Morkel, IG, Wesseloo, J & Harris, P 2015, 'Highlighting and quantifying seismic data quality concerns', in PM Dight (ed.), Proceedings of the Ninth Symposium on Field Measurements in Geomechanics
, Australian Centre for Geomechanics, Perth, pp. 539-549, https://doi.org/10.36487/ACG_rep/1508_37_Morkel
For seismically active mines, the analysis of the mining induced seismicity forms an important part of the geotechnical risk management and design process. However, it appears that the quality of seismic data is seldom scrutinised, resulting in lower quality databases and, therefore, unreliable results. These results are used to make decisions affecting both the safety and productivity of mine sites. For this reason assessing and quantifying the database quality is important.
Many mine sites rely on seismic service providers to help maintain the seismic system and to provide good quality seismic data, which they will use in seismic analysis techniques. The mine personnel generally assume good quality data and do not have the tools or expertise to evaluate the integrity of a database. In our experience, data quality problems are experienced by most mines, to some degree, with the quality at some mines having a serious impact on decision making.
This paper presents a method for the assessment of seismic data quality. This method highlights the areas in the database that is most contaminated with bad data and thus provides a first step towards rectifying the problem. Quality indices are also developed to objectively quantify the database quality. The methods presented in this paper are work in progress on which the authors will improve in the near future.
Harris, PC & Wesseloo, J 2015, mXrap v5 software, Australian Centre for Geomechanics, The University of Western Australia,
Mcrae, TW 1964, The impact of computers on accounting, Wiley, London.
Mendecki, AJ 2013, ‘Frequency range, log E, log P and magnitude’, in A Malovichko, R Dyagilev & DA Malovichko (eds), Proceedings 8th International Symposium on Rockbursts and Seismicity in Mines (RaSiM8), Russian Academy of Sciences, Perm, pp. 167173.
Mendecki, AJ, Van Aswegen, G & Mountfort, P 1999, ‘A guide to routine seismic monitoring in mines’, in AJ Jager & JA Ryder (eds), A handbook on rock engineering practice for tabular hard rock mines, Safety in Mines Research Advisory Committee (SIMRAC), pp. 287-309.
Morkel, IG & Wesseloo, J 2015, mXrap software app, Mining Induced Seismicity – Seismic Event Quality, version 1, Australian Centre for Geomechanics, The University of Western Australia, Perth,
Redman, TC 1998, ‘The impact of poor data quality on the typical enterprise’, Communications of the ACM, vol. 41, pp. 79-82.
Scheepers, LJ, Hofmann, G & Morkel, IG 2012, ‘The study of seismic response to production for a grid mining layout’, Proceedings of the Second Southern Hemisphere International Rock Mechanics (SHIRMS), pp. 387-406.
Tayi, GK & Ballou, DP 1998, ‘Examining data quality’, Communications of the ACM, vol. 41, pp. 54-57.
Van Aswegen, G, Potvin, Y & Hudyma, M 2005, ‘Routine seismic hazard assessment in some South African mines’, in Y Potvin & M Hudyma (eds), Proceedings of the Sixth International Symposium on Rockburst and Seismicity in Mines (RaSiM6), Australian Centre for Geomechanics, Perth, pp. 435-444.
Wesseloo, J 2013, ‘Towards real-time probabilistic hazard assessment of the current hazard state for mines’, in A Malovichko, R Dyagilev & DA Malovichko (eds), Proceedings 8th International Symposium on Rockbursts and Seismicity in Mines (RaSiM8), Russian Academy of Sciences, Perm, pp. 307-312.
Wesseloo, J 2014, ‘Evaluation of the spatial variation of the b-value’, The Journal of The Southern African Institute of Mining and Metallurgy (SAIMM), vol. 114, October 2014, pp. 823-827.
Wesseloo, J, Woodward, K & Pereira, J 2014, ‘Grid-based analysis of seismic data’, The Journal of The Southern African Institute of Mining and Metallurgy (SAIMM), vol. 114, October 2014, pp. 815-823.