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directly provide information on the size and distribution of active hotspots. But more subtle anomalies resulting from subresolution or dormant (but recently active) heat sources could be more difficult to
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of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large
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resolution visualizations of hydrate distributions and fluid migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter
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