Acoustic Detection of Underwater Mines

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to automatically, efficiently and transparently detect underwater mines from sonar echoes?

Goals & benefits

Identify the frequency bands involved in the detection of underwater mines and enhance knowledge by helping submarine staff and acoustic experts understand the causal relationships between specific frequency bands, their combination, and the presence of a mine.

Help to design a virtual “Golden Ear” (expert in underwater acoustics) operating 24/7/365 with the same quality of decision, or to design by simulation undetectable mines.

Assist the military profession in making an earlier and more reliable decision, thanks to rapid, systematic and explainable detection process with limited sensors.


We get a Predictive Model that is:


A Decision System composed of 23 unchained gradual rules using only the 29 variables that xtractis identified as significant (out of the 60 Potential Predictors that are measures characterizing the energy in a specific frequency band).


Good Real Performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 10/2022 (v2.2)

Results by
XTRACTIS® GENERATE 12.2.43406 (09/2022)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 3 cases
  5. Top-Models Benchmark