No description available.

Post-Stack inversion of reflection data in seismic exploration can be used to obtain detailed information about lithology variations in the zone of interest. Generalized Linear Inversion (GLI) has previously been applied as a useful tool to achieve this. The purpose of my investigation is to apply GLI to data from the Coal Measures. It is known that in the Coal Measures the most strongly reflecting horizons are the coal seams, which are the exploration targets. In the seismic bandwidth they are thin beds, which causes particular problems associated with vertical resolution for the inversion. The method is applied to a seismic line from the Belvoir Coalfield supplied by British Coal. In order to get better relative amplitudes and to keep the same bandwidth down the whole section, the data were carefully reprocessed using the ProMAX software. Wireline log data from two boreholes intersected by the seismic line were edited to generate acoustic impedance logs as functions of time. Software was developed to implement GLI, and tested on synthetic data before applying it to the reprocessed data. The initial guesses for earth and wavelet models at the boreholes were obtained after systematic studies to determine the best strategy. The construction of the initial guess for the boundary locations elsewhere on the section is very critical for the success of the search for the global minimum. A combination of structural interpretation and the inversion results obtained from the previous trace was found to do the best job. I have tried to invert separately for the boundary locations, acoustic impedances and the wavelet, with the wavelet parameterized in the frequency domain. I found that, provided that the wavelet extracted at a borehole is a good estimate with low error energy, the most successful strategy is just to invert for the boundary locations, keeping the acoustic impedances and the extracted wavelet fixed. If the extracted wavelet is not a good estimate, then parameterizing the wavelet in the frequency domain and optimizing those parameters at the borehole is a useful approach. None of the implemented inversion strategies produced a perfect result. Discrepancies were due to the difficulty in obtaining true relative amplitude values on the processed section. The inversion results and systematic studies on the field dataset indicate that the assumptions of the convolutional model are not satisfied by the processed section.

No description available.

No description available.

Bibliography: leaves 279-293. / xxxiv, 323 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Temperature-programmed Pyrolysis experiments employing Bowmans and Lochiel low-rank coal and treated Bowmans coals, were conducted to investigate the sulphur transformations during pyrolysis. / Thesis (Ph.D.)--University of Adelaide, Dept. of Chemical Engineering, 2000

A methodology to monitor the real-time cutting forces generated by a longwall shearer is developed. Longwall shearers are the single productive element in an underground longwall coal mine, and are a major source of unscheduled system downtime. The cutting forces constitute the interaction between the longwall and the coal face. Knowledge of the forces can improve the reliability and productivity of longwall mines by providing a means of shearer condition monitoring. A Kalman filter based cutting force estimator is developed, and then tested using a comprehensive shearer simulation incorporating a dynamic representation of the cutterhead. Subsequently, prior work on autonomous shearer guidance with measured pick forces is extended by employing the estimated cutting forces to track changes to the shearer's horizon. This is validated by simulation. The force estimation methodology uses an extended Kalman filter to blend noisy measurement information with a nonlinear system model, the latter combining the dynamics of the shearer and its inputs. A detailed model is developed of the shearer rigid body dynamics, and of the measurements required for observability. The formulation avoids the use of sensors at the coal cutting interface. The methodology uses proven, practicable and reliable sensor technologies, making it suitable for longterm force monitoring. A comprehensive simulation of a longwall shearer is developed using the MATLAB(R) Simulink(R) and MSC.visualNastran 4DTM software environments. This incorporates dynamic representations of the major structures and inputs, including the interaction between the cutterhead and the coal face. The simulation is used to investigate the cutting forces in a number of different operational scenarios. This facilitates the construction of a basic dynamic model of the forces, and provides insight into the relationship between the cutting forces and the condition of the cutting interface. The cutting force estimator is tested using measurements from the shearer simulations. It is shown that the cutting force estimates track the fiducial forces well in each scenario. A number of modifications to the force models are also shown to improve the performance of the estimator. Sensitivity analyses are conducted to investigate the effect of tuned process noise covariance and model parameter errors on the estimates. The application to the problems of shearer condition monitoring and horizon tracking are considered. The first is demonstrated by detecting the effects of a worn pick and a seam hardness gradient on the force estimates, and the second by using the force estimates to track the motion of the cutterhead relative to an embedded stone band. The results show that it is possible, in principle, to estimate the cutting forces generated by a longwall shearer from indirect measurements using a Kalman filter methodology. Furthermore, the force estimates reveal changes to the cutting environment, and can track the vertical motion of the shearer as it operates. This thesis represents the first step in the development of a shearer cutting force estimator. The ultimate goal of the research is to improve the reliability and productivity of Australian longwall operations, using the estimated forces to monitor shearer condition. Autonomous horizon tracking with estimated forces can lead to further improvements to longwall productivity, and to the health and safety of mine personnel. A number of recommendations are made for future field validation, and development of an automated shearer condition monitoring system based on the cutting force estimates.

