Mining-induced seismicity, particularly high-energy seismic events, is a major factor giving rise to dynamic phenomena within the rock strata. Rockbursts and stress relief events produce the most serious consequences in underground mines, are most difficult to predict and tend to interact with other mining hazards, thus making control measures difficult to implement. In the context of steadily increasing mining depth within copper mines in the Legnica-Głogów Copper Belt Area (Poland) alongside the gradually decreasing effective mining thickness, a study of the causes and specificity of mining-induced seismicity in specific geological and mining settings may improve the effectiveness of the prevention and control measures taken to limit the negative impacts of rockbursts in underground mine workings, thus ensuring safe working conditions for miners. This study investigates the presumed relationship between the mined ore deposit thickness and fundamental parameters of mining-induced seismicity, with the main focus on the actual locations of their epicenters with respect to the working face in commonly used room-and-pillar systems. Data recalled in this study was supplied by the O/ZG Rudna geophysics station. Based on information about the actual ore deposit thickness in particular sections of the mines (Rudna Główna, Rudna Północna, Rudna Zachodnia) and recent reports on seismic activity in this area, three panels were selected for further studies (each in different mine region), where the ore deposit thickness was varied (panel G-7/5 – Rudna Główna, panel XX/1 – Rudna Północna, panel XIX/1 – Rudna Zachodnia). Data from seismic activity reports in those regions was used for energetic and quantitative analysis of seismic events in the context of the epicenter location with respect to the selected mining system components: undisturbed strata, working face and abandoned excavations. In consideration of the available rockburst control methods and preventive measures, all events (above 1 × 103 J) registered in the database were analysed to infer about the global rockburst hazard level in the panel and phenomena induced (provoked) by blasting were considered in order to evaluate the effectiveness of the implemented control measures.
With reference to the situation experienced in several Polish collieries where the risk of occurrence of gas-geodynamic phenomena is increasing and decisions to start the mining activities need to take numerous constraints associated with previous mining into account, this paper addresses certain geo-mechanical aspects of longwall mining in the zones of excavation edge interactions giving rise to major changes in the conditions of the deposit and rock strata, as a consequence of previous mining operations in adjacent coalbeds. Starting from the analytical description of displacements and stresses in the proximity of longwall mining systems, the paper summarizes the results of model tests and investiga-tions of the influence that the excavation edge has on the behavior and structural continuity of a portion of the coal body in the coalbed beneath or above an old excavation. Based on selected nonlinear functions emulating the presence of edges in the rock strata, a comparative study is carried out by investigating two opposite directions of workface advance, from the gob area towards the coal body and from the coal body towards the gobs. The discussion of the results relies on the analysis of roof deformation and the concentration factor of the vertical stress component at the workface front.
Rockburst is a common engineering geological hazard. In order to evaluate rockburst liability in kimberlite at an underground diamond mine, a method combining generalized regression neural networks (GRNN) and fruit fly optimization algorithm (FOA) is employed. Based on two fundamental premises of rockburst occurrence, depth, σθ, σc, σt, B1, B2, SCF, Wet are determined as indicators of rockburst, which are also input vectors of GRNN model. 132 groups of data obtained from rockburst cases from all over the world are chosen as training samples to train the GRNN model; FOA is used to seek the optimal parameter σ that generates the most accurate GRNN model. The trained GRNN model is adopted to evaluate burst liability in kimberlite pipes. The same eight rockburst indicators are acquired from lab tests, mine site and FEM model as test sample features. Evaluation results made by GRNN can be confirmed by a rockburst case at this mine. GRNN do not require any prior knowledge about the nature of the relationship between the input and output variables and avoid analyzing the mechanism of rockburst, which has a bright prospect for engineering rockburst potential evaluation.