Accelerating High-Resolution Seismic Imaging by Using Deep Learning
Abstract
:1. Introduction
2. Methods
2.1. High-Resolution Imaging Using QPSTM
2.2. Network Architecture
2.3. End-to-End Learning with Small Patches
2.4. Data Set
3. Results
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Sample Availability: Data associated with this research is confidential and the source code is available after the paper has been published online. You can get the source code from https://github.com/reed-lau/migration-migration.git. |
Layer | Output Shape | Connected to |
---|---|---|
input-1 | (64,128,1) | |
conv2d-1 | (64,128,64) | input-1 |
conv2d-2 | (64,128,64) | conv2d-1 |
max-pooling2d-1 | (32,64,64) | conv2d-2 |
conv2d-3 | (32,64,128) | max-pooling2d-1 |
conv2d-4 | (32,64,128) | conv2d-3 |
max-pooling2d-2 | (16,32,128) | conv2d-4 |
conv2d-5 | (16,32,256) | max-pooling2d-2 |
conv2d-6 | (16,32,256) | conv2d-5 |
max-pooling2d-3 | (8,16,256) | conv2d-6 |
conv2d-7 | (8,16,512) | max-pooling2d-3 |
conv2d-8 | (8,16,512) | conv2d-7 |
dropout-1 | (8,16,512) | conv2d-8 |
max-pooling2d-4 | (4,8,512) | dropout-1 |
conv2d-9 | (4,8,1024) | max-pooling2d-4 |
conv2d-10 | (4,8,1024) | conv2d-9 |
dropout-2 | (4,8,1024) | conv2d-10 |
up-sampling2d-1 | (8,16,1024) | dropout-2 |
conv2d-11 | (8,16,512) | up-sampling2d-1 |
concatenate-1 | (8,16,1024) | dropout-1 |
conv2d-11 | ||
conv2d-12 | (8,16,512) | concatenate-1 |
conv2d-13 | (8,16,512) | conv2d-12 |
up-sampling2d-2 | (16,32,512) | conv2d-13 |
conv2d-14 | (16,32,256) | up-sampling2d-2 |
concatenate-2 | (16,32,512) | conv2d-6 |
conv2d-14 | ||
conv2d-15 | (16,32,256) | concatenate-2 |
conv2d-16 | (16,32,256) | onv2d-15 |
up-sampling2d-3 | (32,64,256) | conv2d-16 |
conv2d-17 | (32,64,128) | p-sampling2d-3 |
concatenate-3 | (32,64,256) | conv2d-4 |
conv2d-17 | ||
conv2d-18 | (32,64,128) | concatenate-3 |
conv2d-19 | (32,64,128) | conv2d-18 |
up-sampling2d-4 | (64,128,128) | conv2d-19 |
conv2d-20 | (64,128,64) | up-sampling2d-4 |
concatenate-4 | (64,128,128) | conv2d-2 |
conv2d-20 | ||
conv2d-21 | (64,128,64) | concatenate-4 |
conv2d-22 | (64,128,64) | conv2d-21 |
conv2d-23 | (64,128,1) | conv2d-22 |
Deep Learning Method (hour) | QPSTM Method (hour) | |
---|---|---|
Computing Resource | One TITAN XP GPU | One TITAN XP GPU |
Computing Process | Data Generation (96 h) Training Process (4 h) Prediction Process (0.4 h) | 32 h * Profile Number (437) |
Total Time | 100.4 h | 13,984 h |
Speedup Ratio | 139 |
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Share and Cite
Liu, W.; Cheng, Q.; Liu, L.; Wang, Y.; Zhang, J. Accelerating High-Resolution Seismic Imaging by Using Deep Learning. Appl. Sci. 2020, 10, 2502. https://doi.org/10.3390/app10072502
Liu W, Cheng Q, Liu L, Wang Y, Zhang J. Accelerating High-Resolution Seismic Imaging by Using Deep Learning. Applied Sciences. 2020; 10(7):2502. https://doi.org/10.3390/app10072502
Chicago/Turabian StyleLiu, Wei, Qian Cheng, Linong Liu, Yun Wang, and Jianfeng Zhang. 2020. "Accelerating High-Resolution Seismic Imaging by Using Deep Learning" Applied Sciences 10, no. 7: 2502. https://doi.org/10.3390/app10072502