PhD Position in Applied Geosciences: Pattern recognition in DAS data

Distributed Acoustic Sensing (DAS) is a fiber-optic technology that transforms optical fibers into dense seismic arrays. When deployed on unused telecommunication fibers (“dark fibers”), DAS provides regularly spaced seismic measurements over tens of kilometers, offering the potential for seismic monitoring over large areas. This research is conducted within the RUBADO project (BMWE, FKZ 03EE4076A), in which DAS is deployed along multiple long-range fiber-optic cables in the Upper Rhine Graben. The goal is to investigate the potential of existing fiber-optic infrastructure for monitoring geothermal reservoirs and induced seismicity at regional scales.

Efficient DAS monitoring requires automated processing of the large volumes of data generated and reliable identification of transient seismic signals, such as microseismic events, within predominantly anthropogenic noise. In addition, the identification of periods of low anthropogenic noise is relevant for applications such as ambient seismic noise interferometry. Machine learning (ML) provides a promising approach for the automated detection and classification of seismic signals in DAS data. The objective of this research is therefore to develop, implement, and validate ML-based methods for improving signal detection and classification in DAS recordings, thereby contributing to the development of efficient monitoring approaches for geothermal reservoirs and induced seismicity, as well as broader seismic applications.

In this framework, the following tasks are expected:

• Data acquisition, signal pre-processing and classification
• Collect and organize datasets acquired within the RUBADO project,
• Perform multi-domain analysis of DAS waveforms in the time, frequency, and space-wavenumber domains (and other array-based representations where relevant),
• Develop robust pre-processing workflows (e.g., denoising and segmentation) tailored to DAS data characteristics.

• Identify and extract physically meaningful signal attributes and recurring waveform patterns that capture the variability of seismic and anthropogenic sources, forming the basis for machine learning feature spaces.

• Training dataset development and pattern recognition framework:
• Build and curate a labelled dataset through manual inspection and expert annotation of transient signals in DAS recordings,
• Define consistent labeling strategies for different signal classes (e.g., seismic events, traffic-induced noise, instrumental artifacts),
• Investigate and implement pattern recognition approaches to identify recurrent waveform structures and spatio-temporal signatures in DAS records,
• Develop machine learning and deep learning workflows for automatic signal classification, including supervised, unsupervised, and/or semi-supervised (hybrid) approaches to use both labeled and unlabeled data.
• Model validation, benchmarking and transfer:
• Apply ML models to DAS datasets from the RUBADO project,
• Benchmark the performance against independent geophone data and existing event catalogs,
• Assess model generalization capability across different DAS deployments, acquisition geometries, and environmental conditions,

• Perform systematic uncertainty and bias analysis to identify limitations and improve model transferability.

• Workflow integration for seismic monitoring and subsurface imaging:
• Integrate the developed processing and machine learning pipeline into the RUBADO analysis framework for near real-time or batch seismic monitoring,
• Enhance event detection, classification, and characterization workflows to improve signal interpretability in DAS data,
• Support improved subsurface imaging by providing cleaner, better-characterized input signals for further seismic processing (e.g., ambient noise analysis, interferometry, or velocity inversion)., The “KIT-Family +” program assists you in reconciling work and family life by offering childcare services, holiday activities, a parent-child office space, and assistance with caring for relatives.

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• Master’s degree in Geophysics, Physics, Computational Earth Sciences, Mathematics or related field.
• Strong background in seismology and signal processing.
• Strong programming skills (e.g. Python, MATLAB, C/C++).
• Proven experience in big data analysis and/or machine learning.
• Interest in geothermal applications.
• Enthusiasm for fieldwork in addition to office work and for interdisciplinary collaboration.

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