The benefits of unconstrained, data-driven velocity models for seismic processing in caving operations

Authors: Haque, I; Törnman, W; Martinsson, J; Vanegas-Palacio, C; Viljoen, N; Primadiansyah, A; Svanberg, E

Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2645_47

Cite As:
Haque, I, Törnman, W, Martinsson, J, Vanegas-Palacio, C, Viljoen, N, Primadiansyah, A & Svanberg, E 2026, 'The benefits of unconstrained, data-driven velocity models for seismic processing in caving operations', in A van As, D Cumming-Potvin & J Wesseloo (eds), Caving 2026: Proceedings of the Sixth International Conference on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 1-12, https://doi.org/10.36487/ACG_repo/2645_47

Download citation as:   ris   bibtex   endnote   text   Zotero


Abstract:
Microseismic monitoring is essential for panel caving operations, but a complex and changing environment makes it challenging to obtain accurate event location. This study evaluates an unconstrained, data-driven rock mass model (including velocity) for high-precision seismic processing at the Deep Mill Level Zone (DMLZ) panel cave in the Grasberg district. The approach uses the Bayesian estimation of mining-induced seismicity (BEMIS) framework, an automated, real-time system designed for the highly irregular and heterogeneous ‘Swiss cheese’ appearance of the underground rock mass. By combining Bayesian inference with adaptive calibration, event location accuracy and precision is improved, revealing clusters, structures and features that improve the understanding of cave propagation. The study also incorporates high-resolution P- and S-wave tomography by BEMIS, producing detailed velocity models with quantified uncertainties that enable dataquality filtering for reliable interpretation. The tomograms show low-velocity zones that closely match the evolving cave shape and high-velocity areas exhibiting stress accumulation, and highlight areas with poor ground conditions. In combination, the precise event locations and tomography provide new and valuable insights into cave evolution and extraction performance at the DMLZ. Overall, the study demonstrates that data-driven, Bayesian seismic processing offers a robust solution for precise velocity model estimation and cave monitoring in complex mining environments.

Keywords: caving, seismic processing, data-driven velocity model, mining-induced seismicity, seismic tomography, event location accuracy

References:
Duplancic, P & Brady, BH 1999, ‘Characterisation of caving mechanisms by analysis of seismicity and rock stress’, Proceedings of the 9th Congress of the International Society for Rock Mechanics, CRC Press/Balkema, Paris, pp. 1049–1053.
Lolon, S, Campbell, R, Edgar, I, Priatna, A & Haque, I 2024, ‘Implementation of major mining strategy changes at the Deep Mill Level Zone (DMLZ) mine, PT Freeport Indonesia’, in D Johansson & H Schunnesson (eds), MassMin 2024: Proceedings of the International Conference & Exhibition on Mass Mining, Luleå University of Technology, Luleå, pp. 73–85, 
10.36487/ACG_repo/2435_A-07
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, no. 1, pp. 1156–1178.
Putra, F, Perdana, A, Hamman, J & Campbell, R 2024, ‘A geotechnical review of the transition from narrow incline undercut to wundercut design at the Deep Mill Level Zone mine, PT Freeport Indonesia’, 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. 1319–1334, 
Satriawan, E, Munandar, A, Immanuela, R, Primadiansyah, A, Soumilena, A & Meiharriko N 2025, ‘Potential cave shape interpretation through a comprehensive monitoring system at the Deep Mill Level Zone Mine, Indonesia’, Proceedings of the ARMA Symposium, American Rock Mechanics Association, Alexandria.
Simanjuntak, K, Primadiansyah, A, Soumilena, N & Teweng, W 2020, ‘Driving and managing stress in the Deep Mill Level Zone caving mine’, in R Castro, F Báez & K Suzuki (eds), MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining, University of Chile, Santiago, pp. 394–405, 
Törnman, W 2021, Towards Reliable Seismic Hazard Assessment in Underground Mines, PhD thesis, Luleå University of Technology, Luleå.
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, 
Törnman, W, Martinsson, J & Dineva, S 2021, ‘Robust Bayesian estimator for S-wave spectra using a combined empirical Green’s function’, Geophysical Journal International, vol. 227, no. 1, pp. 403–438.
Törnman, W, Martinsson, J & Svanberg, E 2024, ‘Enriching seismic data with noise and blasts and the importance of credibility’, 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. 207–216, 
Törnman, W, Martinsson, J & Svanberg, E 2025, ‘High-resolution tomographic investigation at the Kiruna Mine prior to a major seismic event’, Proceedings of the 11th International Symposium on Rockbursts and Seismicity in Mines, Luleå University of Technology, Luleå, pp. 558–578.




© Copyright 2026, Australian Centre for Geomechanics (ACG), The University of Western Australia. All rights reserved.
View copyright/legal information
Please direct any queries or error reports to repository-acg@uwa.edu.au