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Introduction to Python in earth science data analysis : from descriptive statistics to machine learning / / Maurizio Petrelli
| Introduction to Python in earth science data analysis : from descriptive statistics to machine learning / / Maurizio Petrelli |
| Autore | Petrelli Maurizio |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 550.285 |
| Collana | Springer textbooks in earth sciences, geography and environment |
| Soggetto topico | Geology - Data processing |
| ISBN | 3-030-78055-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Overview -- Let me Introduce Myself -- Organization of Book -- Styling Conventions -- Shared Codes -- Involvement and Collaborations -- Contents -- Part I Python for Geologists: A Kickoff -- 1 Setting Up Your Python Environment, Easily -- 1.1 The Python Programming Language -- 1.2 Programming Paradigms -- 1.3 A Local Python Environment for Scientific Computing -- 1.4 Remote Python Environments -- 1.5 Python Packages for Scientific Applications -- 1.6 Python Packages Specifically Developed for Geologists -- 2 Python Essentials for a Geologist -- 2.1 Start Working with IPython Console -- 2.2 Naming and Style Conventions -- 2.3 Working with Python Scripts -- 2.4 Conditional Statements, Indentation, Loops, and Functions -- 2.5 Importing External Libraries -- 2.6 Basic Operations and Mathematical Functions -- 3 Solving Geology Problems Using Python: An Introduction -- 3.1 My First Binary Diagram Using Python -- 3.2 Making Our First Models in Earth Science -- 3.3 Quick Intro to Spatial Data Representation -- Part II Describing Geological Data -- 4 Graphical Visualization of a Geological Data Set -- 4.1 Statistical Description of a Data Set: Key Concepts -- 4.2 Visualizing Univariate Sample Distributions -- 4.3 Preparing Publication-Ready Binary Diagrams -- 4.4 Visualization of Multivariate Data: A First Attempt -- 5 Descriptive Statistics 1: Univariate Analysis -- 5.1 Basics of Descriptive Statistics -- 5.2 Location -- 5.3 Dispersion or Scale -- 5.4 Skewness -- 5.5 Descriptive Statistics in Pandas -- 5.6 Box Plots -- 6 Descriptive Statistics 2: Bivariate Analysis -- 6.1 Covariance and Correlation -- 6.2 Simple Linear Regression -- 6.3 Polynomial Regression -- 6.4 Nonlinear Regression -- Part III Integrals and Differential Equations in Geology -- 7 Numerical Integration -- 7.1 Definite Integrals.
7.2 Basic Properties of Integrals -- 7.3 Analytical and Numerical Solutions of Definite Integrals -- 7.4 Fundamental Theorem of Calculus and Analytical Solutions -- 7.5 Numerical Solutions of Definite Integrals -- 7.6 Computing the Volume of Geological Structures -- 7.7 Computing the Lithostatic Pressure -- 8 Differential Equations -- 8.1 Introduction -- 8.2 Ordinary Differential Equations -- 8.3 Numerical Solutions of First-Order Ordinary Differential Equations -- 8.4 Fick's Law of Diffusion-A Widely Used Partial Differential Equation -- Part IV Probability Density Functions and Error Analysis -- 9 Probability Density Functions and Their Use in Geology -- 9.1 Probability Distribution and Density Functions -- 9.2 The Normal Distribution -- 9.3 The Log-Normal Distribution -- 9.4 Other Useful PDFs for Geological Applications -- 9.5 Density Estimation -- 9.6 The Central Limit Theorem and Normal Distributed Means -- 10 Error Analysis -- 10.1 Dealing with Errors in Geological Measurements -- 10.2 Reporting Uncertainties in Binary Diagrams -- 10.3 Linearized Approach to Error Propagation -- 10.4 The Mote Carlo Approach to Error Propagation -- Part V Robust Statistics and Machine Learning -- 11 Introduction to Robust Statistics -- 11.1 Classical and Robust Approaches to Statistics -- 11.2 Normality Tests -- 11.3 Robust Estimators for Location and Scale -- 11.4 Robust Statistics in Geochemistry -- 12 Machine Learning -- 12.1 Introduction to Machine Learning in Geology -- 12.2 Machine Learning in Python -- 12.3 A Case Study of Machine Learning in Geology -- Appendix A Python Packages and Resources for Geologists -- A.1 Python Libraries for Geologists -- A.2 Python Learning Resources for Geologists -- Appendix B Introduction to Object Oriented Programming -- B.1 Object-Oriented Programming -- B.2 Defining Classes, Attributes, and Methods in Python. Appendix C The Matplotlib Object Oriented API -- C.1 Matplotlib Application Programming Interfaces -- C.2 Matplotlib Object Oriented API -- C.3 Fine Tuning Geological Diagrams Using the OO-Style -- Appendix D Working with Pandas -- D.1 How to Perform Common Operations in Pandas -- Appendix Further Readings. |
| Record Nr. | UNINA-9910502625203321 |
Petrelli Maurizio
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Earth Sciences : Using Python to Solve Geological Problems / / by Maurizio Petrelli
| Machine Learning for Earth Sciences : Using Python to Solve Geological Problems / / by Maurizio Petrelli |
| Autore | Petrelli Maurizio |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xvi, 209 pages) : illustrations |
| Disciplina | 550.028557 |
| Collana | Springer Textbooks in Earth Sciences, Geography and Environment |
| Soggetto topico |
Earth sciences
Machine learning Artificial intelligence Mathematics Application software Earth Sciences Machine Learning Artificial Intelligence Applications of Mathematics Computer and Information Systems Applications |
| ISBN | 3-031-35114-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Basic Concepts of Machine Learning for Earth Scientists -- Chapter 1. Introduction to Machine Learning -- Chapter 2. Setting Up your Python Environments for Machine Learning -- Chapter 3. Machine Learning Workflow -- Part 2: Unsupervised Learning -- Chapter 4. Unsupervised Machine Learning Methods -- Chapter 5. Clustering and Dimensionality Reduction in Petrology -- Chapter 6. Clustering of Multi-Spectral Data -- Part 3: Supervised Learning -- Chapter 7. Supervised Machine Learning Methods -- Chapter 8. Classification of Well Log Data Facies by Machine Learning -- Chapter 9. Machine Learning Regression in Petrology -- Part 4: Scaling Machine Learning Models -- Chapter 10. Parallel Computing and Scaling with Dask -- Chapter 11. Scale Your Models in the Cloud -- Part 5: Next Step: Deep Learning -- Chapter 12. Introduction to Deep Learning. |
| Record Nr. | UNINA-9910746284003321 |
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Petrelli Maurizio
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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