Microseismic Source Research Articles

Unsupervised clustering of mining-induced microseismicity provides insights into source mechanisms

Microseismic source mechanisms in underground mines can provide information about the rock mass response to mining. Conventional approaches to such studies rely upon moment tensor solutions that are susceptible to modeling assumptions and need reliable information about source locations and high-resolution velocity models. We propose the application of unsupervised clustering to group microseismic events into different classes directly from the waveform data such that the events in a specific class have similar source mechanisms. Our method has three main steps, first using spectral decomposition to separate the source terms from the path-receiver contributions in the observed amplitude spectra of events occurring in spatially dense clusters. Second, reducing the number of features from the source spectra using independent component analysis (ICA). Third, applying a Gaussian mixture model (GMM) to the reduced feature matrix to obtain event clusters. To test our method, we generate synthetic waveform data using the receiver network and the recorded microseismic event locations in an underground potash mine in Saskatchewan. Results show the ability of our method to separate events into different classes corresponding to differences in source mechanisms. Application to field data recorded in the mine during February 2021 successfully discriminates between blasts and microseismic events. The data recorded between 1 March and 30 June 2021 that contain microseismic events only are divided into two dominant classes. Using known moment tensors (MT) of some of these events for labeling, we interpret one of the two classes as having dominant double-couple mechanisms as compared to the other which most likely corresponds to the linear dipole-tensile mechanisms. Our method, combined with some expert knowledge such as MT of some larger magnitude events, can offer an assessment of source types of large microseismic populations as often encountered in induced seismicity.

Construction and experimental verification of wave velocity model for source location in goaf overlying rock strata

The geological structure of the goaf overlying rock is complex, a consequence of coal mining that has modified the original stratified structure of the sedimentary strata. To enhance the accuracy of microseismic source location in such intricate geological formations, a wave velocity model for the “three zones” goaf was constructed based on natural divisions within the strata using Snell's law and assuming a homogeneous medium. The model took into account the effects of rock deformation and fracture development, enabling the derivation of formulas for microseismic wave propagation path and travel time calculation. Additionally, the concept of equivalent wave velocity was defined. An indoor simulation test using similar materials was conducted to establish a geological model of the goaf. By comparing the errors between the theoretical and measured values of equivalent wave velocity, assessing the locating effects before and after implementing the wave velocity model of the goaf, and verifying the feasibility of the model, it was demonstrated that establishing a wave velocity model based on the characteristics of the strata structure was crucial for improving the accuracy of the microseismic source location. Notably, as the propagation path of microseismic waves in the goaf increased, the equivalent wave velocity decreased. The wave velocity structure in the goaf exhibited nonuniformity, with the relative error between the theoretical and measured values of equivalent wave velocity being limited to 10 %. The incorporation of this established wave velocity model into the location method resulted in a substantial 58.57 % increase in locating accuracy.

Location method of ill-conditioned microseismic source and its engineering application

Microseismic event location is one of the core parameters in microseismic monitoring, and the accuracy of localization will directly affect the effectiveness of engineering applications. However, limited by spatial factors, the geometry of the sensor installation will be close to linear, which makes the localization equation suffer from the pathological problem, and the localization accuracy is greatly reduced. To address this problem, the reasons for the pathological problem are analyzed from the perspective of the objective function residuals and coefficient matrix. The pathological problem is caused by the combined effect of the poorer sensor array and data errors, and its residual isosurface shows a conical distribution, and as the residual value decreases, the apex of the isosurface gradually extends to the far side, and the localization results do not converge. For this reason, an improved regularized Newton downhill localization algorithm is proposed. In this method, firstly, the Newtonian downhill method is improved so that the magnitudes of the seismic source parameters are the same, and the condition number of the coefficient matrix is reduced; then, the L-curve method is used to calculate the regularization factor for the pathological equations, and the coefficient matrix is improved; finally, the pathological equations are regularized, and the seismic source coordinates are obtained by the improved Newtonian downhill method. The results of engineering applications show that compared with the traditional algorithm based on automatic of P-arrival picking, the number of effective microseismic events calculated by the proposed localization algorithm is increased by 194.7 %, and the localization accuracy is substantially improved. The proposed algorithm reduces the problem of low accuracy of S-arrival picking and allows localization using only P-wave arrival. The method reduces the quality requirements of the data and significantly improves the utilization of microseismic events and positioning accuracy.

