Basin analysis and economic geology of the Northern Mount Isa Basin

The Mount Isa Basin is a new concept used to describe the area of Palaeo- to Mesoproterozoic rocks south of the Murphy Inlier and inappropriately described presently as the Mount Isa Inlier. The new basin concept presented in this thesis allows for the characterisation of basin-wide structural deformation, correlation of mineralisation with particular lithostratigraphic and seismic stratigraphic packages, and the recognition of areas with petroleum exploration potential. The northern depositional margin of the Mount Isa Basin is the metamorphic, intrusive and volcanic complex here referred to as the Murphy Inlier (not the "Murphy Tectonic Ridge"). The eastern, southern and western boundaries of the basin are obscured by younger basins (Carpentaria, Eromanga and Georgina Basins). The Murphy Inlier rocks comprise the seismic basement to the Mount Isa Basin sequence. Evidence for the continuity of the Mount Isa Basin with the McArthur Basin to the northwest and the Willyama Block (Basin) at Broken Hill to the south is presented. These areas combined with several other areas of similar age are believed to have comprised the Carpentarian Superbasin (new term). The application of seismic exploration within Authority to Prospect (ATP) 423P at the northern margin of the basin was critical to the recognition and definition of the Mount Isa Basin. The Mount Isa Basin is structurally analogous to the Palaeozoic Arkoma Basin of Illinois and Arkansas in southern USA but, as with all basins it contains unique characteristics, a function of its individual development history. The Mount Isa Basin evolved in a manner similar to many well described, Phanerozoic plate tectonic driven basins. A full Wilson Cycle is recognised and a plate tectonic model proposed. The northern Mount Isa Basin is defined as the Proterozoic basin area northwest of the Mount Gordon Fault. Deposition in the northern Mount Isa Basin began with a rift sequence of volcaniclastic sediments followed by a passive margin drift phase comprising mostly carbonate rocks. Following the rift and drift phases, major north-south compression produced east-west thrusting in the south of the basin inverting the older sequences. This compression produced an asymmetric epi- or intra-cratonic clastic dominated peripheral foreland basin provenanced in the south and thinning markedly to a stable platform area (the Murphy Inlier) in the north. The fmal major deformation comprised east-west compression producing north-south aligned faults that are particularly prominent at Mount Isa. Potential field studies of the northern Mount Isa Basin, principally using magnetic data (and to a lesser extent gravity data, satellite images and aerial photographs) exhibit remarkable correlation with the reflection seismic data. The potential field data contributed significantly to the unravelling of the northern Mount Isa Basin architecture and deformation. Structurally, the Mount Isa Basin consists of three distinct regions. From the north to the south they are the Bowthorn Block, the Riversleigh Fold Zone and the Cloncurry Orogen (new names). The Bowthom Block, which is located between the Elizabeth Creek Thrust Zone and the Murphy Inlier, consists of an asymmetric wedge of volcanic, carbonate and clastic rocks. It ranges from over 10 000 m stratigraphic thickness in the south to less than 2000 min the north. The Bowthorn Block is relatively undeformed: however, it contains a series of reverse faults trending east-west that are interpreted from seismic data to be down-to-the-north normal faults that have been reactivated as thrusts. The Riversleigh Fold Zone is a folded and faulted region south of the Bowthorn Block, comprising much of the area formerly referred to as the Lawn Hill Platform. The Cloncurry Orogen consists of the area and sequences equivalent to the former Mount Isa Orogen. The name Cloncurry Orogen clearly distinguishes this area from the wider concept of the Mount Isa Basin. The South Nicholson Group and its probable correlatives, the Pilpah Sandstone and Quamby Conglomerate, comprise a later phase of now largely eroded deposits within the Mount Isa Basin. The name South Nicholson Basin is now outmoded as this terminology only applied to the South Nicholson Group unlike the original broader definition in Brown et al. (1968). Cored slimhole stratigraphic and mineral wells drilled by Amoco, Esso, Elf Aquitaine and Carpentaria Exploration prior to 1986, penetrated much of the stratigraphy and intersected both minor oil and gas shows plus excellent potential source rocks. The raw data were reinterpreted and augmented with seismic stratigraphy and source rock data from resampled mineral and petroleum stratigraphic exploration wells for this study. Since 1986, Comalco Aluminium Limited, as operator of a joint venture with Monument Resources Australia Limited and Bridge Oil Limited, recorded approximately 1000 km of reflection seismic data within the basin and drilled one conventional stratigraphic petroleum well, Beamesbrook-1. This work was the first reflection seismic and first conventional petroleum test of the northern Mount Isa Basin. When incorporated into the newly developed foreland basin and maturity models, a grass roots petroleum exploration play was recognised and this led to the present thesis. The Mount Isa Basin was seen to contain excellent source rocks coupled with potential reservoirs and all of the other essential aspects of a conventional petroleum exploration play. This play, although high risk, was commensurate with the enormous and totally untested petroleum potential of the basin. The basin was assessed for hydrocarbons in 1992 with three conventional exploration wells, Desert Creek-1, Argyle Creek-1 and Egilabria-1. These wells also tested and confrrmed the proposed basin model. No commercially viable oil or gas was encountered although evidence of its former existence was found. In addition to the petroleum exploration, indeed as a consequence of it, the association of the extensive base metal and other mineralisation in the Mount Isa Basin with hydrocarbons could not be overlooked. A comprehensive analysis of the available data suggests a link between the migration and possible generation or destruction of hydrocarbons and metal bearing fluids. Consequently, base metal exploration based on hydrocarbon exploration concepts is probably. the most effective technique in such basins. The metal-hydrocarbon-sedimentary basin-plate tectonic association (analogous to Phanerozoic models) is a compelling outcome of this work on the Palaeo- to Mesoproterozoic Mount lsa Basin. Petroleum within the Bowthom Block was apparently destroyed by hot brines that produced many ore deposits elsewhere in the basin.

