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Ge Jin

Ge Jin

Assistant Professor
Co-director of Reservoir Characterization Project
Department of Geophysics

Short Bio

Dr. Ge Jin is Assistant Professor of Geophysics and Co-director of the Reservoir Characterization Project ( He is interested in Distributed Fiber-Optic Sensing (DFOS) and machine-learning applications, as well as seismic imaging and interpretation. He obtained his Ph.D. in Geophysics from Columbia University and dual B.S. in Geophysics and Computer Science from Peking University. 

Dr. Jin managed and participated many cross-functional research projects, and made a major breakthrough by discovering the rich information hidden in the extra-low frequency band of the DAS signal. He also developed a series of algorithms that utilize DFOS data for production logging, hydraulic fracture detection, well production interference evaluation, and microseismic monitoring. In addition to FOS research, he has developed several workflows that use ground roll energy to image near-surface structures, which have been applied on land seismic data from multiple fields.


  • B.S. in Geophysics, Peking University, 2006
  • B.S. in Computer Science, Peking University, 2006
  • M.S. in Geophysics, Peking University, 2009
  • Ph.D. in Geophysics, Columbia University, 2014


  • Fiber Optic Sensing
  • Machine learning
  • Seismic imaging and interpretation


  • GPGN436/536: Geophysical Computing/Advanced Geophysical Computing
    • Offered in fall semesters
    • Co-teach with Jeff Shragge
    • Syllabus: GPGN436, GPGN536
  • GPGN598C: Introduction to Distributed Fiber-Optic Sensing and Its Applications
  • GPGN598C: Reading Seminar
    • Offered in fall semesters


Selected Publications

Liu, Y., Jin, G., Wu, K., & Moridis, G., 2020, November. Hydraulic-Fracture-Width Inversion Using Low-Frequency Distributed-Acoustic-Sensing Strain Data – Part I: Algorithm and Sensitivity Analysis. Society of Petroleum Engineers. doi:10.2118/204225-PA

Titov, A., Jin, G., Fan, Y., Tura, A., Kutun, K. and Miskimins, J., 2020, March. Distributed Fiber-optic Sensing Based Production Logging Investigation: Flowloop Experiments. In First EAGE Workshop on Fibre Optic Sensing (Vol. 2020, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.

Jin*, G., Friehauf*, K., Roy*, B., Constantine, J.J., Swan, H.W., Krueger, K.R. and Raterman, K.T., 2019, October. Fiber Optic Sensing-Based Production Logging Methods for Low-Rate Oil Producers. In Unconventional Resources Technology Conference, Denver, Colorado, 22-24 July 2019 (pp. 1183-1199). Unconventional Resources Technology Conference (URTeC); Society of Exploration Geophysicists.

Jin, G., Mendoza, K., Roy, B. and Buswell, D.G., 2019. Machine learning-based fracture-hit detection algorithm using LFDAS signal. The Leading Edge38(7), pp.520-524.

Jin, G. and Roy, B., 2017. Hydraulic-fracture geometry characterization using low-frequency DAS signal. The Leading Edge36(12), pp.975-980.

Jin, G., Gaherty, J.B., Abers, G.A., Kim, Y., Eilon, Z. and Buck, W.R., 2015. Crust and upper mantle structure associated with extension in the Woodlark Rift, Papua New Guinea from Rayleigh‐wave tomography. Geochemistry, Geophysics, Geosystems16(11), pp.3808-3824.

Jin, G. and Gaherty, J.B., 2015. Surface wave phase-velocity tomography based on multichannel cross-correlation. Geophysical Journal International201(3), pp.1383-1398.


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Colorado School of Mines
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