Abstract: A method for adjusting a surface of an exploratory data model of a geological domain to take into account blast hole data, the method comprising; drilling a plurality of blast holes proximal to the geological domain; recording blast hole data samples for each of the blast holes in an electronic data storage apparatus; operating a processing assembly in data communication with said storage apparatus according to instructions stored in a memory accessible to the processing assembly to perform the following acts: labelling each said data sample as domain or non-domain; determining blast hole boundary samples for the geological domain at each of a number of elevations to produce blast hole boundaries for the geological domain; comparing the blast hole boundaries to a surface of the exploratory data model; adjusting the surface based on the blast hole boundaries for the geological domain to generate an adjusted surface; and mining the geological domain based on the adjusted surface to thereby improve efficiency of

Abstract: A compact hyperspectral imager adapted to operate in harsh environments and to conduct post acquisition signal processing to provide automated and improved hyperspectral processing results is disclosed. The processing includes luminance and brightness processing of captured hyperspectral images, hyperspectral image classification and inverse rendering to produce luminance invariance image processing.

Abstract: A compact hyperspectral imager adapted to operate in harsh environments and to conduct post acquisition signal processing to provide automated and improved hyperspectral processing results is disclosed. The processing includes luminance and brightness processing of captured hyperspectral images, hyperspectral image classification and inverse rendering to produce luminance invariance image processing.

Abstract: The current invention relates to mining systems and mine planning and in particular, to operating a mine that automatically updates a mine plan. The mining system directs operation of mining equipment within a mine based on a mine plan that schedules operations in the mine. The system further includes a mining planning system for updating the mine plan by a learning module configured to determine an inferencing model from initial data obtained from a data input module. The inferencing model is then evaluated by an estimation module using the initial data and the measurement data wherein such evaluation provides a fusion model. Consequently, a mine planner module determines an updated mine plan based on an existing mine plan and the fusion model. Based on the updated mine plan, the mining system directs operation of the mining equipment within the mine.

Abstract: Described herein is a computer implemented method for generating a probabilistic model usable to identify instances of a target feature in geophysical data sets stored on a memory device.

Abstract: A compact hyperspectral imager adapted to operate in harsh environments and to conduct post acquisition signal processing to provide automated and improved hyperspectral processing results is disclosed. The processing includes luminance and brightness processing of captured hyperspectral images, hyperspectral image classification and inverse rendering to produce luminance invariance image processing.

Abstract: Methods and systems are described for tracking material through a production chain or operational process chain in which the material is transferred via a plurality of spatially distinct lumped masses of material (12, 14, 16, 18). A dynamic state space (430) is maintained descriptive of the plurality of spatially distinct lumped masses of material, wherein a quantity of entries in the dynamic state space is augmented or diminished dependent on a quantity of spatially distinct lumped masses being tracked. Measurements relating to an observed lumped mass of material are fused into the dynamic state space and a dynamic covariance matrix to provide an updated estimate of material in the plurality of spatially distinct lumped masses of material.

Abstract: The present disclosure provides a method of characterizing a resource in a block located in an area. The method comprises the step of providing information concerning a property of the resource for a plurality of sample positions in, at or in the environment of the block. The information is obtained from analyzes of samples from the sample positions. The method further comprises modelling a change in the property along a distance at or within the block using the provided information. The method also comprises calculating parameters associated with the modelled change using Bayes' theorem and calculating a property characteristic of the resource for the block using the calculated parameters.

Abstract: This disclosure relates to updating an estimate for a material property of a volume, for example, updating the estimate of iron concentration in a block of a mine block model. The estimate is based on values of one or more model parameters. A processor receives a measurement of the material property outside the volume. Then, the processor determines updated values for the one or more model parameters based on the estimate and the measurement and determines an updated estimate for the material property of the volume based on the updated values for the one or more model parameters and the measurement. Since a measurement outside the volume is used to determine updated model parameters and an updated estimate of that volume, the model is more accurate and the estimate for the material property of the volume is also more accurate although measurements within that volume are not available.

Abstract: Described herein is a computer implemented method for generating a probabilistic model usable to identify instances of a target feature in geophysical data sets stored on a memory device.

Abstract: A system for large scale data modelling is described. The system includes at least one data measurement sensor (230) for generating measured data, a training processor (240) to determine optimized hyperparameter values in relation to a Gaussian process covariance function including a sparse covariance function that is smooth and diminishes to zero outside of a characteristic hyperparameter length. An evaluation processor (260) determines model data from the Gaussian process covariance function with optimised hyperparameter values and measured data. Also described is methods for modelling date, including a method using a Gaussian process including a sparse covariance function that diminishes to zero outside of a characteristic length, wherein the characteristic length is determined from the data to be modelled.

Abstract: Methods and systems are described for tracking material through a production chain or operational process chain in which the material is transferred via a plurality of spatially distinct lumped masses of material (12, 14, 16, 18). A dynamic state space (430) is maintained descriptive of the plurality of spatially distinct lumped masses of material, wherein a quantity of entries in the dynamic state space is augmented or diminished dependent on a quantity of spatially distinct lumped masses being tracked. Measurements relating to an observed lumped mass of material are fused into the dynamic state space and a dynamic covariance matrix to provide an updated estimate of material in the plurality of spatially distinct lumped masses of material.

Abstract: A system (100) for analyzing and synthesizing a plurality of sources of sample data (310, 320) by automated learning and regression. The system includes data storage (110) with a stored multi-task covariance function, and an evaluation processor (102) in communication with the data storage (110). The evaluation processor (102) performs regression using the stored sample data and multi-task covariance function and synthesizes prediction data for use in graphical display or digital control.

Abstract: The present disclosure provides a method of characterising a resource in a block located in an area. The method comprises the step of providing information concerning a property of the resource for a plurality of sample positions in, at or in the environment of the block. The information is obtained from analyses of samples from the sample positions. The method further comprises modelling a change in the property along a distance at or within the block using the provided information. The method also comprises calculating parameters associated with the modelled change using Bayes' theorem and calculating a property characteristic of the resource for the block using the calculated parameters.

Abstract: A system (100) for analysing and synthesising a plurality of sources of sample data (310, 320) by automated learning and regression. The system includes data storage (110) with a stored multi-task covariance function, and an evaluation processor (102) in communication with the data storage (110). The evaluation processor (102) performs regression using the stored sample data and multi-task covariance function and synthesises prediction data for use in graphical display or digital control.