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公开(公告)号：US12153177B2
公开(公告)日：2024-11-26
申请号：US18459153
申请日：2023-08-31
Applicant: X Development LLC
Inventor： Allen Richard Zhao , Kenton Lee Prindle , Kevin Forsythe Smith , Artem Goncharuk
Abstract: This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
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公开(公告)号：US12007519B2
公开(公告)日：2024-06-11
申请号：US18167988
申请日：2023-02-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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公开(公告)号：US20230194750A1
公开(公告)日：2023-06-22
申请号：US18167988
申请日：2023-02-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
CPC classification number: G01V99/005 , G01V1/282
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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Patent Agency Ranking
公开(公告)号：US11774614B2
公开(公告)日：2023-10-03
申请号：US17374320
申请日：2021-07-13
Applicant: X Development LLC
Inventor： Allen Richard Zhao , Kenton Lee Prindle , Kevin Forsythe Smith , Artem Goncharuk
CPC classification number: G01V1/30 , G01V1/18 , G01V2210/121 , G01V2210/1295 , G01V2210/1425 , G01V2210/66
Abstract: This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
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公开(公告)号：US11592594B2
公开(公告)日：2023-02-28
申请号：US17229391
申请日：2021-04-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R Davis , Allen Richard Zhao
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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公开(公告)号：US20230408717A1
公开(公告)日：2023-12-21
申请号：US18459153
申请日：2023-08-31
Applicant: X Development LLC
Inventor： Allen Richard Zhao , Kenton Lee Prindle , Kevin Forsythe Smith , Artem Goncharuk
CPC classification number: G01V1/30 , G01V1/18 , G01V2210/121 , G01V2210/1295 , G01V2210/1425 , G01V2210/66
Abstract: This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
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公开(公告)号：US20230020861A1
公开(公告)日：2023-01-19
申请号：US17374320
申请日：2021-07-13
Applicant: X Development LLC
Inventor： Allen Richard Zhao , Kenton Lee Prindle , Kevin Forsythe Smith , Artem Goncharuk
Abstract: This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
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公开(公告)号：US20210318465A1
公开(公告)日：2021-10-14
申请号：US17229391
申请日：2021-04-13
Applicant: X Development LLC
Inventor： Kenton Lee Prindle , Artem Goncharuk , Neil David Treat , Kevin Forsythe Smith , Thomas Peter Hunt , Karen R. Davis , Allen Richard Zhao
Abstract: This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
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