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The capability of estimating earthquake source locations, together with the appraisal of the relevant uncertainties, plays a crucial role in monitoring and managing both underground anthropogenic activities as well as the natural (micro)seismicity. This is especially true in the close proximity of hydrocarbon production or storage sites, geothermal fields and in general all activities that involve injection/production of fluid or gases in the subsurface. To this end, a monitoring network must be carefully designed to minimize the location errors introduced by geometrically unbalanced networks. In this study, we first review the different sources of errors that are relevant to the localization of seismic events, how they propagate through the localization algorithms, and their impact on the outcome. We then propose a quantitative methodology, based on a Monte-Carlo approach, to estimate the accuracy of earthquake localization, and particularly suited to the design, optimization, and assessment of the performances of a local seismic monitoring network. This work is an effort to propose a more realistic and reliable way to evaluate the location uncertainty of seismic events, going beyond simplified approaches that tend to under- and over-estimate this metric. To illustrate the performance of the proposed approach, we have analyzed the distribution of the localization errors and their related dispersion on a very dense grid of theoretical hypocenters, in both horizontal and vertical directions, by using a real monitoring network layout. The results expand in a quantitative fashion the qualitative indications drawn from purely geometrical parameters (the azimuthal gap), and from classical detectability maps.

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