Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources

The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.     

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.     

GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping

Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments.     

Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat. The control factors were compiled as a spatial database from remotely sensed data combined with GIS analysis. The relative weight of each factor was integrated as a species potential index (SPI), which produced habitat potential maps. The maps were compared with the surveyed habitat locations, revealing a strong correlation between the potential maps and species locations. The combination of a GIS-based weights-of-evidence model and remote sensing techniques is an effective method in determining areas of macrobenthos habitat potential in a tidal flat setting.     

Prospectivity mapping for “Zhuxi-type” copper-tungsten polymetallic deposits in the Jingdezhen region of Jiangxi Province,South China

The Zhuxi deposit is the largest copper-tungsten polymetallic deposit in the world and is in Jiangxi Province in South China. The ore body is characterized by hydrothermal-vein deposits of copper, lead, and zinc minerals at shallow levels, skarn deposits of tungsten and copper minerals at middle levels, and altered-granite-hosted copper and tungsten minerals at depth. Such metallogenic systems are typically intrusion-related. The intrusive granites related to the Zhuxi polymetallic deposit have been dated at 152.9 Ma to 146.9 Ma. The intrusions provided the thermal energy and the source material for the ore mineralization. Skarns mineralization, the main type of ore mineralization, developed in the contact zone of Carboniferous-Permian formations with the granites. Nappe structures changed the dip of the ore bodies from steep in the top part to gentle in the bottom. NE-trending faults provided the fluid pathways and controlled the geological framework and distribution of ore deposits on a regional scale. In this study, recognition exploration criteria were analyzed based on a mineral deposit model and the geological setting. Extraction of favorable geological information and GIS-based data-integration methods were used for mineral-prospectivity mapping of Zhuxi-type polymetallic deposits. Buffering analysis was employed to extract structural information (e.g. faults) and lithologic or stratigraphic information (e.g. granites or geologic units). The singularity method and spatially weighted principal component analysis were used to enhance and delineate geochemical anomalies. The derivative norm was utilized to extract magnetic-gradient anomalies associated with intrusive granites. Student t-test of weights-of-evidence (WofE) proved to be an effective way to optimize threshold values for binarization of variables as evidence layers by evaluating the spatial correlation between known deposits and geological variables. The posterior probabilities of WofE gave a relative estimation of mineralization potential. Areas delineated by high posterior probability had much higher potentiality for the discovery of new deposits where had none had been found yet.     

Modeling the spatial patterns of human wildfire ignition in Yunnan province,China

Despite wildfire being an important regulator of dryland ecosystems, uncontrolled wildfire can be harmful to both forest ecosystems and human society, and wildfire prevention and control continue to raise worldwide concern. Wildfire management depends on knowledge of wildfire ignitions, both for cause and location. The regimes and factors influencing wildfire ignition have been studied at length. Humans have a profound effect on fire regimes and human activity is responsible for igniting the largest number of fires in our study area. Understanding the spatial patterns of ignitions is foremost to achieving efficiency in wildfire prevention. Previous studies mainly concentrate on overall wildfire risk integrating numerous factors simultaneously, yet the importance of human factors on ignition has not received much attention. In this study, we mapped human accessibility to explore the influence of human activity on wildfire ignition in a simple and straightforward way. A Bayesian weights-of-evidence (WofE) method was developed based on fire hotspots in China's Yunnan province extracted from satellite images and verified as known wildfires for the period 2007–2013. We considered a set of factors that impact fire ignition as associated with human accessibility: the locations of settlements, roads, water and farmland susceptible to human wildfire ignition. Known points of likely wildfire ignition were selected as training samples and all suspected thematic maps of the factors were taken as explanatory layers. Next, the weights of each layer in terms of its explanatory power were computed and used to generate evidence based on a threshold to pass a statistical test. The conditional independence (CI) of each layer was checked with the Agterberg-Cheng test. Finally, the posterior probability was calculated and its precision validated using samples of both presence and absence by withheld validation data. A comparison of WofE models was made to test the predictability. Results show proximity to villages, roads and farmland are strongly associated with human wildfire ignition and that wildfire more often occurs at an intermediate distance from high-density human activity. The WofE method proved more powerful than logistic regression, improving predictive accuracy by 10% and was more straightforward in presenting the association of dependence and independence. In addition, WofE with 1000 m buffer bands is more robust in predicting human wildfire ignition risk than binary or 100 m buffers for the ecoregion studied. Our results are significant for advising practical wildfire management and resource allocation, evaluation of human ignition control and also provides a foundation for future efforts toward integrated wildfire prediction.     

