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dc.contributor.authorWaheed, U.B.
dc.contributor.authorAlkhalifah, Tariq Ali
dc.contributor.authorHaghighat, E.
dc.contributor.authorSong, C.
dc.date.accessioned2022-05-30T08:13:57Z
dc.date.available2022-05-30T08:13:57Z
dc.date.issued2022
dc.identifier.citationWaheed, U. B., Alkhalifah, T., Haghighat, E., & Song, C. (2022). A Holistic Approach to Computing First-Arrival Traveltimes Using Physics-Informed Neural Networks. 83rd EAGE Annual Conference & Exhibition. https://doi.org/10.3997/2214-4609.202210613
dc.identifier.doi10.3997/2214-4609.202210613
dc.identifier.urihttp://hdl.handle.net/10754/678312
dc.description.abstractSince the original algorithm by John Vidale in 1988 to numerically solve the isotropic eikonal equation, there has been tremendous progress on the topic addressing an array of computational challenges, including improvement of the solution accuracy, incorporation of surface topography, the addition of accurate physics by accounting for anisotropy/attenuation in the medium, and speeding up computations. Despite these advances, these algorithms have no mechanism to carry information gained by solving one problem to the next. Moreover, these approaches may breakdown for certain complex forms of the eikonal equation, requiring simplification of the equations to estimate approximate solutions. Therefore, we seek an alternate approach to address these challenges holistically. We propose an algorithm based on the emerging paradigm of physics-informed neural network to solve different forms of the eikonal equation. We show how transfer learning can be used to speed up computations by utilizing information gained from prior solutions. Such an approach makes the implementation of eikonal solvers much simpler and puts us on a much faster path to progress. The method paves the pathway to solving complex forms of the eikonal equation that have remained unsolved using conventional algorithms or solved using some approximation techniques at best.
dc.publisherEuropean Association of Geoscientists & Engineers
dc.relation.urlhttps://www.earthdoc.org/content/papers/10.3997/2214-4609.202210613
dc.rightsArchived with thanks to European Association of Geoscientists & Engineers
dc.titleA Holistic Approach to Computing First-Arrival Traveltimes Using Physics-Informed Neural Networks
dc.typeConference Paper
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentKing Abdullah University of Science and Technology
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSeismic Wave Analysis Group
dc.conference.dateJune 6-9, 2021
dc.conference.name83rd EAGE Annual Conference & Exhibition
dc.conference.locationMadrid / Online
dc.eprint.versionPost-print
dc.contributor.institutionKing Fahd University of Petroleum and Minerals
dc.contributor.institutionMassachusetts Institute of Technology
dc.contributor.institutionImperial College London
kaust.personAlkhalifah, Tariq Ali


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