Patents by Inventor Xinming Wu

Xinming Wu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20250029330
    Abstract: A method of modeling interactive intelligent three-dimensional implicit structure based on deep learning is provided, including: generating a plurality of geological simulation structural models by using a data simulation technology, to construct a geological simulation structural model library, where the geological simulation structural model has a diversified fold and a fault feature; acquiring, for each, model, geological fault data and unevenly distributed geological horizon data to obtain a training sample data set; training a neural network by using the training sample data set; inputting multi-source heterogeneous data of a target region into a trained neural network, so as to output a geological structural model corresponding to the multi-source heterogeneous data, where the multi-source heterogeneous data includes fault interpretation data and horizon interpretation data, and the multi-source heterogeneous data includes at least one selected from: geological outcrop observation data, well logging dat
    Type: Application
    Filed: October 14, 2022
    Publication date: January 23, 2025
    Inventors: Xinming Wu, Zhengfa Bi
  • Patent number: 11403495
    Abstract: A machine learning system efficiently detects faults from three-dimensional (ā€œ3Dā€) seismic images, in which the fault detection is considered as a binary segmentation problem. Because the distribution of fault and nonfault samples is heavily biased, embodiments of the present disclosure use a balanced loss function to optimize model parameters. Embodiments of the present disclosure train a machine learning system by using a selected number of pairs of 3D synthetic seismic and fault volumes, which may be automatically generated by randomly adding folding, faulting, and noise in the volumes. Although trained by using only synthetic data sets, the machine learning system can accurately detect faults from 3D field seismic volumes that are acquired at totally different surveys.
    Type: Grant
    Filed: November 25, 2020
    Date of Patent: August 2, 2022
    Assignee: Board of Regents, The University of Texas System
    Inventors: Xinming Wu, Yunzhi Shi, Sergey Fomel
  • Publication number: 20210158104
    Abstract: A machine learning system efficiently detects faults from three-dimensional (ā€œ3Dā€) seismic images, in which the fault detection is considered as a binary segmentation problem. Because the distribution of fault and nonfault samples is heavily biased, embodiments of the present disclosure use a balanced loss function to optimize model parameters. Embodiments of the present disclosure train a machine learning system by using a selected number of pairs of 3D synthetic seismic and fault volumes, which may be automatically generated by randomly adding folding, faulting, and noise in the volumes. Although trained by using only synthetic data sets, the machine learning system can accurately detect faults from 3D field seismic volumes that are acquired at totally different surveys.
    Type: Application
    Filed: November 25, 2020
    Publication date: May 27, 2021
    Inventors: Xinming Wu, Yunzhi Shi, Sergey Fomel