In addition to the various quasi-static stress and strain observations, the FEAR experiments will generate a rich database of continuous seismic records over a large frequency and energy range, from nano-earthquakes and micro-creep events (dimension 10cm-1m) to ruptures larger than 10m. This seismic data will be analyzed in real-time (or near-real time?) with state-of-the-art methodologies (e.g. template matching, deep learning powered seismic detection and characterization methods, waveform-based absolute and relative relocation methods, etc.). The resulting earthquake catalogues as well as other geophysical observations (strain, pressures, etc.) will flow as inputs into a novel test-bench for generating real-time, data-driven earthquake forecasts, which are in turn inputs to an adaptive traffic-light risk mitigation and control system for the safe operation of the tunnel.
The real-time data will drive a range of seismicity forecast models that are based on statistical, physics-based and hybrid modelling approaches (also integrating feedback and learnings from WP2 and WP3). We will develop a first-of-its kind closely-coupled induced seismicity experiment-simulator setup with a high-performance computational framework for analysis and modelling of fluid-rock interaction in near-real time, which will allow us to test and validate earthquake forecasting models and mitigation strategies.
An essential component of the experiments will be a systematic search for precursory signals, observed at laboratory scales. Such precursors systematically observed prior to ruptures of 10-50m length would be the most tantalizing discovery towards possible future applications in earthquake predictions, with potential to transform operational earthquake forecasting.