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Выпуск 23
Издание:Elsevier, 2004 г., 25 стр.
Язык(и)Английский
Sources of Phanerozoic granitoids in the transect Bayanhongor–Ulaan Baatar, Mongolia: geochemical and Nd isotopic evidence, and implications for Phanerozoic crustal growth

The Central Asian Orogenic Belt (CAOB) is renowned for massive generation of juvenile crust in the Phanerozoic. Mongolia is the heartland of the CAOB and it has been subject to numerous investigations, particularly in metallogenesis and tectonic evolution. We present new petrographic, geochemical and Sr–Nd isotopic analyses on Phanerozoic granitoids emplaced in west-central Mongolia. The data are used to delineate their source characteristics and to discuss implications for the Phanerozoic crustal growth in Central Asia. Our samples come from a transect from Bayanhongor to Ulaan Baatar, including three tectonic units: the Baydrag cratonic block (late Archean to middle Proterozoic), the Eo-Cambrian Bayanhongor ophiolite complex and the Hangay–Hentey Basin of controversial origin. The intrusive granitoids have ages ranging from ca. 540 to 120 Ma. The majority of the samples are slightly peraluminous and can be classified as granite (s.s.), including monzogranite, syenogranite and alkali feldspar granite. Most of the rocks have initial 87Sr/86Sr ratios between 0.705 and 0.707. Late Paleozoic to Mesozoic granitoids (#250 Ma) are characterized by near-zero Nd(T) values (0 to 22), whereas older granitoids show lower Nd(T) values (21.5 to 27). The data confirm the earlier observation of Kovalenko et al. [Geochemistry International 34 (1996) 628] who showed that granitoids emplaced outside of the Pre-Riphean basement rocks are characterized by juvenile positive Nd(T) values, whereas those within the Pre-Riphean domain and the Baydrag cratonic block, as for the present case, show a significant effect of ‘contamination’ by Precambrian basement rocks. Nevertheless, mass balance calculation suggests that the granitoids were derived from sources composed of at least 80% juvenile mantle-derived component. Despite our small set of new data, the present study reinforces the general scenario of massive juvenile crust production in the CAOB with limited influence of old microcontinents in the genesis of Phanerozoic granitoids.

Издание:Elsevier, 2003 г., 12 стр.
Язык(и)Английский
Sources of Svecofennian granitoids in the light of ion probe U–Pb measurements on their zircons

The presence of 1.91–1.93 Ga old granitoids at the Archean–Proterozoic boundary along the Raahe–Ladoga zone in Finland has been demonstrated on various occasions. These rocks have been considered to represent juvenile crustal material, as their εNd values are markedly positive. However, as Svecofennian metasediments contain detrital zircons derived from a ca. 2 Ga old source, the possibility has existed that the 1.92 Ga age may have been a mixture between 2 and 1.89 Ga old zircon populations, as such mixing would not markedly affect their neodymium isotopic properties. Also, some syntectonic 1.89 Ga old Svecofennian granitoids contain heterogeneous zircon populations, but it has been impossible to determine the age and origin of the older zircons by conventional methods.

NORDSIM ion probe results on three samples from the 1.92 Ga age group confirm the earlier conclusions. Especially important is that no zircons older than 1.95 Ga were detected in the 1.92 Ga group samples. Thus, the 1.92 Ga event was the beginning of the formation of new continental crust in the primitive Svecofennian island arc and these granitoids formed by partial melting of basaltic magmas derived from a depleted mantle source. One sample also contains a younger zircon population formed during the orogenic culmination at 1.89 Ga. In contrast, one grain from a sample representing the 1.89 Ga age group contains an Archean core, which is considered to represent sedimentary detritus assimilated during either magma formation or intrusion.

While the results prove the true igneous nature of the 1.92 Ga event, they also rule out these rocks as a possible provenance for the ca. 2 Ga old zircons encountered in the Svecofennian metaturbidites. Thus, there is still no direct evidence from granitoid rocks for an extensive Svecofennian protocrust, the existence of which has been postulated on the basis of geochemical and Sm–Nd isotopic data.

Автор(ы):Seeger M.C.
Издание:PGS Publishing, Linden Park, 2002 г., 12 стр.
Язык(и)Английский
Southeast Missouri iron metallogenic province: characteristics and general chemistry

The Southeast Missouri Iron Metallogenic Province is comprised of eight known major and numerous minor magnetite and hematite deposits. It is hosted by the Middle Proterozoic St. Francois granite-rhyolite terrane. Host rocks are rhyolites, trachytes, and andesites. Ore is associated with, although not necessarily hosted by, magnetite trachytes. Deposits are associated with caldera subsidence structures and, sometimes, trachyte ring intrusions. Deposits are within or near margins of these structures. Areal association of the deposits with a major Proterozoic tectonic zone, possibly a transform fault, suggests additional tectonic/ structural control on ore emplacement. Magnetite and hematite have been produced in the province; currently, only magnetite is produced. Potential exists for production of rare earth elements, copper, and gold.

