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Data mining is a promising new tool in mineral exploration. Here, we combined data-mining procedures with spatial prediction modeling for gold exploration targeting in the Buhweju area in southwestern Uganda. It was employed in a data-rich context of unavoidably partly redundant and correlated information that offered challenges in extracting significant relationships. Our study utilized a database of co-registered digital maps related to gold mineralization. It comprised Landsat TM, Shuttle Radar Topographic Mission (SRTM), and geophysical (radiometric and magnetic) datasets for geological and structural mapping. The locations of 15 orogenic gold deposits and 87 gold occurrences were obtained from the Geological Survey of Uganda database. These were considered direct evidence of the presence of gold mineralization. The geological and geophysical settings at the gold deposit/occurrences locations were based on geological units as host rocks, contacts, and structural elements, together with continuous field values of geophysics, radiometry, and other remotely sensed imagery. A gold exploration targeting proposition (Tp) was defined as: "That a point p within the study area contains a gold deposit given the presence of spatial evidence." All outstanding combinations of spatial evidence were obtained using empirical likelihood ratios. With a data-mining strategy, the ratios were filtered and modeled to identify stronger spatial associations, to rank the study area according to the likelihood of future discoveries, to represent ranking quality, to estimate associated uncertainty, and to select prospective target areas. The empirical likelihood ratios facilitated a transparent strategy for generating prediction patterns and extracting small prospective target areas with higher likelihood of discovery and lower-ranking uncertainty. Conclusions are provided on the knowledge extraction for prospectivity with further data and the challenges of reducing the arbitrariness of decisional steps. [ABSTRACT FROM AUTHOR]

Centurion is a fast developing area facing severe karstic sinkhole development. The area is predominantly underlain by fractured dolomite formations that include Oaktree, Monte Christo, Lyttelton, and Eccles of the Malmani Dolomite Group (~ 2600 and ~ 2500 Ma). Fractures of the underlying dolomite formations were investigated as one of the most influencing factors in karstic sinkhole formation using fieldwork mapping, petrology, and remote-sensing techniques (automated lineament extraction methods). Fieldwork results indicate that the study area has two prevalent fracture orientations trending N–S and ENE–WSW with average orientations of 178°/86° E and 073°/67° N. The fieldwork mapping results coincide with those acquired using remote-sensing techniques by automated extraction of lineaments from colour-shaded image and from four Sobel directional filtered images. Remote-sensing techniques indicate that there are extra two prevalent lineament directions; NE–SW and E–W in the study area and majority of the sinkholes develop in areas with high lineament density. Eccles, Monte Christo, and Oaktree dolomite formations exhibit a unidirectional fracture system while Lyttelton dolomite formation exhibit bidirectional fracture system. It is, therefore, concluded that (1) geological lineaments extracted using remote sensing and fractures present in the underlying dolomite formations play a significant role in controlling fluid circulation and thus influencing karstic processes such as dissolution, and therefore, (2) sinkhole development is primarily structurally controlled; however, it is exacerbated by anthropogenic activities due to the influence of water ingress. [ABSTRACT FROM AUTHOR]

Many landslides were triggered by heavy rainfall from 29 June to 1 July 2017 in Ningxiang County, Hunan Province, China. A field investigation into one of the landslides, known as the Wangjiawan landslide, was undertaken on July 3, 2017, to understand the landslide mechanisms and the factors that triggered the event. The landslide is a translational and rotational slide that degraded downslope to an earth flow. It occurred on a steeply dipping mud-rich slate intercalation. Field investigation shows that the landslide had a movement rate of more than 25 m/s and had been triggered by up to 338 mm of continuous rainfall over 2 days. The landslide was fast-moving because it occurred on a steep slope and showed a rapid reduction in shear strength. The landslide resulted in nine fatalities and 19 injuries, most during initial rescue efforts. To mitigate a possible secondary landslide disaster, later emergency measures including evacuation of the survivors, setting up warning signs, and covering landslide cracks with plastic sheeting were adopted. [ABSTRACT FROM AUTHOR]

This paper presents the calibration and application of a Gestalt-based line segment method for automatic geological lineament detection from remote sensing images. This method involves estimation of the scale factor, the angle tolerance and a threshold on the false alarm rate. It identifies major lineaments as objects characterized by two edges on the image, which appear as transitions from dark to bright and vice versa. These objects were modelled as random sets with parameters drawn from their distributions. Following the geometry of detected segments, a novel validation method assesses the accuracy with respect to a linear vector reference. The methodology was applied to a study area in Kenya where lineaments are prominent in the landscape and are well identifiable from an ASTER image. Error rates were based on distance and local orientation, and the study showed that the existence and size of the objects were sensitive to parameter variation. False detection rate and missing detection rate were both equal to 0.50, which is better than values equal to 0.65 and 0.63, observed using the Canny edge detection. Modelling the uncertainty of geological lineaments with random sets further showed that no core set is formed, indicating that there is an inherent uncertainty in their existence and position, and that the variance is relatively high. Comparing the test area with four areas in the same region showed similar results. Despite some shortcomings in identifying full lineaments from partially observed lineaments, it is concluded that the procedure in this paper is well able to automatically extract lineaments from a remote sensing image and validate their existence. [ABSTRACT FROM AUTHOR]

Time series analysis is an important part of geodetic and geodynamic studies, especially when continuous GPS observations are used to explore areas with a low rate of deformation. In this domain, having precise and robust tools for processing and analyzing position time series is a prerequisite. To meet this requirement, a new software package called GPS Interactive Time Series Analysis was developed using the MATLAB language. Along with calculating basic statistics and quality parameters such as mean and variance, the software is capable of importing and visualizing different time series formats, determining and removing jumps and outliers, interpolating data, and producing numerical and publication quality graphical outputs. Furthermore, bivariate statistical analysis (such as correlation coefficients, curvilinear and nonlinear regression), residual analysis, and spectral analysis (such as auto-spectrum, Lomb-Scargle spectrum, evolutionary power spectrum, and wavelet power spectrum) form the main analysis features of the software. [ABSTRACT FROM AUTHOR]

Inadequate water resources management and a general decline in rainfall have aggravated water scarcity problems in the Upper Ewaso Ng'iro North Basin in Kenya. Furthermore, water use conflicts in the basin have escalated in recent decades due to increased competition for available water resources. Excessive abstraction of the declining river water mainly for irrigation in the Mount Kenya and Nyandarua foot zones often leads to reduced water flow during the dry seasons, greatly affecting downstream water users. Increased water use in the basin coupled with deterioration of the vegetative cover has resulted in reduced water flows in the Ewaso Ng'iro river and its major tributaries. In addition, lack of sufficient knowledge about available water resources and current lack of coordination in water resources management in the basin often result in water deficits which have hampered development in the downstream catchment. The goal of this study was to match the water requirements of various competing sectors in the basin with the available water resources in order to attain both economic and ecological sustainability. To achieve this, GIS techniques were used to quantify the spatial and temporal stream flow. The Water Evaluation and Planning (WEAP) model was applied to evaluate water resources development based on an equilibrium scenario of the current water demand. Water use was simulated for five different sectors (domestic, livestock, wildlife, irrigation and reserve). The analyses revealed that high water demand for irrigation was the main cause of excessive water abstraction particularly in the upstream catchments, giving rise to water shortages and consequently, water use conflicts downstream. The study, therefore, recommends that rainwater harvesting be promoted in the basin in order to improve water availability for productive use. [ABSTRACT FROM AUTHOR]