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    SYNTHETIC SUBTERRANEAN SOURCE
    1.
    发明公开
    SYNTHETIC SUBTERRANEAN SOURCE 审中-公开

    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

    IPC: G01V1/30 ,  G01V1/18

    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.

    SYNTHETIC SUBTERRANEAN SOURCE
    2.
    发明申请
    SYNTHETIC SUBTERRANEAN SOURCE 有权

    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

    IPC: G01V1/30 ,  G01V1/18

    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.

    SUBSURFACE LITHOLOGICAL MODEL WITH MACHINE LEARNING
    3.
    发明申请
    SUBSURFACE LITHOLOGICAL MODEL WITH MACHINE LEARNING 有权

    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

    IPC: G01V99/00 ,  G01V1/28

    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.

    DETECTING SEISMIC EVENTS USING MULTISPAN SIGNALS
    5.
    发明申请
    DETECTING SEISMIC EVENTS USING MULTISPAN SIGNALS 有权

    公开(公告)号：US20230120850A1

    公开(公告)日：2023-04-20

    申请号：US18048127

    申请日：2022-10-20

    Applicant: X Development LLC

    Inventor： Artem Goncharuk ,  Kevin Forsythe Smith ,  Valey Kamalov ,  Mattia Cantono ,  Rafael Muller

    IPC: G01H9/00 ,  G01V1/22 ,  G01V1/34 ,  G01V1/24 ,  G01V1/00

    Abstract: Systems and methods for detecting a mechanical disturbance are disclosed. One of the method may comprise the operation steps including: transmitting, by a transmitter, a pulse at a preset frequency along a first cable; receiving, by a receiver, a plurality of signals, wherein each of the plurality of signals travels along the first cable and a second cable connected to the receiver for a corresponding span; calculating one or more differential phases, wherein each differential phase is calculated based on respective phases and the corresponding spans of two of the plurality of signals; and determining a localization of the mechanical disturbance based on the one or more differential phases.

    Synthetic subterranean source
    7.
    发明授权
    Synthetic subterranean source 有权

    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

    IPC: G01V1/30 ,  G01V1/18

    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.

    TRAINING MACHINE LEARNING MODELS WITH SPARSE INPUT
    8.
    发明公开
    TRAINING MACHINE LEARNING MODELS WITH SPARSE INPUT 审中-公开

    公开(公告)号：US20240070459A1

    公开(公告)日：2024-02-29

    申请号：US18456792

    申请日：2023-08-28

    Applicant: X Development LLC

    Inventor： Artem Goncharuk ,  Robert Clapp ,  Kevin Forsythe Smith ,  Shiang Yong Looi ,  Ananya Gupta ,  Joses Bolutife Omojola ,  Min Jun Park

    IPC: G06N3/08 ,  G06N5/04

    CPC classification number: G06N3/08 ,  G06N5/04

    Abstract: This disclosure describes a system and method for effectively training a machine learning model to identify features in DAS and/or seismic imaging data with limited or no human labels. This is accomplished using a masked autoencoder (MAE) network that is trained in multiple stages. The first stage is a self-supervised learning (SSL) stage where the model is generically trained to predict data that has been removed (masked) from an original dataset. The second stage involves performing additional predictive training on a second dataset that is specific to a particular geographic region, or specific to a certain set of desired features. The model is fine-tuned using labeled data in order to develop feature extraction capabilities.

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