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dc.contributor.authorAkram, Jubran
dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorPeter, Daniel
dc.date.accessioned2017-08-23T11:54:05Z
dc.date.available2017-08-23T11:54:05Z
dc.date.issued2017-08-17
dc.identifier.citationAkram 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.
dc.identifier.doi10.1190/segam2017-17761195.1
dc.identifier.urihttp://hdl.handle.net/10754/625376
dc.description.abstractWe 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.
dc.description.sponsorshipThe 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.
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttp://library.seg.org/doi/10.1190/segam2017-17761195.1
dc.rightsArchived with thanks to SEG Technical Program Expanded Abstracts 2017
dc.subjectborehole geophysics
dc.subjectmicroseismic
dc.subjectneural networks
dc.subjectprocessing
dc.subjectalgorithm
dc.titleA robust neural network-based approach for microseismic event detection
dc.typeConference Paper
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalSEG Technical Program Expanded Abstracts 2017
dc.eprint.versionPublisher's Version/PDF
kaust.personAkram, Jubran
kaust.personOvcharenko, Oleg
kaust.personPeter, Daniel
refterms.dateFOA2018-06-14T02:29:43Z
dc.date.published-online2017-08-17
dc.date.published-print2017-08-17


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