Case Study: Black Tusk Geophysics achieves 86% reduction in digs and nearly 50% cost savings with MathWorks MATLAB

Black Tusk Geophysics Detects And Classifies Unexploded Ordnance

Black Tusk Geophysics faced the challenge of locating and classifying unexploded ordnance (UXO) buried at former training and bombing sites, where conventional metal detectors produced many false positives and costly excavations. To develop, test, and deploy advanced, computation‑intensive algorithms for electromagnetic induction sensor data, the team used MathWorks tools—primarily MATLAB and its associated toolboxes—to build robust detection and visualization workflows.

Using MathWorks' MATLAB with Optimization Toolbox, Parallel Computing Toolbox, Statistics and Machine Learning Toolbox, and MATLAB Compiler, Black Tusk implemented nonlinear data‑fitting inversion, visualization, and classification pipelines that prioritize dig targets and quantify confidence. Deployed in over 10 field demonstrations, the solution reduced excavations by up to 86%, yielded nearly 50% project cost savings, and cut computation time by about 75% while improving UXO detection accuracy.

Black Tusk Geophysics

Laurens Beran

Research Geophysicist