A research report submitted to the Faculty of Engineering and the Built Environment, School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. / This study was based on the hypothesis that there are opportunities to maximize production outputs in many existing underground hard rock trackless mining systems using the same or less resources by improvement in decision making paradigms. This is very important in the current operating environments of uncertainties and continued drop in metal prices. The project main goal was thus to carry out a detailed investigation of trackless mining production systems and test how to maximize output by focusing on three objectives, namely: analyzing key technical factors that impact the production rates in terms of tons per hour, identifying major operational activities which impact effective equipment operating hours, and identifying decision support systems (DSS) to improve operational decision making. Regarding the first objective (production rates), through the analysis of trackless mining as a serial production system, it was shown that production rates could be increased by focusing at system level, process level and work station/equipment level decisions. System level decisions must minimize the total residence time of the material (ore) in transit or work in process(WIP). This will open capacity for generating more ore. Process level decisions must reduce the gross cycle times at the work stations to equal or be below the Takt times inorder to smoothen production flow. Takt time is an important factor in a production system which shows the maximum cycle time allowed to meet the daily demand. The third level focuses on the capability of the mining equipment itself through decisions that improves the reliability, maintainability and capacity. Decision tables based on reducing the equipment failure rates (λ), improving the repair rates (µ) and the cycle times were developed to aid in making the reliability, maintainability or capacity decisions. For the second objective (operational activities), the focus is to maximize effective operating times of the equipment through reduction of delays. The study shows this can be achieved through use of real-time decision support systems (DSS) for better control of the operations. The third objective was able to identify functional modern DSS that can be implemented in trackless mining. Effectively, the study was able to highlight opportunities of generating extra capacity for trackless mines at same or less resources by focusing on the above three objectives. / TL2019

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the Degree of Master of Science in Engineering, 2016 / Previously undermined areas pose a significant challenge to mining by opencast due to the risk of sinkhole occurrence. In order to optimise reserve utilisation as well as safeguard personnel and equipment there was need to develop a “Sinkhole Prediction Model” to assist in the prediction of areas prone to sinkhole formation. The aim of this research therefore was to develop a “Sinkhole prediction tool” with a view to pre-identifying areas of potential sinkhole hazard to inform better controls to assist in mining these areas safely. This was done utilising the current Hill (1996) caving height method culminating in the development of a hazard index model dividing the mining zones into high and low hazard. These areas were colour coded Red (High hazard) and Green (Low Hazard). The “Sinkhole Prediction Model” evolved to include over hundred sinkhole incidences that were statistically analysed to firm up on the robustness of the Prediction Model capabilities. The Hill (1996) caving height formula was discounted after the statistical analysis indicated that a good prediction model lies in the interrogation of site specific data. The outcome of the work conducted in this research report indicated a 97% correlation between the refined “Sinkhole Prediction Model” and the actual sinkhole occurrence at the Anglo American case study area (Mine X). Various refinements inclusive of lithological assessments, blast and drilling reconciliations as well as the implementation of the roughening up quality audits led to the implementation of a robust sinkhole management process that has managed to consistently assist in safeguarding equipment and personnel thus allowing for coal extraction optimisation in areas that could have been written off due to the sinkhole hazard. This risk can only be eliminated by mining the areas with the sinkhole risk. Currently the method is being impacted by significant roughening up cost incurred in a drive to make the areas safe to allow for coal extraction. The roughening up process on average costs R3.5 million per sinkhole and is a function of the number of sinkholes found, which translates to an equivalent cost of R7 / sales tonne. The current sinkhole prediction model being employed in deficient in that it cannot pinpoint the actual location of the void in the area previously undermined by bord and pillar and this is a great limitation of this report. Various geophysical techniques were pursued to assist in the precise identification of the actual sinkhole spatially. This process was aimed to reduce the roughening up cost (entire block stabilisation) as opposed to targeted sinkhole excavation and stabilisation. This process proved futile as the void identification systems are highly incapable of identifying the voids / iv sinkholes spatially (x, y and z coordinates) to assist targeted sinkhole treatment as a result of the following:  System inability to penetrate areas comprised of highly conductive strata such as clays.  Inability to distinguish between the underground voids and geological anomalies such as dykes.  Not suitable for penetrating wet strata.  Impacted by noise interference from mining machinery. The major result of this research is the establishment of a site specific “Sinkhole Prediction Model” that can generate hazard plans in real time thus informing the management on areas associated with a potential sinkhole hazard. The hazard plans can be generated timely and decisions made to facilitate safe coal extraction in areas of high sinkhole hazard. This has culminated in a robust sinkhole management process within the group that has managed to eliminate the risk of personnel and equipment exposure at Mine X. The roughening up process is accepted as the primary sinkhole mitigation or rehabilitation process with the need to work towards reducing the roughening up costs through development of the tool capable of precisely identifying the voids routinely to facilitate targeted rehabilitation. Significant research is required in this area as the mining environment is comprised of strata that currently cannot support the use of real time void identification to facilitate targeted void identification and rehabilitation. There is also merit in the future to formulate the database capable of assisting in the prediction of sinkholes in the Witbank coalfield as well as assist in robust management of mining boundaries across the different mining houses. The system implemented at Mine X is currently being deployed to other operations in the group where modification will be made to match the site specific conditions. Future research into understanding the sinkhole occurrence dynamics is quite crucial if targeted rehabilitation is to be achieved for cost reduction and mining sustainability. A combination of the understanding of the sinkhole occurrence driving mechanisms in conjunction with use of modelling packages such as ELFEN (a hybrid Modelling) tool will go a long way in enhancing the development of precise sinkhole prediction point in space.

