Enhanced Hardrock Seismic Imaging Through Multi‐Scale Information‐Guided Unsupervised Learning
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
Abstract In hardrock or crystalline rock geological settings, due to low impedance contrast, reflected energy is usually weak. In addition, often stronger surface waves and noncoherent noise are observed including high‐frequency scattering noise, which seriously covers the useful reflection signal. Therefore, imaging of hardrock seismic data with a low signal‐to‐noise ratio (S/N) is challenging and requires tailored and cumbersome processing workflows. In this study, we propose an unsupervised learning‐based framework with frequency‐guided constraints for pre‐stack seismic data denoising. The proposed label‐free framework contains two input channels, noisy and time‐frequency‐domain data conditioned through a continuous wavelet transform (CWT) filter. The CWT filtered data provide richer feature representations guiding better the reconstruction of seismic signals. The proposed framework consists of several feature attention blocks with the soft attention mechanism to extract the spatial relationship between noisy and CWT filtered data and assign higher weights to significant features. To improve the denoising performance, we designed a hybrid loss function containing the log‐cosh function, amplitude‐weighted constraint, and frequency‐dynamic weighted constraint. We use one synthetic and two real pre‐stack seismic data sets from two mineral‐endowed regions in Sweden and Canada to test the effectiveness of the proposed network. Compared with the three benchmarks, our proposed framework shows stronger reflection signal recovery and is capable of better attenuating the complex noise. The proposed denoising workflow allows improved delineation of near‐surface structures and the mineral deposits targeted in one of the data sets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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