Reconstructing fluvial bar surfaces from compound cross‐strata and the interpretation of bar accretion direction in large river deposits

The interpretation of fluvial styles from the rock record is based for a significant part on the identification of different types of fluvial bars, characterized by the geometric relationship between structures indicative of palaeocurrent and surfaces interpreted as indicative of bar form and bar accretion direction. These surfaces of bar accretion are the boundaries of flood‐related bar increment elements, which are typically less abundant in outcrops than what would be desirable, particularly in large river deposits in which each flood mobilizes large volumes of sediment, causing flood‐increment boundary surfaces to be widely spaced. Cross‐strata set boundaries, on the other hand, are abundant and indirectly reflect the process of unit bar accretion, inclined due to the combined effect of the unit bar surface inclination and the individual bedform climbing angle, in turn controlled by changes in flow structure caused by local bar‐scale morphology. This work presents a new method to deduce the geometry of unit bar surfaces from measured pairs of cross‐strata and cross‐strata set boundaries. The method can be used in the absence of abundant flood‐increment bounding surfaces; the study of real cases shows that, for both downstream and laterally accreting bars, the reconstructed planes are very similar to measured bar increment surfaces.     

Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia

Bogotá is located in the central Andean region of Colombia, which is frequently affected by landslide processes. These processes are mostly triggered during the rainy season in the city. This fact remarks the importance of determining what rain-derived parameters (e.g. intensity, antecedent rain, daily rain) are better related with the occurrence of landslides. For this purpose, the linear discriminant analysis (LDA), a technique derived from multivariate statistics, was used. The application of this type of analysis led to obtain simple mathematical functions that represent the probability of occurrence of landslides in Bogotá. The functions also allow to identify the most relevant variables derived from records of rainfall linked to the generation of landslides. A proof of concept using the proposed methodology was done using historic rainfall data from a 9-km² area of homogenous climatology and geomorphology in the south part of Bogotá. Landslides needed to be grouped for the LDA. Each one of these grouping categories represents landslides that occurred in similar geomorphologic conditions. Another set of events with no landslides was generated synthetically. Results of the proof of concept show that rainfall parameters such as normalized rainfall intensity I _MAP, normalized daily rainfall R _MAP and rainy-days normal RDN have the best statistical correlation with the landslides observed in the zone of analysis.     

Using Two-Point Set Statistics to Estimate the Diameter Distribution in Boolean Models with Circular Grains

This paper deals with the counting and measuring of overlapping circular objects in binary images, a problem that arises in the mineral processing industry when estimating the distribution of bubble diameters in flotation cells in order to predict the geometallurgical performance of the flotation process. By viewing the images as realizations of a stationary planar Boolean model with circular grains and by using two-point set statistics, it is possible to jointly estimate the average number of objects per unit area and their diameter distribution. When applied to real and simulated images, this method is shown to give accurate estimates, to be robust to the presence of noise contaminating the images and of moderate drifts in the number of objects per unit area, and to speed up the processing time with respect to currently used methods. Combined, these properties serve to make the method suitable for an on-line monitoring of the flotation process.     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

Multiple Random Walk Simulation: A Fast Method to Map Grade Uncertainty with Large Datasets

Multiple Random Walk Simulation consists of a methodology adapted to run fast simulations if close-spaced data are abundant (e.g., short-term mining models). Combining kriging with the simulation of random walks attempts to approximate traditional simulation algorithm results but at a computationally faster way when there is a large amount of conditioning samples. This paper presents this new algorithm illustrating the situations where the method can be used properly. A synthetic study case is presented in order to illustrate the Multiple Random Walk Simulation and to analyze the speed and goodness of its results against the ones from using Turning Bands Simulation and Sequential Gaussian Simulation.     

