Microseismic Analysis Research Articles

Determination of mining-induced stress based on mining face hydraulic support stress and micro-seismicity

Understanding dynamic visualization of mining-induced stress is of great significance to disaster prevention and control in coal mining activities. In this study, three theoretical models, including linear, polynomial, and exponential models, are proposed to inverse the mining-induced stress through the acquisition and analysis of hydraulic support stress and micro-seismicity in the coal mining face. The distribution of mining-induced stress in the coal seam are graphed by fitting two key stress parameters including hydraulic support stress and peak stress, and two key zones including goaf zone and in situ stress zone. These key stress parameters and zones are defined based on the critical nodes of the model curve. According to the geological background of Mataihao coal mine in Erdos, Inner Mongolia Autonomous Region, China, the contours of mining-induced stress are graphed through the stress calculation of these three inversion theoretical models. The multi-monitoring data of micro-seismicity, drilling chips, advanced borehole stress and bolts axial force are used to verify the key stress parameters and zones of the theoretical models. It shows that the monitoring data are in good agreement with the distribution of inversed results. It should be emphasized that, if the fault structure exists around the mining face, the mining-induced stress decreases obviously when the mining face is passing through the faults, and the location of the peak stress will be closer to the mining face. The results in this study could provide methods for early prevention of extreme mining-induced stress and disaster control in the mining activities.

Frac-hits and connections of multi-well hydrofracturing fracture network involving the variable factors: well spacing, perforation cluster spacing and injection rate

PurposeWith the development of fracturing technology, the research of multi-well hydrofracturing becomes the key issue. Frac-hits in multi-well hydrofracturing has an important effect on fracture propagation and final production of fractured well; in the process of hydrofracturing, there are many implement parameters that can affect frac-hits, and previous studies in this area have not systematically targeted the influence of a single parameter on multi-well hydrofracturing. Therefore, it is of great significance to study the occurrence rule and influence of frac-hits for optimizing the design of fracturing wells.Design/methodology/approachBased on the proposed numerical models, the effects of different fracturing implement parameters (perforation cluster spacing, well spacing and injection rate) on frac-hits are compared in numerical cases. Through the analysis of fracture network, stress field and microseismic, the effects of different fracturing implement parameters on frac-hits and connections are compared.FindingsThe simulation results show that the effect of perforation cluster spacing and well spacing on frac-hits is greater than that of injection rate. Smaller well spacing makes it easier for fractures between adjacent wells to interact with each other, which increases the risk of frac-hits and reduces the risk of fracture connections. Smaller perforation cluster spacing results in larger individual fracture lengths and greater deflection angles, which makes the possibility of frac-hits and connections greater. The lower the injection rate, the lower the probability of frac-hits.Originality/valueIn this study, the influence of different fracturing implement parameters on frac-hits and connections in multi-well hydrofracturing is studied, and the mechanism of frac-hits and connections is analyzed through fracture network, stress field and microseismic analysis. Different simulation results are compared to optimize fracturing well parameter design and provide reference for engineering application.

Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory

Machine Learning–Enhanced Microseismic Analysis for Evaluating Rock Crack Trajectory

Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.

Laboratory study on the effect of stress cycling pattern and rate on seismicity evolution

Recent laboratory and field studies suggest that temporal variations in injection patterns (e.g., cyclic injection) might trigger less seismicity than constant monotonic injection. This study presents results from uniaxial compressive experiments performed on Red Felser sandstone samples providing new information on the effect of stress pattern and rate on seismicity evolution. Red Felser sandstone samples were subjected to three stress patterns: cyclic recursive, cyclic progressive (CP), and monotonic stress. Three different stress rates (displacement controlled) were also applied: low, medium, and high rates of 10−4 mm/s, 5 × 10−4 mm/s, and 5 × 10−3 mm/s, respectively. Acoustic emission (AE) waveforms were recorded throughout the experiments using 11 AE transducers placed around the sample. Microseismicity analysis shows that (i) Cyclic stress patterns and especially cyclic progressive ones are characterized by a high number of AE events and lower maximum AE amplitude, (ii) among the three different stress patterns, the largest b-value (slope of the log frequency-magnitude distribution) resulted from the cyclic progressive (CP) stress pattern, (iii) by reducing the stress rate, the maximum AE energy and final mechanical strength both decrease significantly. In addition, stress rate remarkably affects the detailed AE signature of the events classified by the distribution of events in the average frequency (AF)—rise angle (RA) space. High stress rates increase the number of events with low AF and high RA signatures. Considering all elements of the AE analysis, it can be concluded that applying cyclic stress patterns in combination with low-stress rates may potentially lead to a more favourable induced seismicity effect in subsurface-related injection operations.

