Triggering Event Research Articles

Bumpless transfer control of switched systems: A dynamic event-triggering switching control approach

This paper studies the bumpless transfer (BT) control issue for switched systems with event-triggered (ET) control input. The aim is to reduce the jumps in control input at both switching and triggering points, while capturing the stability of the switched systems. First, a dynamic ET program is constructed with the triggering condition based on the level of suppression of input signal jumps. This not only reduces input jumps at triggering points, but also prevents Zeno behavior caused by triggering within the operation time interval of subsystems. Second, a sampled state-dependent switching signal is designed to ensure a dwell time for subsystems to prevent Zeno phenomenon induced by switching. Further, a switched ET controller with alleviated jumps is designed. Third, conditions are obtained to simultaneously reach both stabilization and BT of switched systems. Finally, the effectiveness of the theoretical results is demonstrated through a simulation of an engine model.

Human‐AI collaboration: Designing artificial agents to facilitate socially shared regulation among learners

AbstractSocially shared regulation of learning (SSRL) is a crucial process for groups of learners to successfully collaborate. Detecting and supporting SSRL is a challenge, especially in real time, but hybrid intelligence approaches such as Artificial Intelligence (AI) agents may make this possible. Leveraging the concept of trigger events which invite SSRL, we present a design of an AI agent, MAI, which can detect SSRL and prompt students to raise their group‐level metacognitive awareness with the aim of facilitating SSRL. We present the methodology we used to design MAI, drawing on the Echeloned DSR (eDSR) Methodological Framework and making use of the Wizard of Oz prototyping paradigm. We likewise present empirical results evaluating our initial prototype of MAI, using lexical alignment between speakers as a quantitative measure of the effect of MAI's prompts on facilitating SSRL, the Partner Model Questionnaire as a quantitative measure of perceptions of MAI, and interviews as qualitative context for these perceptions. We found that the first prototype of MAI did not facilitate SSRL as hoped, possibly owing to mixed perceptions of MAI's reliability and lack of clarity about MAI's role in the collaborative learning task. From these findings, we offer revised prompts for the next iteration of prototyping this agent and a refined set of design requirements for future development of metacognitive AI agents for supporting SSRL. Practitioner notesWhat is already known about this topic Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning progress. Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. What this paper adds This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. The findings highlight the critical importance of multidisciplinary knowledge in the design of multi‐agent interfaces (MAI) that provide real‐time, adaptive support for group metacognitive processes and decision‐making. Implications for practice and/or policy Educational technologists can utilize the proposed design principles in the development and integration of MAI tools to enhance SSRL. Educators can incorporate the principles of MAI and our relevant findings into their teaching strategies to actively foster and support socially shared regulation of learning among students. Policymakers should consider revising educational frameworks to include the use of AI technologies that support SSRL strategies in collaborative learning.

Dynamic event-triggered fault-tolerant control for nonaffine systems with asymmetric error constraint

This paper focuses on the problem of dynamic event-triggered fault-tolerant control (FTC) for nonaffine systems with unmeasurable state and asymmetric error constrain. At first, an adaptive failure compensation observer is designed to calculate unpredictable state of the nonaffine systems. Then, a dynamic event triggering strategy (DETS) is constructed to adjust the control signal and make it more flexible in transmission. Next, a new error-dependent conversion function (EDCF) is designed to confine tracing error, which can ensure that the designed Lyapunov function is always positive definite, and extend the symmetric constraint boundary to the asymmetric constraint boundary. The tracking performance and stability of closed-loop systems are proven through Lyapunov stability analysis. Finally, the availability of this method is verified by simulation experiments.

Quantized feedback stabilization of impulsive switched linear systems based on event triggering

Quantized feedback stabilization of impulsive switched linear systems based on event triggering

Shifting GnRH neuron ensembles underlie successive preovulatory luteinizing hormone surges.

