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کتاب Applied Geostatistics With SGeMs
نویسنده:
Nicolas Remy ، Alexandre Boucher ، Jianbing Wu
ناشر:
Cambridge University
تعداد صفحه:
۲۸۵
زبان نوشتار:
انگلیسی
سال انتشار:
۲۰۰۹
معرفی کتاب Applied Geostatistics With SGeMs
Geostatistics is a science. It is also an art.
Geostatistics is not simply the application of statistical methods to geologydriven spatial distributions, it also provides a conceptual framework for making inferences from Earth sciences data – data which are, more often than not, incomplete.
Some may say, as I would, that most of the problems addressed in geostatistics are inverse problems, in the sense that data are used to infer parameters of the prior model. However, a gap exists between inverse problems and geostatistical problems: in inverse problems modeling the observations can be computer intensive, but the a priori model is typically simple (or simplistic); in a geostatistical problem the data are directly related to the model parameters, this allows one to consider prior models that encapsulate properties of the real Earth, sometimes with breathtaking realism. The gap is narrowing and will disappear in the near future. For the time being, we should try to do the best we can in each of the two fields.
Most geostatistical solutions involve random functions, and a long way has been trod since the simple Gaussian models used in the beginning. The Stanford team has been at the origin of many non-Gaussian developments that have now become standard. They are at it again with the multiple-point geostatistical concept and related algorithms, which allow defining truly complex random functions.
If a painter has no brushes, or no paints, she/he can not produce art. The same happens here: in order to deal with difficult problems calling for non-simplistic priors and working out practical solutions (the art), one needs computer software. The equations underwriting geostatistics can be laid on paper, but even the simplest application requires dedicated computer software. This is where the Stanford Geostatistical Modeling Software (SGeMS) shines. With this book the reader will learn how to use the software towards solving non-trivial problems.
Nothing can replace my repeated stays at Stanford and interaction with the people there at the Center for Reservoir Forecasting – with, among others, the authors of this book and my dear friend Andr´e Journel. Having this book with me is the closest thing to being at Stanford.
Geostatistics is not simply the application of statistical methods to geologydriven spatial distributions, it also provides a conceptual framework for making inferences from Earth sciences data – data which are, more often than not, incomplete.
Some may say, as I would, that most of the problems addressed in geostatistics are inverse problems, in the sense that data are used to infer parameters of the prior model. However, a gap exists between inverse problems and geostatistical problems: in inverse problems modeling the observations can be computer intensive, but the a priori model is typically simple (or simplistic); in a geostatistical problem the data are directly related to the model parameters, this allows one to consider prior models that encapsulate properties of the real Earth, sometimes with breathtaking realism. The gap is narrowing and will disappear in the near future. For the time being, we should try to do the best we can in each of the two fields.
Most geostatistical solutions involve random functions, and a long way has been trod since the simple Gaussian models used in the beginning. The Stanford team has been at the origin of many non-Gaussian developments that have now become standard. They are at it again with the multiple-point geostatistical concept and related algorithms, which allow defining truly complex random functions.
If a painter has no brushes, or no paints, she/he can not produce art. The same happens here: in order to deal with difficult problems calling for non-simplistic priors and working out practical solutions (the art), one needs computer software. The equations underwriting geostatistics can be laid on paper, but even the simplest application requires dedicated computer software. This is where the Stanford Geostatistical Modeling Software (SGeMS) shines. With this book the reader will learn how to use the software towards solving non-trivial problems.
Nothing can replace my repeated stays at Stanford and interaction with the people there at the Center for Reservoir Forecasting – with, among others, the authors of this book and my dear friend Andr´e Journel. Having this book with me is the closest thing to being at Stanford.
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