MATLAB® Recipes for Earth Sciences

Fourth Interactive eBook Edition

verfasst von: Martin H. Trauth

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data and image analysis. The revised and updated Fourth Edition includes sixteen new sections and most chapters have greatly been expanded so that they now include a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Array Manipulation; Control Flow; Creating Graphical User Interfaces; Hypothesis Testing; Kolmogorov-Smirnov Test; Mann-Whitney Test; Ansari-Bradley Test; Detecting Abrupt Transitions in Time Series; Exporting 3D Graphics to Create Interactive Documents; Importing, Processing and Exporting LANDSAT Images; Importing and Georeferencing TERRA ASTER Images; Processing and Exporting EO-1 Hyperion Images; Image Enhancement; Correction and Rectification; Shape-Based Object Detection in Images; Discriminant Analysis; and Multiple Linear Regression. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. The book’s supplementary electronic material (available online through Springer Link) includes recipes that include all the MATLAB commands featured in the book and the example data.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Data Analysis in Earth Sciences

Abstract

Earth scientists make observations and gather data about the natural processes that operate on planet Earth. They formulate and test hypotheses on the forces that have acted on a particular region to create its structure and also make predictions about future changes to the planet. All of these steps in exploring the Earth involve the acquisition and analysis of numerical data. An earth scientist therefore needs to have a firm understanding of statistical and numerical methods as well as the ability to utilize relevant computer software packages, in order to be able to analyze the acquired data.

Martin H. Trauth

Chapter 2. Introduction to MATLAB

Abstract

MATLAB® is a soft ware package developed by The MathWorks, Inc., founded by Cleve Moler, Jack Little and Steve Bangert in 1984, which has its headquarters in Natick, Massachusetts (http://www.mathworks.com). MATLAB was designed to perform mathematical calculations, to analyze and visualize data, and to facilitate the writing of new soft ware programs. The advantage of this soft ware is that it combines comprehensive math and graphics functions with a powerful high-level language.

Martin H. Trauth

Chapter 3. Univariate Statistics

Abstract

The statistical properties of a single parameter are investigated by means of univariate analysis. Such a parameter could, for example, be the organic carbon content of deep-sea sediments, the sizes of grains in a sandstone layer, or the ages of sanidine crystals in a volcanic ash. Both the number and the size of samples that we collect from a larger population are often limited by financial and logistical constraints. The methods of univariate statistics assist us to draw from the sample conclusions that apply to the population as a whole.

Martin H. Trauth

Chapter 4. Bivariate Statistics

Abstract

Bivariate analysis aims to understand the relationship between two variables, x and y. Examples are the length and width of a fossil, the sodium and potassium content of volcanic glass, and the organic matter content along a sediment core. When the two variables are measured on the same object, x is usually identified as the independent variable and y as the dependent variable. If both variables have been generated in an experiment, the variable manipulated by the experimenter is described as the independent variable. In some cases neither variable is manipulated and neither is independent.

Martin H. Trauth

Chapter 5. Time-Series Analysis

Abstract

Time-series analysis aims to investigate the temporal behavior of a variable x(t). Examples include the investigation of long-term records of mountain uplift, sea-level fluctuations, orbitally-induced insolation variations and their influence on the ice-age cycles, millennium-scale variations in the atmosphere-ocean system, the effect of the El Nino/Southern Oscillation on tropical rainfall and sedimentation (Fig. 5.1), and tidal influences on noble gas emissions from bore holes. The temporal pattern of a sequence of events can be random, clustered, cyclic, or chaotic. Time-series analysis provides various tools with which to detect these temporal patterns. Understanding the underlying processes that produced the observed data allows us to predict future values of the variable.

Martin H. Trauth

Chapter 6. Signal Processing

Abstract

Signal processing involves techniques for manipulating a signal in order to minimize the effects of noise, to correct all kinds of unwanted distortions, and to separate out various components of interest. Most signal processing algorithms include the design and realization of filters. A filter can be described as a system that transforms signals. System theory provides the mathematical background for filter design and realization. A filter has an input and an output, with the output signal y(t) being modified with respect to the input signal x(t) (Fig. 6.1). The signal transformation can be carried out through a mathematical process known as convolution or, if filters are involved, as filtering.

Martin H. Trauth

Chapter 7. Spatial Data

Abstract

Most data in earth sciences are spatially distributed, either as vector data, (points, lines, polygons) or as raster data (gridded topography). Vector data are generated by digitizing map objects such as drainage networks or outlines of lithologic units. Raster data can be obtained directly from a satellite sensor output, but gridded data can also, in most cases, be interpolated from irregularly-distributed field samples (gridding).

Martin H. Trauth

Chapter 8. Image Processing

Abstract

Computer graphics are stored and processed as either vector or raster data. Most of the data types that were encountered in the previous chapter were vector data, i.e., points, lines and polygons. Drainage networks, the outlines of geologic units, sampling locations, and topographic contours are all examples of vector data. In Chapter 7, coastlines are stored in a vector format while bathymetric and topographic data are saved in a raster format. Vector and raster data are often combined in a single data set, for instance to display the course of a river on a satellite image. Raster data are often converted to vector data by digitizing points, lines or polygons. Conversely, vector data are sometimes transformed to raster data.

Martin H. Trauth

Chapter 9. Multivariate Statistics

Abstract

Multivariate analysis aims to understand and describe the relationships between an arbitrary number of variables. Earth scientists often deal with multivariate data sets such as microfossil assemblages, geochemical fingerprints of volcanic ash layers, or the clay mineral content of sedimentary sequences.

Martin H. Trauth

Chapter 10. Directional Data

Abstract

Methods for analyzing circular and spherical data are widely used in earth sciences. For instance, structural geologists measure and analyze the orientation of slickensides (or striae) on fault planes. Circular statistics is also common in paleomagnetic applications. Microstructural investigations include the analysis of grain shapes and quartz c-axis orientations in thin sections.

Martin H. Trauth

Titel: MATLAB® Recipes for Earth Sciences
verfasst von: Martin H. Trauth
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-662-46244-7
Print ISBN: 978-3-662-46243-0
DOI: https://doi.org/10.1007/978-3-662-46244-7