Machine learning automatic picker for geothermal microseismicity analysis for practical procedure to reveal fine reservoir structures

Machine learning automatic picker for geothermal microseismicity analysis for practical procedure to reveal fine reservoir structures

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.

Integrated microseismic and geomechanical analysis of hydraulic fracturing induced fault reactivation: a case study in Sichuan Basin, Southwest China

Because of the special geographical location adjacent to the Tibetan Plateau, the geological setting of the Sichuan Basin is very complex, which brings a great challenge to shale gas development. In this study, we conducted an integrated microseismic and geomechanical analysis on a refracturing well in the Sichuan Basin. We observed that casing deformation occurred in the area of fault reactivation. However, the reactivation of pre-existing faults does not always cause casing deformation. In addition, a cluster of high-magnitude microseismic events will be triggered during the fault reactivation, which is also helpful to recognize the reactivated faults. Furthermore, according to the definition of b-value in the Gutenberg–Richter law, a group of high-magnitude microseismic events usually shows a low b-value and vice versa. Based on the analysis of treatment data and microseismicity, we found that there is a positive relationship between b-value and instantaneous shut-in pressure (ISIP): lower b-value represents lower ISIP. The investigation of the stress shadow effect based on the theoretical solution of a plane strain fracture is also convincing to prove the relationship between b-value and ISIP. Therefore, a significant relationship between hydraulic fracturing, microseismicity, and fault reactivation is established. Importantly, some useful implications of this relationship could be utilized for early warning of fault reactivation (probably casing deformation) and optimization of refracturing design for unconventional shale plays.

Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory

Microseismic monitoring is a common monitoring tool in the mining production process; for supervising a huge amount of microseismic data, effective analysis tools are necessary. In this study, the monitoring results of microseismic events at the Maoping lead-zinc mine in Yiliang County, Yunnan Province, and the spatiotemporal distribution characteristics of microseismic events are analyzed. We analyze the temporal characteristics of microseismic events using fractal theory, combining the change in fractal dimension with the rock burst incubation process. We also construct an observation area model for event anomalies based on the spatial distribution characteristics of microseismic events. The results show that the growth of the fractal dimension is consistent with the trend of the incubation process before rock burst, and the larger the fractal dimension, the higher the rock burst risk. The observation model, based on the density of microseismic events, can effectively refine the rock burst discrimination range and facilitate subsequent observations. An effective and feasible method of microseismic analysis is provided.

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and efficiency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a Saskatchewan, Canada, potash mine are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behavior that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e., the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalog data. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed the identification of potential cluster characteristics in time, space, and magnitude domains. The implemented modeling approaches and obtained results will be used to further advance strategies and protocols for the safe and efficient operation of potash mines.

Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

Abstract. In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness, will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen Fjord (Svalbard), we observe the spatial clustering of icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial seismic sources, we were able to derive a power law of icequake energy and to relate this energy to the size of the cracks that generate the icequakes.

Detection and Characterization of Microseismic Events from Fiber-Optic DAS Data Using Deep Learning

Abstract Microseismic analysis is a valuable tool for fracture characterization in the Earth’s subsurface. Distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, so they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time consuming. We designed, trained, and deployed a deep learning model to detect microseismic events in DAS data automatically. We created a curated dataset of nearly 7000 manually selected events and an equal number of background noise examples. We optimized the deep learning model’s network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieved an accuracy of 98.6% on our benchmark dataset and even detected low-amplitude events missed during manual labeling. Our methodology detected more than 100,000 events, allowing for a far more accurate and efficient reconstruction of spatiotemporal fracture development than would have been feasible by traditional methods.