成矿区三维可视化与立体定量预测——以钦-杭成矿带庞西垌地区下园垌铅锌矿区为例

矿产资源是人类生存与社会进步的根本物质保障。近年来,随着地表矿、浅部矿产资源的日益枯竭,采用新技术、新方法的深部矿产资源预测成为地质勘查的主要研究方向。基于数字化、三维可视化及矿产定量预测为主的三维地质建模技术,为当前矿产资源远景预测与找矿工作提供了有力的工具。本文在现代成矿预测理论研究基础上,运用三维地质建模技术建立了钦杭成矿带下园垌矿区地质、地球物理、地球化学、钻孔等三维模型,揭示了区内构造地质特征、地球化学异常表征及地层岩体要素,据此探讨了矿床的成因及矿体分布特征。并在此基础上,采用证据权方法对研究区地质、地球物理、地球化学等多源信息进行融合,运用断裂缓冲区、地球化学异常、东岗岭组沉积岩地层等为证据因子来计算单位体积成矿后验概率,进行立体成矿预测,并圈定出铁锰矿、方铅闪锌矿、铅锌银综合矿等3处找矿有利靶区及估算出预测区内矿产资源储量总量为88710吨。研究结果表明:综合分析地质、地球物理、地球化学及钻孔数据进行矿区的三维地质空间定位、定量预测研究,可以有效的识别矿致异常信息,圈定找矿远景区,为成矿预测研究领域提供了新方向,可以将此方法应用至其他矿山。     

Local to regional scale structural controls on mineralisation and the importance of a major lineament in the eastern Mount Isa Inlier, Australia: Review and analysis with autocorrelation and weights of evidence

Although major crustal lineaments may play an important role in mineralisation, the relationship between lineaments and mineral deposits can be quite cryptic, and structural controls may vary as a function of scale along lineaments. Major lineaments alone may be of limited use for detailed target generation. The Cloncurry Lineament in the Eastern part of the Mount Isa Inlier is a crustal scale structure defined by potential field-derived ‘worms’. Weights-of-evidence quantifies the association between mineral occurrences and this lineament. Autocorrelation is used to recognise structural controls on mineralisation at different scales, by progressively limiting the lengths of the vectors between mineral occurrence points in the autocorrelation plot. The weights-of-evidence analysis shows that Au, Au–Cu, Cu–Au and Cu deposits have a positive spatial correlation to the Cloncurry Lineament, which suggests it that acted as a primary crustal scale control on the localisation of Cu and Au through focussing mineralisation systems on a broad scale. However, autocorrelation defines a variety of local structural controls, which can be interpreted as shear zones, variably oriented fault sets, en echelon fault arrays, and potentially the orientation of bedding and/or iron formations which localise fluid flow and mineral deposition at finer scales. The results suggest that major lineaments defined by geophysical contrasts can be used in conjunction with techniques of spatial analysis for targeting structurally controlled mineralisation in areas under thin cover adjacent to mineralised terrains such as the Mt Isa Inlier.     

Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence

Landslide hazard mapping is a fundamental tool for disaster management activities in mountainous terrains. The main purpose of this study is to evaluate the predictive power of weights-of-evidence modelling in landslide hazard assessment in the Lesser Himalaya of Nepal. The modelling was performed within a geographical information system (GIS), to derive a landslide hazard map of the south-western marginal hills of the Kathmandu Valley. Thematic maps representing various factors (e.g., slope, aspect, relief, flow accumulation, distance to drainage, soil depth, engineering soil type, landuse, geology, distance to road and extreme one-day rainfall) that are related to landslide activity were generated, using field data and GIS techniques, at a scale of 1:10,000. Landslide events of the 1970s, 1980s, and 1990s were used to assess the Bayesian probability of landslides in each cell unit with respect to the causative factors. To assess the accuracy of the resulting landslide hazard map, it was correlated with a map of landslides triggered by the 2002 extreme rainfall events. The accuracy of the map was evaluated by various techniques, including the area under the curve, success rate and prediction rate. The resulting landslide hazard value calculated from the old landslide data showed a prediction accuracy of > 80%. The analysis suggests that geomorphological and human-related factors play significant roles in determining the probability value, while geological factors play only minor roles. Finally, after the rectification of the landslide hazard values of the new landslides using those of the old landslides, a landslide hazard map with > 88% prediction accuracy was prepared. The methodology appears to have extensive applicability to the Lesser Himalaya of Nepal, with the limitation that the model's performance is contingent on the availability of data from past landslides.