A joint model-based design of experiments approach for the identification of Gaussian Process models in geological exploration

When searching for potential mining sites, accurately modelling mineral concentrations or rock qualities in the subsurface is a crucial task. However, drilling in these locations is an expensive process, so reliable interpolation and efficient sampling techniques are required (Rossi & Deutsch, 2014). Gaussian Processes (GPs), also known as Kriging models, were first developed in the mining industry in the 1950s and continue to be widely used in resource modelling (Sahimi, 2011). As the true nature of the subsurface is unknown, assumptions must be made about the kernel function, which describes correlation structures between probable distributions of spatial phenomena, and its parameters must be estimated. This is typically accomplished through expert judgement and exploratory data analysis of preliminary samples. Model predictions are updated iteratively as more drilling data becomes available, with a focus on balancing expected exploitation (high grade intercepts) and exploration (minimising the Kriging variance) (Jafrasteh & Suarez, 2020). However, problems can arise if the chosen kernel is incorrect or if high uncertainty affects parameters. This poster showcases a joint model-based design approach (Galvanin et al., 2016) aiming to optimise three objectives: 1) reducing parametric uncertainty; 2) increasing the exploration of the design space to avoid local optima; 3) maximising the distinguishability of candidate model predictions to identify the most suitable kernel function with the minimum number of samples. Two different kernels in an Ordinary Kriging GP were used as candidate models and in-silico data was generated using one kernel. Starting from some initial samples, the optimal design strategy iteratively determined sampling locations to maximise the distinguishability between model predictions with a constraint ensuring that each iteration reduces prediction variance. The correct model could be distinguished and the data approximated well with a limited number of drilling experiments while satisfactorily estimating kernel parameters

A joint model-based design of experiments approach for the identification of Kriging models in geological exploration

When exploring prospective mining locations, a central task is modelling rock attributes in the subsurface. The drilling needed to sample these locations is costly, so efficient sampling and reliable interpolation methods are needed. Kriging models (Gaussian Processes) are thus used, with the kernel and its parameters determined from data analysis of preliminary samples and expert judgement. New samples iteratively update the model, targeting exploitation (high ore grades) and exploration (minimising prediction variance). The problem arises if the chosen kernel is incorrect or high uncertainty affects parameters. This paper thus suggests a joint model-based design of experiments (j-MBDoE) approach to target two objectives: maximising the distinguishability of candidate model predictions and reducing model uncertainty from parameter variance. Three different kernels in an Ordinary Kriging GP were used as candidate models. In-silico data was generated using one kernel and the optimal design strategy iteratively determined sampling locations to maximise model distinguishability with a constraint to ensure improved parameter estimates. Two models could be distinguished and the data approximated well with a limited number of drilling experiments while satisfactorily estimating kernel parameters

Group 5: Challenge: Nanopore “Defect detection in graphene sheets”

Graphene is a nanomaterial with excellent super conducting properties, is considered to be the strongest material available and is impermeable to gasses (even Helium-the smallest gas atom). Its properties find applications in fabrication of electronic and optoelectronic devices, gas sensors such as chemiresistors for detection of ammonium [1] and nitrogen dioxide [2], biosensors, composite materials and energy storage devices. Graphene has a regular structure- similar to a honey comb, which consists of fused six membered carbon rings into 2D array. This atomic structure is the foundation of the exceptional properties of the material. The occurrence of defects in the lattice which occur during the manufacturing process can cause significant deterioration in the property of the final material produced [3]. These defects can be detected with a number of spectroscopy-microscopic techniques, one of which is Surface Scanning Electron Microscopy (SEM). In the challenge Nanopore “Defect Detection in Graphene Sheets” a complete dataset of 180 SEM images 256x256 pixels (full-stack), from which were derived 2279 images 48x48 pixels of perfect patches (pp) and 32 images 48x48 pixels of defect containing patches (dp) were provided in numpy arrays, see Figure 1. All pixel values had been normalised to values between 0.00 and 1.00. Figure 1 Nanopore “Defect Detection in Graphene Sheets”. The task was to design a classifier for SEM graphene images using the pp dataset. The two classes are as follows: the first is images without defects and the second class, images with defects, where the type of defect or its localisation are not considered. Finally, the classifier had to be evaluated using an appropriate metric on the dp dataset

Targeting adenosine receptors in the development of cardiovascular therapeutics.

Item does not contain fulltextAdenosine receptor stimulation has negative inotropic and dromotropic actions, reduces cardiac ischemia-reperfusion injury and remodeling, and prevents cardiac arrhythmias. In the vasculature, adenosine modulates vascular tone, reduces infiltration of inflammatory cells and generation of foam cells, and may prevent the development of atherosclerosis as a result. Modulation of insulin sensitivity may further add to the anti-atherosclerotic properties of adenosine signaling. In the kidney, adenosine plays an important role in tubuloglomerular feedback and modulates tubular sodium reabsorption. The challenge is to take advantage of the beneficial actions of adenosine signaling while preventing its potential adverse effects, such as salt retention and sympathoexcitation. Drugs that interfere with adenosine formation and elimination or drugs that allosterically enhance specific adenosine receptors seem to be most promising to meet this challenge.1 maart 201