Comment on “Two Foreshock Sequences Post Gulia and Wiemer (2019)” by Kelian Dascher-Cousineau, Thorne Lay, and Emily E. Brodsky

Author(s):  
Laura Gulia ◽  
Stefan Wiemer

Abstract Dascher-Cousineau et al. (2020) apply the so-called foreshock traffic-light system (FTLS) model proposed by Gulia and Wiemer (2019) to two earthquake sequences that occurred after the submission of the model: the 2019 Ridgecrest (Mw 7.1) and the 2020 Mw 6.4 Puerto Rico earthquakes. We show in this comment that the method applied by Kelian Dascher-Cousineau et al. (2020) deviates in at least six substantial and not well-documented aspects from the original FTLS method. As a consequence, they used for example in the Ridgecrest case only 1% of the data available to estimate b-values and from a small subvolume of the relevant mainshock fault. In the Puerto Rico case, we document here substantial issues with the homogeneity of the magnitude scale that in our assessment make a meaningful analysis of b-values impossible. We conclude that the evaluation by Dascher-Cousineau et al. (2020) is misrepresentative and a not a fair test of the FTLS hypothesis.

Author(s):  
Kelian Dascher-Cousineau ◽  
Thorne Lay ◽  
Emily E. Brodsky

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.


2020 ◽  
Vol 91 (5) ◽  
pp. 2843-2850 ◽  
Author(s):  
Kelian Dascher-Cousineau ◽  
Thorne Lay ◽  
Emily E. Brodsky

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 the 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.


2020 ◽  
Vol 91 (5) ◽  
pp. 2828-2842 ◽  
Author(s):  
Laura Gulia ◽  
Stefan Wiemer ◽  
Gianfranco Vannucci

Abstract The Mw 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.


2021 ◽  
Vol 51 (11) ◽  
pp. 1026-1029
Author(s):  
S Savoviċ ◽  
A Djordjevich ◽  
R Min ◽  
I Savoviċ

Author(s):  
Rashi Maheshwari

Abstract: Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.


2007 ◽  
Vol 10 (3) ◽  
pp. 238-244 ◽  
Author(s):  
Gary Jones ◽  
Miles Richardson

AbstractObjectivePrevious research on nutrition labelling has mainly used subjective measures. This study examines the effectiveness of two types of nutrition label using two objective measures: eye movements and healthiness ratings.DesignEye movements were recorded while participants made healthiness ratings for two types of nutrition label: standard and standard plus the Food Standards Agency's ‘traffic light’ concept.SettingUniversity of Derby, UK.SubjectsA total of 92 participants (mean age 31.5 years) were paid for their participation. None of the participants worked in the areas of food or nutrition.ResultsFor the standard nutrition label, participant eye movements lacked focus and their healthiness ratings lacked accuracy. The traffic light system helped to guide the attention of the consumer to the important nutrients and improved the accuracy of the healthiness ratings of nutrition labels.ConclusionsConsumers have a lack of knowledge regarding how to interpret nutrition information for standard labels. The traffic light concept helps to ameliorate this problem by indicating important nutrients to which to pay attention.


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