Quantification of oceanic carbon dioxide sequestration

Patent metadata

Field	Value
Publication number	US-12560583-B2
Application number	US-202318162078-A
Country	US
Kind code	B2
Filing date	Jan 31, 2023
Priority date	Jan 31, 2023
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

A method for quantifying oceanic carbon dioxide (CO 2 ) sequestration is provided. The method includes obtaining a plurality of oceanic spatiotemporal carbon dioxide (CO 2 ) measurements. A plurality of spatiotemporal chlorophyll a (Chl-a) and a plurality of oceanic temperature measurements are obtained. The obtained plurality of oceanic spatiotemporal CO 2 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 CO 2 estimate is generated based on the multidimensional grid.

First claim

The invention claimed is:

1 . A method for quantifying oceanic carbon dioxide (CO 2 ) sequestration, the method comprising:
obtaining a plurality of oceanic spatiotemporal carbon dioxide (CO 2 ) measurements using a plurality of CO 2 sensors; obtaining a plurality of spatiotemporal chlorophyll a (Chl-a) and a plurality of oceanic temperature measurements using a plurality of spatiotemporal sensors; training a machine learning model to map the plurality of oceanic spatiotemporal CO 2 measurements to the plurality of spatiotemporal Chl-a and the plurality of oceanic temperature measurements in a multidimensional grid; generating at least one a priori oceanic spatiotemporal CO 2 estimate based on the multidimensional grid, improving accuracy and spatial resolution of locating CO 2 sequestration; outputting the at least one priori oceanic spatiotemporal CO 2 estimate to a carbon monitoring system for use in climate and environmental policy modeling; and coordinating movements of an autonomous vessel to obtain measurements in ocean locations identified by the climate and environmental policy modeling as increasing the accuracy and spatial resolution of locating CO 2 sequestration.

2 . The method of claim 1 , wherein the at least one a priori oceanic spatiotemporal CO 2 estimate is generated based on a provided spatiotemporal Chl-a measurement and a provided oceanic surface temperature (SST) measurement.

3 . The method of claim 2 , wherein the obtained plurality of oceanic spatiotemporal CO 2 measurements and the generated at least one a priori oceanic spatiotemporal CO 2 estimate are measured in partial pressures of CO 2 (pCO 2 ).

4 . The method of claim 3 , wherein at least some of the obtained plurality of oceanic spatiotemporal pCO 2 measurements are obtained by the autonomous vessel using Hypertaste.

5 . The method of claim 4 , wherein the obtaining the plurality of spatiotemporal Chl-a and oceanic temperature measurements is performed using satellite imaging within a predetermined time and a predetermined distance of the obtained plurality of oceanic spatiotemporal pCO 2 measurements.

6 . The method of claim 5 , further comprising:
identifying ocean locations with spatiotemporal pCO 2 measurement variances that exceed a predetermined threshold.

7 . A computer program product for quantifying oceanic carbon dioxide (CO 2 ) sequestration, the computer program product comprising:
one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising:
obtaining a plurality of oceanic spatiotemporal carbon dioxide (CO 2 ) measurements using a plurality of CO 2 sensors;
obtaining a plurality of spatiotemporal chlorophyll a (Chl-a) and a plurality of oceanic temperature measurements using a plurality of spatiotemporal sensors;
training a machine learning model to map the plurality of oceanic spatiotemporal CO 2 measurements to the plurality of spatiotemporal Chl-a and the plurality of oceanic temperature measurements in a multidimensional grid;
generating at least one a priori oceanic spatiotemporal CO 2 estimate based on the multidimensional grid, improving accuracy and spatial resolution of locating CO 2 sequestration;
outputting the at least one priori oceanic spatiotemporal CO 2 estimate to a carbon monitoring system for use in climate and environmental policy modeling; and
coordinating movements of an autonomous vessel to obtain measurements in ocean locations identified by the climate and environmental policy modeling as increasing the accuracy and spatial resolution of locating CO 2 sequestration.

8 . The computer program product of claim 7 , further comprising:
generating an a priori oceanic CO 2 estimate model using the multidimensional grid.

9 . The computer program product of claim 8 , wherein the at least one a priori oceanic spatiotemporal CO 2 measurement is generated based on a provided spatiotemporal Chl-a measurement and a provided oceanic surface temperature (SST) measurement.

10 . The computer program product of claim 9 , wherein the obtained plurality of oceanic spatiotemporal CO 2 measurements and the generated at least one a priori oceanic spatiotemporal CO 2 estimate are measured in partial pressures of CO 2 (pCO 2 ).

11 . The computer program product of claim 10 , wherein at least some of the obtained plurality of oceanic spatiotemporal pCO 2 measurements are obtained by an autonomous vessel using Hypertaste.

12 . The computer program product of claim 11 , wherein the obtaining the plurality of spatiotemporal Chl-a and oceanic temperature measurements is performed using satellite imaging within a predetermined time and a predetermined distance of the obtained plurality of oceanic spatiotemporal pCO 2 measurements.

13 . The computer program product of claim 12 , further comprising:
identifying ocean locations with spatiotemporal pCO 2 measurement variances that exceed a predetermined threshold; and coordinating the autonomous vessel to measure more frequently in the identified ocean locations with the spatiotemporal pCO 2 measurement variances that exceed the predetermined threshold.

14 . A computer system for quantifying oceanic carbon dioxide (CO 2 ) sequestration, the computer system comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method, the method comprising:
obtaining a plurality of oceanic spatiotemporal carbon dioxide (CO 2 ) measurements using a plurality of CO 2 sensors;
obtaining a plurality of spatiotemporal chlorophyll a (Chl-a) and a plurality of oceanic temperature measurements using a plurality of spatiotemporal sensors;
training a machine learning model to map the plurality of oceanic spatiotemporal CO 2 measurements to the plurality of spatiotemporal Chl-a and the plurality of oceanic temperature measurements in a multidimensional grid;
generating at least one a priori oceanic spatiotemporal CO 2 estimate based on the multidimensional grid, improving accuracy and spatial resolution of locating CO 2 sequestration;
outputting the at least one priori oceanic spatiotemporal CO 2 estimate to a carbon monitoring system for use in climate and environmental policy modeling; and
coordinating movements of an autonomous vessel to obtain measurements in ocean locations identified by the climate and environmental policy modeling as increasing the accuracy and spatial resolution of locating CO 2 sequestration.

15 . The computer system of claim 14 , wherein the at least one a priori oceanic spatiotemporal CO 2 estimate is generated based on a provided spatiotemporal Chl-a measurement and a provided oceanic surface temperature (SST) measurement.

16 . The computer system of claim 15 , wherein the obtained plurality of oceanic spatiotemporal CO 2 measurements and the generated at least one a priori oceanic spatiotemporal CO 2 estimate are measured in partial pressures of CO 2 (pCO 2 ).

17 . The computer system of claim 16 , wherein at least some of the obtained plurality of oceanic spatiotemporal pCO 2 measurements are obtained by autonomous vessel using Hypertaste.

18 . The computer system of claim 17 , wherein the obtaining the plurality of spatiotemporal Chl-a and oceanic temperature measurements is performed using satellite imaging withi

Assignees

• IBM

Inventors

• O'Donncha Fearghal
• Stanford-Clark Andrew James
• Thompson Adam James
• Martinho Ricardo
• Stäbler Moritz Johannes
• Wright Jonathan

Classifications

• G01N33/0063 Primary

  using a threshold to release an alarm or displaying means · CPC title

• G01N33/004 Primary

  CO or CO2 · CPC title