DOI https://doi.org/10.36487/ACG_repo/2465_63
Cite As:
Martinsson, J, Törnman, W & Svanberg, E 2024, 'Responsive short-term seismic forecasting: a web-based tool for mining efficiency and safety', in P Andrieux & D Cumming-Potvin (eds),
Deep Mining 2024: Proceedings of the 10th International Conference on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, pp. 995-1002,
https://doi.org/10.36487/ACG_repo/2465_63
Abstract:
Predicting the rock mass response to future mining is crucial for ensuring the safety and efficiency of operations in seismically active mines. Long-term forecasts often rely on historical seismic data and various inputs that may be available such as faults, geology, mining sequences, stress measurements and numerical modelling. Short-term predictions – spanning a few days to a week – demand a more dynamic and responsive approach. This paper introduces an online web application based on a hierarchical Bayesian prediction model tailored for continuously updated short-term seismic activity forecasts. Further, the activity can easily serve as inputs to post-processing analysis such as hazard assessments while preserving prediction uncertainties.
The approach integrates recent seismic activity, production plans, depth, size of production, and a seismic exposure term within different volumes of the mine and lets them share information through a hierarchical model structure. Notably, for short-term predictions, many covariates considered in long-term forecasts are constant and summarised by an overall seismic exposure term in each volume.
Implemented as an online web application, this model provides direct seismic insights to personnel across various devices, from control room displays to smartphones. Its dynamic interface visualises historical and predicted seismic activity with prediction intervals, empowering decision-makers for safe and efficient mine operations. This study highlights the effectiveness of combining online web applications and robust Bayesian methods, enhancing safety protocols and operational efficiency in seismically active mines.
Keywords: short-term forecast, mine seismic activity, mining seismicity, production plans, web application, hierarchical Bayesian model
References:
Bezanson, J, Edelman, A, Karpinski, S, & Shah, VB 2017, ‘Julia: A fresh approach to numerical computing’, SIAM Review, vol. 59, no. 1, pp. 65–98.
Chen, MH, Shao, QM & Ibrahim, JG 2000, Monte Carlo Methods in Bayesian Computation, Springer, New York.
Gelman, A, Carlin, JB, Stern, HS & Rubin, DB 2004, Bayesian Data Analysis, 2nd edn, Chapman & Hall/CRC Press, Boca Raton.
Gelman, A & Hill, J 2007, Data Analysis Using Regression and Multilevel/Hierarchical Models. Analytical Methods for Social Research, Cambridge University Press, New York.
Gutenberg, B & Richter, CF 1944, ‘Frequency of earthquakes in California’, Bulletin of the Seismological Society of America, vol. 34, no. 4, pp. 185–188.
Harris, PC & Wesseloo, J 2015, mXrap, version 5, computer software, Australian Centre for Geomechanics, Perth,
https://mxrap.com
Hilbe, JM 2011, Negative Binomial Regression, Cambridge University Press, New York.
Hossain, S 2019, ‘Visualization of bioinformatics data with dash bio’, in C Calloway, D Lippa, D Niederhut & D Shupe (eds), Proceedings of the 18th Python in Science Conference, SciPy, pp. 126–133.
Kruschke, J 2014, Doing Bayesian Data Analysis: A Tutorial Introduction with R, JAGS and Stan, 2nd edn, Elsevier Science. Berlin.
Martinsson, J 2013, ‘Robust Bayesian hypocentre and uncertainty region estimation: The effect of heavy-tailed distributions and prior information in cases with poor, inconsistent and insufficient arrival times’, Geophysical Journal International, vol. 192, pp. 1156–1178.
Martinsson, J & Törnman, W 2020, ‘Modelling the dynamic relationship between mining induced seismic activity and production rates, depth and size: a mine-wide hierarchical model’, Pure and Applied Geophysics, vol. 177, pp. 2619–2639.
Neal, RM 2003, ‘Slice sampling’, The Annals of Statistics, vol. 31, no. 3, pp. 705–767.
Sievert, C 2020, Interactive Web-Based Data Visualization with R, plotly, and shiny, Chapman & Hall/CRC Press, New York.
Törnman, W & Martinsson, J 2020, ‘Reliable automatic processing of seismic events: solving the Swiss cheese problem’, in J Wesseloo (ed.), UMT 2020: Proceedings of the Second International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Perth, pp. 155–172.
Węglarczyk, S & Lasocki, S 2009, ‘Studies of short and long memory in mining-induced seismic processes’, Acta Geophysica, vol. 57, no. 3, pp. 696–715.