Seismic inversion via closed-loop fully convolutional residual network and transfer learning

2020 ◽  
Author(s):  
Lingling Wang ◽  
Delin Meng ◽  
Bangyu Wu ◽  
Naihao Liu
Keyword(s):  
Transfer Learning ◽  
Closed Loop ◽  
Seismic Inversion ◽  
Residual Network

Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Lingling Wang ◽  
Delin Meng ◽  
Bangyu Wu
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Seismic Data ◽  
Closed Loop ◽  
Seismic Inversion ◽  
Open Loop ◽  
Residual Network ◽  
Velocity Inversion ◽  
Good Convergence ◽  
Loop Network

Because deep learning networks can 'learn' the complex mapping function between the labeled inputs and outputs, they have shown great potential in seismic inversion. Conventional deep learning algorithms require a large amount of labeled data for sufficient training. However, in practice, the number of well logs is limited. To address this problem, we propose a closed-loop fully convolutional residual network (FCRN) combined with transfer learning strategy for seismic inversion. This closed-loop FCRN consists of an inverse network and a forward network. The inverse network predicts the inversion target from seismic data, whereas the forward network calculates seismic data from the inversion target. The inverse network is initialized by pre-training on the Marmousi2 model and fine-tuned with the limited labeled data around the wells through transfer learning, to suit the target seismic data. The forward network is initialized by training with the limited labeled data around the wells. In this way, the closed-loop network is well initialized to ensure relatively good convergence. Then, the misfit of the limited labeled data and the error between the true and the forward seismic data are used to regularize the training of the initialized closed-loop network. The inverse network of the optimized closed-loop network is used to obtain the final inversion results. The proposed work flow can be used for velocity, density, and impedance inversion from post-stack seismic data. This paper takes velocity inversion as an example to illustrate the effectiveness of the method. The experimental results show that the closed-loop FCRN with transfer learning is superior than the open-loop FCRN with better lateral continuity and velocity details. The closed-loop FCRN can effectively predict the velocity with high accuracy on the synthetic data, has good anti-noise performance, and also can be effectively used for the field data with spatial heterogeneity.

Application of Closed-loop Neuromodulation Algorithm Based on Transfer Learning(LMPROJ) in Treatment of Epilepsy

2019 ◽  
Vol 12 (2) ◽  
pp. 493-494
Author(s):  
L. Shen ◽  
J. Zhou ◽  
J. Wang ◽  
S. Wang ◽  
Z. Zhao
Keyword(s):  
Transfer Learning ◽  
Closed Loop ◽  
Treatment Of Epilepsy

scholarly journals Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

2021 ◽  
Vol 45 (4) ◽  
pp. 600-607
Author(s):  
I. Hamdi ◽  
Y. Tounsi ◽  
M. Benjelloun ◽  
A. Nassim
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Transfer Learning ◽  
Ground Truth ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Residual Network ◽  
Edge Preservation ◽  
Radar Images ◽  
Aperture Radar

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

scholarly journals Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Mateen ◽  
Junhao Wen ◽  
Nasrullah Nasrullah ◽  
Song Sun ◽  
Shaukat Hayat
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Region Of Interest ◽  
The Other ◽  
Previous Knowledge ◽  
Residual Network ◽  
Softmax Classifier ◽  
Fully Connected ◽  
Visual Geometry

In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates.

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