Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

The results of a pilot study into the application of an unsupervised clustering approach to the analysis of catchment-based National Geochemical Survey of Australia (NGSA) geochemical data combined with geophysical and geological data across northern Australia are documented. NGSA Mobile Metal Ion (R) (MMI) element concentrations and first and second order statistical summaries across catchments of geophysical data and geological data are integrated and analysed using Self-Organizing Maps (SOM). Input features that contribute significantly to the separation of catchment clusters are objectively identified and assessed. A case study of the application of SOM for assessing the spatial relationships between Au mines and mineral occurrences in catchment clusters is presented. Catchments with high mean Au code-vector concentrations are found downstream of areas known to host Au mineralization. This knowledge is used to identify upstream catchments exhibiting geophysical and geological features that indicate likely Au mineralization. The approach documented here suggests that catchment-based geochemical data and summaries of geophysical and geological data can be combined to highlight areas that potentially host previously unrecognised Au mineralization.The NGSA project was part of the Australian Government’s Onshore Energy Security Program 2006 – 2011, from which funding support is gratefully acknowledged

Reflections on undertaking the Probation Qualifying Framework scheme during the transforming rehabilitation changes

This article reflects upon the author’s experience of undertaking the PQF (Probation Qualifying Framework) training scheme during the chaotic period of Transforming Rehabilitation. The author asserts that the uncertainty and precarious nature of the changes were detrimental to an effective learning environment, which ultimately promoted a practice culture of punitiveness and control and did not allow learners the space to be skilful and confident practitioners, comfortable working autonomously. Furthermore, the author contends there is an emerging culture within the NPS (National Probation Service) increasingly fostered on ‘risk management’, which is reflected in the vocational nature of PQF training and is contributing towards a widening cultural gap that is emerging between the community rehabilitation companies and NPS

Post-release reforms for short prison sentences: re-legitimising and widening the net of punishment

Transforming Rehabilitation (TR) promised a ‘revolution’ in the way offenders are managed, providing a renewed focus on short sentence prisoners. The TR reforms extends mandatory post-release supervision and tailored through-the-gate resettlement provisions to a group that has predominately faced a ‘history of neglect’ yet often present with the most acute needs within the criminal justice system. However, existing literature underlines that serving short sentences lack ‘utility’ and can be counter-productive to facilitating effective rehabilitation. This article explores the purposes of providing post release supervision for short sentences, firstly exploring a previous attempt to reform short sentences; (the now defunct) ‘Custody Plus’ within the 2003 Criminal Justice Act and then the Offender Rehabilitation Act 2014 within the TR reforms. This article contends that both post release reforms have sought to re-affirm and re-legitimise prison as the dominant form of punishment in society- or what Carlen refers to as ‘carceral clawback’. This article will also use Cohen’s analysis on social control to establish that post release supervision will serve to ‘widen the net’ extend the period of punishment and oversight and will only reinforce a form of enforced ‘state obligated rehabilitation’ that will undermine efforts made to resettle short sentence prisoners

Quantitative mineral mapping of drill core surfaces II: long-wave infrared mineral characterization using μXRF and machine learning

Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (μXRF). Linear programming-derived mineral abundances from quantified μXRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (±7–15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation

Strengthening the Magnetic Interactions in Pseudobinary First-Row Transition Metal Thiocyanates, M(NCS)2.

Understanding the effect of chemical composition on the strength of magnetic interactions is key to the design of magnets with high operating temperatures. The magnetic divalent first-row transition metal (TM) thiocyanates are a class of chemically simple layered molecular frameworks. Here, we report two new members of the family, manganese(II) thiocyanate, Mn(NCS)2, and iron(II) thiocyanate, Fe(NCS)2. Using magnetic susceptibility measurements on these materials and on cobalt(II) thiocyanate and nickel(II) thiocyanate, Co(NCS)2 and Ni(NCS)2, respectively, we identify significantly stronger net antiferromagnetic interactions between the earlier TM ions-a decrease in the Weiss constant, θ, from 29 K for Ni(NCS)2 to -115 K for Mn(NCS)2-a consequence of more diffuse 3d orbitals, increased orbital overlap, and increasing numbers of unpaired t2g electrons. We elucidate the magnetic structures of these materials: Mn(NCS)2, Fe(NCS)2, and Co(NCS)2 order into the same antiferromagnetic commensurate ground state, while Ni(NCS)2 adopts a ground state structure consisting of ferromagnetically ordered layers stacked antiferromagnetically. We show that significantly stronger exchange interactions can be realized in these thiocyanate frameworks by using earlier TMs.EPSRC NPIF 2018 fund Laboratory Directed Research and Development Program of Oak Ridge National Laboratory NSERC of Canada PGSD fund Trinity College, Cambridge School of Chemistry, University of Nottingham Hobday Fellowship EPSRC Strategic Equipment Grant EP/M000524/

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

AbstractMachine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Hence, there is much scope for the application of MLAs to the rapidly increasing volumes of remotely sensed geophysical data for geological mapping problems. We carry out a rigorous comparison of five MLAs: Naive Bayes, k-Nearest Neighbors, Random Forests, Support Vector Machines, and Artificial Neural Networks, in the context of a supervised lithology classification task using widely available and spatially constrained remotely sensed geophysical data. We make a further comparison of MLAs based on their sensitivity to variations in the degree of spatial clustering of training data, and their response to the inclusion of explicit spatial information (spatial coordinates). Our work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data. Random Forests is straightforward to train, computationally efficient, highly stable with respect to variations in classification model parameter values, and as accurate as, or substantially more accurate than the other MLAs trialed. The results of our study indicate that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically. The use of explicit spatial information generates accurate lithology predictions but should be used in conjunction with geophysical data in order to generate geologically plausible predictions. MLAs, such as Random Forests, are valuable tools for generating reliable first-pass predictions for practical geological mapping applications that combine widely available geophysical data

The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines

Inductive machine learning algorithms attempt to recognize patterns in, and generalize from empirical data. They provide a practical means of predicting lithology, or other spatially varying physical features, from multidimensional geophysical data sets. It is for this reason machine learning approaches are increasing in popularity for geophysical data inference. A key motivation for their use is the ease with which uncertainty measures can be estimated for nonprobabilistic algorithms. We have compared and evaluated the abilities of two nonprobabilistic machine learning algorithms, random forests (RF) and support vector machines (SVM), to recognize ambiguous supervised classification predictions using uncertainty calculated from estimates of class membership probabilities. We formulated a method to establish optimal uncertainty threshold values to identify and isolate the maximum number of incorrect predictions while preserving most of the correct classifications. This is illustrated using a case example of the supervised classification of surface lithologies in a folded, structurally complex, metamorphic terrain. We found that (1) the use of optimal uncertainty thresholds significantly improves overall classification accuracy of RF predictions, but not those of SVM, by eliminating the maximum number of incorrectly classified samples while preserving the maximum number of correctly classified samples; (2) RF, unlike SVM, was able to exploit dependencies and structures contained within spatially varying input data; and (3) high RF prediction uncertainty is spatially coincident with transitions in lithology and associated contact zones, and regions of intense deformation. Uncertainty has its upside in the identification of areas of key geologic interest and has wide application across the geosciences, where transition zones are important classes in their own right. The techniques used in this study are of practical value in prioritizing subsequent geologic field activities, which, with the aid of this analysis, may be focused on key lithology contacts and problematic localities. </jats:p