Detection of weak seismic sequences based on arrival time coherence and empiric network detectability: an application at a near fault observatory

Abstract

SUMMARY Microseismic monitoring is a primary tool for understanding and tracking the progress of mechanical processes occurring in active rock fracture systems. In geothermal or hydrocarbon fields or along seismogenic fault systems, the detection and location of microseismicity facilitates resolution of the fracture system geometry and the investigation of the interaction between fluids and rocks, in response of stress field perturbations. Seismic monitoring aims to detect locate and characterize seismic sources. The detection of weak signals is often achieved at the cost of increasing the number of false detections, related to transient signals generated by a range of noise sources, or related to instrumental problems, ambient conditions or human activity that often affect seismic records. A variety of fast and automated methods has been recently proposed to detect and locate microseismicity based on the coherent detection of signal anomalies, such as increase in amplitude or coherent polarization, at dense seismic networks. While these methods proved to be very powerful to detect weak events and to reduce the magnitude of completeness, a major problem remains to discriminate among weak seismic signals produced by microseismicity and false detections. In this work, the microseimic data recorded along the Irpinia fault zone (Southern Apennines, Italy) are analysed to detect weak, natural earthquakes using one of such automated, migration-based, method. We propose a new method for the automatic discrimination of real vs false detections, which is based on empirical data and information about the detectability (i.e. detection capability) of the seismic network. Our approach allows obtaining high performances in detecting earthquakes without requiring a visual inspection of the seismic signals and minimizing analyst intervention. The proposed methodology is automated, self-updating and can be tuned at different success rates.