Pore-pressure wave and diffusion induced by fluid-mass sources in partially saturated porous media

The dynamic and quasi-static spatiotemporal responses of pore water induced by fluid-mass sources in unsaturated porous media are determined and compared. An explicit fundamental solution for pore water pressure is derived using the known Green's function of dynamic poroelasticity. Based on the order of the low-frequency approximations for the source impulsive time function, different physical regimes are identified, describing the transition from diffusion to wave propagation. The diffusive regime bears only two diffusions equivalent to diffusive slow P2 and P3 waves. Non-dispersion diffusion and dispersion diffusion regimes, where P1 waves and diffusions coexist, are present. The similarity and diversity of induced pore water pressures owing to diffusive slow waves and fast waves may depend on the fluid loading rate (e.g. fluid injection) in contrast to the critical frequencies of a partially saturated porous medium. The short-term fluctuation in the injection rate may create very similar slow diffusive P2 waves along with a fast-compressional P1 wave by low impulsive frequencies and various slow P2 modes with transmitted radiation to the P1 wave by the comparatively high impulsive frequencies in different regimes. The results indicate that the diffusion and wave processes may generate uniform dynamic and quasi-static responses in partially saturated media for hydraulic tests and induced microseismic analysis.

Microseismic analysis over a single horizontal distributed acoustic sensing fiber using guided waves

A single horizontal distributed acoustic sensing (DAS) fiber is notoriously challenging for microseismic analysis even when it is close to recorded events. Due to its uniaxial measurement, locations suffer from circular ambiguity. Nonetheless, in unconventional plays, the presence of dispersive guided waves in the DAS records can partially resolve such ambiguity. If the reservoir has lower seismic velocities than its surrounding medium, it can act as a waveguide. In this case, guided waves are generated only by microseismic events occurring inside or close to the reservoir, and their propagation is confined to the reservoir. We first train a machine learning model for microseismic event detection using a unique data set of almost 7000 manually picked events and an equal number of noise windows. Applying the trained model to 10 stimulation stages from two offset wells yields more than 100,000 event detections, with a higher sensitivity than manual labeling. Detected events undergo a localization procedure based on the dispersion properties of guided waves, estimated in-situ from known perforation shots. Location results allow us to reconstruct the spatio-temporal pattern of fracture development. We observe a dominant fracture propagation direction for all stages, which indicates the effect of the regional stress in the reservoir. We qualitatively validate the direction and extent of the fracture growth by perforation shot analysis. We have found the first application of microseismic event location with a single straight fiber, which is considered impossible without a waveguide structure.

Planetary analogue study using microseismic analysis for near-surface lava tube detection and exploration

The paper presents new results from microseismic data analysis for the detection and delineation of near-surface lava tubes. Single-station, free-field seismic noise data were collected at the Corona Volcano area (Lanzarote, Canary Islands) as part of the PANGAEA-X 2017 European Space Agency (ESA) astronaut training campaign. The site was selected as it represents a suitable analogue for lunar maria and provided the opportunity to cross-validate and ground truth seismic and other geophysical studies. In this paper, we present our observations on the distribution of frequencies and amplitudes of standing waves generated by microtremors in the space between the ground surface and underground cavities by averaging amplitude spectra of microseismic records.This study shows that the frequency of the vertical component peaks is a relatively good indicator of presence, lateral extent and approximate dip of the cavities. However, only the vertical component peaks with a frequency either equal to or close to central to high amplitude (3.1–8.5) horizontal-to-vertical (H/V) peaks are related to lava tubes. If the P-wave velocities in the rocks overlying the lava tubes are known, then these frequencies can also be used to estimate the depth of such cavities.This study also identified some important pitfalls that may limit the use and accuracy of our approach. These include layering of rocks overlying the lava tubes, lateral changes in thickness, lateral variations of the P-wave velocity, and the presence of shallow void-rich zones.Overall, the results of this study confirmed that microseismic data are suitable for cavity detection. It is also, to our knowledge, one of the first studies dealing with microseismic data from near-surface lava tubes. With the recent increase in planetary exploration, this method can be easily adapted to support the geophysical exploration of volcanic terrains on the Moon and Mars.

