Ocean CO2 Sequestration Estimation Using Satellite and ML Grids
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Solution Overview
Problem
Accurate quantification of oceanic carbon dioxide sequestration across spatial and temporal domains is challenging due to the vastness of the global ocean, making continuous and in situ measurements economically and practically infeasible, leading to uncertainties in CO2 flux estimation.
Innovation Solution
A method involving the mapping of oceanic spatiotemporal CO2 measurements to chlorophyll a (Chl-a) and temperature measurements in a multidimensional grid, using machine learning models like CNNs and LSTM, to generate a priori estimates of CO2 sequestration, leveraging adaptive sampling and satellite data for improved estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous in situ measurements are conducted across the global ocean, then measurement precision of CO2 sequestration is improved, but device complexity and cost become prohibitively high
Solution Approach 1:
The patent introduces satellite remote sensing as an intermediary tool to measure oceanic CO2 parameters. Instead of deploying complex in situ measurement systems across the entire ocean, satellite sensors act as intermediaries to obtain surface CO2 partial pressure, temperature, and chlorophyll-a data, which are then used to estimate subsurface CO2 fluxes through modeling approaches.
Solution Approach 2:
The patent replaces mechanical in situ measurement systems with remote sensing technology. Satellite-based optical and thermal sensors substitute for physical oceanographic instruments, eliminating the need for extensive deployment of buoys, moorings, and ship-based measurement systems while achieving comparable or superior spatial coverage.
2Measurement precision
If continuous in situ measurements are conducted across the global ocean, then measurement precision of CO2 sequestration is improved, but loss of time and resources become prohibitively high
Solution Approach 1:
The patent performs preliminary action by using satellite remote sensing to obtain surface ocean parameters (temperature, chlorophyll-a, CO2 partial pressure) that serve as inputs for predictive models. These models pre-process the data to estimate CO2 fluxes before detailed in situ verification is needed, reducing the overall time and resources required for comprehensive measurement.
Solution Approach 2:
The patent applies partial action by focusing satellite and modeling efforts on key ocean regions and time periods where CO2 fluxes are most significant or variable. Rather than continuously measuring every location, the system strategically samples critical areas to achieve accurate overall estimates with reduced temporal and spatial coverage requirements.
3Device complexity
If sparse sampling is used across the global ocean, then device complexity is reduced, but measurement precision of CO2 sequestration deteriorates
Solution Approach 1:
The patent transitions from two-dimensional surface sampling to three-dimensional estimation by incorporating vertical profiling through modeling. Satellite data provide surface conditions, while oceanographic models extrapolate these conditions to subsurface layers, adding the depth dimension without requiring physical sensors at multiple depths. This dimensional transformation allows sparse surface sampling to yield comprehensive volumetric estimates.
Solution Approach 2:
The patent changes parameters by transforming directly measurable surface parameters (temperature, chlorophyll-a, surface CO2 partial pressure) into estimated subsurface parameters (CO2 flux, acidification rates). Through biogeochemical models, surface observations are converted into deeper ocean conditions, allowing accurate estimation of three-dimensional CO2 sequestration from two-dimensional satellite data.
Data Source
AI summary
A method for quantifying oceanic carbon dioxide (CO2) sequestration is provided. The method includes obtaining a plurality of oceanic spatiotemporal carbon dioxide (CO2) measurements. A plurality of spatiotemporal chlorophyll a (Chl-a) and a plurality of oceanic temperature measurements are obtained. The obtained plurality of oceanic spatiotemporal CO2 measurements is mapped to the obtained plurality of spatiotemporal Chl-a and the plurality of oceanic temperature measurements in a multidimensional grid. At least one a priori oceanic spatiotemporal CO2 estimate is generated based on the multidimensional grid.