Remote Methane Detection Using MWIR and LWIR Diffraction Gratings
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Solution Overview
Problem
Current remote methods for detecting methane, such as spectral analysis, require robust atmospheric corrections and scene-dependent analytics, making them inefficient for quantitatively mapping methane presence and concentration over wide areas.
Innovation Solution
A system utilizing thermal infrared energy collection and multiple optical subsystems with diffraction gratings to disperse energy across mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) spectral regions, allowing for the detection of atmospheric gases like methane through spectral component data comparison between these regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral analysis techniques are used for remote methane detection, then detection capability is achieved, but robust atmospheric corrections and scene-dependent analytics are required, reducing efficiency
Solution Approach 1:
The system divides the infrared spectrum into multiple discrete wavelength bands (e.g., 2.3 µm, 3.3 µm, 7.6 µm, 8.6 µm) and uses separate detectors for each band. This segmentation allows independent optimization of detection algorithms for each wavelength, reducing the complexity of atmospheric corrections needed for full-spectral analysis while maintaining high detection accuracy through targeted spectral feature measurement.
Solution Approach 2:
The detector system is designed to measure multiple spectral bands simultaneously using a single integrated platform with multiple detectors. This multi-functional approach enables the system to perform both atmospheric correction and methane detection in one pass, eliminating the need for separate scene-dependent analytics and improving overall detection efficiency.
2Measurement precision
If quantitative mapping of methane concentration is performed over wide areas, then spatial distribution information is obtained, but robust atmospheric corrections are required, increasing system complexity
Solution Approach 1:
The system measures atmospheric conditions at multiple discrete spectral locations (wavelength bands) rather than attempting to model the entire spectrum. By focusing on specific absorption features of methane at predetermined wavelengths, the system performs localized spectral measurements that require simpler, more robust atmospheric corrections compared to full-spectral quantitative mapping.
Solution Approach 2:
The system changes the measurement parameters by selecting specific discrete wavelength bands where methane has characteristic absorption features. This parameter selection approach transforms the complex problem of continuous spectral analysis into a simpler multi-band measurement problem, where atmospheric effects can be corrected using standardized procedures for each band without requiring complex scene-dependent analytics.
3Adaptability or versatility
If detection under various atmospheric conditions and surface types is achieved, then adaptability is improved, but false positives increase without multi-regional spectral comparison
Solution Approach 1:
The system merges measurements from multiple spectral bands and multiple detectors into a unified detection algorithm. By combining information from detectors measuring different wavelength regions (including both methane-specific bands and reference bands), the system creates a more robust detection signature that distinguishes true methane signals from false positives caused by varying atmospheric conditions or surface emissions.
Solution Approach 2:
The system uses reference wavelength bands (e.g., 2.3 µm, 7.6 µm) that do not correspond to methane absorption features as feedback signals. These reference measurements provide real-time information about atmospheric transmission and surface emissions, which are fed back into the detection algorithm to adjust thresholds and reduce false positives in the methane-specific bands (e.g., 3.3 µm, 8.6 µm).
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of methane detection by co-acquiring LWIR and MWIR hyperspectral imagery, providing robust and flexible tools for detection under various atmospheric conditions and surface types, improving spatial resolution and reducing false positives.
Implementation Method 1
a collector that receives thermal infrared energy from a column of atmosphere
Implementation Method 2
The diffraction gratings may disperse the thermal infrared energy at a wavelength within a mid-wavelength infrared (MWIR) spectral region and a wavelength within a long-wavelength infrared (LWIR) spectral region
Data Source
AI summary
Disclosed systems and methods for the remote detection of atmospheric gas may include (1) receiving, at a collector, thermal infrared energy from at least one atmospheric column, (2) receiving, at optical subsystems, the thermal infrared energy over optical paths, (3) focusing the thermal infrared energy onto diffraction gratings that disperse the thermal infrared energy at a wavelength within a mid-wavelength infrared (MWIR) spectral region and a wavelength within a long-wavelength infrared (LWIR) spectral region, (4) receiving, at detectors, the thermal infrared energy dispersed from the diffraction gratings within the MWIR spectral region and the LWIR spectral region, (5) determining spectral component data associated with the thermal infrared energy in the MWIR spectral region and the LWIR spectral region, (6) sending the spectral component data to a computing device, and (7) identifying an atmospheric gas based on the spectral component data.