A methodology to monitor the real-time cutting forces generated by a longwall shearer is developed. Longwall shearers are the single productive element in an underground longwall coal mine, and are a major source of unscheduled system downtime. The cutting forces constitute the interaction between the longwall and the coal face. Knowledge of the forces can improve the reliability and productivity of longwall mines by providing a means of shearer condition monitoring. A Kalman filter based cutting force estimator is developed, and then tested using a comprehensive shearer simulation incorporating a dynamic representation of the cutterhead. Subsequently, prior work on autonomous shearer guidance with measured pick forces is extended by employing the estimated cutting forces to track changes to the shearer's horizon. This is validated by simulation. The force estimation methodology uses an extended Kalman filter to blend noisy measurement information with a nonlinear system model, the latter combining the dynamics of the shearer and its inputs. A detailed model is developed of the shearer rigid body dynamics, and of the measurements required for observability. The formulation avoids the use of sensors at the coal cutting interface. The methodology uses proven, practicable and reliable sensor technologies, making it suitable for longterm force monitoring. A comprehensive simulation of a longwall shearer is developed using the MATLAB(R) Simulink(R) and MSC.visualNastran 4DTM software environments. This incorporates dynamic representations of the major structures and inputs, including the interaction between the cutterhead and the coal face. The simulation is used to investigate the cutting forces in a number of different operational scenarios. This facilitates the construction of a basic dynamic model of the forces, and provides insight into the relationship between the cutting forces and the condition of the cutting interface. The cutting force estimator is tested using measurements from the shearer simulations. It is shown that the cutting force estimates track the fiducial forces well in each scenario. A number of modifications to the force models are also shown to improve the performance of the estimator. Sensitivity analyses are conducted to investigate the effect of tuned process noise covariance and model parameter errors on the estimates. The application to the problems of shearer condition monitoring and horizon tracking are considered. The first is demonstrated by detecting the effects of a worn pick and a seam hardness gradient on the force estimates, and the second by using the force estimates to track the motion of the cutterhead relative to an embedded stone band. The results show that it is possible, in principle, to estimate the cutting forces generated by a longwall shearer from indirect measurements using a Kalman filter methodology. Furthermore, the force estimates reveal changes to the cutting environment, and can track the vertical motion of the shearer as it operates. This thesis represents the first step in the development of a shearer cutting force estimator. The ultimate goal of the research is to improve the reliability and productivity of Australian longwall operations, using the estimated forces to monitor shearer condition. Autonomous horizon tracking with estimated forces can lead to further improvements to longwall productivity, and to the health and safety of mine personnel. A number of recommendations are made for future field validation, and development of an automated shearer condition monitoring system based on the cutting force estimates.

A methodology to monitor the real-time cutting forces generated by a longwall shearer is developed. Longwall shearers are the single productive element in an underground longwall coal mine, and are a major source of unscheduled system downtime. The cutting forces constitute the interaction between the longwall and the coal face. Knowledge of the forces can improve the reliability and productivity of longwall mines by providing a means of shearer condition monitoring. A Kalman filter based cutting force estimator is developed, and then tested using a comprehensive shearer simulation incorporating a dynamic representation of the cutterhead. Subsequently, prior work on autonomous shearer guidance with measured pick forces is extended by employing the estimated cutting forces to track changes to the shearer's horizon. This is validated by simulation. The force estimation methodology uses an extended Kalman filter to blend noisy measurement information with a nonlinear system model, the latter combining the dynamics of the shearer and its inputs. A detailed model is developed of the shearer rigid body dynamics, and of the measurements required for observability. The formulation avoids the use of sensors at the coal cutting interface. The methodology uses proven, practicable and reliable sensor technologies, making it suitable for longterm force monitoring. A comprehensive simulation of a longwall shearer is developed using the MATLAB(R) Simulink(R) and MSC.visualNastran 4DTM software environments. This incorporates dynamic representations of the major structures and inputs, including the interaction between the cutterhead and the coal face. The simulation is used to investigate the cutting forces in a number of different operational scenarios. This facilitates the construction of a basic dynamic model of the forces, and provides insight into the relationship between the cutting forces and the condition of the cutting interface. The cutting force estimator is tested using measurements from the shearer simulations. It is shown that the cutting force estimates track the fiducial forces well in each scenario. A number of modifications to the force models are also shown to improve the performance of the estimator. Sensitivity analyses are conducted to investigate the effect of tuned process noise covariance and model parameter errors on the estimates. The application to the problems of shearer condition monitoring and horizon tracking are considered. The first is demonstrated by detecting the effects of a worn pick and a seam hardness gradient on the force estimates, and the second by using the force estimates to track the motion of the cutterhead relative to an embedded stone band. The results show that it is possible, in principle, to estimate the cutting forces generated by a longwall shearer from indirect measurements using a Kalman filter methodology. Furthermore, the force estimates reveal changes to the cutting environment, and can track the vertical motion of the shearer as it operates. This thesis represents the first step in the development of a shearer cutting force estimator. The ultimate goal of the research is to improve the reliability and productivity of Australian longwall operations, using the estimated forces to monitor shearer condition. Autonomous horizon tracking with estimated forces can lead to further improvements to longwall productivity, and to the health and safety of mine personnel. A number of recommendations are made for future field validation, and development of an automated shearer condition monitoring system based on the cutting force estimates.

Thesis (M.S.)--Ohio University, Novmeber, 1999. / Title from PDF t.p.

Thesis (B.Sc. (Hons.)) -- University of Adelaide, Dept. of Geology, 1975. / Typescript (photocopy).