The analysis of marsh cores from the tidal zones of the Siuslaw, Umpqua, and Coos River systems on the south-central Oregon coast provides supporting evidence of coseismic subsidence resulting from megathrust earthquakes and reveals the landward extent of the zero-isobase. The analysis is based on lithostratigraphy, paleotidal indicators, microfossil paleotidal indicators, and radiocarbon age. Coseismic activity is further supported by the presence of anomalous thin sand layers present in certain cores. The analysis of diatom assemblages provides evidence of relative sea-level displacement on the order of 1 to 2 m. The historic quiescence of local synclinal structures in the Coos Bay area together with the evidence of prehistoric episodic burial of wetland sequences suggests that the activity of these structures is linked to megathrust releases. The distribution of cores containing non-episodically buried marshes and cores that show episodically buried wetlands within this area suggests that the landward extent of the zero-isobase is between 100 km and 120 km from the trench. The zero-isobase has a minimum width of 10 to 15 km. Radiocarbon dating of selected buried peat sequences yields an estimated recurrence interval on the order of 400 years. The apparent overlapping of the landward margin of both the upperplate deformation zone (fold and/or thrust fault belt) and the landward extent of the zero-isobase is interpreted to represent the landward limit of the locked zone. The earthquake magnitude is estimated to be 8.5 based on an arbitrary rupture length of 200 km and a locked zone width of 105 km. The identification of the zero-isobase on the southcentral Oregon coast is crucial to the prediction of regional coseismic subsidence and tsunami hazards, the testing of megathrust dislocation models, and the estimation of megathrust rupture areas and corresponding earthquake magnitudes in the Cascadia Margin.

The Cascadia subduction zone has been characterized as a typical Chilean-type subduction zone based on qualitative comparisons of plate age and convergence rate, with simple forearc structure. However, the discovery of unusual structural styles of deformation, variations in the morphology of the forearc, and its absence of seismic activity suggest differences from the Chilean analog. The manuscripts presented here (McNeill et al., 1997, in press, submitted) illustrate this complexity and provide examples of contrasting deformation throughout the offshore forearc. The Washington and northern Oregon shelf and upper slope are characterized by extension in the form of listric normal faults. These faults have been active since the late Miocene and are driven by detachment and extension of the underlying overpressured m��lange and broken formation. This region of the forearc is partly to wholly decoupled from convergence-driven compression which dominates deformation elsewhere in the forearc. One exception to convergence-driven compression is a region of N-S compression of the inner shelf and coastal region which reflects the regional stress field. N-S compressional structures apparently influence the positions of coastal lowlands and uplands and may contribute to the record of coastal marsh burials interepreted as the result of coseismic subsidence during subduction zone earthquakes. Modeling of subduction zone earthquake characteristics based on marsh stratigraphy is likely to be inaccurate in terms of rupture zone position, magnitude, and recurrence interval. The Cascadia shelf and upper slope are underlain by a sequence of deformed basinal strata which reflects the tectonic evolution of the margin. The surface of a regional late Miocene angular unconformity (7.5-6 Ma: a global hiatus) indicates deformation by uplifted submarine banks and subsided synclines (coincident with low recent uplift onshore), which control the current shelf break position. The basin is currently filled behind a N-S-trending outer-arc high, which uplifted in the early-middle Pliocene following truncation and erosion of the seaward edge of the basin. Breaching of the outer-arc high occurred in the early Pleistocene leading to the formation of the Astoria Submarine Fan and increased growth rates of the accretionary wedge. / Graduation date: 1999

This dissertation provides the first demonstration of scanning synthetic aperture radar (ScanSAR) advanced interferometry processing for measuring surface deformation. ScanSAR data are synthesized from ERS-1/2 stripmap SAR images over known deformation in Phoenix, Arizona. The strategy is to construct a burst pattern similar to Envisat ScanSAR data and to create a realistic variable-burst synchronization scenario in which any image pair has at least 50% burst overlap. The Small Baseline Subsets technique is applied to the synthesized data to demonstrate ScanSAR time series analysis for a scenario generally conducive for interferometry. The same processing approach is employed with the stripmap data to validate the results. The differences in ScanSAR and stripmap velocities have a mean and standard deviation of 0.02±0.02 cm/year. 96.3% and 99.1% of the velocity differences are within ±0.1 cm/year and ±0.2 cm/year, respectively. The RMS deviations between the ScanSAR and stripmap displacement estimates are 0.40±0.30 cm. 68.5% and 94.6% of the differences are within ±0.5 cm and ±1.0 cm, respectively. The Permanent Scatterer (PS) technique also is adapted and applied to the synthesized data to demonstrate the presence of PS in ScanSAR data. The atmospheric and nonlinear motion phase derived from a PS analysis of stripmap data are removed from the ScanSAR interferograms. Even for this idealized scenario, the final PS identification yields fewer ScanSAR PS (10 PS/km²) than the stripmap PS results (312 PS/km² or 15.6 PS/km² at the ScanSAR pixel resolution). Based on the calculated likelihood of finding multiple stripmap PS within a ScanSAR pixel, it is concluded that the ScanSAR single scatterer PS model is flawed. A model is introduced that considers multiple PS within a ScanSAR pixel. The search for two PS per pixel yields 120 PS/km². The ScanSAR and stripmap PS velocity differences mean is zero and standard deviation is 0.02 cm/year. However, while the differences between the ScanSAR and stripmap PS DEM error estimates are zero-mean, they have a 7-meter standard deviation. One possible explanation for this relatively large deviation is the differencing of the wrong ScanSAR and stripmap PS as the result of a misalignment between the ScanSAR and stripmap images. / text