The mining industry is rapidly moving into new and potentially much higher risk environments. Deep mining is expected to be dominated at depths that are much greater than the current, ie greater than 1000 m. During the underground excavation at such depths, more and more geological disasters such as water inrush, gas emission, debris flow, rock burst and roof falling may more easily occur because the resource body characteristics and rock mass response are much more complicated in the higher stress regimes and/or fractured/fault zones. This poster project aims to extend and apply an existing research outcome - PANDAS computational model and software platform - to numerically investigate the related issues for an improved understanding of the deep mine in fault zones. PANDAS is a novel finite element based software and has been applied to simulate fault systems and heterogeneously fractured geomaterials for underground issues. This will further reduce uncertainties of related geohazards and fault mobilisation induced by deep underground mining.However, little work has been done to systematically study the interaction between mining and fault-slip before mine design. More specifically, the scientific issue needs to conquer that which is the dominant factor to lead to instability of surrounding rock of stope and longwall panel when mining at footwall and hanging wall. Therefore, this poster project aims to obtain systematic analyses of influence of mining at hanging wall and footwall on fault-slip using a numerical approach.The fault system of a certain reservoir will be set up based on known geological settings if possible and then numerically simulated and assessed by using PANDAS on supercomputers to investigate potential risks during deep mining processes. This may include those built on relevant experimental and field observation results, considering geological settings and mechanical characteristics of the target mining site to study the coupling effect of rock mass response in fractured/fault zones for deep mines, and finally provide the safety evaluation to a certain mining for optimised design.CITATION:Han, Z and Xing, H, 2016. Using PANDAS to investigate improved understanding of fault zones, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), p 889 (The Australasian Institute of Mining and Metallurgy: Melbourne).