Quantum Sensor Data Pipeline for Subsurface Imaging
The Challenge
Quantum gravity sensors achieve unprecedented sensitivity for subsurface imaging—but raw measurements are dominated by noise. Tidal forces, atmospheric pressure variations, and instrument drift can obscure the signals we're trying to detect. The challenge: extract true gravitational anomalies from noisy time-series data and reconstruct what lies beneath the surface.
Solution Overview
I architected an end-to-end production platform handling the complete workflow from raw sensor data to 3D subsurface visualization. The system combines physics-based signal processing with JAX-accelerated inverse problem solving.
Key Outcomes: - Robust signal extraction from noisy quantum sensor measurements - High-resolution 3D density reconstructions - Production-grade pipeline deployed on HPC infrastructure
Technical Approach
Signal Processing Pipeline
Raw gravimeter time-series require systematic corrections to isolate true gravitational signals:
Physics-Based Corrections: - Tidal effects (Earth-Moon-Sun gravitational interactions) - Atmospheric pressure variations - Instrument drift (linear trends and non-linear residuals) - Spatial corrections (elevation, terrain effects)
Filtering & Analysis: - Moving average and Gaussian smoothing for noise reduction - FFT-based Butterworth filters for frequency-domain analysis - Gaussian Process regression for non-linear drift modeling
Inverse Problem Solution
The core challenge: infer 3D subsurface density from 2D surface measurements. This is a computationally massive, ill-posed optimization problem.
I applied: - Smart parameterization reducing dimensionality by orders of magnitude - Physics-based forward modeling predicting surface signals from subsurface models - JAX-accelerated gradient computation for efficient optimization - Constrained optimization respecting physical bounds and conservation laws
Production Infrastructure
- Docker containerization for reproducible deployments
- HPC cluster execution for computationally intensive inversions
- Robust configuration management with Pydantic
- Comprehensive logging with Loguru
Technical Stack
| Category | Technologies |
|---|---|
| ML Framework | JAX (automatic differentiation, JIT) |
| Signal Processing | FFT, Butterworth filters, Gaussian Processes |
| Python Tools | Pydantic, NumPy, SciPy, Click, Loguru |
| Infrastructure | Docker, Linux HPC clusters |
Skills Demonstrated
- Time-series signal processing and filtering
- Physics-based data corrections
- JAX automatic differentiation and JIT compilation
- Constrained optimization for inverse problems
- Production pipeline engineering
- HPC deployment patterns
Business Value
This platform transforms raw quantum sensor data into: - 3D subsurface density visualizations - Detection and characterization of buried anomalies - Actionable insights for exploration and monitoring applications
Code Repository: Private (proprietary)
For questions about this project, contact me.
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