Dynamic stress characterization and instability risk identification using multisource acoustic signals in cut-and-fill stopes

The quantitative characterization of rock mass and stress changes induced by mining activities is crucial for structural stability monitoring and disaster early warning. This paper investigates the time–space–intensity distribution of microseismic sources during the pillar-free large-area continuous extraction. Furthermore, it explores a method involving collaborative evolution patterns of the velocity field and spatial b-value to identify stress and structural changes at the panel stope. Results show that anomalous zones in wave velocities and b-values form at the intersections of extraction drifts, strike drifts, cross drifts, and connection roadways influenced by mining activities, as well as in footwall ore-rock contacts, often accompanied by the nucleation of microseismic events. The synergistic use of wave velocity fields and spatial b-value models reveals the relationship between stress migration behavior and stope structure changes due to mining disturbances. The velocity field primarily reflects macroscopic changes in the structure and stress distribution, while spatial b-values further explain stress gradients in specific areas. Additionally, we have advanced the identification of an instability disaster at the connection roadway and cross drift intersection based on increases in wave velocity and abnormal changes in b-value. This paper demonstrates the potential of risk identification using the proposed method, providing insights into predicting geotechnical engineering disasters in complex stress environments.

A microseismic source location method considering diffraction used in tunnel engineering

A microseismic source location method considering diffraction used in tunnel engineering

High-Precision Microseismic Source Localization Using a Fusion Network Combining Convolutional Neural Network and Transformer

High-Precision Microseismic Source Localization Using a Fusion Network Combining Convolutional Neural Network and Transformer

Uncertainty of microseismic sources identification and probabilistic location in underground excavation

Microseismic (MS) source location is an integral component of MS technology and essential to understanding the rock failure mechanism and avoiding potential geological hazards in underground rock excavation. However, accurate location remains challenging owing to the complex geological conditions and unknown rock failure mechanisms. In this study, a novel location framework was developed to locate the MS source positions and their uncertainties based on probabilistic programming. Probabilistic programming was utilized to determine the coordinates of the MS source and its variation using the Markov Chain Monte Carlo (MCMC) method based on the waveform equation. A classical benchmark problem was utilized to verify and illustrate the developed framework. The developed framework can not only locate the position of the MS source but also determine its variation due to the uncertainty during the monitoring and excavation. The located MS source is in agreement with the actual positions. The results show that the developed framework is a scientific, accurate, reasonable, and promising tool for the location of MS sources. Then, the developed framework was applied to locate the position of the blasting in a practical mine. This further proved that the developed framework could locate the MS source, providing an excellent uncertainty analysis tool for underground rock excavation.

Microseismic source location using deep learning: A coal mine case study in China

Microseismic source location is crucial for the early warning of rockburst risks. However, the conventional methods face challenges in terms of the microseismic wave velocity and arrival time accuracy. Intelligent techniques, such as the full convolutional neural network (FCNN), can capture spatial information but struggle with complex microseismic sequence. Combining the FCNN with the long short-term memory (LSTM) network enables better time-series signal classification by integrating multi-scale information and is therefore suitable for waveform location. The LSTM-FCNN model does not require extensive data preprocessing and it simplifies the microseismic source location through feature extraction. In this study, we utilized the LSTM-FCNN as a regression learning model to locate the seismic focus. Initially, the method of short-time-average/long-time-average (STA/LTA) arrival time picking was employed to augment spatiotemporal information. Subsequently, oversampling the on-site data was performed to address the issue of data imbalance, and finally, the performance of LSTM-FCNN was tested. Meanwhile, we compared the LSTM-FCNN model with previous deep-learning models. Our results demonstrated remarkable location capabilities with a mean absolute error (MAE) of only 7.16 m. The model can realize swift training and high accuracy, thereby significantly improving risk warning of rockbursts.

On the Stability of Mantle‐Sensitive P‐Wave Interference During a Secondary Microseismic Event

AbstractBody wave extraction from oceanic secondary microseismic sources with seismic interferometry provides alternative information to better constrain the Earth's structure. However, sources' spatiotemporal variations raise concerns about travel time measurement robustness. Therefore, we study the cross‐correlations’ stability during a single oceanic event. This study focuses on 3 days of data and three seismic arrays' combinations between 8 and 11 December 2014 during storm Alexandra, a “weather bomb” event in southern Greenland. We use the WAVEWATCH III hindcast to model P‐wave noise sources and assess the impact of short‐term source variations on cross‐correlations. Model‐based cross‐correlations compared to data show coherent delays to reference 3D Earth models (∼0–3 s) confirming the robustness of the source model which could explain minor travel time variations (≤1 s).

