation formula was applied to the 3,103 fully mechanized mining face of a certain mine in southwest China, and compared and analyzed the results with numerical simulation and on-site measurement using microseismic monitoring technology. The research findings demonstrate that the formula effectively quantifies the ‘three zones’ range within the overlying strata in the goaf of steep coal seams. The ‘three zones’ range in steep coal seams is primarily influenced by factors such as the length of the filling area in the lower part of the goaf, changes in roof lithology, coal seam dip angle, length of the working face, and overburden load. The longer the length of the filling area, the larger the upper breaking boundary angle and the smaller the lower breaking boundary angle of the fracture zone. Based on the research results of three methods, the height of the caving zone in the 3,103 fully mechanized mining face is from 6.93 m to 7.7 m, the height of the fracture zone is from 28.91 m to 34.2 m, the lower breaking boundary angle of the fracture zone is from 40° to 44.5°, and the upper breaking boundary angle of the fracture zone is from 57.7° to 62°. The research results offer robust technical support and theoretical guidance for the determination of the ‘three zones’ range within the overlying strata during the future mining of steep coal seams.

Rockburst hazard assessment and prevention and control strategies in the Ruihai gold mine

Ruihai gold mine is a super large underwater gold mine with an initial mining depth of more than 1,400 m. Deep mining may face rockburst disaster. In order to scientifically control the potential rockburst risk, ensure the safety of underground personnel and equipment, and realize the safe and efficient mining of the mine, it is necessary to evaluate the rockburst risk of the mine and formulate appropriate rockburst risk warning and mitigation strategies. XRD mineral composition analysis and conventional rock mechanical tests were carried out on the parent rock granite samples. The results show that there are differences in the lithologic composition and rock mechanics parameters of the three colors of granite, which are preliminarily determined as three kinds of granite; The rockburst tendency of three kinds of granites is evaluated by using three classical rockburst criteria, and the discrimination results of the three rockburst criteria are not completely consistent or even contradictory. The application limitations rockburst risk assessment based on index criteria are discussed and analyzed in detail. It is proposed to apply microseismic monitoring technology to warn and manage the rockburst risk of Ruihai gold mine in development and mining stages, and put forward microseismic monitoring layout schemes and targeted rockburst prevention and control measures in different stages. The research results can provide reference for similar projects.

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields.

Microseismic crack monitoring technology

Microseismic crack monitoring technology

Identification of goaf instability under blasting disturbance using microseismic monitoring technology

Micro-seismic (MS) technique can be used to appreciate rock failure process and severity of damage by quantifying micro-earthquakes in terms of time, location, grade, and energy release. Based on 3D laser detection and high-precision micro-seismic monitoring, this study analyzes the MS activity, stress and displacement evolution linked with the instability/failure property of hidden goaf incorporating several blasting disturbances in an open pit mine. MS monitoring outcomes reveal two arch fracture zones in the goaf roof, and MS occasions are mostly intense at rock-intersecting interface of north–south goafs. Evolution of apparent stress, apparent volume, and displacement reveals that the goaf is currently in a stable state. The current investigation offers a novel field method and view of inspecting the stability of goaf subjected to blasting disturbance in mines.

Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.

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.

Investigation on the Height of Fracture Zone in Goaf of Steep Coal Seam Based on Microseismic Monitoring

The height of the fracture zone in the mined-out area is the basic parameter for controlling mine strata, preventing and controlling mine water, and arranging drilling holes for high-level gas extraction. Aiming at the development law of fracture zone in the steep seam and the limitation of traditional drilling inspection method, microseismic monitoring technology is used to investigate the height of fracture zone in goaf. Taking the 3103 south working face of a mine in Sichuan as an example, the scheme of microseismic monitoring and data collection of the working face is worked out. Through data analysis and processing, the range of the fracture zone in the goaf is 6.8 m– 34.2 m, and compared with the numerical simulation analysis results, the relevant results are basically consistent. It provides a new technical means for the investigation of “three zones” of overlying strata in the goaf of a steep seam.