Spatiotemporal variations in seismic attenuation during hydraulic fracturing: A case study in a tight oil reservoir in the Ordos Basin, China

During hydraulic fracturing (HF) stimulation for unconventional reservoir development, seismic attenuation has a significant influence on high-frequency microseismic data. Attenuation also provides important information for characterizing reservoir structure and changes to it due to HF injections. However, the attenuation effect is typically not considered in microseismic analysis. We have adopted the spectral ratio and centroid-frequency shift methods to estimate the subsurface attenuation (the factor [Formula: see text]) in a tight oil reservoir in the Ordos Basin, China. The P- and S-wave attenuations are calculated using the 3C waveform data recorded by a single-well downhole geophone array during a 12-stage HF stimulation. Both methods provide similar results (with differences in [Formula: see text] of absolute values less than 0.010 for P- and S-waves). For individual events, their median [Formula: see text] values calculated from different geophones are selected to represent the average attenuation. Spatiotemporal variations in attenuation are obtained by investigating [Formula: see text] values along propagating rays linking different source–receiver pairs. The [Formula: see text] values derived at different HF stages reveal significant attenuation in the targeted tight sandstone layer (0.030–0.062 for [Formula: see text] and 0.026−0.058 for [Formula: see text]), and the attenuation is apparently increased by fluid injection activities. We explain the sudden decrease in attenuation near the geophone array as a result of high shale content using log data from a horizontal treatment well. The consistency between the [Formula: see text] values and horizontal well-log data, as well as the HF process, indicates the reliability and robustness of the attenuation results. By studying spatiotemporal variations in attenuation, the changes in subsurface structures may be quantitatively characterized, thereby creating a reliable basis for microseismic modeling and data processing and providing additional information on monitoring the HF process.

Mechanism of Rock Bursts Induced by the Synthetic Action of “Roof Bending and Rock Pillar Prying” in Subvertical Extra-Thick Coal Seams

When studying the rock burst mechanism in subvertical extra-thick coal seams in the Wudong coal mine in Xinjiang, China, most studies focus on rock pillars, while the effect of the roof on rock bursts is usually ignored. In this paper, a rock burst mechanism in subvertical extra-thick coal seams under the control of a “roof-rock pillar” is proposed. A theoretical analysis is first performed to explain the effect of roof-rock pillar combinations on rock bursts in coal seams. Numerical modeling and microseismic analysis are implemented to further study the mechanism of rock burst. The main conclusions are as follows: 1) During the mining of the B3+6 coal seam, an obvious microseismic concentration phenomenon is found in both the roof and rock pillar of B3+6. The rock bursts exhibited obvious directionality, and its main failure characteristics are floor heave and sidewall heave, but there will also be some failures such as shoulder socket subsidence in some parts. 2) The stress transfer caused by rock pillar prying is the main reason for the large difference in rock burst occurrence near the vertical and extra thick adjacent coal seams under the same mining depth. 3) Under the same cantilever length, the elastic deformation energy of the roof is much greater than that of the rock pillar, which makes it easier to produce high-energy microseismic events. With an increasing mining depth, the roof will become the dominant factor controlling the occurrence of rock bursts. 4) The high-energy event produced by the rock mass fracture near the coal rock interface easily induces rock bursts, while the high-energy event produced by the fracture at the far end of the rock mass is less likely to induce rock burst. 5) Roof deformation extrusion and rock pillar prying provide high static stress conditions for the occurrence of rock bursts in the B3+6 coal seam. The superposition of the dynamic disturbance caused by roof and rock pillar failure and the high static stress of the coal seam is the main cause of rock burst in the B3+6 coal seam.

