Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

Several earthquake early warning (EEW) algorithms have been developed worldwide for rapidly estimating real-time information (i.e., location, magnitude, ground shaking, and/or potential consequences) about ongoing seismic events. This study quantitatively compares the operational performance of two popular regional EEW algorithms for European conditions of seismicity and network configurations. We specifically test PRobabilistic and Evolutionary early warning SysTem (PRESTo) and the implementation of the Virtual Seismologist magnitude component within SeisComP, VS(SC), which we use jointly with the SeisComP scanloc module for locating events. We first evaluate the timeliness and accuracy of the location and magnitude estimates computed by both algorithms in real-time simulation mode, accounting for the continuous streaming of data and effective processing times. Then, we focus on the alert-triggering (decision-making) phase of EEW and investigate both algorithms’ ability to yield accurate ground-motion predictions at the various temporal instances that provide a range of warning times at target sites. We find that the two algorithms show comparable performances in terms of source parameters. In addition, PRESTo produces better rapid estimates of ground motion (i.e., those that facilitate the largest lead times); therefore, we conclude that PRESTo may have a greater risk-mitigation potential than VS(SC) in general. However, VS(SC) is the optimal choice of EEW algorithm if shorter warning times are permissible. The findings of this study can be used to inform current and future implementations of EEW systems in Europe

Accuracy and Uncertainty Analysis of Selected Methodological Approaches to Earthquake Early Warning in Europe

Earthquake early warning (EEW) is becoming an increasingly attractive real‐time strategy for mitigating the threats posed by potentially devastating incoming seismic events. As efforts accelerate to develop practical EEW‐based solutions for earthquake‐prone countries in Europe, it is important to understand and quantify the level of performance that can be achieved by the underlying seismological algorithms. We conduct a conceptual study on EEW performance in Europe, which explicitly focuses on the accuracy and associated uncertainties of selected methodological approaches. Twenty‐three events from four diverse European testbeds are used to compare the quality of EEW predictions produced by the Virtual Seismologist and PRobabilistic and Evolutionary early warning SysTem algorithms. We first examine the location and magnitude estimates of the algorithms, accounting for both bias and uncertainty in the resulting predictions. We then investigate the ground‐shaking prediction capabilities of the source‐parameter estimates, using an error metric that can explicitly capture the propagation of uncertainties in these estimates. Our work highlights the importance of accounting for EEW parameter uncertainties, which are often neglected in studies of EEW performance. Our findings can be used to inform current and future implementations of EEW systems in Europe. In addition, the evaluation metrics presented in this work can be used to determine EEW accuracy in any worldwide setting

Assessing the potential implementation of earthquake early warning for schools in the Patras region, Greece

Earthquake early warning (EEW) is currently deemed a credible approach to seismic resilience enhancement in modern societies, especially if part of a more holistic earthquake mitigation strategy involving other risk reduction tools such as structural upgrading/retrofit. Yet, there remains a strong need to 1) assess the feasibility of EEW in various seismotectonic contexts, considering specific target applications/end users; and 2) develop next-generation decision-support systems relying on interpretable probabilistic impact-based estimates toward more risk-informed decision-making on EEW installation/alert triggering. These challenges are addressed in this paper, which showcases a series of recent significant EEW contributions by the authors. First, we present the results of a state-of-the-art feasibility study for EEW in schools performed across the Patras region of Greece, attempting to spatially combine traditional seismologically-driven EEW decision criteria (i.e., warning time) with proxy risk-oriented measures for earthquake impact (i.e., building fragility and the number of exposed school students). These results show that, under certain conditions, EEW could be effective for the schools in the considered case-study region. We then demonstrate an advanced end-user-centred approach for improved risk-informed decision-making on triggering EEW alerts. The proposed methodology integrates earthquake-engineering-related seismic performance assessment procedures and metrics with multi-criteria decision-making (MCDM) within an end-to-end probabilistic framework. The performance-based earthquake engineering component of such a framework facilitates the computation of various damage/loss estimates (e.g., repair cost, downtime, and casualties) by combining target-structure-specific models of seismic response, fragility, and vulnerability with real-time ground-shaking estimates. Additionally, the incorporated MCDM methodology enables explicit consideration of end-user preferences (importance) towards the estimated consequences in the context of alert issuance. The developed approach is demonstrated using an archetype school building for the case-study region, for which we specifically investigate the optimal decision (i.e., “trigger” or “don't trigger” an EEW alert) across a range of ground-motion intensity measures. We find that the best action for a given level of ground shaking can vary as a function of stakeholder preferences

