Enhanced Hardrock Seismic Imaging Through Multi‐Scale Information‐Guided Unsupervised Learning
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".