Targeted location of microseismic events based on a 3D heterogeneous velocity model in underground mining

Peng, Pingan; Wang, Liguan

doi:10.1371/journal.pone.0212881

Cited by 17 publications

(20 citation statements)

References 29 publications

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“…To the best of our knowledge, only a few studies have applied simple 3-D velocity models for microseismic source location in mines. Collins et al [23] tried to use a 1-D layered velocity model with a low velocity zone for microseismic source location in a mine, and Peng and Wang [24] treated the P wave velocity in the mined-out regions and tunnels as the wave velocity of air and other zones as a homogeneous velocity model. Gharti et al [25] considered velocities of air, rock and ore in their mining applications.…”

Section: A the Velocity Modelmentioning

confidence: 99%

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

2020

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Microseismic source location is a fundamental research interest in real-time microseismic monitoring and hazard risk assessment, and it provides the basis for determining the fracture zones and calculating seismic source parameters of the microseismicity (e.g., the event magnitude, the focal mechanism). In the present work, we carefully relocate some microseismic earthquakes that occurred in the Yongshaba mine (China) using the shooting 3-D ray-tracing (3D-RT) method based on a high-resolution 3-D velocity model, which is determined by a large amount of seismic data. Furthermore, a semiautomatic waveform cut method based on the cross-correlation (CC) technique (WCC) is developed for a quick, robust and precise determination of the direct P-phase relative delay times. Compared with the full waveform crosscorrelation (FWCC)-based method, the WCC-based method is free from the influence of complex later-phase waveform spectra. To verify the proposed method, we artificially generate the locations of 892 synthetic microseismic events, the P-phase travel times of which are calculated based on the given 3-D velocity model and the 3-D ray-tracing technique. The relocated hypocentres of these synthetic events obtained by the developed method are improved compared with those obtained by a homogeneous velocity model and the straight line-ray tracing based L1 norm time difference (TD) method (HV-SL-TD1), as well as those obtained by the 3-D velocity model and the straight line-ray tracing based L1 norm and L2 norm TD methods (3D-SL-TD1 and 3D-SL-TD2). Finally, we apply the proposed method to eight experimental blasts with pre-measured locations. The synthetic and application tests show that, despite some multi-path phenomena existing in the ray-tracing methods, the WCC-based method performs as well as the experienced manual picking method, and the results obtained by the 3D-RT location have smaller location errors (∼30 m) than those of the homogeneous velocity model-based methods (>40 m). The TD-based method is slightly better than the trigger time (TT)-based method when using the same norm, and the location precision of the L1 norm-based methods have a better resilience to larger picking errors than that of the L2 norm-based methods. Further improvements can be obtained by improving the resolution of the 3-D velocity model and including spatial voids in the model (e.g., tunnels and cavities) for the inversion.

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Section: A the Velocity Modelmentioning

confidence: 99%

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

2020

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“…With the use of nonpillar sublevel caving, two large goaf areas have been formed in this mine. The volumes of these two goaves are 120068.60 m 3 (No. 1 goaf in Fig.…”

Section: A Datasetmentioning

confidence: 99%

“…A microseismic monitoring system can indicate the stability of a rock mass via an analysis of microseismic events [1]. Consequently, to analyze microseismic events in real time, scientists worldwide have proposed a variety of automatic processing methods, such as P-and S-wave arrival picking [2], source localization [3], and source parameter calculation [4]. However, the microseismic records collected for rock masses are commonly triggered by rock fracturing, blasting, the use of a drill jumbo, and ore extraction.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

2020

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The identification of suspicious microseismic events is the first crucial step in processing microseismic data. In this paper, we present an automatic classification method based on a deep learning approach for classifying microseismic records in underground mines. A total of 35 commonly used features in the time and frequency domains were extracted from waveforms. To examine the discriminative ability of these features, a genetic algorithm (GA)-optimized correlation-based feature selection (CFS) method was applied. As a result, 11 features were selected to represent microseismic records. By dividing each microseismic record into 50 frames, an 11 × 50 feature matrix was utilized as the input. A convolutional neural network (CNN) with 35 layers was trained on 20,000 samples recorded at the Huangtupo Copper and Zinc Mine. There are 5 types of events: microseismic events, blasting, ore extraction, mechanical noise, and electromagnetic interference. The event type was correctly determined by the trained CNN classifier 98.2% of the time, outperforming traditional machine learning methods.

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“…These software packages use grid modeling to build complex 3D geological bodies with tetrahedrons or hexahedrons. For microseismic localization, Peng proposed a grid-based velocity model [26]. Below, we briefly introduce the construction of a grid-based velocity model for a tunnel, consisting of the following four steps.…”

Section: Establishment Of the Velocity Model Around Tunnelsmentioning

confidence: 99%

“…Therefore, establishing a complex velocity model that is consistent with the actual engineering scenario is an important factor for improving the accuracy of MEL in a tunnel. To solve this problem, Peng proposed a mesh-based velocity model [26], which can accurately generate an arbitrarily complex 3D velocity model. In this paper, based on this model, a 3D heterogeneous velocity model (HVM) is proposed for MEL around tunnels.…”

Section: Introductionmentioning

confidence: 99%

Microseismic Event Location by Considering the Influence of the Empty Area in an Excavated Tunnel

Peng

Jiang

Wang

2020

Sensors

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The velocity model is a key factor that affects the accuracy of microseismic event location around tunnels. In this paper, we consider the effect of the empty area on the microseismic event location and present a 3D heterogeneous velocity model for excavated tunnels. The grid-based heterogeneous velocity model can describe a 3D arbitrarily complex velocity model, where the microseismic monitoring areas are divided into many blocks. The residual between the theoretical arrival time calculated by the fast marching method (FMM) and the observed arrival time is used to identify the block with the smallest residual. Particle swarm optimization (PSO) is used to improve the location accuracy in this block. Synthetic tests show that the accuracy of the microseismic event location based on the heterogeneous velocity model was higher than that based on the single velocity model, independent of whether an arrival time error was considered. We used the heterogeneous velocity model to locate 7 blasting events and 44 microseismic events with a good waveform quality in the Qinling No. 4 tunnel of the Yinhanjiwei project from 6 June 2017 to 13 June 2017 and compared the location results of the heterogeneous-velocity model with those of the single-velocity model. The results of this case study show that the events located by the heterogeneous velocity model were concentrated around the working face, which matched the actual conditions of the project, while the events located by the single-velocity model were scattered and far from the working face.

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Targeted location of microseismic events based on a 3D heterogeneous velocity model in underground mining

Cited by 17 publications

References 29 publications

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

Microseismic Event Location by Considering the Influence of the Empty Area in an Excavated Tunnel

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