Application of Micro-Seismic Monitoring in Post-Fracturing Evaluation of Shale Gas: A Case Study of Well X from Puguang Area, China

Multi-stage fracturing of horizontal wells is an indispensable technology to create complex fracture networks, which can unlock production potential and enable commercial productivity for shale gas with low porosity and permeability. Real-time monitoring of fracture networks is essential for adjusting key parameters, mitigating fracturing risks, and achieving optimal fracturing effects. Micro-seismic monitoring technology accurately captures and describes the development of fracture networks by detecting micro-seismic waves generated through rock ruptures, providing valuable insights into the evaluation of post-fracturing. In this study, we first introduced the basic parameters of well X that were obtained by laboratory experiments and logging interpretation, including porosity, gas-bearing properties, mineral composition, rock mechanics, and crustal stress. Then, the hydraulic fracturing scheme was designed on the basis of the geological engineering characteristics of well X. Finally, we conducted a comprehensive analysis of various factors that can affect hydraulic fracturing. This included an examination of the impact of pre-fluid temporary plugging and fracture complexity on the overall effectiveness of the operation. Based on the laboratory experiments and theoretical analysis, the following conclusions can be drawn: (1) fracture size is essentially formed when the fluid strength exceeds 35 m3/m; (2) both preflush with high viscosity and the amount of power sand exceeding 20 cubic meters are conducive to the propagation of fracture height; (3) temporary plugging balls facilitate the balanced propagation of multiple fracture clusters within a stage, whereas temporary plugging particles promote the formation of complex fractures; and (4) geological conditions are a prerequisite for creating a complex network of fractures, and only engineering techniques can facilitate the appropriate enhancement of fracture complexity. This study provides an essential method for the fracturing design of shale gas.

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.

Mechanism and Empirical Study of Rockburst in the Adjacent Area of a Fully Mechanized Top-Coal Caving Face Based on Microseismic Technology

Monitoring and providing warnings for coal mine rockburst disasters is a worldwide problem. Several rockburst accidents have occurred in a 1301 belt transport chute near a 1300 fully mechanized caving mine face. To address this issue, an empirical study of the occurrence mechanism of rockbursts in the adjacent area of the fully mechanized top-coal caving face was carried out. This paper mainly addresses the following issues: (1) based on microseismic monitoring technology, the distribution characteristics of the host-rock-supported pressure of the 1300 working face were measured, and the evolution and distribution of the deep-well caving working face host-rock-supported pressure were analyzed. It is revealed that the occurrence mechanism of rockburst in the adjacent area is actually caused by the evolution and superposition of the lateral abutment pressure of the 1300 stope, and the stress of the original rock along the 1301 belt transport down chute; (2) a theoretical calculation model of dynamic and static abutment pressure in longwall stope is built, and an example is tested. The results show that the peak position of lateral abutment pressure of the coal body outside the 1300 goaf is around 63 m, and the peak value of abutment pressure is around 47 MPa; (3) coal body stress monitoring, bolt dynamometer detection, and other means are compared and analyzed. At the same time, with the help of CT geophysical prospecting and drilling cutting measurements, it is concluded that the 1301 belt transport down chute is in the bearing pressure influence zone (superimposed zone), which further verifies the validity of microseismic analysis results and the accuracy of the above theoretical model. Based on this, the early warning system and prevention measures for rockburst based on microseismic monitoring are proposed. The engineering practice shows that the dynamic and static bearing pressure distribution and evolution law of the working face can be dynamically obtained by using microseismic technology, which provides a basis for the accurate prediction and treatment of rockbursts.

Microseismic Monitoring Technology Developments and Prospects in CCUS Injection Engineering

CO2 geological storage projects are an essential tool for China to achieve the double carbon target of energy savings and emission reductions. In order to safely and effectively control the implementation of injection projects and monitor the dynamics of CO2 injection, multi-dimensional and multi-disciplinary monitoring tools are required. Among them, microseismic monitoring is a key technology for predicting reservoir dynamics and reflecting reservoir geomechanical behavior. Such monitoring has been carried out previously for reservoirs in other countries, but experimental projects are also gradually being developed in China. In this paper, we focus on the research and analysis results of microseismic monitoring of carbon storage projects in various work areas. For different reservoir conditions, we explore combinations of the monitoring implementation methods in China, comparing the differences in each work area. We propose a joint well and ground microseismic monitoring method and a multi-spatial and multi-physical field coupling research system for use in the implementation of domestic demo projects for the future research and development of microseismic monitoring of carbon storage projects. The monitoring program can meet the requirements for certain periodic repeated or continuous observations and can intelligently assess the risk and handle the alert behavior. The foundation is laid for the development of the future microseismic monitoring technology to achieve the goal of developing cost-controllable, permanent, and real-time monitoring equipment. The application of the monitoring system in China has been effective, and this experience can contribute to the development of injection engineering in the future.

