Environmental Measurement Data Fusion via Machine Learning
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Solution Overview
Problem
It is challenging and costly to measure certain physical phenomena with sufficient accuracy, precision, and coverage in space and time due to the expense and complexity of instrumentation, which often results in systematic and random errors.
Innovation Solution
A system and method that combine information from multiple environmental instruments to enhance measurement accuracy, precision, and coverage by relating environmental response data with driver data using co-variability, machine learning, and coordinate representations to produce improved space and time-aligned datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive instrumentation is used to measure physical phenomena directly, then measurement accuracy and precision are improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces inexpensive proxy measurements (environmental drivers) as intermediaries to infer expensive-to-measure physical phenomena. Instead of directly measuring complex fluxes with expensive instruments, the system uses readily available data from multiple inexpensive sources that correlate with the target measurements, thereby reducing device complexity while maintaining measurement precision through statistical relationships.
Solution Approach 2:
The patent creates virtual copies of expensive measurements by synthesizing data from multiple inexpensive measurements. Through machine learning models that learn relationships between inexpensive proxy data and expensive target measurements, the system generates synthetic datasets that replicate the information content of expensive instruments without requiring the actual expensive hardware.
2Measurement precision
If multiple expensive instruments are deployed at all required locations, then measurement coverage and resolution are improved, but device complexity and deployment difficulty increase
Solution Approach 1:
The patent segments the measurement task by separating the expensive measurement function from the data collection function. Instead of deploying expensive instruments at every location, the system uses a network of inexpensive sensors distributed across multiple locations, then segments and processes this distributed data through machine learning to reconstruct the full spatial field, thereby reducing deployment complexity while maintaining resolution.
Solution Approach 2:
The patent transitions from physical deployment dimension to data processing dimension. Rather than physically placing expensive instruments at all required locations (spatial dimension), the system uses inexpensive instruments at fewer locations and compensates through computational dimensionality, using machine learning models to infer missing spatial information from the limited physical measurements.
3Measurement precision
If measurements are taken at high repetition rate and duration, then measurement accuracy is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary learning of relationships between inexpensive proxy measurements and expensive target measurements using historical data. Once these statistical models are trained, the system can rapidly infer expensive measurements from inexpensive ones in real-time without requiring time-consuming direct measurements, thereby reducing measurement time while maintaining accuracy through pre-learned relationships.
4Measurement precision
If direct measurement of physical phenomena is performed, then measurement accuracy is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent uses environmental drivers as intermediary measurements that indirectly capture the physical phenomena of interest. Instead of directly measuring fluxes that may disturb the environment, the system measures related environmental conditions (temperature, humidity, wind speed) that serve as proxies, thereby reducing harmful interactions with the measured environment while maintaining measurement accuracy through statistical correlation.
Data Source
AI summary
Methods and systems for enhancing environmental response data from natural and/or anthropogenic environments can use physical laws and data science principles to combine the information contained in environmental response input data and at least two independent types of environmental driver input data to produce environmental response data that has been enhanced from the environmental response input data in at least one of the following ways:the environmental response output data has improved accuracy;the environmental response output data has improved precision;the environmental response output data comprises a greater number of times;the environmental response output data comprises a greater number of locations in one-dimensional, two-dimensional, or three-dimensional space.