Seismic Signal Processing and Source Location Based on Deep Learning
Bibliographic record
Abstract
Seismic signal processing plays a critical role in geophysics, enabling the detection, classification, and localization of seismic events for applications ranging from earthquake monitoring to resource exploration.Traditional methods such as beamforming, matched filtering, and cross-correlation have demonstrated efficacy but suffer from limitations including sensitivity to noise, dependence on predefined templates, and computational inefficiency when handling large datasets.To address these challenges, this study explores the integration of deep learning techniques for seismic signal processing and source location.The proposed method leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract spatiotemporal features from raw seismic waveforms, enabling accurate classification and localization of seismic events.Experimental results on benchmark seismic datasets indicate that the deep learning-based approach outperforms traditional methods in terms of accuracy, robustness to noise, and computational speed.Additionally, transfer learning techniques are employed to adapt the model to new geographical regions with limited labeled data, further enhancing its applicability.The results demonstrate that deep learning offers a transformative approach to seismic signal analysis, paving the way for real-time seismic monitoring and improved decision-making in geophysical research.This advancement has significant implications for disaster management, resource exploration, and understanding Earth's subsurface dynamics.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".