Search Results (24)

The analysis of earthquake source mechanisms is key for seismotectonic studies, but it is often limited to traditional methods plagued with issues of precision and automation. This is particularly true in low-seismicity areas with deep and/or hidden seismogenic sources, where the identification of precise source mechanisms is a difficult and non-trivial task. In this study, we present a detailed application of TESLA (Tool for automatic Earthquake low-frequency Spectral Level estimAtion), a novel tool designed to overcome these limitations. We demonstrated TESLA’s effectiveness in defining source mechanism analysis by applying it to seismic sequences that occurred near Asti (AT), in the Monferrato area (Southern Piedmont, Italy). Our analysis reveals that the observed clusters consist of two distinct seismic sequences, occurring in 1991 and 2012, which were activated by the same seismogenic source. We relocated a total of 36 events with magnitudes ranging from 1.1 to 3.7, using a 3D velocity model, and computed 12 well-constrained focal mechanism solutions using the first motion polarities and the low-frequency spectral level ratios. The results highlight a relatively small seismogenic source located at approximately 5 km north of Asti (AT), at a depth of between 10 and 25 km, trending SW–NE with strike-slip kinematics. A smaller cluster of three events shows an activation of a different fault segment at around 60 km of depth, also showing strike-slip kinematics. These findings are in good agreement with the regional stress field acting in the Monferrato area and support the use of investigation tools such as TESLA for microseismicity analysis. Full article

Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but play a crucial role in source location and focal mechanism inversion. This study innovatively proposes a deep learning model suitable for simultaneous phase picking and polarity determination with continuous microseismic waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed for three tasks of P-wave picking, S-wave picking, and P-wave first-motion polarity determination and named the model EQT-Plus (EQTP). Notably, the polarity determination task was decomposed into two independent decoders to enhance the learning of polarity characteristics. Through training on a northern California dataset and testing on microseismic events (Md < 3) in the Geysers region, the results demonstrate that the EQTP model achieves superior performance in both phase picking and polarity determination compared to the PhaseNet+ model. It not only provides accurate phase picking but also shows higher consistency with manual picking results in polarity determination. We further validated the good generalization ability of the model with the DiTing dataset from China. This study not only advances the adaptation of the Transformer model in seismology but also reliably delivers fundamental information essential for refined microseismic inversion, offering an alternative and advanced tool for the seismological community. Full article

Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of ±1%, ±3%, and ±5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm. Full article

The layout of acoustic emission sensors plays a critical role in non-destructive structural testing. This study proposes a grid-based optimization method focused on multi-source location results, in contrast to traditional sensor layout optimization methods that construct a correlation matrix based on sensor layout and one source location. Based on the seismic source travel time theory, the proposed method establishes a location objective function based on minimum travel time differences, which is solved through the particle swarm optimization (PSO). Furthermore, based on location accuracy across various configurations, the method systematically evaluates potential optimal sensor locations through grid search. Synthetic tests and laboratory pencil-lead break (PLB) experiments are conducted to compare the effectiveness of PSO, genetic algorithm (GA), and simulated annealing (SA), with the following conclusions. (1) In the synthetic tests, the proposed method achieved an average location error of 1.78 mm, outperforming that based on the traditional layout, GA and SA. (2) For different noise cases, the location accuracy separately improved by 24.89% (σ = 0.5 μs), 12.59% (σ = 2 μs), and 15.06% (σ = 5 μs) compared with the traditional layout. (3) For the PLB experiments, the optimized layout achieved an average location error of 9.37 mm, which improved the location accuracy by 59.15% compared with the traditional layout. Full article

The construction of accurate anisotropic velocity models is essential for effective microseismic monitoring in hydraulic fracturing. Ignoring anisotropy can result in significant distortions in microseismic event locations and their interpretation. Although methods exist to simultaneously invert anisotropic parameters and event locations using microseismic arrival times, the results heavily depend on accurate initial models and sufficient ray coverage due to strong trade-offs among multiple parameters. Microseismic waveform inversion for anisotropic parameters remains challenging due to the low signal-to-noise ratio of the data and the high computational cost. To address these challenges, we propose a method for jointly inverting event locations and velocity updates based on arrival times and vertical slowness estimates, under the assumption of small horizontal velocity variations. Vertical slowness estimates, which are independent of source information and easily obtainable, provide an additional constraint that enhances inversion stability. We test the proposed method in four synthetic examples under various conditions. The results demonstrate that incorporating vertical slowness effectively constrains and stabilizes conventional travel-time inversion, especially in scenarios with poor raypath coverage. Additionally, we apply this method to a field case and find that it produces more reasonable event locations compared to inversions using arrival times alone. This joint inversion method can enhance the accuracy of anisotropic structures and event locations, which thus help with fracture characterization in tight and low-permeability reservoirs. It may serve as an effective downhole monitoring approach for hydrocarbon and geothermal energy production. Full article

