Earthquake prediction method, device, medium and equipment based on earthquake precursor information
By extracting features and correcting attenuation of atmospheric tidal gravity anomaly signals, and combining gradient boosting models with kernel density estimation, the problem of far-field overestimation and near-field underestimation of atmospheric tidal gravity anomaly signals in strong earthquake prediction in existing technologies has been solved. This enables quantitative prediction of strong earthquake magnitude and focal range, improving the accuracy and interpretability of predictions.
Patent Information
- Application Number
- CN202511609949.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-05
- Publication Date
- 2026-06-05
- Estimated Expiration
- 2045-11-05
AI Technical Summary
Existing technologies for predicting strong earthquakes using atmospheric tidal gravity anomaly signals lack systematic quantitative methods and suffer from problems such as overestimation in the far field and underestimation in the near field. They also struggle to achieve a stable mapping between magnitude and source distance and lack a verifiable and transferable quantitative prediction system.
Feature extraction and screening are performed using atmospheric tidal gravity anomaly signals. A decay function is introduced for physical normalization. A joint prediction framework is constructed by combining a gradient boosting model and kernel density estimation. Through spatial partitioning simulation and decay correction, quantitative prediction of strong earthquake magnitude and focal range is achieved.
It improves the accuracy of strong earthquake prediction, provides spatial information on magnitude and high-risk locations, enhances the interpretability and stability of prediction results, and can provide quantitative prediction results several days to twenty days before an earthquake, supporting earthquake risk assessment and emergency response.
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Abstract
Citation Information
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