A Convolution Model for Earthquake Forecasting Derived from Seismicity Recorded During the ST1 Geothermal Project on Otaniemi Campus, Finland

Abstract

We analyse and model the spatio-temporal evolution of seismicity induced by hydraulic stimulations during the initial phase of a 6.1 km deep EGS development on Aalto University’s Otaniemi campus. We use the records from surface accelerometers and borehole seismometers installed at depth between 0.3 and 2.7km. We analyze the seismicity induced by stimulations over 50 days. The data were processed using Machine Learning techniques for phase detection (the Generalised Phase Detection), a back-projection technique for phase associations and location (Quake Migrate). Relative locations were refined using a cross-correlation techniques. The procedure yielded a catalog of ~70,000 events, including 10,000 which could be located, with local magnitudes between -1.5 and 2. We analyze how the seismicity relates in time and space to the injection history and propose a simple convolution method to predict the evolution of the seismicity in time and space. The parameters of the models can be calibrated from the seismicity data themselves, in particular the Omori-like decay of seismicity during shut-in periods. We demonstrate the performance the method and investigate how the model parameters relate to the poroelastic mechanical, and hydraulic properties of the medium. This approach can be used for seismic hazard assessment, to feed a traffic light system or eventually for optimization and control during a stimulation.