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. Full article

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. Full article

The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels. Full article

To reveal the deformation and failure law of the gob-side roadway (GSR) and the main influencing factors in close extra-thick coal seams, the research methods of field monitoring, theoretical analysis, and numerical simulation are adopted in this paper. Field monitoring data shows that microseismic events occur and accumulate frequently in the surrounding rock and some overlying key layers of the GSR. Large deformation is experienced in the middle part of roadway near the solid coal side, the middle and upper parts of the roadway near the coal pillar side, and the roadway floor. The overlying strata of the GSR are fractured to form a composite structure as “low-level cantilever beam and high-level masonry beam”. The coal pillar is squeezed and effected by the composite beam structure and the rotation moment M, causing serious bulge in middle and upper part of the coal pillar side. The stability of the solid coal side of the roadway is affected by the stress transferred from gangue contact point. Numerical simulation shows that the immediate roof and key layer breakage are induced by the mining of the 30,501 working face. Shear and tension failures happen in the GSR due to overburden subsidence and rotary extrusion. The stress and displacement at the middle and upper of the roadway on the coal pillar side are larger than the other area. Compared with the solid coal side, the coal on the coal pillar side is obviously more fractured, with a lower bearing capacity. The peak stress in the coal pillar shows up 2 m away from the roadway, which is close to the length of bolt support. The mining-induced stress and the stress transferred from gangue contact point are the direct reasons for solid coal bulge beside the roadway. The peak stress on the solid coal side is located 7 m away from the roadway, at the gangue contact point where overburden fractures. The overburden strata loads and the transferred stress near the gangue contact point are transferred from the sides to the roadway floor. Their coupling effect with the in situ horizontal stress acts as the force source for the plastic floor heave. Full article

Microseismic source location is the core of microseismic monitoring technology in coal mining; it is also the advantage of microseismic monitoring technology compared with other monitoring methods. The source location method directly determines the accuracy and stability of the source location results. Based on the problem of non-benign arrays of microseismic monitoring sensors in the coal mining process, a fast location method of microseismic source in coal mining based on the NM-PSO algorithm is proposed. The core idea of the NM-PSO algorithm is to use the particle swarm optimization (PSO) algorithm for global optimization, reduce the size of the solution space and provide the optimized initial value for the Nelder Mead simplex algorithm (NM), and then use the fast iteration characteristics of the NM algorithm to accelerate the convergence of the model. The NM-PSO algorithm is analyzed by an example and verified by the microseismic source location engineering. The NM-PSO algorithm has a significant improvement in the source location accuracy. The average location errors in all directions are (5.65 m, 5.01 m, and 7.21 m), all Within the acceptable range, and they showed good universality and stability. The proposed NM-PSO algorithm can provide a general fast seismic source localization method for different sensor array deployment methods, which significantly improves the stability and result in the accuracy of the seismic source localization algorithm and has good application value; this method can provide new ideas for research in microseismic localization in coal mining. Full article

Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning. Full article

The layout of microseismic monitoring (MSM) station networks is very important to ensure the effectiveness of source location inversion; however, it is difficult to meet the complexity and mobility requirements of the technology in this new era. This paper proposes a network optimization method based on the geometric parameters of the proposed sensor-point database. First, according to the monitoring requirements and mine-working conditions, the overall proposed point database and model are built. Second, through the developed model, the proposed coverage area, envelope volume, effective coverage radius, and minimum energy level induction value are comprehensively calculated, and the evaluation reference index is constructed. Third, the effective maximum envelope volume is determined by taking the analyzed limit of monitoring induction energy level as the limit. Finally, the optimal design method is identified and applied to provide a sensor station layout network with the maximum energy efficiency. The method, defined as the S-V-E-R-V model, is verified by a comparison with the existing layout scheme and numerical simulation. The results show that the optimization method has strong practicability and efficiency, compared with the mine’s layout following the current method. Simulation experiments show that the optimization effect of this method meets the mine’s engineering requirements for the variability, intelligence, and high efficiency of the microseismic monitoring station network layout, and satisfies the needs of event identification and location dependent on the station network. Full article

As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation. Micro-seismic data are generated in complex underground environments which are significantly affected by random noise. These data greatly influence subsequent micro-seismic source location, energy estimation, and disaster monitoring. In this paper, a new denoising method based on Time-Frequency Peak Filtering (TFPF) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed for micro-seismic data. Micro-seismic data are decomposed into several Intrinsic Mode Functions (IMFs) by CEEMDAN. Then, discriminant factors are used to determine which IMF needs to be denoised by TFPF. Finally, the denoised result is reconstructed by inverse CEEMDAN. By comparing and analyzing different entropies, Fuzzy Entropy (FE) is selected as the best discriminant factor. The CEEMDAN-FE-TFPF denoising method can effectively avoid the influence of fixed window length of the conventional TFPF method. The effectiveness and superiority of this method are verified by experiments of synthetic and actual data. Full article

The diversion tunnel of a hydropower station is characterized by low quality surrounding rock and weak structural planes. During excavation, rock mass spalling and cracking frequently occur. To evaluate the stability of a rock mass during tunnel excavation, high-precision microseismic monitoring technology was introduced to carry out real-time monitoring. Based on the temporal and spatial distribution characteristics of microseismic events, the main damage areas and their influencing factors of tunnel rock mass were studied. By analyzing the source characteristic parameters of the concentration area of microseismic activities, the rock fracture mechanism of the concentration area was revealed. The 3D numerical model of diversion tunnel was established, and the deformation characteristics of the rock mass under the control of different combination types of weak structural planes were obtained. The results showed that the microseismic event was active between 29 October 2020 and 6 November 2020, and the energy release increased sharply. The main damage areas of the rock mass were located at Stakes K0 + 500–K0 + 600 m. Microseismic source parameters revealed that shear failure or fault-slip failure induced by geological structures had an important influence on the stability of the surrounding rock. The numerical simulation results were consistent with the microseismic monitoring results and indicated that among the three kinds of structural plane combination types, including “upright triangle”, “inverted triangle” and “nearly parallel”, the “upright triangle” structure had the most significant influence on the stability of the surrounding rock. In addition, the maximum displacement of the surrounding rock had a trend of lateral migration to the larger dip angle in the three combined structural plane types. The research results will provide significant references for the safety evaluation and construction design of similar tunnels. Full article

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests. Full article