Guanyu Yuan
+49 345 / 55 – 26076
lisa.ringel@geo.uni-halle.de
Room 4.23
von-Seckendorff-Platz 3
06120 Halle (Saale)
Lisa Maria Ringel, MEng
+49 345 / 55 – 26076
lisa.ringel@geo.uni-halle.de
Room 4.23
von-Seckendorff-Platz 3
06120 Halle (Saale)
About Me
My research focuses on the stochastic characterization of discrete fracture networks (DFNs). I use data from tomographic experiments to infer the properties of DFNs. For this purpose, I work on the implementation of fast forward models by suitable numerical methods and I compare different inversion approaches by their efficiency and performance.
Research Interests
CV
2019 – today | PhD student
Martin Luther University Halle-Wittenberg, Applied Geology
2018 – 2019 | Research Assistant
Ingolstadt University of Applied Sciences, Institute of New Energy Systems (InES)
2018 | Master‘s thesis
German Aerospace Center, Institute of Aeroelasticity
Title: Preliminary design of a wind tunnel experiment for transient shock boundary layer interaction
2017 – 2018 | M. Eng. Technical Development in Mechanical Engineering
Ingolstadt University of Applied Sciences
2015 – 2018 | Scholarship holder
Max Weber-Program
2013 – 2017 | B. Eng. Renewable Energy Technologies
Ingolstadt University of Applied Sciences
Publications
Ringel, L. M., Jalali, M., Bayer, P. (2022). Characterization of the highly fractured zone at the Grimsel Test Site based on hydraulic tomography. Hydrol. Earth Syst. Sci., 26, 6443–6455.
Ringel, L. M., Jalali, M., and Bayer, P., (2022). Inversion of
Hydraulic Tomography Data from the Grimsel Test Site with a Discrete Fracture Network Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2220, https://doi.org/10.5194/egusphere-egu22-2220.
Ringel, L. M., Jalali, M., Bayer, P., (2022). Estimation of hydraulic and geometrical characteristics of fractured geothermal reservoirs using in-situ tomographic methods, European Geothermal Congress 2022, Berlin, Germany, 17–21 October 2022.
Ringel, L. M., Jalali, M., Bayer, P. (2021). Stochastic Inversion of Three-Dimensional Discrete Fracture Network Structure With Hydraulic Tomography. Water Resources Research. 57(12), e2021WR030401.
Ringel, L. M., Jalali, M. & Bayer, P. (2020). Inversion of three-dimensional discrete fracture networks using hydraulic tomography. Paper presented at the AGU Fall Meeting, virtual.
Ringel, L. M., Somogyvari, M., Jalali, M. & Bayer, P. (2020). Characterization of discrete fracture networks by invasive tomographic methods. Paper presented at Computational Methods in Water Resources, virtual.
Bayer, P., Afshari Moein, M. J., Somogyvári, M., Ringel, L. M., & Jalali, M. (2020). Stress-based tomography: potential, open-questions and future developments. Paper presented at the EGU General Assembly, virtual.
Ringel, L. M., Somogyvari, M., Jalali, M., & Bayer, P. (2020). A fast and robust approach for simulating the pressure diffusion in three-dimensional discrete fracture networks applied to inversion problems. Paper presented at the EGU General Assembly, virtual.
Ringel, L. M., Somogyvári, M., Jalali, M., & Bayer, P. (2019). Comparison of Hydraulic and Tracer Tomography for Discrete Fracture Network Inversion. Geosciences, 9(6), 274.
Ringel, L. M., Somogyvári, M., Jalali, M., & Bayer, P. (2019). Inversion of discrete fracture networks by hydraulic and tracer tomography. Paper presented at the EGU General Assembly, Vienna.
