Fluvial sediment transport and deposition following the 1991 eruption of Mount Pinatubo

The 1991 eruption of Mount Pinatubo generated extreme sediment yields from watersheds heavily impacted by pyroclastic flows. Bedload sampling in the Pasig–Potrero River, one of the most heavily impacted rivers, revealed negligible critical shear stress and very high transport rates that reflected an essentially unlimited sediment supply and the enhanced mobility of particles moving over a smooth, fine-grained bed. Dimensionless bedload transport rates in the Pasig–Potrero River differed substantially from those previously reported for rivers in temperate regions for the same dimensionless shear stress, but were similar to rates identified in rivers on other volcanoes and ephemeral streams in arid environments. The similarity between volcanically disturbed and arid rivers appears to arise from the lack of an armored bed surface due to very high relative sediment supply; in arid rivers, this is attributed to a flashy hydrograph, whereas volcanically disturbed rivers lack armoring due to sustained high rates of sediment delivery. This work suggests that the increases in sediment supply accompanying massive disturbance induce morphologic and hydrologic changes that temporarily enhance transport efficiency until the watershed recovers and sediment supply is reduced.     

Water use, growth and yield of green gram under rainfed upland crop sequences in Gangetic Plains of Indian sub-tropics

A field experiment was conducted to study the effect of different crop sequences on water use, growth and yield of green gram (Vigna radiata (L) Wilezek) during 1992-93 and 1993-94 under rainfed upland condition in Indo-Gangetic plains of West Bengal. Green gram sown in the month of March as pre-rainy (summer) season crop, as and when the winter crops vacated the land, produced highest dry matter of 372 gm-2 which was significantly highest in black gram-yellow sarson sequence. The results of the field experimentation revealed that green gram gave highest grain yield to the extent of 10.80 q/ha when sown after black gram (rainy season) followed by yellow sarson (winter season) while the crop produced 10.63 q/ha under sesame-yellow sarson sequence. Highest water use of 267 mm was achieved in green gram under black gram-yellow sarson sequence and the crop gave water use efficiency of 4.07 kg ha-1mm-1 under black gram-yellow sarson sequences.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.     

Modelling detrital cooling-age populations: insights from two Himalayan catchments

The distribution of detrital mineral cooling ages in river sediment provides a proxy record for the erosional history of mountain ranges. We have developed a numerical model that predicts detrital mineral age distributions for individual catchments in which particle paths move vertically toward the surface. Despite a restrictive set of assumptions, the model permits theoretical exploration of the effects of thermal structure, erosion rate, and topography on cooling ages. Hypsometry of the source‐area catchment is shown to exert a fundamental control on the frequency distribution of bedrock and detrital ages. We illustrate this approach by generating synthetic ⁴⁰Ar/³⁹Ar muscovite age distributions for two catchments with contrasting erosion rates in central Nepal and then by comparing actual measured cooling‐age distributions with the synthetic ones. Monte Carlo sampling is used to assess the mismatch between observed and synthetic age distributions and to explore the dependence of that mismatch on the complexity of the synthetic age signal and on the number of grains analysed. Observed detrital cooling ages are well matched by predicted ages for a more slowly eroding Himalayan catchment. A poorer match for a rapidly eroding catchment may result from some combination of large analytical uncertainties in the detrital ages and inhomogeneous erosion rates within the basin. Such mismatches emphasize the need for more accurate thermal and kinematic models and for sampling strategies that are adapted to catchment‐specific geologic and geomorphic conditions.