Synthetic subterranean source
    22.
    发明授权

    公开(公告)号:US12153177B2

    公开(公告)日:2024-11-26

    申请号:US18459153

    申请日:2023-08-31

    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

    公开(公告)号:US20240070459A1

    公开(公告)日:2024-02-29

    申请号:US18456792

    申请日:2023-08-28

    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.

    TECHNIQUES FOR SELECTION OF LIGHT SOURCE CONFIGURATIONS FOR MATERIAL CHARACTERIZATION

    公开(公告)号:US20230324284A1

    公开(公告)日:2023-10-12

    申请号:US18329993

    申请日:2023-06-06

    CPC classification number: G01N21/255 G06N3/126 G01N33/442 G01N21/3563

    Abstract: Techniques for selecting a spectroscopic light source include obtaining a light source dataset and a spectroscopic dataset, initializing a genetic algorithm, selecting a first individual solution and a second individual solution from an initial generation of solutions, generating a new individual solution from the first and second individual solutions by combining their respective chromosome encodings, evaluating a specificity of the new individual solution to a target material, adding the new individual solution to a new generation of solutions, populating the new generation of solutions with a plurality of additional individual solutions, generating one or more descendent generations of solutions by iterating the genetic algorithm, selecting one or more implementation individual solutions exhibiting a threshold specificity to the target material, and outputting the one or more implementation individual solutions.

    IMPROVING BLAST PATTERNS
    26.
    发明申请

    公开(公告)号:US20230065981A1

    公开(公告)日:2023-03-02

    申请号:US18047127

    申请日:2022-10-17

    Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.

    DETERMINING ORE CHARACTERISTICS
    27.
    发明申请

    公开(公告)号:US20220347725A1

    公开(公告)日:2022-11-03

    申请号:US17832910

    申请日:2022-06-06

    Abstract: Techniques for processing ore include the steps of causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility; correlating the plurality of images of the stream of ore fragments with at least one or more characteristics of the ore fragments using a machine learning model that includes a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments; determining, based on the correlation, at least one of the one or more characteristics of the ore fragments; and generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.

    Deformulation techniques for deducing the composition of a material from a spectrogram

    公开(公告)号:US11353394B2

    公开(公告)日:2022-06-07

    申请号:US16948760

    申请日:2020-09-30

    Abstract: The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

    GENERATION AND IMPLEMENTATION OF GEOSPATIAL WORKFLOWS

    公开(公告)号:US20250077566A1

    公开(公告)日:2025-03-06

    申请号:US18816539

    申请日:2024-08-27

    Abstract: Implementations are described herein for automatically generating multimodal geospatial workflows for accomplishing geospatial tasks. In various implementations, a natural language request may be processed based on generative model(s) such as LLM(s) to generate workflow output tokens that identify high-level actions for completing a geospatial task conveyed in the natural language request. First data indicative of the high-level actions may be processed using one or more of the generative models to generate dataset output tokens that identify responsive dataset(s) that likely contain data responsive to the geospatial task. Second data indicative of both the high-level actions and the responsive dataset(s) may be processed based on one or more of the generative models to generate data manipulation output tokens that identify data manipulation instructions for assembling data from the responsive dataset(s) into a response that fulfills the geospatial task.

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