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Currently Active Research Projects


GMG-4 (2018) - Understanding conditions for stable/unstable fault slip induced by fluid injections

Experimental plan:

  • Use the existing codes to model a field experiment on fluid injection.
  • Develop codes for coupling between evolving compaction/dilation of the fault gouge and fluid flow.
Publications:

Larochelle, S., Lapusta, N., Ampuero, J.-P., & Cappa, F. (2021). Constraining fault friction and stability with fluid-injection field experiments. Geophysical Research Letters, 48, e2020GL091188. https://doi.org/10.1029/2020GL091188. [PDF] *GMGPUB2

Heimisson, Elías Rafn and Rudnicki, John and Lapusta, Nadia (2021) Dilatancy and Compaction of a Rate-and-State Fault in a Poroelastic Medium: Linearized Stability Analysis. Journal of Geophysical Research. Solid Earth, 126 (8). Art. No. e2021JB022071. ISSN 2169-9313. doi:10.1029/2021jb022071. [PDF] *GMGPUB9


GMG-6 (2018) - Experimental investigation of the interaction between fluids and failure of rock faults in shear

Experimental plan:

  • Conduct controlled and highly instrumented laboratory experiments with fluid injection into a pre-existing fault to study evolution in friction/pore pressure and triggering of fast/slow slip under various conditions.
  • Measure slip, slip rate, and shear stress evolution along the fault during the injection process.
  • Compare measurements with existing theories on the stability of fault slip.
Publications:

Gori, Marcello and Rubino, Vito and Rosakis, Ares J. et al. (2021) Dynamic rupture initiation and propagation in a fluid-injection laboratory setup with diagnostics across multiple temporal scales. Proceedings of the National Academy of Sciences of the United States of America, 118 (51). Art. No. e2023433118. ISSN 0027-8424. doi:10.1073/pnas.2023433118. [PDF] [SUP] *GMGPUB8



GMG-7 & 8 (2018) - Microseismic Monitoring with Deep Learning

Experimental plan:

  • Adapt the method for vertical only data. Apply to SAF and SBB arrays, and the geothermal data.

Publications:

Smith, Jonthan D. and Ross, Zachary E. and Azizzadenesheli, Kamyar et al. (2022) HypoSVI: Hypocentre inversion with Stein variational inference and physics informed neural networks. Geophysical Journal International, 228 (1). pp. 698-710. ISSN 0956-540X. doi:10.1093/gji/ggab309. [PDF]

Smith, Jonathan D. and Azizzadenesheli, Kamyar and Ross, Zachary E. (2021) EikoNet: Solving the Eikonal Equation with Deep Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 59 (12). pp. 10685-10696. ISSN 0196-2892. doi:10.1109/TGRS.2020.3039165. https://resolver.caltech.edu/CaltechAUTHORS:20200526-084219717 *GMGPUB10


GMG-9 (2018) - Application of DAS in monitoring microseismicity and subsurface structure changes

Experimental plan:

  • Use the DAS instrument contributed by OptaSense through GMG to collect data in the Pasadena area.
  • Analyze the DAS data and develop new methods in microseismicity detection, and structure monitoring.
  • Optimize the data collection and processing procedures to improve the monitoring accuracy and efficiency.
Publications:

Wang, Xin and Williams, Ethan F. and Karrenbach, Martin et al. (2020) Rose Parade Seismology: Signatures of Floats and Bands on Optical Fiber. Seismological Research Letters. ISSN 0895-0695. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20200506-105533707

Zhan, Zhongwen (2020) Distributed Acoustic Sensing Turns Fiber‐Optic Cables into Sensitive Seismic Antennas. Seismological Research Letters, 91 (1). pp. 1-15. ISSN 0895-0695. https://resolver.caltech.edu/CaltechAUTHORS:20200116-083302517


GMG-10 (2020) - Characterizing geothermal tremor

Experimental plan:

  • WP1: Noise discrimination study source-path-receiver analysis to discriminate what resonances are not associated with geothermal tremor (e.g., environmental or anthropogenic)
  • WP2 Time-frequency analysis. a search for relationships between injection / production flow and pressure changes and amplitude / frequency responses
  • Numerical model building of sources. Use known geothermal reservoir rock and fluid properties (e.g., viscosity), well-field performance (e.g., flow rate) and known crack-wave (e.g., Krauklis waves) and fluid-flow physics (e.g., turbulent flow) to iteratively forward model for geothermal tremor by perturbing fracture properties (e.g., fracture width, aperture and geometry).

GMG-11 (2022) - Forecast and control of injection-induced seismicity

Experimental plan:

  • WP1: Develop and test a probabilistic method to disentangle direct and indirect triggering of injection induced earthquakes. The method will provide an estimate of the probability that any particular earthquake was caused by an injection or a previous earthquake.
  • WP2: Test the method on a selection of examples of injection-induced seismicity, in particular from Oklahoma or the Montney Basin (British Columbia).
  • WP3: Compare the empirical spatio-temporal kernel functions with predictions from stress-based simulations (combining poroelastic stress calculation an earthquake nucleation based on rate-and-state friction). Assess the possibility of seismicity control through numerical experiments.
Publications:

Kim, T., & Avouac, J.-P. (2023). Stress-based and convolutional forecasting of injection-induced seismicity: Application to the Otaniemi geothermal reservoir stimulation. Journal of Geophysical Research: Solid Earth, 128, e2022JB024960. https://doi.org/10.1029/2022JB024960. [PDF]


GMG-12 (2022) - A vertically-integrated multiphase reservoir model to enable real-time forecasting of seismicity during carbon storage operation

Experimental plan:

  • Task 1: Incorporate real thermodynamic properties of CO2 into single-phase flow model to understand how initial temperature and in-situ pressure variations impact pressure diffusion in the Gronnigen site
  • Task 2: Implement the vertically-integrated two-phase flow framework proposed by Jenkins et al 2019 to simulate two-phase injection into a single aquifer laye uniform thickness
  • Task 3: Extend the model to consider two-phase injection into a single aquifer layer of variable thickness, hydraulic and elastic properties.Test this model using parameters from the Gronnigen site.
  • Task 4: Incorporate the new model into the seismicity forecasting framework at GMG (Smieth et al. 2022).