The gonadotropin-releasing hormone (GnRH) neurons operate as a neuronal ensemble exhibiting coordinated activity once every reproductive cycle to generate the preovulatory GnRH surge. Using GCaMP fibre photometry at the GnRH neuron distal dendrons to measure the output of this widely scattered population in female mice, we find that the onset, amplitude, and profile of GnRH neuron surge activity exhibits substantial variability from cycle to cycle both between and within individual mice. This was also evident when measuring successive proestrous luteinizing hormone surges. Studies combining short (c-Fos and c-Jun) and long (genetic Robust Activity Marking) term indices of immediate early gene activation revealed that, while ∼50% of GnRH neurons were activated at the time of each surge, only half of these neurons had been active during the previous proestrous surge. These observations reveal marked inter- and intra-individual variability in the GnRH surge mechanism. Remarkably, different sub-populations of overlapping GnRH neurons are recruited to the ensemble each estrous cycle to generate the GnRH surge. While engendering variability in the surge mechanism itself, this likely provides substantial robustness to a key event underlying mammalian reproduction.Significance Statement The mid-cycle luteinizing hormone (LH) surge driven by the gonadotropin-releasing hormone (GnRH) neurons represents the key event triggering ovulation in all mammals. Using GCaMP fibre photometry and genetic activation markers, we unexpectedly find that different sub-populations of GnRH neurons are responsible for driving consecutive LH surges every 4-5 days in cycling female mice. This remarkable oscillatory pattern of network plasticity within the ensemble occurs under normal physiological conditions and likely contributes to the variable timing of the onset of LH surge both within and between individuals. The ability of individual GnRH neurons to take turns within the ensemble in driving the LH surge likely provides a robust fail-safe mechanism for ovulation and contributes to the robustness of mammalian fertility.

Contagious yawning and scratching in captive lemurs

Behavioral contagion is thought to play a significant role in social synchronization and coordination across animal taxa. While there is extensive evidence of behavioral contagion in Haplorrhines (i.e. monkeys and apes), limited research exists in Strepsirrhines (i.e. lemurs). Here, we aimed to investigate the presence of contagious yawning and scratching in two captive groups of black-and-white ruffed lemurs (Varecia variegata) (N = 4) and red ruffed lemurs (Varecia rubra) (N = 4), and further test whether behavioral contagion is modulated by the model’s social integration in the group. We conducted all occurrence sampling to examine whether individuals observing a yawning or scratching event (i.e. trigger event) were more likely to yawn or scratch in the following 2 min, as compared to individuals who did not observe it. We ran generalized linear mixed models and found that the likelihood of yawning and scratching was higher for subjects observing the trigger event than for subjects who did not observe the event, although the model’s social integration had no modulating effect on the probability of showing behavioral contagion. Our findings represent the first evidence of behavioral contagion in this genus and contribute to shed light on the distribution and the possible adaptive function of this phenomenon in primates.

Acute carpal tunnel syndrome after atraumatic rupture of the flexor tendons: a case report

Acute carpal tunnel syndrome (ACTS) is a rare entity, mainly associated with traumatic causes, although patients with predisposing factors such as taking anticoagulants or tendinopathies may debut with an atraumatic ACTS. This case study presents the case of a 77-year-old male patient, anticoagulated, who began with rapidly progressive pain and swelling in the right wrist, radiating towards the medial territory, with no history of traumatic triggering event. Examination showed paraesthesia in the volar aspect of the first to third fingers of the hand and increased pain on passive mobilisation of the fingers. Given the progressive evolution of the condition and the lack of response to both physical and pharmacological measures, the carpal tunnel was opened surgically. During surgery, a significant haematoma was observed secondary to the rupture of the deep flexor tendons of the fourth and fifth fingers inside the canal. Following release of the carpal tunnel, complete recovery of the symptomatology occurred. In conclusion, a high degree of clinical suspicion is necessary in a patient with an examination suggestive of ACTS. The treatment of choice is urgent surgical decompression of the carpal tunnel. Early intervention allows resolution of symptoms and reduces complications.

Clinical and angiographic features of patients experiencing exercise-induced out-of-hospital cardiac arrest

Abstract Background Out-of-hospital cardiac arrest (OHCA) is a devastating condition that exhibits significant clinical heterogeneity. Previous studies have reported coronary artery disease (CAD) as the most common cause of OHCA. Possible causes of exercise-induced OHCA in patients with CAD include ST-elevation myocardial infarctions (STEMI), exercise-induced myocardial ischaemia and other triggers. We investigated whether there are differences in the clinical and angiographic features between 'exercise-induced (EX)' and 'non-exercise-induced (non-EX)' OHCA secondary to CAD. Methods In total, resuscitated 2300 patients (from 72 institutions) with OHCA due to CAD were registered within our regional CCU network database between 2010 and 2021. The population of the coverage area is 16.8 million during the day and 14.1 million at night. Patients with a trigger were grouped based on their triggering event: at rest, sleeping, eating, drinking, watching television, bathing, excretion, mild physical activity, driving, exercise and other activity. We classified the patients into 2 groups based on their triggering event as exercise: exercise (EX) group(n=169) and Non-Ex group(n=2131). The primary clinical outcome of interest was favorable 30-day neuroprognosis. Results Baseline characteristics was shown in the Table (below). Acute coronary syndrome (ACS) was the most common type of clinical presentation in each group. In ACS, the EX group had less ST elevation and/or left bundle branch block in electrocardiogram (57% vs. 68%; P=0.01), and initial good flow (TIMI grade 3) of culprit lesion in coronary artery was more frequently seen in the EX group than in the Non-EX group (23% vs. 12%; P< 0.01). A Kaplan-Meier survival curve analysis showed that EX group had favorable clinical outcome compared with Non-EX group (71% vs 58%, p<0.001:log-rank test). Multivariate analysis demonstrated that exercise-related trigger for OHCA was an independent predictor of favorable outcome (OR:0.59, 95%CI:0.38-0.90; P=0.015). Conclusion EX-OHCA had favorable clinical outcome compared with non-EX-OHCA. Reason may be that these patient present with culprit lesion that has good blood flow, possibly leading to fewer STEMI events.Baseline charactaristicComparison in Patients with ACS