Induced seismic activity and source parameter characteristics in the Datong coal mine, China

To clarify the activity patterns and source characteristics of coal mining–induced microseismicity, this study analyzed the spatial distribution characteristics of microseismic events in the Datong coal mining area based on records from the regional digital seismic network. We conducted a detailed characterization of the depth distribution characteristics of microseismic events using the double-difference localization method. Additionally, the source parameters, including corner frequency ( fc), source rupture radius ( r), seismic moment ( M0), source radiated energy ( Es), and stress drop (Δσ), were calculated for 136 mine-induced earthquakes with magnitudes ranging from ML1.3 to ML3.2. The results show that ML ≥ 2.0 mining-induced seismic events occur mainly within numerous microfractures in the Datong mining area. The depth of the seismic sources in the mining area is concentrated at 200∼500 m, with significant north–south differences and a close correlation with the mining depth. The displacement spectra of microseismic sources show agreement with the Brune source model [Formula: see text] attenuation pattern. As M0 gradually increases, r, Δσ, and Es show an increasing trend, while fc gradually decreases, exhibiting characteristics similar to those of tectonic earthquakes. Compared to tectonic earthquakes, coal mining-induced earthquakes have lower corner frequencies and stress drop levels mainly because mining activities alter the originally stable geological structure and stress state, leading to weakened rock strength, decreased elastic modulus, and shallower source depths. These factors contribute to the reduction in corner frequencies. As mining operations continue, microfracturing occurs in the coal and surrounding rock mass, intensifying the dynamic instability of the rock mass that was already under high stress conditions. This situation triggers larger-magnitude, mining-induced seismic events under lower stress conditions.

Microseismic Source Imaging Using Physics-Informed Neural Networks With Hard Constraints

Microseismic Source Imaging Using Physics-Informed Neural Networks With Hard Constraints

Microseismic Source Localization Method Based on Neural Network Algorithm and Dynamic Reduction of Solution Interval

Microseismic Source Localization Method Based on Neural Network Algorithm and Dynamic Reduction of Solution Interval

Real-time microseismic evaluation of coalbed methane reservoir stimulation based on improved metaheuristic inversion strategy

For the enhancement of coalbed methane production, the microseismic source location plays a crucial role in evaluating fracture information during hydraulic fracturing (HF). A swift and precise microseismic source location is essential for optimizing HF schemes. This optimization necessitates an inversion strategy characterized by low computational costs and high optimization accuracy. In this study, we propose an SM-MFO inversion strategy that functions in parallel with the simplex (SM) algorithm and is founded on the original moth flame optimizer (MFO) algorithm. This strategy was validated through gas blast microseismic monitoring field tests to assess the source location performance of the SM-MFO inversion strategy, followed by its application in real-time evaluation of staged HF for the Xinji 1# horizontal well. The results reveal that within the SM-MFO inversion strategy, SM can prevent local convergence by leveraging the capabilities of MFO. Conversely, MFO can bolster local search abilities with the assistance of SM. As a result, both the convergence speed and global optimization abilities of the strategy are enhanced. In field tests, the average location error of the SM-MFO algorithm was 18.11 m. This figure is more favorable compared to the errors in the MFO algorithm (22.15 m) and the SM algorithm (35.83 m). Moreover, the SM-MFO inversion strategy facilitates real-time evaluation of HF with a computational cost of just 1.01 s for a single microseismic event. Upon analysis of the overall incidence across the nine stages in the radial direction, a span of 270 m is attained, with fewer frequency of fractures observed in the southeast direction at each respective stage Additionally, the geometric parameters of the fractures and the stimulated reservoir volume (SRV) exhibit a positive correlation with the total fluid volume. The injected fluid volume peaks in the second and sixth stages, leading to fracture mesh dimensions of 312 m and 316 m, respectively. In summary, the total SRV of the Xinji 1# horizontal well is 201.7 × 105 m³, and the reservoir stimulation effect aligns with the anticipated results. This work highlights the efficacy of the SM-MFO inversion strategy in enhancing the efficiency and accuracy of microseismic source location for hydraulic fracturing, contributing valuable insights to the field.

Hydraulic fracturing distributed acoustic sensing monitoring data source mechanism inversion: A Hessian-based method

Distributed acoustic sensing (DAS) microseismic monitoring during hydraulic fracturing provides microseismic data with high spatial samplings for fracturing zones. However, sources located perpendicular to the single horizontal well of the DAS acquisition system have limited source-receiver geometries with extremely poor azimuthal coverage, resulting in high uncertainty in source mechanism inversion. To address this problem, we introduce the Hessian matrix, which governs the blurring effect caused by the source-receiver geometry, into the DAS microseismic source mechanism inversion. Our Hessian-based source mechanism inversion method consists of three main steps: (1) construct the Hessian matrix of the source mechanism based on the source-receiver geometry, (2) obtain an initial source mechanism using a conventional source mechanism inversion method, and (3) update the initial source mechanism using a Hessian-based [Formula: see text]-regularized least-squares algorithm. We assess the robustness of our method using synthetic DAS microseismic data with the consideration of noise, source location error, and different regularization parameters, and we compare the results with those of the conventional method. The results demonstrate that the Hessian-based method has a remarkable ability to mitigate the blurring effect of the Hessian matrix caused by limited DAS source-receiver geometry with poor azimuthal coverage, thereby reducing the uncertainty of the inverted source mechanism even in the presence of real noise and/or source location error. Finally, we use our method to invert the source mechanism of a real DAS microseismic event acquired during hydraulic fracturing. Our Hessian-based method provides low uncertainty in the source mechanism inversion of the real DAS microseismic data.

