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    A robust neural network-based approach for microseismic event detection

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    segam2017-17761195.1.pdf
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    Description:
    Expanded Abstract
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    Type
    Conference Paper
    Authors
    Akram, Jubran
    Ovcharenko, Oleg cc
    Peter, Daniel cc
    KAUST Department
    Earth Science and Engineering Program
    Extreme Computing Research Center
    Physical Science and Engineering (PSE) Division
    Date
    2017-08-17
    Online Publication Date
    2017-08-17
    Print Publication Date
    2017-08-17
    Permanent link to this record
    http://hdl.handle.net/10754/625376
    
    Metadata
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    Abstract
    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.
    Citation
    Akram J, Ovcharenko O, Peter D (2017) A robust neural network-based approach for microseismic event detection. SEG Technical Program Expanded Abstracts 2017. Available: http://dx.doi.org/10.1190/segam2017-17761195.1.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). For computer time, this research used the resources of the Supercomputing Laboratory, Information Technology Division and Extreme Computing Research Center at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia.
    Publisher
    Society of Exploration Geophysicists
    Journal
    SEG Technical Program Expanded Abstracts 2017
    DOI
    10.1190/segam2017-17761195.1
    Additional Links
    http://library.seg.org/doi/10.1190/segam2017-17761195.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2017-17761195.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program

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