One Health literacy and governance of health leaders across three countries following the pandemic

Abstract Background To address health threats and promoting health of all, One Health is an important approach. To foster the implementation of One Health, a framework proposed by Blankart et al. (2024) postulates a reinforcing relationship between One Health literacy and governance after being triggered by an external event. We build on this model and aim to explore how the postulated mechanism differs among health policy and practice leaders across the UK, Germany, and Switzerland after the external triggering event of the COVID-19 pandemic. Methods We developed an online survey instrument for each of the 5 elements of the framework and conducted it in April 2024. We targeted mid- and end-career leaders and senior experts from the Sciana network that reported about their personal experiences and perceived changes (scale from -5 to + 5). We performed descriptive and inferential statistics to analyse the data. The survey was complemented by structured interviews to gather contextual information. Results Leaders and experts (N = 30, response rate: 30%) increased their One Health literacy (mean: 2.2), increasingly engaged with One Health principles (mean: 1.0), which resulted in more calls for optimized One Health governance (mean: 2.4). The interviewees reported that they adopted and implemented a more consistent One Health governance (mean: 2.6), which led to more familiarity with One Health principles (mean: 2.3). According to the results, the average effect on One Health literacy and Governance was stronger in Germany, followed by Switzerland and the UK. Conclusions We have demonstrated that triggering events such as the COVID-19 pandemic have led to improvements in One Health literacy and subsequently One Health governance. We therefore suggest that the postulated framework holds in practice and have showed that there are important differences in uptake between leaders in the three countries. Further research may build on these findings and develop more effective health policy. Key messages • We have contributed to the understanding of how pandemics improve One Health literacy and subsequent One Health governance using the example through a survey of leaders in health policy and practice. • Based on the understanding of triggering events’ effect on One Health literacy and governance specific recommendations for the implementation of One Health in public health can be developed.

A regional multicentre prospective survey addressing sport-related myocardial infarction: clinical characteristics of 155 patients and one year follow-up

Abstract Background Exercise is a keystone of cardiovascular prevention; however, it is also a powerful trigger of acute cardiovascular events including sudden cardiac arrest and acute myocardial infarction. Contemporary studies addressing these sport-related myocardial infarction (SR-MI) are scarce. Aim To describe clinical characteristics and follow-up of SR-MI. Methods We conducted a regional multicentric prospective survey addressing patients admitted for a SR-MI in 2 intensive coronary units. SR-MI was defined as MI onset occurring during sport or within the 1st hour of recovery. A one-year follow-up was performed by phone call. Results From April 1st 2018 to February 23th 2023, 155 SRMI were included, of whom 13 (8.4%) were female, median age was 61-y. (IQR 54-66). Cycling was the most frequent sport (32.5%) followed by fitness and hiking (12.6% each) then jogging (11.3%) (Figure). Most were ST elevation MI (68.8%). The main risk factors were dyslipidaemia (33.8%), hypertension (27.3%) and tobacco smoking (24.5%). No cardiovascular risk factor other than age was identified in 38 patients (25.7%). Prodromal symptoms were reported by 66 subjects (42.6%). SR-MI occurred mostly during exercise (60.6%). Out of hospital cardiac arrest (OHCA) occurred in 20 (12.6%) patients, more commonly among patients with prior coronary artery disease (CAD) (p = 0.02) and tobacco smoking (p=0.047). A significant single vessel disease was observed in 67 patients (43.2%); revascularization was performed by stenting in 134 patients (86.5%) and surgery in 11 (7.1%). In-hospital death occurred in 4 patients and was associated to OHCA (p=0.007). Among the survivors at time of discharge (151 patients), 141 were eligible to follow-up, none one lost: one patient died suddenly at home, one suffered from a recurrent MI, one experienced a cardioversion from his implantable cardioverter defibrillator (ICD) and an ICD was implanted in 4 others. A cardiovascular rehabilitation was performed in 101 patients (65.2%); from the 30 smokers at the time of SRMI, 26 declared smoking cessation. Conclusions Among patients admitted for a SR-MI, most were male. Prodromal symptoms were common but neglected. OHCA occurred in 1 patient out of 8 and was associated with history of CAD, tobacco smoking and in-hospital death. Such findings highlight the need for mass education to prevention including recognition of prodromal symptoms and management of cardiac arrest. In this exercising population, rehabilitation rate was high and quitting tobacco was commonly achieved.Table

Impaired striatal glutathione-ascorbate metabolism induces transient dopamine increase and motor dysfunction.