Microseismic wavefield modelling in anisotropic elastic media using integral equation method

AbstractIn this paper, we present a frequency‐domain volume integral method to model the microseismic wavefield in heterogeneous anisotropic‐elastic media. The elastic wave equation is written as an integral equation of the Lippmann–Schwinger type, and the seismic source is represented as a general moment tensor. The actual medium is split into a background medium and a scattered medium. The background part of the displacement field is computed analytically, but the scattered part requires a numerical solution. The existing matrix‐based implementation of the integral equation is computationally inefficient to model the wavefield in three‐dimensional earth. An integral equation for the particle displacement is, hence, formulated in a matrix‐free manner through the application of the Fourier transform. The biconjugate gradient stabilized method is used to iteratively obtain the solution of this equation. The integral equation method is naturally target oriented, and it is not necessary to fully discretize the model. This is very helpful in the microseismic wavefield computation at receivers in the borehole in many cases; say, for example, we want to focus only on the fluid injection zone in the reservoir–overburden system and not on the whole subsurface region. Additionally, the integral equation system matrix has a low condition number. This provides us flexibility in the selection of the grid size, especially at low frequencies for given wave velocities. Considering all these factors, we apply the numerical scheme to three different models in order of increasing geological complexity. We obtain the elastic displacement fields corresponding to the different types of moment tensor sources, which prove the utility of this method in microseismic. The generated synthetic data are intended to be used in inversion for the microseismic source and model parameters.

Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study

Microseismic source location based on improved artificial bee colony algorithm: Performance analysis and case study

Graphical neural networks based on physical information constraints for solving the eikonal equation

Abstract Accurate temporal resolution of the eikonal equation forms the cornerstone of seismological studies, including microseismic source localization, and travel-time tomography. Physics-informed neural networks (PINNs) have gained significant attention as an efficient approximation technique for numerical computations. In this study, we put forth a novel model named Eiko-PIGCNet, a graph convolutional neural network that incorporates physical constraints. We demonstrate the effectiveness of our proposed model in solving the 3D eikonal equation for travel-time estimation. In our approach, the discretized grid points are converted into a graph data structure, where every grid point is regarded as a node, and the neighboring nodes are interconnected via edges. The node characteristics are defined by incorporating the velocity and spatial coordinates of the respective grid points. Ultimately, the efficacy of the Eiko-PIGCNet and PINNs is evaluated and compared under various velocity models. The results reveal that Eiko-PIGCNet outshines PINNs in terms of solution accuracy and computational efficiency.

Retracted: Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment.

[This retracts the article DOI: 10.1155/2022/5479007.].

Gaussian-weighted crosscorrelation imaging condition for microseismic source localization

Time-reversal imaging is a powerful method for the localization of microseismic events. Conventional time-reversal imaging methods such as autocorrelation imaging or grouped crosscorrelation imaging may suffer from imaging artifacts, e.g., caused by less refined velocity models, noise-contaminated data, and data acquired from sparse receiver networks. These artifacts typically reduce imaging quality and may cause subsequent misinterpretations leading to false positives. To address these issues, we develop a new imaging condition that comprises three steps for each time step. First, we divide the back-propagated wavefield into parts according to their maximum absolute amplitudes. Second, the amplitudes of the back-propagated wavefield are weighted by a Gaussian function with the spatial extent of the prevailing wavelength of the event, centered at the absolute maximum of that part of the wavefield. Finally, we zero-lag crosscorrelate these weighted wavefields at each space point to obtain an image for this time step. The final image is gained by summing the images for each time step. This process collects all energy concentrations along the back projection process, and the energy on the wavefront overlaps and collapses at the hypocenter leading to high-resolution images displaying little to no imaging artifacts. Numerical examples using the Marmousi-II and the 3D SEG overthrust models and a 3D field data example indicate the performance of our method. High-resolution low-noise source images allow unique identification of sources even for source clusters, noisy data, and sparse acquisitions. The source localization errors are smaller than the dominant wavelength of the signal, where a smooth model with a mean velocity error of approximately 5% was considered in the synthetic examples, and a homogeneous model was used in the field data example.

An Inverted Heterogeneous Velocity Model for Microseismic Source Location in Deep Buried Tunnels

An Inverted Heterogeneous Velocity Model for Microseismic Source Location in Deep Buried Tunnels