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Simulating mining-induced seismicity using the material point method

journal contribution
posted on 2023-05-21, 04:17 authored by Basson, G, Andrew BassomAndrew Bassom, Brian SalmonBrian Salmon
Micro-seismic events within a mine have the potential to disturb planned daily operations as unpredictable events can lead to safety concerns. Mines are typically large-scale underground endeavours which cannot easily be modelled on the macro scale. One increasingly popular technique for simulating activity in mines is the material point method (MPM) which is a numerical algorithm designed for analysing large deformations. One attraction of the MPM over many other numerical strategies is that it does not require repeated remeshing of the underlying computational grid but this means that the MPM is not particularly suited to analysis of small-scale events. In an effort to address this dichotomy, in this work we propose an adaptation to the regular grid used in MPM together with a novel time-stepping strategy. This assists greatly in the solution of large modeling problems typical of those arising in mining contexts. We present a new approach of extracting mining induced micro-seismic events from a numerical model by converting simulated plastic strain to seismic potency with the elastoplastic Coulomb criterion used to describe tensile and shear fracturing. Our results are benchmarked against a standard geotechnical problem in order to validate our formulation. The potential practical usefulness of the model is demonstrated by applying it to a real case study consisting of 4 months of recorded micro-seismic events taken from a mine in Tasmania.

History

Publication title

Rock Mechanics and Rock Engineering

Volume

54

Pagination

4483-4503

ISSN

0723-2632

Department/School

School of Natural Sciences

Publisher

Springer-Verlag Wien

Place of publication

Sachsenplatz 4-6, Po Box 89, Vienna, Austria, A-1201

Rights statement

© The Author(s) 2021

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the mathematical sciences

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