Seismogenic Structure Orientation and Stress Field of the Gargano Promontory (Southern Italy) From Microseismicity Analysis

Historical seismic catalogs report that the Gargano Promontory (southern Italy) was affected in the past by earthquakes with medium to high estimated magnitude. From the instrumental seismicity, it can be identified that the most energetic Apulian sequence occurred in 1995 with a main shock of MW = 5.2 followed by about 200 aftershocks with a maximum magnitude of 3.7. The most energetic earthquakes of the past are attributed to right-lateral strike-slip faults, while there is evidence that the present-day seismicity occur on thrust or thrust-strike faults. In this article, we show a detailed study on focal mechanisms and stress field obtained by micro-seismicity recorded from April 2013 until the present time in the Gargano Promontory and surrounding regions. Seismic waveforms are collected from the OTRIONS Seismic Network (OSN), from the Italian National Seismic Network (RSN), and integrated with data from the Italian National Accelerometric Network (RAN) in order to provide a robust dataset of earthquake localizations and focal mechanisms. The effect of uncertainties of the velocity model on fault plane solutions (FPS) has been also evaluated indicating the robustness of the results. The computed stress field indicates a deep compressive faulting with maximum horizontal compressive stress, SHmax, trending NW-SE. The seismicity pattern analysis indicates that the whole crust is seismically involved up to a depth of 40 km and indicates the presence of a low-angle seismogenic surface trending SW-NE and dipping SE-NW, similar to the Gargano–Dubrovnik lineament. Shallower events, along the eastern sector of the Mattinata Fault (MF), are W-E dextral strike-slip fault. Therefore, we hypothesized that the seismicity is locally facilitated by preexisting multidirectional fractures, confirmed by the heterogeneity of focal mechanisms, and explained by the different reactivation processes in opposite directions over the time, involving the Mattinata shear zone.

A novel method for automatic identification of rock fracture signals in microseismic monitoring

Microseismic (MS) monitoring technology is able to offer 3-dimensional and real-time rupture characteristics of rocks by capturing and analyzing detectable elastic waves released from rock fracture, which has been widely applied in rock/mining engineering. The prerequisite of MS analysis is to identify MS signals with high efficiency and accuracy. However, the identification work is inevitably disturbed by vibration signals from the blasting activities involved in rock/mining engineering. Currently, the classification work of MS and blasting signals is mainly carried out by the on-site operators based on their own experience, which is time-consuming and subjectively sensitive. In this work, a new discriminant method is developed to automatically recognize these signals based on their time-frequency spectrum characteristics to offer more reliable data for MS monitoring analysis. Singular value decomposition is adopted to reduce data volume and obtain unique features. Random Forest (RF) is applied to achieve automatic recognition and get the most important features based on the variation of ‘out-of-bag’ error and Gini Index. These most important features can be used to optimize the RF classifier. The numerical experiments are conducted to verify our new method. The results prove that the method reaches 95.99%±0.1% accuracy in 100 repetitions for events collected from the field, and the optimized RF produces a more accurate result (97%) using fewer characteristics, which is higher than the methods reported in the previous literature. The proposed method diminishes the sensitivity of operators in the classification and promotes the accuracy and efficiency of the identification of MS signals data in the application of MS monitoring technology. Moreover, this strategy also offers a new strategy to process the acquired monitoring signals in other monitoring techniques.

Quantifying hydraulically induced fracture height and density from rapid time-lapse distributed acoustic sensing vertical seismic profile data

Several recent studies have advanced the use of time-lapse distributed acoustic sensing vertical seismic profile data in horizontal wells for determining hydraulically stimulated fracture properties. Hydraulic fracturing in a horizontal well typically generates vertical fractures in the rock medium around each stage. We have modeled the hydraulically stimulated formation with vertical fracture sets about the lateral wellbore as a horizontally transverse isotropic medium. Rock-physics modeling is used to relate the anisotropy parameters to the fracture properties. This modeling is used to develop an inversion for compressional wave time delay to the fracture height and density of each stage. Field data from two horizontal wells are analyzed, and the fracture height evaluated using this technique is consistent with the microseismic analysis.