JEF 454E - Machine Learning in Geophysical Engineering

Dersin Amaçları

Enhance the knowledge of Artificial Intelligence and Machine learning through practical applications in Geoscience and everyday life.

Demonstrate proficiency in Python and Jupyter Notebooks to highlight computing skills.

Develop and apply standard machine-learning workflows:
Preparing the data
Designing the model
Training, validating, and
Evaluating the model

Apply standard data manipulation strategies in the Geosciences including working with different data types, visualizing data, reducing dimensionality, and performing feature engineering.

Dersin Tanımı

Introduction to Artificial Intelligence & Machine Learning Challenges in Geoscience
What, why, and how Machine Learning and Importance in Geophysics
Classifications of ML, Supervised & Unsupervised: application in Seismic
Regression: Ordinary Least Square Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Orthogonal Matching Pursuit for wave classification
Classification & Regularization: Logistic Regression, Sigmoid & Softmax functions
Introduction to Python and Juypter Noted book, loading and analyzing geophysical data, i.e., Seismic
Clustering, Anomaly Detection, Association Rule Mining, Principal Component Analysis
Geoscience data application and attribute analysis for ML
AI, ML, DL, Generative AI and its application with limitations in 3D seismic modeling
Nearest Neighbor, KNN, Decision Tree for subsurface fault detection
Random forest, Gradient Descent, Over-Fitting. Underfitting, Variance, Bias
Naive Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes
Deep Learning, Convolutional Neural Networks
Training Machine Learning Model for Seismic Exploration