Feasibility of identifying microseismic events at the geothermal system of Mt. Princeton using surface passive seismic data

Passive seismic data is becoming a useful monitoring tool in studies related to the fluid flow (such as heavy oil, tight gas, and enhanced geothermal) in the subsurface. Extracting information from this data, however, requires correctly identifying microseismic events. Here, we study the passive seismic data recorded at Mt. Princeton geothermal system in Colorado to understand the microseismicity of this system. The microseismicity could potentially provide information about the fracture pattern and fluid-flow path, which would help to ascertain locations for water injection and steam production. It is necessary to verify the quality of the passive seismic data for identifying microseismic events. To automate the identification of events, we introduce a localized cross-correlation approach. Our algorithm has the advantage of identifying events common to all stations and providing information about the signal to noise ratio of the events at different stations. This algorithm has the additional advantage of giving an estimate of the background noise at different stations, which helps us identify stations with poor data quality.