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• W3135210147 endingPage "IM71" @default.
• W3135210147 startingPage "IM63" @default.
• W3135210147 abstract "With the expanding size of 3D seismic data, manual seismic interpretation becomes time-consuming and labor-intensive. For automating this process, recent progress in machine learning, in particular the convolutional neural network (CNN), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation aims at mimicking the intelligence of experienced seismic interpreters to annotate subsurface geology accurately and efficiently. However, most of the implementations and applications are relatively simple in their CNN architectures, which primary rely on the seismic amplitude but undesirably fail to fully use the preknown geologic knowledge and/or solid interpretational rules of an experienced interpreter who works on the same task. We have developed a generally applicable framework for integrating a seismic interpretation CNN with such commonly used knowledge and rules as constraints. Three example use cases, including relative geologic time-guided facies analysis, layer-customized fault detection, and fault-oriented stratigraphy mapping, are provided for illustrating how one or more constraints can be technically imposed and demonstrating what added values such a constrained CNN can bring. It is concluded that the imposition of interpretational constraints is capable of improving CNN-assisted seismic interpretation and better assisting the tasks of subsurface mapping and modeling." @default.
• W3135210147 created "2021-03-15" @default.
• W3135210147 creator A5003731949 @default.
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• W3135210147 date "2021-04-21" @default.
• W3135210147 modified "2023-09-30" @default.
• W3135210147 title "Imposing interpretational constraints on a seismic interpretation convolutional neural network" @default.
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• W3135210147 doi "https://doi.org/10.1190/geo2020-0449.1" @default.
• W3135210147 hasPublicationYear "2021" @default.
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