TCSC control design for transient stability improvement of a multi-machine power system using trajectory sensitivity

This paper discusses the effects of thyristor controlled series compensator (TCSC), a series FACTS controller, on the transient stability of a power system. Trajectory sensitivity analysis (TSA) has been used to measure the transient stability condition of the system. The TCSC is modeled by a variable capacitor, the value of which changes with the firing angle. It is shown that TSA can be used in the design of the controller. The optimal locations of the TCSC-controller for different fault conditions can also be identified with the help of TSA. The paper depicts the advantage of the use of TCSC with a suitable controller over fixed capacitor operation.

Performance study of two different compensating devices in a custom power park

Simulation study of a custom power park (CPP) is presented. It is assumed that the park contains unbalanced and nonlinear loads in addition to a sensitive load. Two different types of compensators are used separately to protect the sensitive load against unbalance and distortion caused by the other loads. It has been shown that a shunt compensator can regulate the voltage of the CPP bus, whereas the series compensator can only regulate the sensitive load terminal voltage. Additional issues such as the load transfer through a static transfer switch, detection of sag/fault etc. are also discussed. The concepts are validated through PSCAD/EMTDC simulation studies on a sample distribution system.

Diesel engine condition monitoring and simulated diesel knock

This paper discusses diesel engine condition monitoring (CM) using acoustic emissions (AE) as well as some of the commonly encountered diesel engine problems. Also discussed are some of the underlying combustion related faults and the methods used in past studies to simulate diesel engine faults. The initial test involved an experimental simulation of two common combustion related diesel engine faults, namely diesel knock and misfire. These simulated faults represent the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank angle encoder and top-dead centre (TDC) signals. Using these signals, it was possible to characterise the effect of different combustion conditions and hence, various diesel engine in-cylinder pressure profiles.

Enhancing acoustic emission signals from multi-cylinder diesel engine

This paper presents techniques which can be viewed as pre-processing step towards diagnosis of faults in a small size multi-cylinder diesel engine. Preliminary analysis of the acoustic emission (AE) signals is outlined, including time-frequency analysis, selection of optimum frequency band. Some results of applying mean field independent component analysis (MFICA) to separate the AE root mean square (RMS) signals are also outlined. The results on separation of RMS signals show this technique has the potential of increasing the probability to successfully identify the AE events associated with the various mechanical events.

Life on the edge : housing experiences in three remote Australian indigenous settlements

This analysis of housing experiences and aspirations in three remote Indigenous settlements in Australia (Mimili, Maningrida and Palm Island) reveals extreme liveability problems directly related to the scale and form of housing provision. Based upon field visits to each of the settlements and extensive interviews with residents and local housing and community officers, the paper analyses two aspects of living in such housing conditions at two spatial scales, the layout of the settlement and the design of individual houses. The failings at both scales are shown to be the fault of a dysfunctional housing system that is only recently been addressed.

