Why RockSigma

Because basic processing of seismic data isn’t enough.

In mining and subsurface operations, traditional monitoring solutions and regulatory compliance are only the baseline.

The degree of processing and accuracy of seismic data touches several critical aspects of an operation – from reducing unplanned downtime and improving both short- and long-term planning and designs, to reducing risks of disastrous events that can lead to multi-million-dollar setbacks.

Poor precision

Scattered event locations hide the realities in vague clouds of data, prohibiting data driven decision making.

Non real-time

High dependence on manual processing cripples ownership and action on the ground, leading to lost production hours, and higher operational costs.

Siloed data

Vertically integrated, closed systems, makes data exchange across different systems difficult, curtailing higher order analysis and insights. 



RockSigma benefits

The RockSigma solution, based on the BEMIS System, offers a way to combat the aforementioned challenges:

Fully automated workflow

scalable to activity levels of any mine. No manual intervention required.

Reliable real-time results

radically reducing dangerous and costly waiting periods.

Unparallelled event location accuracy

boosting confidence in decision making.

Tomographic imaging

elevating understanding of the rock mass.

Predictions of future activity levels

enabling seismicity-aware production planning.

Self-calibrating

no need to do expensive reference blasts and manual calibration.

Supports multiple sensor and measurement systems

eliminating vendor lock-in.

Open interfaces

supporting multiple applications, each tailored for the specific process it supports.

DOCUMENTATION

Trusting RockSigma means trusting science.

The foundation of our work is built on more than a decade of dedicated research, and hundreds of scientific pages authored by our founders themselves. The passion we share for deepening our understanding of the rock-mass response isn’t an abstract idea; it’s pure science made accessible not just to researchers, but to every engineer, operator, and decision-maker underground.

Martinsson, J. Törnman, W. and Mozaffari, S (2024)

Innovative bayesian-based seismic anomaly detection: A real-time solution for enhancing safety and productivity in mining operations, in D Johansson & H Schunnesson (eds), Massmin 2024: Proceedings of the 9th International Conference and Exhibition on Mass Mining, Kiruna, Sweden, pp. 1013-1021 

Martinson, J. Törman, W. and 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,

Törnman, W. Martinsson, J and 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. and Martinsson, J and Dineva.S. (2021).

Robust Bayesian estimator for S-wave spectra, using a combined empirical Green’s function. Geophys. J. Int. (2021)

Törnman, W. and Martinsson, J (2020)

Reliable automatic processing of seismic events: solving the Swiss cheese problem’, in J Wesseloo (ed.), Proceedings of the Second International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Perth, pp. 155-172

Martinsson, J. and Törnman, W. (2019).

Modelling the dynamic relationship between mining induced seismic activity and production rates, depth and size – a mine-wide hierarchical model. Pure and Applied Geophysics.

Martinsson, J. and Jonsson, A. (2018).

A new model for the distribution of observable earthquake magnitudes and applications to b-value estimation. IEEE Geoscience and Remote Sensing Letters.

Wettainen, T. and Martinsson, J. (2014).

Estimation of future ground vibration levels in Malmberget town due to mining-induced seismic activity. Journal of the Southern African Institute of Mining and Metallurgy 114:835–843.

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. Geophys. J. Int. (2013).

Why change something that already works?

Because it could simply work better. With RockSigma, you don’t need to revolutionize your systems to revolutionize your results. We utilize your existing data to help your team thoroughly understand their mine and subsurface environments without spending hours on follow-ups and double-checks.

By working with us you can: optimize shifts, cut downtime, stay compliant, and improve productivity, safety, and reliability – for planning both day-to-day objectives and year-round strategies.

Ready to Connect?

Whether you’re looking to enhance your monitoring capabilities, explore a partnership, or simply learn more about our approach, we’d love to hear from you. Phone call, or an e-mail – whichever way works best for you.