Seismic Monitoring of a Geothermal Plant Under Development Using Distributed Acoustic Sensing (DAS)

Background

Deep geothermal energy projects require reliable seismic monitoring throughout their development, from drilling and reservoir stimulation to long-term operation. Distributed Acoustic Sensing (DAS) uses fiber-optic cables as dense arrays of seismic sensors and can provide measurements continuously along several kilometers of fiber, offering new perspectives for seismic monitoring.

Within a joint research project between the Karlsruhe Institute of Technology (KIT) and Lithium de France (LdF), a dedicated approximately 2-km-long horizontal fiber-optic cable was installed near the surface, in a trench, in Schwabwiller (Upper Rhine Graben, Alsace, France), during the development of a geothermal power plant. The dataset contains continuous DAS recordings acquired during well-drilling activities and reservoir testing. It complements the more conventional seismological data collected during the same period with a network of seismometers. This dataset gives the possibility to evaluate the capabilities and limitations of fiber-optic sensing for passive seismic monitoring in a real geothermal environment.

Objectives

The main objective of the subject is to investigate the potential of DAS for seismic monitoring and the detection and characterization of microseismic and other weak seismic events. At a broader scale, the work will also explore the potential of the existing dataset and surface fiber layout for regional seismic monitoring and related applications.

Depending on the student's interests and background, possible tasks include:

• Characterization of the DAS array and its spatially varying fiber response
• Analysis and characterization of ambient seismic noise
• Detection, characterization, and localization of seismic events
• Development of a synthetic seismic event catalogue for testing and benchmarking detection methods
• Application and evaluation of advanced techniques for weak-event detection
• Comparison of DAS-based observations with more conventional seismometer observations (from surface, from wells)

Depending on the student's interests, additional approaches involving advanced signal processing, array 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 seismology, signal processing, and data analysis
• Motivation to work with large-scale, real-world geophysical datasets