Prediction of Relatively High-Energy Seismic Events Using Spatial–Temporal Parametrisation of Mining-Induced Seismicity

• Published in: Rock mechanics and rock engineering 2020-11, Vol.53 (11), p.5111-5132
• Main Authors: Si, Guangyao, Cai, Wu, Wang, Shuyu, Li, Xu
• Format: Article
• Language: English
• Subjects: Accuracy; Civil Engineering; Clustering; Coal mines; Density; Density distribution; Earth and Environmental Science; Earth Sciences; Earthquake prediction; Earthquakes; Energy; Geological hazards; Geophysics/Geodesy; Mining; Original Paper; Parameterization; Principal components analysis; Probability density functions; Probability theory; Rockbursts; Seismic activity; Seismic data; Seismic hazard; Seismic properties; Seismicity; Spatial data; Statistical analysis; Two dimensional analysis; Underground mines
• ISSN: 0723-2632; 1434-453X
• DOI: 10.1007/s00603-020-02210-3

Mining-induced seismicity has been reported almost in every mining country. Large seismic events with high-energy (HE) radiation can pose a serious threat to safe mining operations, which are the direct cause for rockbursts in underground mines. Over the last 30 years, statistical techniques to parameterise seismic data and predict seismic hazard have been developed significantly with promising results. However, similar to earthquake prediction, the prediction accuracy of HE seismic events remains a challenging task due to the complex nature of mining-induced seismic events. This paper aims to parameterise spatial, temporal and energy information conveyed by past seismic activities to provide early warnings for HE events. The 4D spatial–temporal seismic data were transferred to a 2D plane using principal component analysis (PCA) and then applied with a kernel density estimator to calculate their probability density function. The mode of probability density distribution, ρ ( x ) max , was proposed as a measure to quantify the clustering degree of past seismic events in the PCA space. The peaks of ρ ( x ) max were found as an effective precursor to predicting the onset time of future HE events. After inverse transformation, the spatial locations of ρ ( x ) max also indicate the potential high-risk areas that susceptible to HE events, which can be used to predict the onset location of HE events. As a supplement to the spatial – temporal information, energy/magnitude information was parameterised by the modified cumulative Benioff strain and b value. The accuracy of using these seismic parameters for the prediction of HE events has been assessed based on the seismic data collected from a Chinese coal mine.