The b-value evolution of mining-induced seismicity and mainshock occurrences at hard-rock mines

Ma, Xu; Westman, Erik; Slaker, Brent; Thibodeau, Denis; Counter, Dave

doi:10.1016/j.ijrmms.2018.02.003

Cited by 36 publications

(17 citation statements)

References 31 publications

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“…It is well-known that the calculation of the b-value mainly depends on the selection of the spatiotemporal range of microseismic events. Ma et al [63] stated that the b-value varies with mining-induced seismicity sequences. Accordingly, the a-value and b-value can be used as different indicators.…”

Section: Mining-induced Earthquake Tendency Based On G-r Relationshipmentioning

confidence: 99%

Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine

Deng

Wen

et al. 2022

Minerals

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The risks associated with underground mining at Fankou Lead/Zinc Mine in South China are growing due to the large-scale mining activities there. To recognize mining-induced earthquakes and assess the risk per mining level, a microseismic monitoring system, which is used to record microseismic events, is installed at multiple mining levels in Fankou Lead/Zinc Mine. The purpose of this study is to identify mining-induced earthquakes and to evaluate the risk per mining level by analyzing the spatiotemporal characteristics of microseismic activities in the Fankou Lead/Zinc Mine. In this study, the Gutenberg-Richter (G-R) relationship is applied to compute the b-value, which is used to obtain the maximum magnitude (M (max)) of microseismic event that probably occurs at each mining level. Then, the evaluation of the recurrence period for M (max) and the probability of the microseismic event with the magnitude M (max) is carried out and the M (max) at each mining level is determined based on the recording period of microseismic events. The results show that factors such as the maximum rock vibration velocity, source parameters, displacement, microseismic waveform and energy ratio (ES/EP) can be used to distinguish whether a recorded microseismic event is mining-induced earthquake. Additionally, we propose a method to assess the possibility of mining-induced earthquake at each mining level based on M (max) and predict the recurrence time of microseismic event with the magnitude M (max). The of two years results of microseismic events monitoring demonstrate that the current study is promising for identifying mining-induced earthquakes, assessing the risk of mining-induced earthquakes, predicting the potential maximum microseismic event in a region and estimating its recurrence period and its probability in the Fankou Lead/Zinc Mine.

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Section: Mining-induced Earthquake Tendency Based On G-r Relationshipmentioning

confidence: 99%

Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine

Deng

Wen

et al. 2022

Minerals

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“…In view of the limited energy detection range of the geophones and relatively long distance between microseismic sources and geophones, a proportion of microseismicity with low energy levels could not have been picked up by the monitoring system. The minimum complete detection magnitude (or the magnitude of completeness), defined as the minimum magnitude at which all the microseismic events are detected in spatial and temporal scales, can be an indicator (Woessner and Wiemer 2005;Ma et al 2018b). The minimum complete detection magnitude for the whole dataset was estimated to be 3.9 by visualising the frequency-magnitude relationship of microseismicity, while those for microseismicity around each panel varied from 3.3 to 4.2.…”

Section: Energy Magnitude and Scalingmentioning

confidence: 99%

The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

et al. 2020

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Knowledge regarding microseismic characteristics associated with longwall coal mining is crucial in evaluating the potential for underground mining hazards. Although microseismicity is induced by mining activities, it still remains uncertain as to what extent mining activities influence the spatial, temporal, and magnitude characteristics of microseismicity. To establish a thorough understanding of the relationship between microseismic characteristics and mining activities, a 27-month long microseismic monitoring campaign was conducted around a highly stressed coal zone and eight producing longwall panels at Coal Mine Velenje in Slovenia. Each microseismic event was classified to be associated with the producing longwall panel that triggered it, and the microseismic response to multi-panel longwall top coal caving face advance was analysed. Monitoring data have shown that locations of microseismic events coincided with stress concentrated regions. It was established that both seismic count and energy-intensive regions associated with coal mining in different panels are spatially connected, but they do not fully overlap with mined-out or stress concentrated areas. In addition, microseismic event counts frequency was found to be well correlated with mining intensity, while seismic energy magnitude and spatial distribution are poorly correlated with the same. Therefore, microseismic characteristics could not be explained solely by the mining-induced stress transfer and mining intensity, but are believed to be dominated by pre-existing natural fractures throughout the coal seam. Analyses of these observations helped the development of a conceptual seismic-generation model, which provides new insights into the causes of microseismicity in coal mining.

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“…Importantly, the location, frequency, and source parameters of seismic events provide mining engineers with valuable information (J. P. Liu, et al., 2019; Ma et al., 2018a; Tierney & Morkel, 2017; Wang et al., 2019). For instance, this includes the in‐situ stress level (Konicek & Waclawik, 2018; Kozłowska & Orlecka‐Sikora, 2017; Ma et al., 2016), its orientation (Ma et al., 2019b; Mahdevari et al., 2016), burst proneness of rock, the evolution of mining‐induced stress re‐distribution (Abolfazlzadeh & Hudyma, 2016; Beer et al., 2017; Pariseau & McCarterr, 2017), potential for the occurrence of seismic events with large magnitudes (Ma et al., 2018), discrimination between natural and anthropogenic seismicity (Lizurek, 2017), and so forth. Due to the depletion of shallow ore deposits, mining depths have been continuously increasing around the world over time.…”

Section: Introductionmentioning

confidence: 99%

Numerical Modeling of Complex Stress State in a Fault Damage Zone and Its Implication on Near‐Fault Seismic Activity

et al. 2021

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The b-value evolution of mining-induced seismicity and mainshock occurrences at hard-rock mines

Cited by 36 publications

References 31 publications

Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine

Method for Identifying and Forecasting Mining-Induced Earthquakes Based on Spatiotemporal Characteristics of Microseismic Activities in Fankou Lead/Zinc Mine

The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

Numerical Modeling of Complex Stress State in a Fault Damage Zone and Its Implication on Near‐Fault Seismic Activity

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