An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks
As gravity exploration technology advances, gravity gradient measurement is becoming an increasingly important method for gravity detection. Airborne gravity gradient measurement is widely used in fields such as resource exploration, mineral detection, and oil and gas exploration. However, the motio...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/421 |
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| author | Shuai Zhou Changcheng Yang Yi Cheng Jian Jiao |
| author_facet | Shuai Zhou Changcheng Yang Yi Cheng Jian Jiao |
| author_sort | Shuai Zhou |
| collection | DOAJ |
| description | As gravity exploration technology advances, gravity gradient measurement is becoming an increasingly important method for gravity detection. Airborne gravity gradient measurement is widely used in fields such as resource exploration, mineral detection, and oil and gas exploration. However, the motion and attitude changes of the aircraft can significantly affect the measurement results. To reduce the impact of the dynamic environment on the accuracy of gravity gradient measurements, compensation algorithms and techniques have become a research focus. This paper proposes a post-error compensation algorithm using convolutional and long short-term memory neural networks (CNN-LSTMs). By leveraging convolution feature extraction capabilities and considering the temporal dependencies of dynamic measurement parameters with LSTM, the model demonstrates a stronger ability to learn from severely coupled time series data, resulting in a significant improvement in the compensation performance. This method outperforms traditional neural networks’ multi-layer perceptrons (MLPs) in terms of compensation accuracy on both simulated and measured airborne gravity gradient data from Heilongjiang Province. |
| format | Article |
| id | doaj-art-67ee0c861d9246cfa303540940babb63 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-67ee0c861d9246cfa303540940babb632025-01-24T13:48:52ZengMDPI AGSensors1424-82202025-01-0125242110.3390/s25020421An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural NetworksShuai Zhou0Changcheng Yang1Yi Cheng2Jian Jiao3College of Geoexploration Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Geoexploration Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Geoexploration Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Geoexploration Science and Technology, Jilin University, Changchun 130012, ChinaAs gravity exploration technology advances, gravity gradient measurement is becoming an increasingly important method for gravity detection. Airborne gravity gradient measurement is widely used in fields such as resource exploration, mineral detection, and oil and gas exploration. However, the motion and attitude changes of the aircraft can significantly affect the measurement results. To reduce the impact of the dynamic environment on the accuracy of gravity gradient measurements, compensation algorithms and techniques have become a research focus. This paper proposes a post-error compensation algorithm using convolutional and long short-term memory neural networks (CNN-LSTMs). By leveraging convolution feature extraction capabilities and considering the temporal dependencies of dynamic measurement parameters with LSTM, the model demonstrates a stronger ability to learn from severely coupled time series data, resulting in a significant improvement in the compensation performance. This method outperforms traditional neural networks’ multi-layer perceptrons (MLPs) in terms of compensation accuracy on both simulated and measured airborne gravity gradient data from Heilongjiang Province.https://www.mdpi.com/1424-8220/25/2/421gravity gradientdeep learninglong short-term memoryconvolutionpost-error compensation |
| spellingShingle | Shuai Zhou Changcheng Yang Yi Cheng Jian Jiao An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks Sensors gravity gradient deep learning long short-term memory convolution post-error compensation |
| title | An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks |
| title_full | An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks |
| title_fullStr | An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks |
| title_full_unstemmed | An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks |
| title_short | An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks |
| title_sort | airborne gravity gradient compensation method based on convolutional and long short term memory neural networks |
| topic | gravity gradient deep learning long short-term memory convolution post-error compensation |
| url | https://www.mdpi.com/1424-8220/25/2/421 |
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