Rock mass dynamic response to extraction

许多土木和采矿工程项目需要挖掘岩石以满足地下空间设计要求，或用于从地壳中提取矿石材料。竖井、隧道和采场只是土木和采矿工程项目中广泛使用的岩石开挖的众多例子中的一部分。岩石开采过程扰乱了围岩的平衡，因为现场岩石压力在开挖周围的某些位置降低，而在其他位置增加。这可能会导致不稳定问题，导致在岩石压力较低的情况下松散的岩石块掉落。在采后岩石压力较高的情况下，开挖可能会发生剧烈坍塌，特别是当岩石是坚硬和脆性的时。这就是所谓的岩爆或应变爆。它总是伴随着在寄主岩石中的微震活动的发生。当主岩重新调整自身以适应开采后岩石压力的突然上升时，会发生微震活动。这种效应被称为岩石对开采的动态响应-这是本研究的主题。为了避免由于剧烈的岩石破坏或岩爆而造成的灾害，在地下安装了微震传感器网络，以24/7的基础上监测岩石对开采活动的反应。当数据被分析时，有可能确定微震事件的时间、强度和位置，并估计岩石震动的速度（称为峰值粒子速度）以及作为提取过程的结果从寄主岩石释放的地震能量。在过去的几十年里，进行了大量的研究，以帮助提高对岩石对开采的反应的认识和理解。现在可以开发复杂的数值模型，能够模拟地质复杂环境中发现的岩石开挖的复杂形状，以及现场岩石压力和计划的开采顺序。然而，迄今为止，这些努力的大部分集中在模拟在更简单的模型“静态”条件下的提取过程，即一个过程，不伴随着地震反应。在这项研究中，这一不足之处是处理，因为它已经创建了一个开发的静态模型和现实之间的差距，即表现出爆裂和微震活动的岩石的动态响应。近年来，申请人已经开始通过开发描述岩石对开采的动态响应的算法来解决这种建模缺陷，该算法能够计算开采后压力、峰值粒子速度和释放的地震能量。这项研究将集中在开发和验证算法的动态岩石响应提取的特性。随后，它将把开发的算法应用到加拿大矿山的实际应用中，在那里，深度很大的震动对矿山运营商的安全非常重要。

Many civil and mining engineering projects require that the rock be excavated to meet underground space design requirements, or for the purpose of extracting ore material from the earth crust. Shafts, tunnels and mining stopes are only some of the numerous examples of rock excavations widely used in civil and mining engineering projects. The process of rock extraction disturbs the equilibrium of the host rock in that the in-situ rock pressure decreases at some locations and increases at others around the excavation. This may cause instability problems leading to the fall of loose rock blocks in case of low rock pressure. In the case of high post-extraction rock pressure, the excavation may collapse violently, especially when the rock is strong and brittle. This is known as rockburst or strainburst. It is invariably accompanied by the occurrence of microseismic activities in the host rock. Microseismicity occurs as the host rock is re-adjusting itself to the sudden rise in rock pressure following extraction. Such effects are known as the dynamic response of rock to extraction – the topic of this research. In an effort to avoid disasters due to violent rock failure or rockburst, networks of microseismic sensors are installed underground to monitor the response of the rock to extraction activities on a 24/7 basis. When the data is analyzed, it is possible to identify the time, intensity and location of microseismic events and estimate the speed of rock shaking, known as peak particle velocity, and the seismic energy released from the host rock as a result of an extraction process. Much research was done in the past few decades to help advance the knowledge and understanding of the rock response to extraction. It is now possible to develop sophisticated numerical models capable of simulating complex shapes of rock excavations found in geologically complex environments, as well as the in situ rock pressure, and the planned extraction sequence. However, to date, the majority of these efforts focused on simulating the extraction process in the simpler-to-model “static” conditions, i.e. a process that is not accompanied by seismic response. This deficiency is dealt with in this research as it has created a gap between the developed static models and reality, i.e. dynamic response of the rock exhibiting burst and microseismicity. The applicant has begun in recent years to address this modelling deficiency, by developing algorithms depicting the dynamic response of rock to extraction, capable of computing post-extraction pressure, peak particle velocity and seismic energy released. This research will focus on the development and verification of algorithms for the characterization of the dynamic rock response to extraction. Subsequently, it will take the developed algorithms to real-life applications for validation in Canadian mines where tremors at great depth are of much concern to the safety of the mine operators.