Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Attention-Based Algorithm for Gravity Adaptation Zone Calibration

Chen Yu

Abstract

Accurate calibration of gravity adaptation zones is of great significance in fields such as underwater navigation, geophysical exploration, and marine engineering. With the increasing application of gravity field data in these areas, traditional calibration methods based on single features are becoming inadequate for capturing the complex characteristics of gravity fields and addressing the intricate interrelationships among multidimensional data. This paper proposes an attention-enhanced algorithm for gravity adaptation zone calibration. By introducing an attention mechanism, the algorithm adaptively fuses multidimensional gravity field features and dynamically assigns feature weights, effectively solving the problems of multicollinearity and redundancy inherent in traditional feature selection methods, significantly improving calibration accuracy and robustness.In addition, a large-scale gravity field dataset with over 10,000 sampling points was constructed, and Kriging interpolation was used to enhance the spatial resolution of the data, providing a reliable data foundation for model training and evaluation. We conducted both qualitative and quantitative experiments on several classical machine learning models (such as SVM, GBDT, and RF), and the results demonstrate that the proposed algorithm significantly improves performance across these models, outperforming other traditional feature selection methods. The method proposed in this paper provides a new solution for gravity adaptation zone calibration, showing strong generalization ability and potential for application in complex environments. The code is available at \href{this link} {https://github.com/hulnifox/RF-ATTN}.

An Attention-Based Algorithm for Gravity Adaptation Zone Calibration

Abstract

Accurate calibration of gravity adaptation zones is of great significance in fields such as underwater navigation, geophysical exploration, and marine engineering. With the increasing application of gravity field data in these areas, traditional calibration methods based on single features are becoming inadequate for capturing the complex characteristics of gravity fields and addressing the intricate interrelationships among multidimensional data. This paper proposes an attention-enhanced algorithm for gravity adaptation zone calibration. By introducing an attention mechanism, the algorithm adaptively fuses multidimensional gravity field features and dynamically assigns feature weights, effectively solving the problems of multicollinearity and redundancy inherent in traditional feature selection methods, significantly improving calibration accuracy and robustness.In addition, a large-scale gravity field dataset with over 10,000 sampling points was constructed, and Kriging interpolation was used to enhance the spatial resolution of the data, providing a reliable data foundation for model training and evaluation. We conducted both qualitative and quantitative experiments on several classical machine learning models (such as SVM, GBDT, and RF), and the results demonstrate that the proposed algorithm significantly improves performance across these models, outperforming other traditional feature selection methods. The method proposed in this paper provides a new solution for gravity adaptation zone calibration, showing strong generalization ability and potential for application in complex environments. The code is available at \href{this link} {https://github.com/hulnifox/RF-ATTN}.
Paper Structure (18 sections, 13 equations, 3 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 13 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Earth Gravity Field Models
  4. Gravity Adaptation Zone Calibration Methods
  5. Preliminary
  6. Model Overview
  7. Feature Fusion
  8. Feature Prediction
  9. Training Process
  10. Experiments
  11. Dataset Construction
  12. Evaluation Metrics
  13. Accuracy
  14. Recall
  15. F1 Score
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overview of our proposed model, Left: Feature extraction from gravity anomaly data using the method described in Section \ref{['p']}, Middle: Feature fusion method based on the attention mechanism, Right: Calibration of the gravity adaptation zone using the Random Forest model with the extracted features.
  • Figure 2: Topography maps of the four selected sea areas
  • Figure 3: Heatmap of gravity anomaly values obtained from the EGM2008 model for the four selected sea regions, Left: Before interpolation, Right: After interpolation