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Inaccuracies in spatial modelling of coal properties can impact coal resource and reserve estimates. Geostatistical methods which account for compositional data have previously been recommended as a preferred method for modelling of coal properties. These methods can quantify uncertainty, can use auxiliary information to improve estimates, and ensure that compositional data (such as coal proximate analysis) is modelled in a mathematically consistent way. However, geostatistical methods have drawbacks in that have rigid statistical assumptions about the distribution and stationarity of the target variable and require variogram interpretation which can be onerous when then number of domains and target variables are numerous. Due to these drawback, inverse distance weighting (a simple deterministic method) remains the most popular method for modelling coal properties in Australian resource estimates. To address the drawbacks of geostatistical methods, a machine learning approach based on the quantile regression forest algorithm is proposed as an alternative method to spatially model coal properties. This newly proposed method, accounts for spatial arrangement of data, requires minimal pre-processing steps, can quantify uncertainty and can easily incorporate auxiliary information of various types. To evaluate the performance of this method, the accuracy of predictions of coal proximate analysis properties, and coal density are compared to the predictions of inverse distance weighting and regression kriging. All methods incorporate isometric log ratio transform and back-transform of data in order to account for the compositional nature of coal proximate analysis. Data from an active coal mine in the Bowen Basin, Queensland Australia was used as basis of the results. A unique feature of the mine site in this basin is the presence of intrusion which impacts coal quality properties to various degrees. Using evaluation metrics from leave-one-out cross-validation, this paper demonstrates that quantile regression forest has higher accuracy, lower bias and higher precision than inverses distance weighting across all coal properties. The paper also shows that results of the new method are very similar or better than regression kriging. It is also demonstrated that the prediction maps produced by quantile regression forest could be used to more accurately map out coal impacted by intrusion compared to inverse distance weighting, and that inverse distance weighting overestimates the impact of intrusion and intrusion extent. A drawback of the method compared to inverse distance weighting, is that it is more computationally demanding, less intuitive and is currently not available in existing geological packages. [ABSTRACT FROM AUTHOR]

A large, incomplete fragment of arthropod cuticle with distinctive tuberculate ornamentation from the late Permian Kaloola Member, Baralaba Coal Measures of the Bowen Basin in central Queensland, is assigned to the mycteropoid clade of sea scorpions. It is Changhsingian (254–252 Ma) in age and occurs in a freshwater, possibly fluvial, environment. The cuticle ornamentation consists of round to oval tubercles in two size classes and differs, in detail, from all previously described mycteropoids. We therefore propose a new species, ?Woodwardopterus freemanorum sp. nov., to accomodate this new specimen. It is at least 11 Ma younger than any previously known eurypterid fossil. This significant discovery demonstrates that large sweep-feeding eurypterids persisted until the end-Permian extinction in the southern high latitudes of Gondwana. Ecological factors that may have contributed to the survival of these freshwater eurypterids into the late Palaeozoic are also briefly discussed. [ABSTRACT FROM AUTHOR]

Coal resource tonnage estimates are reliant on spatial modeling of critical data including in-situ density. To model coal in-situ density, numerous methods are available including deterministic, geostatistical, and machine learning-based methods. In most studies the focus is on comparing differing spatial model method accuracy, however, in this study, the focus is on determining if different in-situ density spatial model methods materially impact resource tonnage estimates. This study used data from an active open-cut coal mine located in the Bowen Basin, Queensland Australia. The coal mine has extensive areas that are impacted by lamprophyre and dolerite intrusion. This study compared tonnage estimates derived from in-situ density modeling from four spatial model methods including inverse distance weighting, ordinary kriging, geographic quantile regression forest, and random forest regression kriging. This study found that, at local scale and in areas highly impacted by intrusion, variation in tonnage estimates between in-situ density model methods was up to 4.40%. This variation was considered material as it was higher than the error of each spatial model. It is, therefore recommended that, especially in coal deposits which are impacted by intrusion, spatial model methods are carefully selected and evaluated. [ABSTRACT FROM AUTHOR]