Els mapes del medi natural terrestre com a coneixements de base per als estudis sobre la prevenció de la contaminació

Sobre els mapes del medi natural terrestre que aglutinaran els trets del rocam, la disposició dels materials i les seves formes i que reuniran la informació suficient per poder ser utilitzats per als estudis sobre la prevenció de la contaminació

Lectura ambiental de la cartografía geológica

Geological and geomorphological maps are, apart from a source of scientific information, a necessary tool in order to take proper decisions to solve the geo-environmental problems that arise when dealing with territorial planning. In this paper, the social and economical utility of such maps is described, and some exercises meant for Science of Earth and Environmental Sciences students are proposed

Prevenção e controle da erosão urbana: bacia do Córrego do Limoeiro e bacia do Córrego do Cedro, municípios de Presidente Prudente e Álvares Machado, SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

Mapeamento geomorfológico da Ilha do Maranhão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Zoneamento geoarqueológico aplicado à gestão de recursos culturais

Pós-graduação em Geociências e Meio Ambiente - IGCE

Mt. Tremblant -- Catsk

Map drawn with oil based pastels on brown craft paper.

Memoirs, maps, &c., issued by the Geological Survey of the United Kingdom and the Museum of Practical Geology.

At head of title: List F.

Geology alive. GeoBizirik.org

Geology is the science that studies the Earth, its composition, structure and origin in addition to past and present phenomena that leave their mark on rocks. So why does society need geologists? Some of the main reasons are listed below: - Geologists compile and interpret information about the earth’s surface and subsoil, which allows us to establish the planet’s past history, any foreseeable changes and its relationship with the rest of the solar system. - Society needs natural resources (metals, non-metals, water and fossil fuels) to survive. The work of geologists is therefore a key part of finding new deposits and establishing a guide for exploring and managing resources in an environmentally-friendly way. - The creation of geological maps allows us to identify potential risk areas and survey different land uses; in other words, they make an essential contribution to land planning and proposing sustainable development strategies in a region. - Learning about Geology and the proper use of geological information contributes to saving lives and reducing financial loss caused by natural catastrophes such as earthquakes, tsunamis, volcanic eruptions, flooding and landslides, while also helping to develop construction projects, public works, etc. Through the proposed activities we aim to explain some of the basic elements of the different specialities within the field of Geological Sciences. In order to do this, four sessions have been organised that will allow for a quick insight into the fields of Palaeontology, Mineralogy, Petrology and Tectonics.

Geology alive. GeoBizirik.org

Geology is the science that studies the Earth, its composition, structure and origin in addition to past and present phenomena that leave their mark on rocks. So why does society need geologists? Some of the main reasons are listed below: - Geologists compile and interpret information about the earth’s surface and subsoil, which allows us to establish the planet’s past history, any foreseeable changes and its relationship with the rest of the solar system. - Society needs natural resources (metals, non-metals, water and fossil fuels) to survive. The work of geologists is therefore a key part of finding new deposits and establishing a guide for exploring and managing resources in an environmentally-friendly way. - The creation of geological maps allows us to identify potential risk areas and survey different land uses; in other words, they make an essential contribution to land planning and proposing sustainable development strategies in a region. - Learning about Geology and the proper use of geological information contributes to saving lives and reducing financial loss caused by natural catastrophes such as earthquakes, tsunamis, volcanic eruptions, flooding and landslides, while also helping to develop construction projects, public works, etc. Through the proposed activities we aim to explain some of the basic elements of the different specialities within the field of Geological Sciences. In order to do this, four sessions have been organised that will allow for a quick insight into the fields of Palaeontology, Mineralogy, Petrology and Tectonics.

Compositional data-driven alternatives to single geochemical component maps

Conventional practice in Regional Geochemistry includes as a final step of any geochemical campaign the generation of a series of maps, to show the spatial distribution of each of the components considered. Such maps, though necessary, do not comply with the compositional, relative nature of the data, which unfortunately make any conclusion based on them sensitive
to spurious correlation problems. This is one of the reasons why these maps are never interpreted isolated. This contribution aims at gathering a series of statistical methods to produce individual maps of multiplicative combinations of components (logcontrasts), much in the flavor of equilibrium constants, which are designed on purpose to capture certain aspects of the data.
We distinguish between supervised and unsupervised methods, where the first require an external, non-compositional variable (besides the compositional geochemical information) available in an analogous training set. This external variable can be a quantity (soil density, collocated magnetics, collocated ratio of Th/U spectral gamma counts, proportion of clay particle fraction, etc) or a category (rock type, land use type, etc). In the supervised methods, a regression-like model between the external variable and the geochemical composition is derived in the training set, and then this model is mapped on the whole region. This case is illustrated with the Tellus dataset, covering Northern Ireland at a density of 1 soil sample per 2 square km, where we map the presence of blanket peat and the underlying geology. The unsupervised methods considered include principal components and principal balances
(Pawlowsky-Glahn et al., CoDaWork2013), i.e. logcontrasts of the data that are devised to capture very large variability or else be quasi-constant. Using the Tellus dataset again, it is found that geological features are highlighted by the quasi-constant ratios Hf/Nb and their ratio against SiO2; Rb/K2O and Zr/Na2O and the balance between these two groups of two variables; the balance of Al2O3 and TiO2 vs. MgO; or the balance of Cr, Ni and Co vs. V and Fe2O3. The largest variability appears to be related to the presence/absence of peat.

Weighted Logratio Biplots, Correspondence Analysis and Spectral Maps

Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data