A characteristic alteration suite is associated with the iron oxide mineralization. The suite includes silicification, potassium metasomatism, and alteration of host rock to actinolite, chlorite, garnet and epidote. While several alteration types are associated with each deposit, every type is not seen at each deposit.

Chemistry suggests that magnetite and hematite deposits in the province have a unique chemical signature when compared to magnetite not directly associated with the major deposits. In addition, hematite that is an oxidation product of magnetite has a different chemical signature than presumed primary hematite.

Автор(ы):Corbett G., Leach T.
Издание:1996 г., 215 стр.
Язык(и)Английский, Русский
Southwest pacific rim gold-copper system:Structure, Alteration, and Mineralization / Типы Au-Cu систем юго-западной части Тихоокеанского кольца

Эта публикация классифицирует и описывает типы Au-Cu систем ЮЗ части Тихоокеанского кольца (рис.S.1) и анализирует гидротермальные рудообразующие процессы. Исследование этих систем с точки зрения геологического строения, гидротермальных изменений и типов рудной минерализации дает информацию, которая может помочь в определении направления миграции потоков гидротерм в действующих гидротермальных системах.

Главные структуры локализуют магматические гидротермальные системы в условиях магматических дуг и создают предпосылки для рудообразования в условиях растяжения оперяющих структур (разломов). Различные типы конвергенсии влияют на тип главных структур и условия рудообразования в них. Брекчии встречаются в большинстве Au-Cu месторождений и могут рассматриваться в качестве ведущего признака в понимании условий  рудообразования, так как наблюдается тесная зависимость между образованием брекчий и типами рудной минерализации.

Предполагается, что температура и рН гидротерм являются наиболее важными из большинства факторов, которые контролируют типы гидротермальных изменений. Гидротермальные минералы классифицируются с позиций этих двух факторов, что придает осмысленность интерпретации данных о гидротермальных метаморфитах. Возможные механизмы переноса металлов и их отложения дают основу понимания распространения металлов в системах, связанных с интрузиями

Редактор(ы):Bilodeau M., Meyer F., Schmitt M.
Издание:Springer, 2005 г., 401 стр., ISBN: 978-0387-20331-7
Язык(и)Английский
Space, structure and randomness. Contributions in honor of Georges Matheron in the field of geostatistics, random sets and mathematical morphology / Пространство, структура и случайность. Вклад в честь Джорджа Матерона в области геостатистики

Пространство, структура и случайность. Вклад в честь Джорджа Матерона в области геостатистики, случайных множеств и математической морфологии

Personal Reminiscences of Georges Matheron Dietrich Stoyan
A few words about Georges Matheron (1930-2000) Jean Serra
Introduction From the editors
Part I Geostatistics

Автор(ы):Fischer M.M.
Издание:Springer, 2006 г., 332 стр., ISBN: 978-3-540-35729-2
Язык(и)Английский
Spatial analysis and GeoComputation / Пространственный анализ и геокомпьютеризация

The dissemination of digital spatial databases, coupled with the ever wider use of GISystems, is stimulating increasing interest in spatial analysis from outside the spatial sciences. The recognition of the spatial dimension in social science research sometimes yields different and more meaningful results than analysis which ignores it

Автор(ы):Nogueira P.M.
Издание:CRC Press, 2024 г., 451 стр., ISBN: 978-1-032-65032-6
Язык(и)Английский
Spatial analysis in geology using R / Пространственный анализ в геологии с использованием R

The integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems.

Автор(ы):Rigaux P., Scholl M., Voisard A.
Издание:Elsevier, 2002 г., 439 стр., ISBN: 1-55860-588-6
Язык(и)Английский
Spatial databases with application to GIS / Пространственные базы данных с применением в ГИС

If we move away from the traditional paper map and the explanation or journal that usually accompanies it, we have to consider a new type of digital information, characterized by its large volume (for instance, the amount of images recorded per day by a satellite is in the terabyte range) and its intrinsic complex structure. In addition, geographic information differs in nature according to the type of application and the way it was obtained. Basically, it may be derived <...>

Издание:2023 г., 33 стр.
Язык(и)Английский
Spatial interpolation using machine learning: from patterns and regularities to block models / Пространственная интерполяция с использованием машинного обучения: от паттернов и закономерностей к блочным моделям

In geospatial data interpolation, as in mapping, mineral resource estimation, modeling and numerical modeling in geosciences, kriging has been a central technique since the advent of geostatistics. Here, we introduce a new method for spatial interpolation in 2D and 3D using a block discretization technique (i.e., microblocking) using purely machine-learning algorithms and workflow design.

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