🧠 Machine Learning for Seismology | 🌋 Earthquake & Volcano Research
- I develop signal processing and machine learning algorithms for time series data such as seismic and strainmeter records.
- My research focuses on earthquake and volcanic preparatory processes, with particular emphasis on tremor signals and Slow Slip Events (SSEs).
- My goal is to advance data-driven seismology to improve our understanding of fault slip and deformation processes, and move toward more reliable earthquake and eruption forecasting.
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DeepStrain
Pipeline for detecting and clustering Slow Slip Events from borehole strainmeter data. -
AutoencoderZ
Advanced autoencoder for feature extraction and dimensionality reduction of seismic/strain data. -
ClusTremor ⭐
Deep learning–based clustering of tremor signals to study eruption phases and pre-eruptive activity. -
NoiseCut
Denoising package for seismic and OBS records using harmonic–percussive separation. -
TremorExtractor-EqDetector
Algorithms for volcanic tremor extraction and earthquake detection using MIR techniques.
- 📧 Email: zali@gfz.de
- 🌐 Google Scholar
- 🏢 GFZ Profile Page
⭐️ Feel free to explore my repositories or get in touch if you are interested in collaboration.