-
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.
-
公开(公告)号：US20240169030A1
公开(公告)日：2024-05-23
申请号：US17990569
申请日：2022-11-18
Applicant: X Development LLC
Inventor： Ray Jr. Anthony Nagatani , Allen Richard Zhao , Antonio Raymond Papania-Davis , Weishi Yan , Jeffrey Bush , Charles Stephen Spirakis , Brian Howell
IPC: G06F18/214 , G06F18/25
CPC classification number: G06F18/214 , G06F18/251
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterization of aggregate particles. A method includes obtaining, from a set of low fidelity sensors, first sensor data of a first portion of particles; obtaining, from a set of high fidelity sensors, second sensor data of the first portion of particles, the second sensor data comprising a higher fidelity representation of characteristics of the first portion of particles than the first sensor data; training a characterization model using the first sensor data and the second sensor data, the training comprising: providing, as training data to the characterization model, the second sensor data; and processing the second sensor data with the characterization model to correlate the first sensor data with the second sensor data. The first sensor data can indicate shape characteristics of each particle; and the second sensor data indicates a surface area of each particle.
-
Patent Agency Ranking
公开(公告)号：US20230243880A1
公开(公告)日：2023-08-03
申请号：US18160651
申请日：2023-01-27
Applicant: X Development LLC
Inventor： Alex S. Miller , Artem Goncharuk , Allen Richard Zhao , Kevin Forsythe Smith
CPC classification number: G01R29/0842 , G01R31/085 , G01R27/28 , G01R29/0878
Abstract: This disclosure describes a system and method for generating images by performing TEM surveys using pre-existing infrastructure such as transmission lines, or power lines, and naturally occurring transients such as lightning strikes or load switching. A relatively inexpensive sensor array can be installed on overhead power lines (e.g., electrical transmission or sub-transmission lines) which can detect transients in the overhead power lines. Transients in the overhead power lines can cause the power lines to emit pulses of electromagnetic (EM) radiation, which propagate into the earth's subsurface. This sudden change in electromagnetic field in the subsurface can induce eddy currents, which in turn emit return EM radiation that can propagate back to the overhead power line and induce secondary voltage and current transients. The magnitude of these secondary transients, and their time delay from the original transient are influenced by the properties of the subsurface in which the eddy currents formed.
-
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.
-
公开(公告)号：US11651602B1
公开(公告)日：2023-05-16
申请号：US17060002
申请日：2020-09-30
Applicant: X Development LLC
Inventor： Vadim Tschernezki , Lance Co Ting Keh , Hongxu Ma , Allen Richard Zhao , Jie Jacquot
CPC classification number: G06V20/64 , G06K9/627 , G06K9/6256 , G06N3/08 , G06N20/00
Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for machine learning classification based on separate processing of multiple views. In some implementations, a system obtains image data for multiple images showing different views of an object. A machine learning model is used to generate a separate output based on each the multiple images individually. The outputs for the respective images are combined to generate a combined output. A predicted characteristic of the object is determined based on the combined output. An indication of the predicted characteristic of the object is provided.
-
公开(公告)号：US20220383606A1
公开(公告)日：2022-12-01
申请号：US17834269
申请日：2022-06-07
Applicant: X Development LLC
Inventor： Jie Jacquot , Hongxu Ma , Allen Richard Zhao , Vadim Tschernezki , Ronald Votel
Abstract: Methods, systems, apparatus, and computer-readable media for data band selection using machine learning. In some implementations, image data comprising information for each of multiple wavelength bands is obtained. A multi-layer neural network is trained using the image data to perform one or more classification or regression tasks. A proper subset of the wavelength bands is selected based on parameters of a layer of the trained multi-layer neural network, where the parameters were determined through training of the multi-layer neural network using the image data. Output is provided indicating that the selected wavelength bands are selected for the one or more classification or regression tasks.
-
公开(公告)号：US20210374448A1
公开(公告)日：2021-12-02
申请号：US16887037
申请日：2020-05-29
Applicant: X Development LLC
Inventor： Jie Jacquot , Hongxu Ma , Allen Richard Zhao , Vadim Tschernezki , Ronald Votel
Abstract: Methods, systems, apparatus, and computer-readable media for data band selection using machine learning. In some implementations, image data comprising information for each of multiple wavelength bands is obtained. A multi-layer neural network is trained using the image data to perform one or more classification or regression tasks. A proper subset of the wavelength bands is selected based on parameters of a layer of the trained multi-layer neural network, where the parameters were determined through training of the multi-layer neural network using the image data. Output is provided indicating that the selected wavelength bands are selected for the one or more classification or regression tasks.
-
-
-
-
-
-
Search Results
27
records
(took 0.026 seconds)
