Geological observation in southeastern Zanskar and adjacent Lahul area (northern Himalaya).

The paper deals with the stratigraphic and structural setting of the sedimentary sequence cropping out in southeastern Zanskar and adjacent Lahul areas. The Tibetan Zone succession of southeastern Zanskar consists of about 6000 m of sediments, Late Precambrian~ ?Eocene in age, arranged in two superposed slabs (Pugh tal Unit, below, and Zangla Unit, above) tectonically resting upon the High Himalayan Crystalline. The Pughtal sequence, mostly terrigenous with carbonate units in the Cambrian, Silurian and Carboniferous, is about 2500 m thick. It was deposited from ?Late Precambrian to Carboniferous or ?Early Permian. The Permian Panjal Traps constitute the "sole" of the Zangla Unit, whose sedimentary sequence, about 3000 m thick, mainly carbonatic, spans from Late Permian (Kuling Formation) to Middle Jurassic (Kioto Limestone) in eastern Zanskar. In the Zangla area Late Jurassic/Cretaceous formations (Spiti Shales, Giumal Sandstone, Chikkim Limestone) are also present. Towards northwest, the sequence ranges up to Paleocene (Spanboth Formation) and ?Eocene (Chulung La Slates). Au nord de la Haute Chaine, dans la partie septentrionale de I'Himalaya, la marge continentale indienne a vu plus de 6000 m de sediments se deposer depuis I'Infracambrien jusqu'a I'Eocene. Lors de l'orogenese himalayenne, ces sediments ont ete decolles de leur substratum originel, dMormes et metamorphises de maniere differenciee suivant leur position. Ils reposent en contact tectonique sur la nappe cristalline du Haut-Himalaya. L'unite inferieure ou unite de Pughtal consiste, la ou elle est complete, en plus de 2500 m de sediments en partie detritiques terrigenes mais marque par l'edification de plates-formes carbonatees au Cambrien, Silurien et Carbonifere. Dans cette unite on releve deux grandes sequences sedimentaires separees par l'evenement epirogenique et magmatique tardi-Cambrien (500 rna), contrecoup de l'orogenese pan-africaine. Un niveau massif de vo1canites basaltiques permiennes ~ les Panjal Traps ~ forme la base ou sole de I'unite superieure (nappe de Zangla). Cette unite, plissee de maniere disharmonique, recouvre progressivement vers l'ouest des niveaux de plus en plus anciens de l'unite inferieure, niveaux eux-memes replisses en grands plis couches kilometriques a vergence nord. Dans la partie occidentale (Ringdom) l'unite superieure repose directement sur la nappe cristalline. Cette unite montre une serie sedimentaire avec des carbonates de plate-forme bien developpes au Trias superieur et au Lias puis des sediments surtout pelagiques et en partie detritiques terrigenes au Jurassique superieur et au Cretace. Des la fin du Cretace et jusqu'au Paleocene superieur s'edifie a nouveau une plate-forme peu profonde. La serie se termine par des couches continentales attribuees a l'Eocene. L'evolution geodynamique durant Ie Paleozoique et Ie Mesozoique est analysee. II en ressort que la sedimentation, a partir de I'Ordovicien, est regJee plus par des grands cycles eustatiques que par des mouvements tectoniques ou epirogeniques regionaux (les orogeneses caledoniennes, hercyniennes et cretacees des auteurs).

Quelques abus de la statistique dans la recherche médicale [Various abuses of statistics in medical research].

Statistics has become an indispensable tool in biomedical research. Thanks, in particular, to computer science, the researcher has easy access to elementary "classical" procedures. These are often of a "confirmatory" nature: their aim is to test hypotheses (for example the efficacy of a treatment) prior to experimentation. However, doctors often use them in situations more complex than foreseen, to discover interesting data structures and formulate hypotheses. This inverse process may lead to misuse which increases the number of "statistically proven" results in medical publications. The help of a professional statistician thus becomes necessary. Moreover, good, simple "exploratory" techniques are now available. In addition, medical data contain quite a high percentage of outliers (data that deviate from the majority). With classical methods it is often very difficult (even for a statistician!) to detect them and the reliability of results becomes questionable. New, reliable ("robust") procedures have been the subject of research for the past two decades. Their practical introduction is one of the activities of the Statistics and Data Processing Department of the University of Social and Preventive Medicine, Lausanne.

Detection of clusters using space-time scan statistics

This paper aims at detecting spatio-temporal clustering in fire sequences using space?time scan statistics, a powerful statistical framework for the analysis of point processes. The methodology is applied to active fire detection in the state of Florida (US) identified by MODIS (Moderate Resolution Imaging Spectroradiometer) during the period 2003?06. Results of the present study show that statistically significant clusters can be detected and localized in specific areas and periods of the year. Three out of the five most likely clusters detected for the entire frame period are localized in the north of the state, and they cover forest areas; the other two clusters cover a large zone in the south, corresponding to agricultural land and the prairies in the Everglades. In order to analyze if the wildfires recur each year during the same period, the analyses have been performed separately for the 4 years: it emerges that clusters of forest fires are more frequent in hot seasons (spring and summer), while in the southern areas, they are widely present during the whole year. The recognition of overdensities of events and the ability to locate them in space and in time can help in supporting fire management and focussing on prevention measures.

Populating ACT-R's Declarative Memory with Internet Statistics

Decision situations are often characterized by uncertainty: we do not know the values of the different options on all attributes and have to rely on information stored in our memory to decide. Several strategies have been proposed to describe how people make inferences based on knowledge used as cues. The present research shows how declarative memory of ACT-R models could be populated based on internet statistics. This will allow to simulate the performance of decision strategies operating on declarative knowledge based on occurrences and co-occurrences of objects and cues in the environment.

3-D density structure and geological evolution of Stromboli volcano (Aeolian Islands, Italy) inferred from land-based and sea-surface gravity data

We present the first density model of Stromboli volcano (Aeolian Islands, Italy) obtained by simultaneously inverting land-based (543) and sea-surface (327) relative gravity data. Modern positioning technology, a 1 x 1 m digital elevation model, and a 15 x 15 m bathymetric model made it possible to obtain a detailed 3-D density model through an iteratively reweighted smoothness-constrained least-squares inversion that explained the land-based gravity data to 0.09 mGal and the sea-surface data to 5 mGal. Our inverse formulation avoids introducing any assumptions about density magnitudes. At 125 m depth from the land surface, the inferred mean density of the island is 2380 kg m(-3), with corresponding 2.5 and 97.5 percentiles of 2200 and 2530 kg m-3. This density range covers the rock densities of new and previously published samples of Paleostromboli I, Vancori, Neostromboli and San Bartolo lava flows. High-density anomalies in the central and southern part of the island can be related to two main degassing faults crossing the island (N41 and NM) that are interpreted as preferential regions of dyke intrusions. In addition, two low-density anomalies are found in the northeastern part and in the summit area of the island. These anomalies seem to be geographically related with past paroxysmal explosive phreato-magmatic events that have played important roles in the evolution of Stromboli Island by forming the Scari caldera and the Neostromboli crater, respectively. (C) 2014 Elsevier B.V. All rights reserved.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.