Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2023.3262679
Abstract: Deep learning methods, especially convolutional neural networks, achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1-D) convolution, tending to yield lateral discontinuities of impedance on field data applications.… read more here.
Keywords: network; seismic impedance; inversion; hybrid loss ... See more keywords
Seismic Impedance Inversion Using Fully Convolutional Residual Network and Transfer Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2963106
Abstract: In this letter, we use a fully convolutional residual network (FCRN) for seismic impedance inversion. After training with appropriate data, the FCRN can effectively predict impedance with high accuracy, and have good robustness against noise… read more here.
Keywords: seismic impedance; fully convolutional; impedance; residual network ... See more keywords
Seismic Impedance Inversion Using Conditional Generative Adversarial Network
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3090108
Abstract: Deep-learning methods, such as convolutional neural networks (CNNs), have been successfully applied to seismic impedance inversion in recent years. Compared with traditional geophysical inversion, deep-learning inversion can give inversion results with higher resolution. In this… read more here.
Keywords: generative adversarial; conditional generative; inversion; impedance inversion ... See more keywords