Abstract: A method of computerized data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution.

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: A system and method are described for generating a model of an environment in which a plurality of equipment units are deployed for the extraction of at least one resource from the environment. The system comprises a pre-extraction modeling unit configured to receive data from a first plurality of heterogeneous sensors in the environment and to fuse the data into a pre-extraction model. An equipment modeling unit is configured to receive equipment data relating to the plurality of equipment units and to combine the equipment data into an equipment model. A post-extraction modeling unit is configured to receive data from a second plurality of sensors and to fuse the data into a post-extraction model. Information from the pre-extraction model, the equipment model and/or the post-extraction model is communicable to the equipment units for use in controlling operation of the equipment units in the environment.

Abstract: Methods and systems are described for generating a data representation of a geographical region as an adjunct to conducting autonomous operations within the region. The method comprises receiving information specifying a plurality of localised caused zones having operation-defined geographical boundaries within the region; receiving heterogeneous data descriptive of the region; associating the received data with respective localised zones; fusing the received data associated with the localised zones into data representations of the localised zones; and integrating the data representations of the localised zones into a common data representation of the geographical region.

Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbour selection step. The method may include generating a model at a selected resolution.

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.

Abstract: A system and method are described for generating a model of an environment in which a plurality of equipment units (104) are deployed for the extraction of at least one resource from the environment. The system comprises a pre-extraction or in-ground modelling unit (204) configured to receive data from a first plurality of heterogeneous sensors in the environment and to fuse the data into a pre-extraction model descriptive of the environment and the at least one resource. An equipment modelling unit (208) is configured to receive equipment data relating to the plurality of equipment units operating in the environment and to combine the equipment data into an equipment model. A post-extraction or out-of-ground modelling unit (206) is configured to receive data from a second plurality of sensors and to fuse the data into a post-extraction model descriptive of material extracted from the environment, wherein at least one of the equipment units operates to extract the at least one resource from the environment.