Seismic Hazard Estimate at the Iberian Peninsula

—?Seismic hazard at the Iberian Peninsula has been evaluated by using a methodology which combines both zonified and non-zonified probabilistic methods. Seismic sources are used when considering zones where certain calculation parameters may be considered homogeneous, as in zonified methods, while, on the other hand, earthquakes are considered wherever it has taken place, as in non-zonified methods. The methodology which is applied in this paper has been originally used to calculate the seismic hazard maps in the United States. In our case, it has been necessary to adapt the method to the specific features of the seismicity in the Iberian Peninsula and its geographical surroundings, not only with respect to its distribution and characteristics, but also with respect to the properties of the seismic catalog used.¶Geographically, the main feature of the result is the fact that it reflects both historical seismicity and current seismic clusters of the region. Despite the smoothing, maps show marked differences between several seismic zones; these differences becoming more noticeable as exposure time increases. Maximum seismic hazard is found to be in the southwestern region of the Peninsula, especially in the area of the Cape St. Vicent, and around Lisbon. The uncertainty of the results, without considering that due to the attenuation laws, as deduced from the other evaluation parameters, is quite stable, being more sensitive to the parameters b and m _max of the Gutenberg-Richter relation.     

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.     

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.     

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.