Personal Information
Name: Suzhen Shi
Title: Professor
E-mail: ssz@cumtb.edu.cn
Research Interests
(1) Artificial Intelligence and Geophysics
(2)Digital rock and rock physics theory
(3)Reservoir lithology and fluid interpretation
Education/Work Background
2000.09-2004.07，School of Resources and Environment, Henan Polytechnic University, Bachelor;
2004.09-2007.01，School of Resources, China University of Mining and Technology-Beijing, Master；
2007.03-2012.01，College of Earth Science and Surveying Engineering, China University of Mining and Technology-Beijing, PhD；
2012.03-2014.05，College of Mechanics and Architectural Engineering, China University of Mining and Technology-Beijing, Postdoc；
2014.06-2018.06，State Key laboratory of Coal Resources and Safe Mining, China University of Mining and Technology-Beijing, Lecturer；
2018.07-2023.06，State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Associate Professor；
2023.07-Now，State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Professor.
Teaching Courses
Geophysical Exploration
Seismic Data Interpretation and Inversion
Seismic Data Processing and interpretation
Key Research Funding
[1]. Suzhen Shi. Seismic response mechanism of fractures in coal reservoirs. National Natural Science Foundation of China (No. 41702173), 2018-2021, PI
[2]. Geophysical prediction for key parameters of tight reservoir geology-engineering sweet spot, 2021-2024, PI
[3] Post-stack wave impedance in Xinjing Coal Mine, 2023-2024, PI
Honors
2019.06, I won the Coal Youth Science and Technology Award.
2018.12, I was awarded the Green Mine Youth Science and Technology Award.
Selected Publications (* denotes Corresponding author)
[1] Shi, S., Li, M., Chang, W., et al. Seismic impedance inversion based on semi-supervised learning. Computer & Geosciences, 2024, 182, 105497.
[2] Shi, S., Shi G., Pei J., et al. Porosity prediction in tight sandstone reservoirs based on a one–dimensional convolutional neural network–gated recurrent unit model. Applied Geophysics, 2023, 20(4), 1-13.
[3] Shi, S., Qi, Y., Chang, W., et al. Acoustic impedance inversion in coal strata using the priori constraint-based TCN-BiGRU method. Advances in Geo-Energy Research, 2023, 9(1): 13-24.
[4] Wang, X., Shi, S.*, Yao, X., et al. Automatic Identification of Seismic Faults via the Integration of ResNet-50 Residual Blocks and Convolutional Attention Modules. Applied Geophysics, 2023: 1-16.
[5] Shi, S., Feng, J., Bai, J., et al. In situ stress field prediction based on seismic data in Sijiazhuang mining area. Interpretation, 2023, 11(1): T7-T19.
[6] Shi, S., Li, M., Gao, W., et al. Improved Unet in Lithology Identification of Coal Measure Strata. Lithosphere, 2022(Special 12): 4087265.
[7] Shi, S., Gu, J., Liu, Z., et al. Tunneling route prediction of shield machine based on random forest P-wave generation. Applied Geophysics, 2021, 18(3): 1-11.
[8] Shi, S., Feng, J., Liu, Z., et al. Seismic inversion-based prediction of the elastic parameters of rocks surrounding roadways: a case study from the Shijiazhuang mining area of North China. Acta Geophysica, 2021, 69(6): 2219-2230.
[9] Shi, S., Gu, J., Feng, J., et al. Intelligent identification method for near-surface ground fissures based on seismic data. Applied Geophysics, 2020, 17(5-6): 639-648.
[10] Shi, S., Liu, Z., Feng, J., et al. Using 3D seismic exploration to detect ground fissure. Advances in Geo-Energy Research, 2020, 4(1): 13-19.