1. 1 Solution of geological problems-are mathematical methods necessary? A question which is often asked is whether it is necessary for geologists to know and to use mathematics in the practise of their science. There is no simple answer to this question, and it is true that many geologists have had successful careers without ever needing to get involved in anything other than simple mathematics, and all the indications are that this is likely to continue into the future. However, in many branches of the subject the trend has been towards using a numerical approach for the solution of suitable problems. The extent to which this occurs depends on the nature of the area being studied; thus, in structural geology, which is con cerned in its simplest aspects with the geometrical relationships between various features, there are many problems which are easily solved. More recently the use of analytical methods has allowed the solution of more-difficult problems. In another area, geochemistry, two things have happened. On the theoretical side there has been a greater integration with physical chemistry, which itself is a highly mathematical subject; and on the practical side there is the need to analyse and interpret the vast quantities of data which modem instrumentation produces. Within geology the application of numerical methods has been given various names, so we have numerical geology, geo mathematics, geostatistics and geosimulation.

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.

Uses geological examples to illustrate mathematical ideas. Contains a large number of worked examples, and problems for students to attempt themselves. Answers to all the questions are given at the end of the book.

The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.

This undergraduate textbook presents a unique comprehensive overview on Mathematics in Earth Sciences and Geography. It deals with fundamental theoretical and applied mathematics, needed by bachelor students in a wide range of subjects. The book is illustrated with many examples and over a hundred practical exercises, with solutions included in the book. In addition, this textbook highlights numerical resources by using two free software packages (R and Xcas) and introducing their use.

This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The book provides an overview of this new field in three parts. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used. Part II covers in detail various algorithms and methodologies starting from basic building blocks in statistical science and computer science. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated. Part III covers three example application areas, namely, reservoir modelling, mineral resources modelling and climate model downscaling. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed.

First Published in 1994. Routledge is an imprint of Taylor & Francis, an informa company.