IEEE transactions on neural networks and learning systems
Jul 6, 2023
Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification t...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-noise ratio (SNR) of registered seismic signals improves subsequent processing and, eventually, data analysis and interpretation. In this work, a novel noise r...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
3-D salt segmentation is important for many research topics spanning from exploration geophysics to structural geology. In seismic exploration, 3-D salt segmentation is directly related to the velocity modeling building that affects many processing s...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Passive seismic interferometry is a vastly generalized blind deconvolution question, where different paths through the Earth correspond to different channels called Green's functions; the sources are completely incoherent and not shared by the channe...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolution...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments o...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
In this article, an unsupervised domain adaptation strategy has been investigated using a deep Siamese neural network in scene-level land cover classification using remotely sensed images. At the onset, the soft class label and probability scores of ...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
Understanding the dynamics of deforestation and land uses of neighboring areas is of vital importance for the design and development of appropriate forest conservation and management policies. In this article, we approach deforestation as a multilabe...
IEEE transactions on neural networks and learning systems
Jul 6, 2023
In spite of achieving promising results in hyperspectral image (HSI) restoration, deep-learning-based methodologies still face the problem of spectral or spatial information loss due to neglecting the inner correlation of HSI. To address this issue, ...