Introduction to Python in Earth Science Data Analysis - Maurizio Petrelli

Introduction to Python in Earth Science Data Analysis

From Descriptive Statistics to Machine Learning
Buch | Softcover
XV, 229 Seiten
2022 | 1st ed. 2021
Springer International Publishing (Verlag)
978-3-030-78057-9 (ISBN)
64,19 inkl. MwSt
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.

Maurizio Petrelli works as a researcher in petrology and volcanology at the Department of Physics and Geology, University of Perugia. In 2001, he graduated in Geology and obtained his PhD in February 2006 at the University of Perugia.His current studies are focused on the petrological, volcanological and geochemical characterization of magmatic systems with particular emphasis on time-scales estimates of magmatic processes. He combines the use of numerical simulations, experimental petrology and the study of natural samples. Since 2016, he has developed a new line of research at the Department of Physics and Geology, University of Perugia focused on the application of Machine Learning techniques to petrological and volcanological studies.

Part I Python for Geologists, a kick-off.- Setting Up Your Python Environment, Easily.- Python Essentials for a Geologist.- Start Solving Geological Problems Using Python.- Part II Describing Geological Data.- Graphical Visualization of a Geological Dataset.- Descriptive Statistics.- Part III Integrals and Differential Equations in Geology.- Numerical Integration.- Ordinary Differential Equations (ODE).- Partial Differential Equations (PDE).- Part IV Probability Density Functions and Error Analysis.- Probability Density Functions and their Use in Geology.- Error Analysis.- Part V Robust Statistics and Machine Learning.- Introduction to Robust Statistics.- 12. Machine Learning.

Erscheinungsdatum
Reihe/Serie Springer Textbooks in Earth Sciences, Geography and Environment
Zusatzinfo XV, 229 p. 112 illus., 104 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 385 g
Themenwelt Mathematik / Informatik Informatik Theorie / Studium
Mathematik / Informatik Mathematik
Naturwissenschaften Geowissenschaften Geologie
Schlagworte Earth Science Python Programming • Machine Learning in Geology • Modelling Geological Data • Ordinary differential equations • Partial differential equations • Python Learning for Geologists
ISBN-10 3-030-78057-0 / 3030780570
ISBN-13 978-3-030-78057-9 / 9783030780579
Zustand Neuware
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