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A Deep Learning-Enhanced Adaptive Kalman Filter with Multi-Scale Temporal Attention for Airborne Gravity Denoising
Lili Li1, Junxiang Liu1, Guoqing Ma1
1State Key Laboratory of Deep Earth Exploration and Imaging, College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China.
Sensors (Basel, Switzerland)
|April 14, 2026
Summary
A new adaptive Kalman Filter (AKF) method using multi-scale CNN, Bi-LSTM, and attention enhances airborne gravity data denoising. This approach improves geological interpretation by accurately estimating filter parameters and reducing noise.
Area of Science:
- Geophysics
- Remote Sensing
- Signal Processing
Background:
- Airborne gravity surveys are crucial for mapping subsurface geology and mineral targets.
- Raw gravity data is susceptible to noise from airflow and platform instability.
- Kalman Filter (KF) is effective for denoising but sensitive to parameter selection.
Purpose of the Study:
- To develop a novel method for high-precision airborne gravity data denoising.
- To adaptively estimate Kalman Filter parameters for improved accuracy.
- To enhance geological interpretation of airborne gravity data.
Main Methods:
- Proposed the multi-scale CNN-LSTM-attention adaptive Kalman Filter (MSC-LA-AKF).
- Utilized multi-scale CNN for feature extraction at various scales.
- Employed bidirectional LSTM (Bi-LSTM) for time-varying noise identification.
- Integrated a multi-head attention mechanism for adaptive parameter optimization.
Main Results:
- MSC-LA-AKF demonstrated superior denoising accuracy compared to FIR and wavelet filters on simulated data.
- The method effectively removed noise from real airborne gravity data.
- Enhanced geological interpretation was achieved through noise reduction.
Conclusions:
- The MSC-LA-AKF method offers a significant advancement in airborne gravity data processing.
- Adaptive parameter estimation is key to improving KF-based denoising performance.
- This technique provides more reliable data for geological exploration.
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