Seismic Modelling and Pattern Recognition in Oil Exploration (Paperback, Softcover reprint of the original 1st ed. 1992)

The reasons for writing this book are very simple. We use and teach com puter aided techniques of mathematical simulation and of pattern recogni tion. Life would be much simpler if we had a suitable text book with methods and computer programmes which we could keep referring to. Therefore, we have presented here material that is essential for mathematical modelling of some complex geological situations, with which earth scientists are often confronted. The reader is introduced not only to the essentials of computer modelling, data analysis and pattern recognition, but is also made familiar with the basic understanding with which they can plunge into when solving related and more complex problems. This book first makes a case for seismic stratigraphy and then for pattern recognition. Chapter 1 provides an extensive review of applications of pattern recognition methods in oil exploration. Simulation procedures are presented with examples that are fairly simple to understand and easy to use on the computer. Several geological situations can be formulated and simulated using the Monte Carlo method. The binary lithologic sequences, discussed in Chapter 2, consist of alternating layers of any two of sand, shale and coal."

General

Imprint:	Springer
Country of origin:	Netherlands
Release date:	September 2012
First published:	1992
Authors:	A. Sinvhal
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	178
Edition:	Softcover reprint of the original 1st ed. 1992
ISBN-13:	978-9401051347
Categories:	Books > Professional & Technical > Other technologies > Mining technology & engineering Books > Science & Mathematics > Physics > Applied physics & special topics > Geophysics Books > Earth & environment > Earth sciences > Structure & properties of the Earth > Volcanology & seismology Books > Earth & environment > Earth sciences > Geology & the lithosphere > Economic geology > General Promotions
LSN:	9401051348
Barcode:	9789401051347

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