Deep learning has achieved a great success in various fields in recent years. As a powerful tool, deep learning is used to interpolate the gravity and magnetic data with block lack of data in this paper. We firstly designed a deep convolution neural network (CNN), and mask dataset was created to simulate the block lack in the data. The network was trained by using the earth’s gravity anomalies dataset (bouguer, isostatic and surface free-air) and the earth magnetic anomaly dataset. We demonstrated the feasibility of this approach by numerical experiments, which indicated that this method is able to effectively recover the gravity and magnetic data with block lack. The experiments also demonstrated that CNN gains competitive performance compared with traditional interpolation methods.
Presentation Date: Wednesday, September 18, 2019
Session Start Time: 9:20 AM
Presentation Time: 10:35 AM
Location: Poster Station 1
Presentation Type: Poster
