Stage‐Wise Stochastic Deep Learning Inversion Framework for Subsurface Sedimentary Structure Identification
The stochastic models and deep‐learning models are the two most commonly used methods for subsurface sedimentary structures identification. The results from the stochastic models typically involve uncertainty due to their nature. For the deep‐learning models, sufficient structure samples are necessa...
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| Published in: | Geophysical research letters Vol. 49; no. 1 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Washington
John Wiley & Sons, Inc
16.01.2022
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| Subjects: | |
| ISSN: | 0094-8276, 1944-8007 |
| Online Access: | Get full text |
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| Summary: | The stochastic models and deep‐learning models are the two most commonly used methods for subsurface sedimentary structures identification. The results from the stochastic models typically involve uncertainty due to their nature. For the deep‐learning models, sufficient structure samples are necessary for training, but they are practically difficult to obtain. This study develops an inversion framework to combine the strength of these two models to overcome the limitations. The stochastic model is first adopted to generate the structure samples required by the deep‐learning models by integrating available observations. Then the trained deep‐learning model is utilized to reduce the uncertainty of the structures generated by the stochastic models. This integrated framework can successfully estimate the structures using available observations. Importantly, no additional structure training samples are required in the identification process. To summarize, the combination of the stochastic and the deep‐learning models shows great advantages in identifying subsurface sedimentary structures.
Plain Language Summary
This study develops an integrated inversion framework that uses sparse data obtained from different geophysical measurements to identify subsurface sedimentary structures. By combining the advantages of physically‐based geological models and advanced deep‐learning techniques, the developed framework can identify complex subsurface sedimentary structures in a more practical way than is currently possible. The advantage of this method is that the identification process relies only on sparse measurement data and does not need additional structure samples for deep‐learning model training. It has significant practical value because most deep‐learning‐based methods require a large number of training samples, which are practically difficult to obtain. Therefore, the developed framework has great advantages in practical subsurface sedimentary structures identification applications.
Key Points
A stage‐wise inversion framework is developed by combining the transition probability approach and the deep‐learning methods
The developed framework can identify subsurface sedimentary structures using sparse geological data and flow and transport responses
The estimated structures can honor the available flow and transport responses and capture the preferential flow pathways |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0094-8276 1944-8007 |
| DOI: | 10.1029/2021GL095823 |