Introduction to Machine Learning in Geosciences

This course introduces students to the exciting world of machine learning (ML) with a specific focus on geosciences. By the end of this course, students will understand basic ML concepts and apply them to geoscience problems such as seismological data analysis, geochemical data analysis, climate modeling, and satellite images classification.

Learning Outcomes:
Understand key machine learning techniques (both supervised and unsupervised).
Apply ML techniques to geoscience datasets.
Develop critical thinking for model evaluation and optimization.

Introduction
Introduction to Machine Learning (ML)
Overview of machine learning and its relevance in geosciences.
Applications in geosciences: seismic interpretation, mineral exploration, and climate forecasting.
Brief on types of machine learning: supervised, unsupervised, and reinforcement learning.
Fundamental Concepts: Training, validation, and testing datasets. Overfitting and underfitting in models.
Hands-on: Explore a basic geoscience dataset (e.g., seismic data, temperature records, logs data) and understand the workflow for ML applications.

Optimization Techniques
Introduction to Optimization: Definition and relevance of optimization in machine learning models.
Optimization in geosciences: Finding best-fitting models for geological patterns.
Global and Local Optimization:
Local optimization: gradient descent, stochastic gradient descent.
Global optimization: simulated annealing, genetic algorithms.
Hands-on: Implement a gradient descent algorithm to fit a simple geoscience-related regression problem (e.g., predicting hydrological parameters from historical data).

Supervised Learning
Linear and Non-linear Regression:
Linear regression: fitting models to predict geological attributes (e.g., mineral concentrations).
Polynomial regression and decision trees for non-linear relationships.
Classification:
Logistic regression for binary classification tasks in geosciences (e.g., rock type classification).
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) for multiclass problems such as identifying land cover from satellite imagery.
Hands-on: Apply regression and classification techniques to real geoscience datasets. Predict and classify geological data (e.g. facies classification from well logs).

Unsupervised Learning
Clustering:
K-means clustering for geological feature discovery (e.g., identifying seismic clusters).
Hierarchical clustering and DB-Scan for deeper data segmentation (e.g. earthquake epicenter analysis).
Dimensionality Reduction: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for reducing complex geoscience data.
Hands-on: Use clustering to identify natural groups in seismic activity data. Perform PCA to reduce dimensions of images.

Deep Learning
Introduction to Neural Networks:
Basics of artificial neural networks (ANN) and their architecture.
Activation functions (ReLU, Sigmoid, etc.) and backpropagation.
Training and Optimization: Overfitting and regularization techniques (L1, L2). Optimizers: Adam, RMSProp, and learning rate scheduling.
Hands-on: Build a basic neural network for image classification.

Deep Learning for Geosciences
Convolutional Neural Networks (CNNs):
Understanding CNNs and their relevance in image-based geoscience data (e.g., satellite images, seismic waveforms).
CNN layers: convolution, pooling, fully connected layers.
Applications in Geosciences:
Satellite image analysis for land cover mapping.
Seismic image analysis for detecting geological structures.
Hands-on: Build a CNN to classify satellite images of different geological formations or map land cover changes.

Project: Machine Learning in Geosciences
In the last day, students will work on a small project where they apply the concepts learned throughout the course to a specific geoscience problem.