Prediction of Terrestrial Heat Flow in Songliao Basin Based on Deep Neural Network Article Swipe
Lige Bai
,
Jing Li
,
Zhaofa Zeng
,
Deqiang Tao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1029/2023ea003186
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1029/2023ea003186
Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m 2 , the average is 67.1 mW/m 2 , and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1029/2023ea003186
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023EA003186
- OA Status
- gold
- Cited By
- 9
- References
- 59
- Related Works
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- OpenAlex ID
- https://openalex.org/W4389904210
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4389904210Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2023ea003186Digital Object Identifier
- Title
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Prediction of Terrestrial Heat Flow in Songliao Basin Based on Deep Neural NetworkWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-01Full publication date if available
- Authors
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Lige Bai, Jing Li, Zhaofa Zeng, Deqiang TaoList of authors in order
- Landing page
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https://doi.org/10.1029/2023ea003186Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023EA003186Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2023EA003186Direct OA link when available
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Geothermal gradient, Geology, Structural basin, Petrology, Heat flow, Sedimentary basin, Geophysics, Geomorphology, Thermal, Meteorology, PhysicsTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4298145301, https://openalex.org/W2883478268, https://openalex.org/W2114118688, https://openalex.org/W2073437977, https://openalex.org/W1986738852, https://openalex.org/W4303699996, https://openalex.org/W4381380752, https://openalex.org/W2089173587, https://openalex.org/W2765179261, https://openalex.org/W239093348, https://openalex.org/W3198265750, https://openalex.org/W3134608805, https://openalex.org/W1995028274, https://openalex.org/W2026572557, https://openalex.org/W1941867818, https://openalex.org/W2053257485, https://openalex.org/W2991100041, https://openalex.org/W2133505387, https://openalex.org/W4205596357, https://openalex.org/W4223477478, https://openalex.org/W2909248743, https://openalex.org/W2512297284, https://openalex.org/W1969521780, https://openalex.org/W1967908930, https://openalex.org/W2280180419, https://openalex.org/W3167977710, https://openalex.org/W3020162125, https://openalex.org/W3107144884, https://openalex.org/W2965785417, https://openalex.org/W2496694577, https://openalex.org/W2986950891, https://openalex.org/W2768051969, https://openalex.org/W2161724321, https://openalex.org/W2955661526, https://openalex.org/W2353860730, https://openalex.org/W1900588704, https://openalex.org/W2980213693, https://openalex.org/W2114104437, https://openalex.org/W2136050663, https://openalex.org/W2769340760, https://openalex.org/W1973012528, https://openalex.org/W3004442222, https://openalex.org/W2076063813, https://openalex.org/W3153153518, https://openalex.org/W3122626956, https://openalex.org/W2128135732, https://openalex.org/W2888014304, https://openalex.org/W4205931396, https://openalex.org/W2097275551, https://openalex.org/W4319336406, https://openalex.org/W3092764301, https://openalex.org/W4210308187, https://openalex.org/W2907616800, https://openalex.org/W4319441886, https://openalex.org/W3005830954, https://openalex.org/W7053024666, https://openalex.org/W2347368170, https://openalex.org/W2352356010, https://openalex.org/W2883329331 |
| referenced_works_count | 59 |
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| abstract_inverted_index.2 | 142, 149 |
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| corresponding_author_ids | https://openalex.org/A5100336995 |
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| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I194450716, https://openalex.org/I4210134929 |
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| citation_normalized_percentile.is_in_top_10_percent | False |