Towards improvement of autonomous glider navigation accuracy through the use of regional ocean models

Autonomous underwater gliders are robust and widely-used ocean sampling platforms that are characterized by their endurance, and are one of the best approaches to gather subsurface data at the appropriate spatial resolution to advance our knowledge of the ocean environment. Gliders generally do not employ sophisticated sensors for underwater localization, but instead dead-reckon between set waypoints. Thus, these vehicles are subject to large positional errors between prescribed and actual surfacing locations. Here, we investigate the implementation of a large-scale, regional ocean model into the trajectory design for autonomous gliders to improve their navigational accuracy. We compute the dead-reckoning error for our Slocum gliders, and compare this to the average positional error recorded from multiple deployments conducted over the past year. We then compare trajectory plans computed on-board the vehicle during recent deployments to our prediction-based trajectory plans for 140 surfacing occurrences.

Geometry‐specified troposphere decorrelation for subcentimeter real‐time kinematic solutions over long baselines

Real‐time kinematic (RTK) GPS techniques have been extensively developed for applications including surveying, structural monitoring, and machine automation. Limitations of the existing RTK techniques that hinder their applications for geodynamics purposes are twofold: (1) the achievable RTK accuracy is on the level of a few centimeters and the uncertainty of vertical component is 1.5–2 times worse than those of horizontal components and (2) the RTK position uncertainty grows in proportional to the base‐torover distances. The key limiting factor behind the problems is the significant effect of residual tropospheric errors on the positioning solutions, especially on the highly correlated height component. This paper develops the geometry‐specified troposphere decorrelation strategy to achieve the subcentimeter kinematic positioning accuracy in all three components. The key is to set up a relative zenith tropospheric delay (RZTD) parameter to absorb the residual tropospheric effects and to solve the established model as an ill‐posed problem using the regularization method. In order to compute a reasonable regularization parameter to obtain an optimal regularized solution, the covariance matrix of positional parameters estimated without the RZTD parameter, which is characterized by observation geometry, is used to replace the quadratic matrix of their “true” values. As a result, the regularization parameter is adaptively computed with variation of observation geometry. The experiment results show that new method can efficiently alleviate the model’s ill condition and stabilize the solution from a single data epoch. Compared to the results from the conventional least squares method, the new method can improve the longrange RTK solution precision from several centimeters to the subcentimeter in all components. More significantly, the precision of the height component is even higher. Several geosciences applications that require subcentimeter real‐time solutions can largely benefit from the proposed approach, such as monitoring of earthquakes and large dams in real‐time, high‐precision GPS leveling and refinement of the vertical datum. In addition, the high‐resolution RZTD solutions can contribute to effective recovery of tropospheric slant path delays in order to establish a 4‐D troposphere tomography.

Test and evaluation of non conventional instrument transformers and sampled value process bus on Powerlink's transmission network

IEC 61850 Process Bus technology has the potential to improve cost, performance and reliability of substation design. Substantial costs associated with copper wiring (designing, documentation, construction, commissioning and troubleshooting) can be reduced with the application of digital Process Bus technology, especially those based upon international standards. An IEC 61850-9-2 based sampled value Process Bus is an enabling technology for the application of Non-Conventional Instrument Transformers (NCIT). Retaining the output of the NCIT in its native digital form, rather than conversion to an analogue output, allows for improved transient performance, dynamic range, safety, reliability and reduced cost. In this paper we report on a pilot installation using NCITs communicating across a switched Ethernet network using the UCAIug Implementation Guideline for IEC 61850-9-2 (9-2 Light Edition or 9-2LE). This system was commissioned in a 275 kV Line Reactor bay at Powerlink Queensland’s Braemar substation in 2009, with sampled value protection IEDs 'shadowing' the existing protection system. The results of commissioning tests and twelve months of service experience using a Fibre Optic Current Transformer (FOCT) from Smart Digital Optics (SDO) are presented, including the response of the system to fault conditions. A number of remaining issues to be resolved to enable wide-scale deployment of NCITs and IEC 61850-9-2 Process Bus technology are also discussed.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.