AI-Augmented Geophysical Remote Sensing Instrument for High-SNR Scanning
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Solution Overview
Problem
Existing remote sensing systems face challenges in achieving adequate signal-to-noise ratio (SNR) without compromising spatial or temporal resolution, often requiring longer dwell times that can lead to image blurring on moving platforms.
Innovation Solution
Augment measured geophysical data with model-generated data using artificial intelligence, specifically through a machine learning model like a recurrent neural network, to enhance the SNR and improve data accuracy with fewer samples and lower dwell times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If longer dwell times are used to increase signal-to-noise ratio, then measurement accuracy is improved, but scan rate decreases resulting in image blurring on moving platforms
Solution Approach 1:
The patent applies preliminary action by using a machine learning model to predict and generate synthetic signal data before actual measurements are completed. The model is trained on historical measurement data to learn underlying patterns, then uses these learned patterns to generate synthetic data that augments limited actual measurements, effectively preparing enhanced data in advance without requiring longer dwell times
Solution Approach 2:
The patent implements copying by creating synthetic signal data that replicates the characteristics of actual measurements. The machine learning model generates synthetic I and Q components that mirror real measurement patterns, allowing the system to use copies of measurement data to augment the actual limited samples and achieve better signal-to-noise ratio without extending dwell time
2Measurement precision
If longer dwell times are used to increase signal-to-noise ratio, then measurement accuracy is improved, but temporal resolution decreases
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on extensive historical data, enabling it to rapidly generate synthetic measurements that capture temporal variations. This allows the system to maintain high temporal resolution while achieving improved signal-to-noise ratio through the augmented dataset combining actual and synthetic measurements
Solution Approach 2:
The patent uses copying to generate synthetic time-series data that replicates temporal patterns observed in historical measurements. By creating multiple synthetic copies of measurement signals with varying temporal characteristics, the system augments the actual data without requiring longer dwell times, thereby preserving temporal resolution
3Productivity
If fewer measurement samples are used to reduce dwell time, then scan rate increases, but signal-to-noise ratio decreases
Solution Approach 1:
The patent applies copying by generating synthetic measurement samples that replicate the statistical and physical characteristics of actual measurements. The machine learning model creates synthetic I and Q components that mirror real signal patterns, allowing the system to augment a small number of actual samples with multiple synthetic copies, thereby maintaining high scan rates while recovering the signal-to-noise ratio that would otherwise be lost
Solution Approach 2:
The system employs parameter changes by transforming the limited actual measurement data into an augmented dataset through the machine learning model. The model learns optimal parameter representations from actual data and generates synthetic samples with varied parameters that expand the effective dataset size, enabling high scan rates while maintaining adequate signal-to-noise ratio through the transformed and augmented data
Data Source
AI summary
A method may receive measured data including a first time series of I component of signal data and a Q component of signal data. A method may execute a machine model using the measured data as input to generate model-generated data including a second time series of the I component of signal data and the Q component of signal data. A method may combine the measured data and the model-generated data into an augmented data. A method may generate a hybrid data product based on the augmented data.