Real-Time Magnetometry Noise Reduction via Multivariate Regression
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Solution Overview
Problem
Magnetometry surveys face significant delays due to the lack of real-time noise reduction capabilities, which is undesirable in military and commercial applications, as they often require offline noise reduction processes that incur time and cost inefficiencies.
Innovation Solution
A magnetic signal noise reduction and detection system that includes inputs from a total field scalar magnetometer, vector magnetometer, and position, velocity, and heading sensors, utilizing a pre-processor system, adaptive noise cancellation system, and detection system to perform multivariate regression and reduce noise in real-time, enabling immediate feedback on magnetic anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline noise reduction processing is used, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary noise characterization by collecting reference noise data during periods when no magnetic anomalies are present. This pre-collected noise profile is then used during actual survey operations to enable real-time noise reduction without requiring lengthy post-processing, thus maintaining measurement precision while eliminating time delays.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring magnetic field measurements and comparing them against the characterized noise profile. When deviations indicating potential anomalies are detected, the system provides immediate feedback for further investigation, eliminating the traditional hours-or-days delay between survey completion and result availability.
2Productivity
If real-time noise reduction is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The noise reduction function is segmented into separate, modular components: noise characterization module, real-time filtering module, and anomaly detection module. Each component performs a specific function and can be independently optimized or adjusted, reducing overall system complexity while maintaining real-time processing capability and productivity.
3Measurement precision
If multiple sensors are used for noise reduction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (magnetometers and accelerometers) into a unified processing framework. By combining these sensors and processing their data together through integrated algorithms, the system achieves improved noise reduction quality while managing complexity through unified rather than separate processing paths.
Data Source
AI summary
A magnetic signal noise reduction and detection system has inputs configured to receive data from a first total field scalar magnetometer, data from a vector magnetometer, and data from a position, velocity and heading sensor, a signal processor configured with a pre-processor system, an adaptive noise cancellation system and a detection system, the pre-processor system configured to carry out initial processing of data received. The pre-processor is configured to convert data to the frequency domain and pass the converted data to the adaptive noise cancellation system. The adaptive noise cancellation system is configured to carry out multivariate regression on the converted data to reduce detected noise. The detection system is configured to detect magnetic anomalies and output information in real time about the magnetic anomalies to a user interface.