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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.

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. [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.

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.


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. *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.

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)

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.

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 a an earthquake nucleation based on rate-and-state friction). Assess the possibility of seismicity control through numerical experiments.

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).

Currently Active Enhancement Projects

GMG-EP-2 (2020-2022) (funded by Shell): Stress-based seismicity forecasting for CO2 storage.
Experimental plan:
  • WP1:Development of a modeling framework to  estimate pore pressure, reservoir deformation, and  stress variations with uncertainties quantification. Estimate seismicity rate with account for the nucleation process represented using the rate&state formalism.
  • WP2: Evaluation of the forecasting performance using the  Groningen  test case. This task involves the production of a seismicity catalog produced with Machine Learning techniques.

Elías R Heimisson, Jonathan D Smith, Jean-Philippe Avouac, Stephen J Bourne, Coulomb threshold rate-and-state model for fault reactivation: application to induced seismicity at Groningen, Geophysical Journal International, Volume 228, Issue 3, March 2022, Pages 2061–2072, [PDF]

Past Projects

GMG-1 (2018 - 2021) - Infrastructure system resiliency via InSAR ground deformation monitoring

Experimental plan:

  • Identify ground deformation (landslides, settlement) in Los Angeles affecting the risk of water infrastructure.
  • Develop automated process of deformation map retrieval using interferometric data and image correlation.
  • Compare with ground-based measurements from state-of-practice site surveying of said features.


Li, B. Q., Khoshmanesh, M., & Avouac, J.-P. (2021). Surface deformation and seismicity induced by poroelastic stress at the Raft River geothermal field, Idaho, USA. Geophysical Research Letters, 48, e2021GL095108. [PDF] *GMGPUB5

GMG-2 (2018 - 2021) - Seismicity due to hydraulic stimulation for geothermal energy production.

Experimental plan:

  • WP1: Develop a code to model thermo-poro-elastic stress variations and deformation due to fluid injection. (Y1)
  • WP2:Use Observations from Brawley to test/calibrate the model and analyze the relation to seismicity (Y1)

Im, K. & Avouac, J.P., (2021). On the role of thermal stress and fluid pressure in triggering seismic and aseismic faulting at the Brawley Geothermal Field, California, Geothermics, 97. [PDF] *GMGPUB7

Im, Kyungjae and Avouac, Jean-Philippe (2021) Tectonic tremor as friction-induced inertial vibration. Earth and Planetary Science Letters, 576 . Art. No. 117238. ISSN 0012-821X. doi:10.1016/j.epsl.2021.117238. [PDF] *GMGPUB6

Im, K., Avouac, JP., Heimisson, E.R. et al. Ridgecrest aftershocks at Coso suppressed by thermal destressing. Nature 595, 70–74 (2021). *GMGPUB4

Avouac, J-P, Vrain, M., Kim, T., Smith, J., Ader, T., Ross, Z., Saarno, T.,  (2021) A Convolution Model for Earthquake Forecasting Derived from Seismicity Recorded During the ST1 Geothermal Project on Otaniemi Campus, Finland, Proceedings World Geothermal Congress. [PDF] *GMGPUB3

GMG-3 (2018) - Relating ground subsidence, seismicity and reservoir operations at Groningen

Experimental plan:

  • WP1: Enhanced seismicity seismicity catalog with AI methods for phase detection, association, & EQs location
  • WP2:Use production data to estimate pore pressure, assimilating surface subsidence information. Test effect of heterogeneities of elastic properties.

GMG-5 (2018-2019) - Modeling and simulation of hydraulic fracturing processes, microseismicity, and environmental impact

Experimental plan:

  • Further validate MMHF against imaging experiments..
  • Apply MMHF to estimate fracking performance in the desired settings.
  • Apply MMHF to estimate fracking fluid leak off to nearby groundwater formations.

GMG-EP-1 (2019-2020) (funded by Total): Evaluation of the effect of pore pressure diffusion and poro-elastic stress on seismicity induced by fluid injections.
Experimental plan:
  • WP1: Application of machine learning algorithms  to detect and located induced earthquakes, using in particular sites in West Texas. Estimate stress variations due to pore pressure diffusion and poroelastic effect and assess the relationship to seismicity.

Li, B., Avouac, J-P., Ross, Z., Du, J., Rebel, E., (2020) Induced seismicity in the Dallas-Fort Worth Basin: Enhanced seismic catalogue and evaluation of fault slip potential, SEG Technical Program Expanded Abstracts 2020. September 2020, 1304-1308. *GMGPUB1