Nothing Special   »   [go: up one dir, main page]

Shen et al., 2021 - Google Patents

Array‐based convolutional neural networks for automatic detection and 4D localization of earthquakes in Hawai 'i

Shen et al., 2021

View PDF
Document ID
11852477430201324264
Author
Shen H
Shen Y
Publication year
Publication venue
Seismological Society of America

External Links

Snippet

The growing amount of seismic data necessitates efficient and effective methods to monitor earthquakes. Current methods are computationally expensive, ineffective under noisy environments, or labor intensive. We leverage advances in machine learning to propose an …
Continue reading at par.nsf.gov (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/364—Seismic filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/16—Receiving elements for seismic signals; Arrangements or adaptations of receiving elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
    • G01V3/10—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices using induction coils
    • G01V3/104—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices using induction coils using several coupled or uncoupled coils
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Ross et al. P wave arrival picking and first‐motion polarity determination with deep learning
Münchmeyer et al. Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
Shen et al. Array‐based convolutional neural networks for automatic detection and 4D localization of earthquakes in Hawai ‘i
Li et al. Machine learning seismic wave discrimination: Application to earthquake early warning
Mousavi et al. Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking
Zhu et al. An end‐to‐end earthquake detection method for joint phase picking and association using deep learning
Wang et al. SeismoGen: Seismic waveform synthesis using GAN with application to seismic data augmentation
Saad et al. Scalodeep: A highly generalized deep learning framework for real‐time earthquake detection
Li et al. High-resolution seismic event detection using local similarity for Large-N arrays
Yang et al. Simultaneous earthquake detection on multiple stations via a convolutional neural network
Saad et al. Real‐time earthquake detection and magnitude estimation using vision transformer
Xiao et al. Siamese earthquake transformer: A pair‐input deep‐learning model for earthquake detection and phase picking on a seismic array
Magrini et al. Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale
Kim et al. Graph convolution networks for seismic events classification using raw waveform data from multiple stations
Zhang et al. Extracting dispersion curves from ambient noise correlations using deep learning
Shi et al. MALMI: An automated earthquake detection and location workflow based on machine learning and waveform migration
Majstorović et al. Designing convolutional neural network pipeline for near‐fault earthquake catalog extension using single‐station waveforms
Johnson et al. Application of a convolutional neural network for seismic phase picking of mining-induced seismicity
Feng et al. Edgephase: A deep learning model for multi‐station seismic phase picking
Rojas et al. Artificial neural networks as emerging tools for earthquake detection
Wang et al. Seismology with dark data: Image‐based processing of analog records using machine learning for the Rangely earthquake control experiment
Cianetti et al. Comparison of deep learning techniques for the investigation of a seismic sequence: An application to the 2019, Mw 4.5 Mugello (Italy) earthquake
Chen et al. CubeNet: Array‐based seismic phase picking with deep learning
Tous et al. Deep neural networks for earthquake detection and source region estimation in north‐central Venezuela
Bornstein et al. PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning