Seismic Network Performance Estimation: Comparing Predictions of Magnitude of Completeness and Location Accuracy to Observations from an Earthquake Catalogue
Friday, 19 December 2014
The design of seismic networks for the monitoring of induced seismicity is of critical importance. The recent introduction of regulations in various locations around the world (with more upcoming) has created a need for a priori confirmation that certain performance standards are met. We develop a tool to assess two key measures of network performance without an earthquake catalogue: magnitude of completeness and location accuracy. Site noise measurements are taken at existing seismic stations or as part of a noise survey. We then interpolate between measured values to determine a noise map for the entire region. The site noise is then summed with the instrument noise to determine the effective station noise at each of the proposed station locations. Location accuracy is evaluated by generating a covariance matrix that represents the error ellipsoid from the travel time derivatives (Peters and Crosson, 1972). To determine the magnitude of completeness we assume isotropic radiation and mandate a minimum signal to noise ratio for detection. For every gridpoint, we compute the Brune spectra for synthetic events and iterate to determine the smallest magnitude event that can be detected by at least four stations. We apply this methodology to an example network. We predict the magnitude of completeness and the location accuracy and compare the predicted values to observed values generated from the existing earthquake catalogue for the network. We discuss the effects of hypothetical station additions and removals on network performance to simulate network expansions and station failures. The ability to predict hypothetical station performance allows for the optimization of seismic network design and enables prediction of network performance even for a purely hypothetical seismic network. This allows the operators of networks for induced seismicity monitoring to be confident that performance criteria are met from day one of operations.