Passive seismic monitoring with nonstationary noise sources

Abstract

Heterogeneous, nonstationary noise sources can cause traveltime errors in noise-based seismic monitoring. The effect worsens with increasing temporal resolution. This may lead to costly false alarms in response to safety concerns and limit our confidence in the results when these systems are used for quasi-real-time monitoring of subsurface changes. Therefore, we have developed a new method to quantify and correct these traveltime errors to more accurately monitor subsurface conditions at daily or even hourly timescales. It is based on the inversion of noise correlation asymmetries for the time-dependent distribution of noise sources. The source model is then used to simulate time-dependent ambient noise correlations. The comparison with correlations computed for homogeneous noise sources yields traveltime errors that translate into spurious changes of the subsurface. The application of our method to data acquired at Statoil’s SWIM array, a permanent seismic installation at the Oseberg field, demonstrates that fluctuations in the noise source distribution may induce apparent velocity changes of 0.25% within one day. Such biases thereby likely mask realistic subsurface variations expected on these timescales. These errors are systematic, being primarily dependent on the noise source location and strength, and not on the interstation distance. Our method can then be used to correct for source-induced traveltime errors by subtracting these quantified biases in either the data or model space. It can furthermore establish a minimum threshold for which we may reliably attribute traveltime changes to actual subsurface changes, should we not correct for these errors. In addition to the aforementioned real data scenario, we apply our method to a synthetic case for a daily passive monitoring overburden feasibility study. Synthetics and field experiments validated the method’s theory and application.