Updating geostatistically simulated models of mineral deposits in real-time with incoming new information using actor-critic reinforcement learning
Why this work is in the frame
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Bibliographic record
Abstract
The existing technologies that update geostatistically simulated models of mineral deposits cannot self-learn from incoming new information generated in operating mines and do not account for high-order spatial statistics. This work proposes a novel self-learning artificial intelligence algorithm that learns from incoming new information and accounts for high-order spatial statistics, in order to update the geostatistically simulated models of mineral deposits in real-time. The proposed algorithm uses deep policy gradient reinforcement learning with an actor and a critic agent. The grid nodes of the geostatistically simulated model are visited sequentially in a random path, the environment generates the states for each grid node, and feeds the state to the actor and critic agents that respectively predict and evaluate the updated property of the grid node The data is stored in a replay memory, which is sampled at regular intervals to train the agents. The trained agents are then used for further rounds of self-learning. An application of the proposed algorithm at a copper mining operation with incoming drilling machine sensor data (collected spatially), and processing mill sensor data (collected over time), demonstrates its applied aspects in updating the geostatistically simulated models of copper grades of the mineral deposit in real-time, while also reproducing spatial patterns and high-order spatial statistics.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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