Assessment of earthquake location uncertainties for the design of local seismic networks

Abstract

The ability to estimate earthquake source locations, along with the appraisal of relevant uncertainties, is paramount in monitoring both natural and human-induced micro-seismicity. For this purpose, a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks. In this study, we first review different sources of errors relevant to the localization of seismic events, how they propagate through localization algorithms, and their impact on outcomes. We then propose a quantitative method, based on a Monte Carlo approach, to estimate the uncertainty in earthquake locations that is suited to the design, optimization, and assessment of the performance of a local seismic monitoring network. To illustrate the performance of the proposed approach, we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout. The results expand, quantitatively, the qualitative indications derived from purely geometrical parameters (azimuthal gap (AG)) and classical detectability maps. The proposed method enables the systematic design, optimization, and evaluation of local seismic monitoring networks, enhancing monitoring accuracy in areas proximal to hydrocarbon production, geothermal fields, underground natural gas storage, and other subsurface activities. This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties, which are crucial for assessing and mitigating seismic risks, thereby enabling the implementation of proactive measures to minimize potential hazards. From an operational perspective, reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.