This repository contains the workflow for integrating seismic and well log data, applying machine learning models, and visualizing subsurface properties as part of the Geohackathon project.
datasets/: Organizes the raw, processed, and combined data.src/: Contains the main source code files for processing and modeling.notebooks/: Includes Jupyter notebooks for analysis and testing.outputs/: Stores the results, such as predictions and visualizations.team_approaches/: Includes team-specific alternative approaches or experiments.main.py: Entry point for executing the project pipeline.requirements.txt: Lists the Python libraries required for the project.README.md: Provides an overview of the project.LICENSE: Contains the license details..gitignore: Specifies files and folders for Git to ignore. quirements.txt # Python dependencies └── README.md # Project documentation (this file)
- Data Integration: Combines seismic data with well logs using depth-matching algorithms.
- Feature Engineering: Extracts seismic attributes (amplitude, frequency, phase) and engineered features for machine learning.
- Machine Learning Models: Models for predicting porosity, permeability, and temperature gradients.
- Prediction Pipeline: Predicts properties for unknown wells using seismic data and trained models.
- Visualization: Depth-wise property plots for validation and insights.
git clone https://github.com/yourusername/repository-name.git
cd repository-name
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## **License**
This project is licensed under the [MIT License](LICENSE).