Characterization of Environmental and Subsurface Processes Using Distributed Acoustic and Temperature–Strain Sensing at a Geothermal Site

Background

Fiber-optic sensing technologies offer new opportunities for continuous and spatially distributed monitoring of geothermal systems. In particular, Distributed Acoustic Sensing (DAS) provides measurements of dynamic strain associated with seismic and other transient signals, while Distributed Temperature and Strain Sensing (DTSS) can be used to monitor low-frequency strain and temperature variations over long distances.

At the Schwabwiller geothermal site, located in the Upper Rhine Graben (Alsace, France), a dedicated multi-fiber cable has been deployed as part of a joint research project between the Karlsruhe Institute of Technology (KIT) and Lithium de France (LdF). The installation allows investigating the complementary information provided by different fiber-optic sensing technologies and study how changes in environmental conditions – such as temperature, precipitation, groundwater levels, and atmospheric conditions – are reflected in distributed strain, temperature, and acoustic measurements. This dataset or future ones offers the opportunity to explore whether variations within the geothermal reservoir, induced by natural processes or operational activities, can be identified through these distributed sensing observations.

Objectives

The aim of the work is to explore the complementary information provided by DAS and DTSS and to investigate their sensitivity to environmental and subsurface processes.

Possible research topics include:

• Characterization of the recording infrastructure and its spatially varying fiber response
• Processing, visualization, and interpretation of DTSS measurements
• Analysis of low-frequency DAS data and long-period strain variations
• Joint analysis and interpretation of DAS and DTSS measurements
• Investigation of the influence of environmental conditions, including temperature, precipitation, groundwater level, and atmospheric pressure
• Identification and characterization of seasonal, diurnal, and transient strain and temperature signatures
• Investigation of the relationship between observed fiber-optic signals and environmental or site-specific processes

Depending on the student's interests, additional approaches involving time-series analysis, statistical methods, signal processing, or machine learning may be explored.

The work will be carried out at KIT (Germany) in collaboration with Lithium de France.

Required Skills

• Experience with scientific programming (e.g., Python and/or MATLAB)
• Interest in geophysics, geotechnics, environmental monitoring, or data analysis
• Motivation to work with large, multi-dimensional time-series datasets