Artificial Intelligence in Deep Geothermal Energy: Trends, Insights, and Future Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deep geothermal energy, known for its stable base load power and resilience to environmental fluctuations, is increasingly recognized as an important renewable energy source. Yet, its development is constrained by subsurface variability, high exploration costs, and operational inefficiencies. Artificial intelligence (AI) can analyze complex data, reveal patterns, and support predictive modeling to lower costs, shorten timelines, and improve efficiency. This review aims to evaluate how AI can address these barriers by systematically synthesizing its applications in deep geothermal research. A structured Web of Science search and multi-stage screening yielded 183 peer-reviewed journal papers, classified across eight research areas: reservoir characterization, exploration and resource identification, system optimization, seismic monitoring and risk assessment, drilling optimization, hybrid energy systems, environmental impact and sustainability, and techno-economic analysis. Our analysis shows that since 2020, AI applications in geothermal energy have expanded exponentially, surpassing overall AI growth rates. China and the United States dominate research output, followed by Germany, Turkey, Canada, and India. Advanced algorithms are increasingly preferred: convolutional neural networks for spatial modeling and image interpretation, recurrent neural networks for time-series forecasting, physics-informed AI, Bayesian frameworks, and autoencoders advance uncertainty quantification and data reconstruction. The novelty of this review lies in its comprehensive cross-domain synthesis of AI applications in deep geothermal energy, using a unified algorithm–input–output–performance lens. This structured mapping enables comparisons not possible in earlier overviews, reveals methodological strengths, identifies effective approaches for different geothermal tasks, and uncovers underexplored areas such as environmental assessment and techno-economic analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it