Innovations in earthquake risk reduction for resilience: Recent advances and challenges

The Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR) highlights the importance of scientific research, supporting the ‘availability and application of science and technology to decision making’ in disaster risk reduction (DRR). Science and technology can play a crucial role in the world’s ability to reduce casualties, physical damage, and interruption to critical infrastructure due to natural hazards and their complex interactions. The SFDRR encourages better access to technological innovations combined with increased DRR investments in developing cost-effective approaches and tackling global challenges. To this aim, it is essential to link multi- and interdisciplinary research and technological innovations with policy and engineering/DRR practice. To share knowledge and promote discussion on recent advances, challenges, and future directions on ‘Innovations in Earthquake Risk Reduction for Resilience’, a group of experts from academia and industry met in London, UK, in July 2019. The workshop focused on both cutting-edge ‘soft’ (e.g., novel modelling methods/frameworks, early warning systems, disaster financing and parametric insurance) and ‘hard’ (e.g., novel structural systems/devices for new structures and retrofitting of existing structures, sensors) risk-reduction strategies for the enhancement of structural and infrastructural earthquake safety and resilience. The workshop highlighted emerging trends and lessons from recent earthquake events and pinpointed critical issues for future research and policy interventions. This paper summarises some of the key aspects identified and discussed during the workshop to inform other researchers worldwide and extend the conversation to a broader audience, with the ultimate aim of driving change in how seismic risk is quantified and mitigated

Environmental monitoring : phase 5 final report (April 2019 - March 2020)

This report presents the results and interpretation for Phase 5 of an integrated environmental monitoring programme that is being undertaken around two proposed shale gas sites in England – Preston New Road, Lancashire and Kirby Misperton, North Yorkshire. The report should be read in conjunction with previous reports freely available through the project website1 . These provide additional background to the project, presentation of earlier results and the rationale for establishment of the different elements of the monitoring programme

Interdisciplinarity in practice: reflections from early-career researchers developing a risk-informed decision support environment for Tomorrow's cities

The concept of disaster risk is cross-disciplinary by nature and reducing disaster risk has become of interest for various disciplines. Yet, moving from a collection of multiple disciplinary perspectives to integrated interdisciplinary disaster risk approaches remains a fundamental challenge. This paper reflects on the experience of a group of early-career researchers spanning physical scientists, engineers and social scientists from different organisations across the global North and global South who came together to lead the refinement, operationalisation and testing of a risk-informed decision support environment for Tomorrow's Cities (TCDSE). Drawing on the notions of subjects and boundary objects, members of the group reflect on their individual and collective journey of transgressing disciplinary boundaries across three case studies between June–December 2021: operationalisation process of the TCDSE; development of a virtual urban testbed as a demonstration case for the implementation of the TCDSE; and consolidation of frequently asked questions about the TCDSE for communication purposes. The paper argues that (1) the production of boundary objects in interdisciplinary research nurtures relations of reciprocal recognition and the emergence of interdisciplinary subjects; (2) the intrinsic characteristics of boundary objects define the norms of engagement between disciplinary subjects and constrain the expression of interdisciplinary contradictions; and (3) affects and operations of power explain the contingent settlement of interdisciplinary disagreements and the emergence of new knowledge. Activating the interdisciplinary capacities of early-career researchers across disciplines and geographies is a fundamental step towards transforming siloed research practices to reduce disaster risk

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability