Python recipes for earth sciences

by Trauth, Martin H.

Type Book

Rate 0.0 /5

Details

Files

Reviews

Download

Title Statements

Full title :Python recipes for earth sciences / Martin H. Trauth.

Summary

Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains the example data as well as recipes that include all the Python commands featured in the book. The Author Martin H. Trauth studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree from the University of Kiel in 1995 and subsequently became a permanent member of the scientific staff at the University of Potsdam. Following his habilitation in 2003, he became a lecturer, and in 2011, he was granted a titular professorship at the University of Potsdam. Since 1990, Martin H. Trauth has worked on various aspects of past changes in the climates of eastern Africa and South America. His projects have aimed to provide a better understanding of (1) the role that the tropics play in terminating ice ages, (2) the relationship between climatic changes and human evolution, and (3) the influence that climate anomalies have had on mass movements in the central Andes. Each of these projects has involved numerical and statistical methods (e.g., time series analysis and signal processing) with paleoclimate time series, lake balance modeling, stochastic modeling of bioturbation, age-depth modeling of sedimentary sequences, or satellite- and microscopic image processing. Martin H. Trauth has taught a variety of courses on data analysis in the earth sciences for more than 25 years both at the University of Potsdam and at other universities around the world.

Edition Statements

Publication Date:2022.

Publication Statements

Publisher(s):Springer
Place of Publication:Cham, Switzerland
Language:eng
Publication Date:2022.

The Physical Object

Description:1 online resource

Edition Identifiers

ISBN:9783031077197, 3031077199
DOI:10.1007/978-3-031-07719-7
oclc:on1346532473
LC Classification:QE48.8
Dewey Decimal:550.285/5133

Intro -- Preface -- Contents -- 1 Data Analysis in the Earth Sciences -- 1.1 Introduction -- 1.2 Data Collection -- 1.3 Types of Data -- 1.4 Methods of Data Analysis -- Recommended Reading -- 2 Introduction to Python -- 2.1 Introduction -- 2.2 Getting Started -- 2.3 Python Syntax -- 2.4 Array Manipulation -- 2.5 Data Types in Python -- 2.6 Data Storage and Handling -- 2.7 Control Flow -- 2.8 Scripts and Functions -- 2.9 Basic Visualization Tools -- 2.10 Generating Code to Recreate Graphics -- 2.11 Publishing and Sharing MATLAB Code -- 2.12 Creating Graphical User Interfaces

4.4 Analyzing the Residuals -- 4.5 Bootstrap Estimates of the Regression Coefficients -- 4.6 Jackknife Estimates of the Regression Coefficients -- 4.7 Cross-Validation -- 4.8 Reduced Major Axis Regression -- 4.9 Curvilinear Regression -- 4.10 Nonlinear and Weighted Regression -- 4.11 Classical Linear Regression of Log-Transformed Data -- Recommended Reading -- 5 Time Series Analysis -- 5.1 Introduction -- 5.2 Generating Signals -- 5.3 Auto-Spectral and Cross-Spectral Analysis -- 5.4 Examples of Auto-Spectral and Cross-Spectral Analysis -- 5.5 Interpolating and Analyzing Unevenly Spaced Data

5.6 Evolutionary P…