Research on microseismic denoising method based on CBDNet

Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose a new denoising method based on convolutional blind denoising network (CBDNet). The method is partially modified for image denoising network CBDNet to make it suitable for one–dimensional data denoising. At present, most of the existing filtering methods are proposed for the Gaussian white noise denoising. In contrast, the proposed method also learns the wind noise, construction noise, traffic noise and mixed noise through the strategy of residual learning. The full convolution subnetwork is used to estimate the noise level, which significantly improves the signal-to-noise ratio and its performance of removing the correlated noise. The model is trained with different types of real noise and random noise. The denoising result is evaluated by corresponding indexes and compared with other denoising methods. The results show that the proposed method has better denoising performance than traditional methods, and it has a superior noise suppression level for oil well construction noise and mixed noise. The proposed method can suppress the interference of time–frequency overlapped end to end and still have noise suppression and event detection capability even if the signal is superimposed on other types of noise.

Fracture propagation induced by hydraulic fracturing using microseismic monitoring technology: Field test in CBM wells in Zhengzhuang region, Southern Qinshui Basin, China

Hydraulic fracturing is an important technical measure for coalbed methane (CBM) development, and the propagation of fractures in the target coal seam induced by hydraulic fracturing is related to the stimulation performance in CBM recovery. Therefore, effective monitoring of fracture development during reservoir fracturing is critical for CBM engineering. In this paper, the microseismic technology was used to monitor the spatial and temporal characteristics of the fracture extension in the CBM well during hydraulic fracturing in Zhengzhuang Region, Southern Qinshui Basin, China. Based on the microseismic fracture scanning data and imaging processing, the three-dimensional shape of fractures in vertical wells after fracturing can be quantified, and for the vertical well ZHSY-1, the main fracture direction is identified as NE106°, and the fracture length is 426 m, and the fracture area of coal seam is 1.6 × 105 m2. It is found that fracturing does not develop continuously in time and space with fracturing fluid injection, and the fracture regions are scattered throughout the space, and the formation and development of fracture regions are intermittent. After fracturing with a large amount of fracturing fluid injection in Well ZHSY-1, the fracture area has been significantly increased, and the well gas production has been significantly improved, which is confirmed by the field CBM well data. This study provides a field application case for studying the effect of hydraulic fracturing fracture propagation using microseismic technology, which can be used as a reference for fracturing engineering in CBM development.

Research on in situ stress inversion of deep‐buried tunnel based on pressure/tension axis mechanism and geological structure

AbstractThe investigation of the in situ stress distribution has always been a key condition for engineering design of deep tunnels and analysis of surrounding rock stability. In this paper, a comprehensive judgment method coupled with pressure/tension (P/T) axis mechanism and geological structure is proposed to invert the in situ stress in the Duoxiongla tunnel in Tibet. In the process of TBM tunnel excavation, 3887 groups of microseismic events were collected by means of microseismic monitoring technology. By studying the temporal and spatial distribution of 3887 groups of microseismic events, 42 groups of microseismic data were selected for in situ stress inversion. Then the focal mechanisms of 42 groups of microseisms were inverted. Combined with the analysis of the previous geological survey, the inversion results of the in situ stress were analyzed. According to the focal mechanism of the tunnel area, the linear in situ stress inversion method was used to invert the in situ stress in the source area. Finally, according to the PTGS (pressure/tension axis mechanism and geological structure) comprehensive judgment method proposed in this paper, the in situ stress of the tunnel microseismic region was determined. The results show that there are mainly three groups of fissures and joint surfaces in the tunnel area, and the in situ stress is dominated by the horizontal tectonic stress; the main driving force of the rupture surface in the excavation process of Duoxiongla tunnel is the horizontal tectonic stress; the distribution of the maximum and minimum principal stress obtained by the inversion is consistent with the distribution of the P/T axis; combined with the linear in situ stress inversion method and the comprehensive judgment of PTGS, the azimuth and dip angles of the three principal stresses are finally determined as N90.71°E, 4.06°, N5.35°W, 3.06°, and N8.10W, 85.32°, respectively. The study verifies the feasibility of microseismic inversion of in situ stress.

A Study on Classification Method of Mine Vibration Based on Microseismic Monitoring Cloud Service Platform

With the gradual deepening of mining depth, sudden ground pressure disasters such as rock burst and collapse caused by deep high stress and high rock pressure are major hidden dangers affecting mine safety production. Microseismic monitoring technology has been widely applied in the field of mine ground pressure disaster warning. The existing microseismic monitoring system has some problems, such as inaccurate automatic recognition and classification of waveform signals, low quality and low efficiency of manual processing. In this paper, the automatic classification of vibration signals in the process of mining is studied, and the automatic classification results are uploaded to the cloud service platform in real time, which solves the technical bottleneck of the existing microseismic monitoring system. Meanwhile, the real-time monitoring of mine ground pressure safety is guaranteed based on the cloud service platform of microseismic monitoring.