Upper Oligocene-Lower Miocene shallowing-upwardlacustrine sequences controlled by periodic and non-periodic processes(Ebro Basin, northeastern Spain)

The lacustrine facies from two sections (Candasnos and Fraga) ofthe Oligocene-Miocene Torrente de Cinca lithostratigraphic Unit in thecentral part of the Ebro Basin (Spain) have been analysed to determine theinfluence of orbital parameters in lacustrine sedimentation. The unit ispredominantly composed of limestones and marls, and represents a shallowlacustrine freshwater system. The sedimentological features of the faciesstudied demonstrate that the lower part of the Candasnos section representsoffshore lacustrine subenvironments whereas the upper part, and the whole ofthe Fraga section, characterise marginal lacustrine areas. Series of stratalthickness variations of limestone, marl, and limestone/marl couplets fromboth sections have been analysed using spectral analysis. This shows thatinformation corresponding to periodic cycles only appears in the offshorefacies, that is to say, in the lower part of Candasnos section, and disappearsin the marginal facies where non-periodic cycles exist. Furthermore, thespectral analysis of the offshore facies highlights the existence of a peak inthe power spectrum with a period of around 7 (6.8 to 7.8) that can berecognised in the field as shallowing-upward lacustrine sequences.Magnetostratigraphic data from the Candasnos section allow us to establish atime span of 2,808 years for the limestone/marl couplet from the lower partof this section, and between 19,000 and 22,000 years for the periodic cycleidentified, thus representing the climatic precession cycle. Shallowingsequences from marginal areas do not correspond with any periodiccycle.     

Sea cliffs resulting from late Miocene extensional tectonics: the Serra Gelada case study (Betic Cordillera, Spain)

The Serra Gelada sea cliffs are carved in Mesozoic carbonate rocks belonging to the External Zones of the eastern Betic Cordillera (Alicante, SE Spain). Several normal faults with vertical slips of more than a hundred metres have played an important role in the origin of this coastline. Some previous studies propose that the present cliff morphology was mainly originated by Quaternary fault activity. However, the integration of geomorphological features, stratigraphical and sedimentological data, together with the results of the tectonic analysis of fractures occurring in Serra Gelada, and a detailed study of seismic reflection profiles carried out in the adjacent continental shelf, indicate that these normal faults were active mainly during the late Miocene. Therefore, the Serra Gelada sea cliffs represent a tectonically controlled long-term landscape. Thus, normal faults have not significantly modified the Serra Gelada relief since then. Furthermore, the northern part of the Serra Gelada cliff may be considered as an inherited pre-Quaternary relict palaeocliff since it has only undergone very little erosive recession.     

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.     

Distribution and effectiveness of nivation in Mediterranean mountains: Peñalara (Spain)

Snow accumulation is responsible for geomorphic and biogeographic processes taking place in the southern sector of the Peñalara massif in central Spain (40°51′N, 3°57′W; max. altitude 2428 m at Pico de Peñalara). This work compares the intensity of nivation on the eastern slope, leeward of the prevailing westerly winds and heavily eroded by glacial activity during the Pleistocene, to that of the western slope on the windward side, unaffected by glacial erosion and completely covered by a thick weathering mantle. On the eastern slope, nivation is effective only where the weathering mantle is exposed or on morainic formations. It does not occur on the landforms derived from glacial erosion. In contrast, the western side shows almost no evidence of snow action except where catastrophic mass movements have altered the regularity of the slope. During the post-glacial epoch, nivation cirques formed in the scars left by mass wasting. In the last 30 years, spring temperatures have increased and this activity has diminished. The pattern of evolution observed at Peñalara can be extrapolated to other Mediterranean mountains with similar characteristics such as marginal glacial activity during the Pleistocene, unconsolidated formations on the summits caused by chemical weathering, and dry, hot summers that can increase the effectiveness of nivation.     

Optimal Model for Geoid Determination from Airborne Gravity

Two different approaches for transformation of airborne gravity disturbances, derived from gravity observations at low-elevation flying platforms, into geoidal undulations are formulated, tested and discussed in this contribution. Their mathematical models are based on Green's integral equations. They are in these two approaches defined at two different levels and also applied in a mutually reversed order. While one of these approaches corresponds to the classical method commonly applied in processing of ground gravity data, the other approach represents a new method for processing of gravity data in geoid determination that is unique to airborne gravimetry. Although theoretically equivalent in the continuous sense, both approaches are tested numerically for possible numerical advantages, especially due to the inverse of discretized Fredholm's integral equation of the first kind applied on different data. High-frequency synthetic gravity data burdened by the 2-mGal random noise, that are expected from current airborne gravity systems, are used for numerical testing. The results show that both approaches can deliver for the given data a comparable cm-level accuracy of the geoidal undulations. The new approach has, however, significantly higher computational efficiency. It would be thus recommended for real life geoid computations. Additional errors related to regularization of gravity data and the geoid, and to accuracy of the reference field, that would further deteriorate the quality of estimated geoidal undulations, are not considered in this study.     

Extracting flow structures from tracer data

A method of visualizing structures in closed chaotic flows out of homogenous particle distributions is presented in the example of models of a meandering jet. To this end, the system will be leaked or opened up by defining a region of the flow, so that a particle is considered to be escaped if it leaves this region. By applying this method to an ensemble of nonescaped tracers, we are able to characterize mixing processes by visualizing the converging and stretching filamentations (stable and unstable manifolds) in the flow without using additional mathematical tools. The possibility of applying the algorithm to analyze buoy data, and a comparison with the finite time manifolds are discussed.     

Dublin days: the 2003 NAM

Mark E Bailey and Tom Ray give an overview of the National Astronomy Meeting in Dublin, where record numbers of astronomers went to talk, listen and network.     

Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data

This paper presents the extension of the self-calibrating method to the coupled inverse modelling of groundwater flow and mass transport. The method generates equally likely solutions to the inverse problem that display the variability as observed in the field and are not affected by a linearisation of the state equations. Conditioning to the state variables is measured by an objective function including, among others, the mismatch between the simulated and measured concentrations. Conditioning is achieved by minimising the objective function by gradient-based methods. The gradient contains the partial derivatives of the objective function with respect to: log conductivities, log storativities, prescribed heads at boundaries, retardation coefficients and mass sources. The derivatives of the objective function with respect to log conductivity are the most cumbersome and need the most CPU-time to be evaluated. For this reason, to compute this derivative only advective transport is considered. The gradient is calculated by the adjoint-state method. The method is demonstrated in a controlled, synthetic study, in which the worth of concentration data is analysed. It is shown that concentration data are essential to improve transport predictions and also help to improve aquifer characterisation and flow predictions, especially in the upstream part of the aquifer, even in the case that a considerable amount of other experimental data like conductivities and heads are available. Besides, conditioning to concentration data reduces the ensemble variances of estimated transmissivity, hydraulic head and concentration.