Work
Applied AI & Data Analytics
Borehole Log ML Analyzer
Applied Machine Learning for Geological Stratigraphy & Subsurface Data
- By
- Muhammad Haider Akhtar
- Field
- Geotechnical / Environmental Science
- Time
- Applied ML & Data Engineering
- Kind
- Founder's Professional Experience
The problem
Geotechnical engineers manually analyze hundreds of feet of continuous drilling borehole logs to classify underground soil and rock strata, a tedious process subject to subjective human interpretation.
The work
Engineered an applied machine learning pipeline in Python to preprocess multivariate borehole log sensor readings, mitigate class imbalances, and classify geological layers automatically using ensemble decision trees.
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- Decision Trees
- K-Means
What it does
- Achieved high classification accuracy across diverse lithological units while maintaining explainable feature importances
- Reduced manual log interpretation time from hours per borehole to minutes of automated processing
- Structured reproducible Jupyter and Python script workflows for geotechnical research teams
Academic and applied ML research project completed by founder Muhammad Haider Akhtar.
NextUrdu Document OCR PreprocessingComputer Vision & OCR