A5005093259- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Seismic Imaging and Inversion Techniques
- Remote-Sensing Image Classification
- Drilling and Well Engineering
- Seismic Waves and Analysis
- Seismology and Earthquake Studies
- Infrared Target Detection Methodologies
- Underwater Acoustics Research
- Video Surveillance and Tracking Methods
Jilin University
2022-2024
Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, article proposes a semisupervised oriented framework (S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...
Suppressing random noise in seismic data is a significant problem processing. Often, there serious aliasing between the effective signal and noise, affecting identification of weak signals, even resulting great difficulties suppression conventional signals. We propose an improved attention-guided convolutional neural network (ADNet) to eliminate interference noise. After sufficient amount training, removes by transferring features learned from synthetic dataset tests with complex field data....
Marine vibrators have been favored by seismic acquisition in recent years because of their greater waveform control, repeatability, and lower environmental damage. However, it presents a processing challenge not found with airguns: the Doppler effect. The current industry standard method for source motion correction is based on spatiotemporal filtering or frequency–wavenumber (F-K) domain division. both methods generate spatial aliasing when shot interval coarse. passage...