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Abstract Measures of foreshock occurrence are systematically examined using earthquake catalogs for eight regions (Italy, southern California, northern California, Costa Rica, Onshore Japan, Alaska, Turkey, and Greece) after imposing a magnitude ≥3.0 completeness level. Foreshocks are identified using three approaches: a magnitude-dependent space + fixed-time windowing method, a nearest-neighbor clustering method, and a modified magnitude-dependent space + variable-time windowing method. The method with fixed-time windows systematically yields higher counts of foreshocks than the other two clustering methods. We find similar counts of foreshocks across the three methods when the magnitude aperture is equalized by including only earthquakes in the magnitude range M*−2≤ M< M*, in which M* is the mainshock magnitude. For most of the catalogs (excluding Italy and southern California), the measured b-values of the foreshocks of all region-specific mainshocks are lower by 0.1–0.2 than b-values of respective aftershocks. Allowing for variable-time windows results in relatively high probabilities of having at least one foreshock in Italy (∼43%–56%), compared to other regional catalogs. Foreshock probabilities decrease to 14%–41% for regions such as Turkey, Greece, and Costa Rica. Similar trends are found when requiring at least five foreshocks in a sequence to be considered. Estimates of foreshock probabilities for each mainshockmore »are method dependent; however, consistent regional trends exist regardless of method, with regions such as Italy and southern California producing more observable foreshocks than Turkey and Greece. Some regions with relatively high background seismicity have comparatively low probabilities of detectable foreshock activity when using methods that account for variable background, possibly due to depletion of near-failure fault conditions by background activity.« less

On 15 January 2022, unusual waves appeared in Earth’s atmosphere and oceans ( 1 – 3 ). The origin of the waves was clearly the catastrophic volcanic eruption in Tonga, which pummeled the atmosphere with the largest eruptive plume since the 1883 eruption of Krakatoa, Indonesia. On page 95 of this issue, Matoza et al. ( 4 ) show that the 2022 Tonga eruption generated waves in the water, air, and even in the ionosphere that wrapped around Earth multiple times. Tsunamis appeared to hop across the land into all of the major ocean basins. And on page 91 of this issue, Kubota et al. ( 5 ) explain that the tsunamis arrived much earlier than expected on the basis of conventional tsunami modeling, and the wave trains lasted much longer than for even the largest earthquakes ( 5 ).

Abstract Gulia and Wiemer (2019; hereafter, GW2019) proposed a near-real-time monitoring system to discriminate between foreshocks and aftershocks. Our analysis (Dascher-Cousineau et al., 2020; hereinater, DC2020) tested the sensitivity of the proposed Foreshock Traffic-Light System output to parameter choices left to expert judgment for the 2019 Ridgecrest Mw 7.1 and 2020 Puerto Rico Mw 6.4 earthquake sequences. In the accompanying comment, Gulia and Wiemer (2021) suggest that at least six different methodological deviations lead to different pseudoprospective warning levels, particularly for the Ridgecrest aftershock sequence which they had separately evaluated. Here, we show that for four of the six claimed deviations, we conformed to the criteria outlined in GW2019. Two true deviations from the defined procedure are clarified and justified here. We conclude as we did originally, by emphasizing the influence of expert judgment on the outcome in the analysis.

Oceanic internal gravity waves propagate along density stratification within the water column and are ubiquitous. They can propagate thousands of kilometers before breaking in shoaling bathymetry and the ensuing turbulent mixing affects coastal processes and climate feedbacks. Despite their importance, internal waves are intrinsically difficult to detect as they result in only minor amplitude deflection of the sea surface; the need for global detection and long time series of internal waves motivates a search for geophysical detection methods. The pressure coupling of a propagating internal wave with the sloping seafloor provides a potential mechanism to generate seismically observable signals. We use data from the South China Sea where exceptional oceanographic and satellite time series are available for comparison to identify internal wave signals in an onshore passive seismic data set for the first time. We analyze potential seismic signals on broadband seismometers in the context of corroborating oceanographic and satellite data available near Dongsha Atoll in May–June 2019 and find a promising correlation between transient seismic tilt signals and internal wave arrivals and collisions in oceanic and satellite data. It appears that we have successfully detected oceanic internal waves using a subaerial seismometer. This initial detection suggests that the onshoremore »seismic detection and amplitude determination of oceanic internal waves is possible and can potentially be used to expand the historical record by capitalizing on existing island and coastal seismic stations.« less

Abstract Recognizing earthquakes as foreshocks in real time would provide a valuable forecasting capability. In a recent study, Gulia and Wiemer (2019) proposed a traffic-light system that relies on abrupt changes in b-values relative to background values. The approach utilizes high-resolution earthquake catalogs to monitor localized regions around the largest events and distinguish foreshock sequences (reduced b-values) from aftershock sequences (increased b-values). The recent well-recorded earthquake foreshock sequences in Ridgecrest, California, and Maria Antonia, Puerto Rico, provide an opportunity to test the procedure. For Ridgecrest, our b-value time series indicates an elevated risk of a larger impending earthquake during the Mw 6.4 foreshock sequence and provides an ambiguous identification of the onset of the Mw 7.1 aftershock sequence. However, the exact result depends strongly on expert judgment. Monte Carlo sampling across a range of reasonable decisions most often results in ambiguous warning levels. In the case of the Puerto Rico sequence, we record significant drops in b-value prior to and following the largest event (Mw 6.4) in the sequence. The b-value has still not returned to background levels (12 February 2020). The Ridgecrest sequence roughly conforms to expectations; the Puerto Rico sequence will only do so if a larger event occurs in themore »future with an ensuing b-value increase. Any real-time implementation of this approach will require dense instrumentation, consistent (versioned) low completeness catalogs, well-calibrated maps of regionalized background b-values, systematic real-time catalog production, and robust decision making about the event source volumes to analyze.« less