A robust debris-flow and GLOF risk management strategy for a data-scarce catchment in Santa Teresa,Peru

The town of Santa Teresa (Cusco Region, Peru) has been affected by several large debris-flow events in the recent past, which destroyed parts of the town and resulted in a resettlement of the municipality. Here, we present a risk analysis and a risk management strategy for debris-flows and glacier lake outbursts in the Sacsara catchment. Data scarcity and limited understanding of both physical and social processes impede a full quantitative risk assessment. Therefore, a bottom-up approach is chosen in order to establish an integrated risk management strategy that is robust against uncertainties in the risk analysis. With the Rapid Mass Movement Simulation (RAMMS) model, a reconstruction of a major event from 1998 in the Sacsara catchment is calculated, including a sensitivity analysis for various model parameters. Based on the simulation results, potential future debris-flows scenarios of different magnitudes, including outbursts of two glacier lakes, are modeled for assessing the hazard. For the local communities in the catchment, the hazard assessment is complemented by the analysis of high-resolution satellite imagery and fieldwork. Physical, social, economic, and institutional vulnerability are considered for the vulnerability assessment, and risk is eventually evaluated by crossing the local hazard maps with the vulnerability. Based on this risk analysis, a risk management strategy is developed, consisting of three complementing elements: (i) standardized risk sheets for the communities; (ii) activities with the local population and authorities to increase social and institutional preparedness; and (iii) a simple Early Warning System. By combining scientific, technical, and social aspects, this work is an example of a framework for an integrated risk management strategy in a data scarce, remote mountain catchment in a developing country.     

Crossover from Nonlinearity Controlled to Heterogeneity Controlled Mixing in Two-Phase Porous Media Flows

We use high resolution Monte Carlo simulations to study the dispersive mixing in two-phase, immiscible, porous media flow that results from the interaction of the nonlinearities in the flow equations with geologic heterogeneity. Our numerical experiments show that distinct dispersive regimes occur depending on the relative strength of nonlinearity and heterogeneity. In particular, for a given degree of multiscale heterogeneity, controlled by the Hurst exponent which characterizes the underlying stochastic model for the heterogeneity, linear and nonlinear flows are essentially identical in their degree of dispersion, if the heterogeneity is strong enough. As the heterogeneity weakens, the dispersion rates cross over from those of linear heterogeneous flows to those typical of nonlinear homogeneous flows.     

Analysis of aquifer vulnerability and water quality using SINTACS and geographic weighted regression

Aquifer vulnerability and water quality were assessed in the Central Valleys of Oaxaca (Mexico) using the SINTACS method, based on a geographic information system. SINTACS layers were prepared using data such as climate (rainfall and temperature), water table, hydraulic conductivity, geology, soil type and topographic model. Maps for water quality index (WQI), contamination index and pollution sources index (PSI) were also obtained by this work. Groundwater quality in the Central Valleys may be affected by two factors, those with an anthropogenic origin and those with natural origin. High vulnerability values are located in the valleys of the basin, where granular sediments are exposed. Low vulnerability values are distributed in the basin??s ranges, where metamorphic rocks are found. Given that many of the zones with the highest groundwater vulnerability values correspond to zones with the greatest PSI values, there is great risk of groundwater contamination for the area of study because external (indicated by PSI) and internal (indicated by SINTACS) factors that cause pollution can be frequently observed in the same place. Geographic weighted regression (GWR) is used to test the dependency between WQI as dependent variable and SINTACS, PSI, Urban localities, Agriculture, Pastures and Rivers as predictors. The results indicate the non-stationary behavior of the dependent variable with respect to the predictors. While the obtained GWR models used to model WQI cannot be used in practical situations to predict the behavior of said variable, they can be used to estimate the degree to which the predictors influence the variable of interest.     

Sulphur partitioning in sediments and biodeposits below mussel rafts in the Ria de Arousa (Galicia, NW Spain)

Forty four core samples were analyzed to determine sediment particle size, total organic carbon (TOC), total inorganic carbon (TIC), total nitrogen and total sulphur. Sequential extraction of S was also carried out, differentiating AVS (acid volatile sulphide), elemental S, organic S and pyrite S. The results obtained show that the presence of mussel rafts causes intense changes in the physicochemical composition and properties of the sea floor in the Ria de Arousa. The percentage of silt and clay, TOC, TIC and total N were significantly higher in the biodeposit than in the sediment. In contrast, there were no differences between the biodeposit and the sediment in terms of pH (8.0-8.7) and redox potential. The sediment and biodeposit were always anoxic, with values of redox potentials below -100 mV. In accordance with these conditions, the dominant fraction of S was pyrite S (FeS(2)). The AVS fraction and elemental S were present at low concentrations, except in the uppermost part of each core. Pyrite was relatively stable when the biodeposit and sediment were maintained in suspension for 8 days in oxic sea water; unlike the AVS fraction, which disappeared within a few hours.