Subject: [Applied Sciences] Manuscript ID: applsci-3831145 - Invitation to Review for Applied Sciences
From: Applied Sciences Editorial Office <[email-masked]>
To: "Sergey Y. Khomutov" <[email-masked]>
Cc: Applied Sciences Editorial Office <[email-masked]>, Siraphat Somchit <[email-masked]>
Date: Mon, 18 Aug 2025 03:07:41 +0200

Dear Dr. Khomutov,

Based on your background and expertise, we invite you to review the following 
manuscript for the journal Applied Sciences 
(https://www.mdpi.com/journal/applsci/). 

Type of manuscript: Article 
Title: Quantitative Hazard Assessment of Mining-Induced Seismicity Using 
Spatiotemporal b-Value Dynamics from Microseismic Monitoring
Authors: Hao Wang, Jianjun Wang, Xinxin Yin *, Xiaonan Liang
Special Issue: Earthquake Detection, Forecasting and Data Analysis
https://www.mdpi.com/journal/applsci/special_issues/48X4E487NP

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* Applied Sciences 2024 CiteScore - 5.5 (Q1 in “General 
Engineering",“Instrumentation" and “Fluid Flow and Transfer Processes") 
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Multidisciplinary")
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----------------------------------------- 
Manuscript details: 
Journal: Applied Sciences 
Manuscript ID: applsci-3831145 
Type of manuscript: Article 
Title: Quantitative Hazard Assessment of Mining-Induced Seismicity Using 
Spatiotemporal b-Value Dynamics from Microseismic Monitoring  
Authors: Hao Wang, Jianjun Wang, Xinxin Yin *, Xiaonan Liang
Earth Sciences
https://www.mdpi.com/journal/applsci/sections/earth_sciences_geography

Special Issue: Earthquake Detection, Forecasting and Data Analysis
https://www.mdpi.com/journal/applsci/special_issues/48X4E487NP  

Abstract: Based on 15,584 microseismic events recorded in 2024 by the 
Seismological Observation System (SOS) microseismic monitoring network at a 
coal mine in Gansu Province, China, this study systematically investigates 
the spatiotemporal characteristics of mining-induced seismicity and its 
quantitative relationship with excavation disturbances. The methodology 
integrates Gaussian Mixture Model (GMM) clustering analysis with maximum 
likelihood estimation of b-value. Key findings include: (1) GMM clustering 
effectively identifies distinct seismic activity zones associated with 
different stress states. Significant variations in b-values (ranging from 
0.64 to 0.70) are observed across clusters, with low b-value zones indicating 
high stress concentration and potential for strong seismic events, thereby 
enabling refined quantitative assessment of seismic hazard; (2) The 
time-sliding window analysis reveals the dynamic evolution of the b-value, 
which exhibits a clear negative correlation between b-value and high-energy 
events. When the b-value drops sharply to 0.6 or below, the likelihood of 
high-energy events increases markedly. Notably, 7 out of 8 strong seismic 
events—defined as those with logarithmic energy (lgE) greater than 
5.0—occurred below this threshold. (3) The spatial distribution of 
seismicity synchronously migrates with the advancement of the working face, 
and a significant positive correlation is observed between monthly excavation 
length and monthly seismic energy release, confirming excavation disturbance 
as the dominant triggering factor for mining-induced seismicity. The proposed 
b-value spatiotemporal analysis framework provides a scientific basis for 
early warning of dynamic disasters and optimization of mining layouts in deep 
coal mines. 

Keywords: mining-induced seismicity monitoring;induced 
seismicity;b-value;clustering analysis;time-series analysis

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