TheMw 7.1 Ridgecrest earthquake sequence in California in July 2019 offered an opportunity to evaluate in near-real time the temporal and spatial variations in the average earthquake size distribution (the b-value) and the performance of the newly introduced foreshock traffic-light system. In normally decaying aftershock sequences, in the past studies, the b-value of the aftershocks was found, on average, to be 10%–30% higher than the background b-value. A drop of 10% or more in “aftershock” b-values was postulated to indicate that the region is still highly stressed and that a subsequent larger event is likely. In this Ridgecrest case study, after analyzing the magnitude of completeness of the sequences, we find that the quality of the monitoring network is excellent, which allows us to determine reliable b-values over a large range of magnitudes within hours of the two mainshocks. We then find that in the hours after the first Mw 6.4 Ridgecrest event, the b-value drops by 23% on average, compared to the background value, triggering a red foreshock traffic light. Spatially mapping the changes in b values, we identify an area to the north of the rupture plane as the most likely location of a subsequent event. After the second, magnitude 7.1 mainshock, which did occur in that location as anticipated, the b-value increased by 26% over the background value, triggering a green traffic light. Finally, comparing the 2019 sequence with the Mw 5.8 sequence in 1995, in which no mainshock followed, we find a b-value increase of 29% after the mainshock. Our results suggest that the real-time monitoring of b-values is feasible in California and may add important information for aftershock hazard assessment.

A systematic decay of the aftershock rate over time is one of the most fundamental empirical laws in Earth science. However, the equally fundamental effect of a mainshock on the size distribution of subsequent earthquakes has still not been quantified today and is therefore not used in earthquake hazard assessment. We apply a stacking approach to well-recorded earthquake sequences to extract this effect. Immediately after a mainshock, the mean size distribution of events, or b value, increases by 20–30%, considerably decreasing the chance of subsequent larger events. This increase is strongest in the immediate vicinity of the mainshock, decreasing rapidly with distance but only gradually over time. We present a model that explains these observations as a consequence of the stress changes in the surrounding area caused by the mainshocks slip. Our results have substantial implications for how seismic risk during earthquake sequences is assessed.

In this paper, we review the procedures involved in establishing a contemporary workflow for real-time microseismic monitoring using a local-scale seismographic network. We draw on our specific experience in monitoring potentially induced or triggered seismicity at two adjacent oil extraction sites in the Basilicata region of Southern Italy. This monitoring effort is conducted within a regulatory framework that adheres to established guidelines. However, we perceive our undertaking as generally appropriate for any local-scale seismographic network, irrespective of the specific application planned. For this reason, here we limit ourselves to discussing issues related to accurate detection, location, and quantification of microearthquakes as discrimination of their causative process is beyond the scope of this paper. We provide an overview of the tools and methodologies essential for effective monitoring, as well as the necessary approaches for assessing the reliability and accuracy of results, with the aim of offering practical guidance to those undertaking similar projects. The paper outlines the acquisition and analysis systems, including both hardware and software components, that we routinely deploy. We assessed the spatial variability of the detection threshold of the network employing an approach based on the comparison of the actual noise level recorded at the seismic stations with the theoretical spectra corresponding to the rupture models for small earthquakes. Furthermore, various tests were conducted to ascertain the significance of noise levels and hypocentral depths on the computed detection thresholds. Special attention is given to the critical issue of addressing the inherent uncertainties in the determination of hypocenters, a factor often underappreciated in such operations. While many advanced tools are available to network operators, optimal results require proper configuration and rigorous testing of these systems.