Книга посвящена методике объемного геологического картирования рудных районов с вольфрам-молибденовым, молибденовым, олово-вольфрамовым и оловянным магматогенным оруденением. Рассматриваются основные задачи объемного картирования и дается геолого-геофизическая характеристика модели ред-кометалльного рудного района. Анализируются геолого-петрографические, петрофизические, гравитационные, магнитные и сейсмические методы изучения формы и внутреннего строения гранитных массивов, описываются основные этапы работ, комплексирование различных методов, оцениваются достоверность и точность исследований.

Настоящий отчет является окончательным по теме "Составление геологической карты и карты прогнозов полезных ископаемых масштаба 1:1500000 Центральной и Западной частей Сибирской платформы", проводимой по заданию Редсовета ВСЕГЕИ и утвержденной МГ СССР. <...>

Учебно-методическое пособие составлено по курсу лекций «Структурная геология и геологическое картирование», который с 2007 года читается автором на геологическом факультете Московского государственного университета имени М.В. Ломоносова для студентов кафедры региональной геологии и истории Земли и кафедры динамической геологии.

A geological map is a medium of communication that uses graphic symbols to represent spatial relationships among geographical and geological feautures. Lines, points, symbols, and texture markings are used to separate various classes of information. But the interpretation of geological maps goes beyond identifying the individual items displayed on the map. A connection must be made between what is perceived on the map and other knowledge. The interpretation of geological maps is basically an attempt to visualize and understand the complex shapes of rock units in the subsurface. A thorough understanding of the variety of geological structures is particularly important, because it helps to determine the nature of subsurface structures from geological maps. Structural geology, therefore, is a major cornerstone of the art of geological map interpretation. <...>

After decades of using only one map projection, the Polyconic, for its mapping program, the U.S. Geological Survey (USGS) now uses several of the more common projections for its published maps. For larger scale maps, including topographic quadrangles and the State Base Map Series, conformal projections such as the Transverse Mercator and the Lambert Conformal Conic are used. Equal-area and equidistant projections appear in the National Atlas.

The map area, covering 2,250 km", lies in the central part of Spitsbergen, south of Isfjorden. Geological investigations were started by Swedish expeditions in the last half of the 19th century, and the work was continued in the present century mainly by Norwegian expeditions. The region is mountainous, moderately glaciated, but shows a juvenile glacial morphology. The climate is polar. The rock sequence exposed in the area is c. 3,400 m thick and consists of two Permian, six Mesozoic, and six Tertiary formations. The Permian deposits are mainly eherts , cherty limestones, limestones, and sandstones deposited under marine conditions. The Mesozoic-Tertiary sequence is composed generally of shales, siltstones, and sandstones deposited mainly in marine environments, and to a lesser extent under terrestrial conditions. Surficial deposits of Quaternary age are largely of glacial, colluvial, alluvial, and marine origin <...>

In various geosciences branches, including mineral exploration, geometallurgical characterization on established mining operations, and remote sensing, the regionalized input variables are spatially well-sampled across the domain of interest, limiting the scope of spatial uncertainty quantification procedures. In turn, response outcomes such as the mineral potential in a given region, mining throughput, metallurgical recovery, or in-situ estimations from remote satellite imagery, are usually modeled from a much-restricted subset of testing samples, collected at certain locations due to accessibility restrictions and the high acquisition costs.

Many Third World and developing countries rely mainly on the utilisation of their natural resources to sustain their economic development. In the Philippines in particular, the exploitation of mineral resources has traditionally been a significant component of the Philippine economy (Domingo, 1993).

Modelling and prediction of spatially distributed data such as the secondary cassiterite mineral distributions are often affected by spatial autocorrelation (SAC); a phenomenon that violates attributes data independence in space, which leads to type1 errors in classical statistics and overfitting or underfitting in machine learning (ML) classification respectively. The concept of overfitting and underfitting of spatially distributed datasets in an ML classification has not been properly addressed by the traditional random holdout technique of model validation, and this is a challenge to the assessment of predictive spatial model performance in spatially distributed datasets.