Identifying initial triggering events in neurodegenerative disorders is critical to developing preventive therapies. In Huntington's disease (HD), hyperdopaminergia-probably triggered by the dysfunction of the most affected neurons, indirect pathway spiny projection neurons (iSPNs)-is believed to induce hyperkinesia, an early stage HD symptom. However, how this change arises and contributes to HD pathogenesis is unclear. Here, we demonstrate that genetic disruption of iSPNs function by Ntrk2/Trkb deletion in mice results in increased striatal dopamine and midbrain dopaminergic neurons, preceding hyperkinetic dysfunction. Transcriptomic analysis of iSPNs at the pre-symptomatic stage showed de-regulation of metabolic pathways, including upregulation of Gsto2, encoding glutathione S-transferase omega-2 (GSTO2). Selectively reducing Gsto2 in iSPNs in vivo effectively prevented dopaminergic dysfunction and halted the onset and progression of hyperkinetic symptoms. This study uncovers a functional link between altered iSPN BDNF-TrkB signalling, glutathione-ascorbate metabolism and hyperdopaminergic state, underscoring the vital role of GSTO2 in maintaining dopamine balance.

The Theory of Dyadic Morality and Moral Identity Explain the Public's Response to Harm Done by Government and Organisations

ABSTRACTHarm functions complexly in moral judgement but has been treated differently in the Social Intuitionist Model (SIM) and the Theory of Dyadic Morality (TDM). Both the SIM and TDM see felt harm as an outcome of experienced negative moral emotions (e.g., disgust), but the SIM regards harm as a kind of epiphenomenon in the sense that it does not affect moral judgement or behaviour, whereas the TDM interprets harm as an essential mediator of the link between negative moral emotions and immorality. The TDM also develops an explanation for how harm functions to initiate reactions to triggering events marked by an intentional agent causing injury to a vulnerable social actor. Here norms serve a regulatory function modulating the effects of harm committed by the agent on negative moral emotions. We conduct two experiments on representative samples of adults () and address two different moral contexts (companies doing badly and government doing badly with respect to a threatened health crisis), two different normative moderators (moral identity and belief in conspiracies) and two different moral action tendencies (intentions and word of mouth). Harm is found to have direct and contingent mediated effects on moral action tendencies, in accord with the TDM. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Relaxed Event Triggered Control of Discrete‐Time Takagi‐Sugeno Systems Based on Homogeneous Polynomial Method

ABSTRACTThis article presents an event triggered control approach for discrete‐time T‐S fuzzy systems, which integrates the homogeneous polynomial method and a dynamic event triggering mechanism. First, a kind of discrete‐time loop functions and the dynamic event triggering scheme based on periodic sampling are constructed to establish stability conditions with known system information. Second, by introducing the homogeneous polynomial method to relax the stability criteria, the sampling interval is increased and the system conservatism is reduced. Additionally, an integrated framework for collaboratively designing the event triggering functions and controllers is proposed, which reduces the complexity of sampling transmission while ensuring system stability. Finally, numerical and practical examples are used to illustrate the feasibility and effectiveness of the proposed method.

Distributed Optimization on Matrix‐Weighted Networks

ABSTRACTThis article is intended to tackle the optimization problems for continuous‐time first‐order and second‐order multi‐agent systems (MASs) operating over matrix‐weighted networks. A matrix‐weighted network serves as a powerful framework to model the interdependence among agents' multidimensional states, providing an effective approach to analyze smart grids, intelligent transportation systems, and so forth. Our optimization objective is to facilitate the convergence of all agents toward the optimal value of a global cost function, which is formed by a sum of local cost functions. To achieve this goal, distributed optimization algorithms based on Hessian matrix and gradient information are constructed. Additionally, an edge‐based event‐triggered mechanism is utilized to avoid communicating with all neighbors at the time of event triggering. It is proved that this mechanism theoretically excludes Zeno behavior. The results show that the proposed algorithms ensure that the agents can achieve the optimization goal while reducing energy consumption. Finally, an application is presented to substantiate the theoretical results.

Asynchronous Sliding Mode Control of Networked Markov Jump Systems via an Asynchronous Observer Approach Based on a Dynamic Event Trigger

This paper explores the utilization of sliding mode control, which relies on an asynchronous observer, for Markov jump systems subject to external disturbances. Firstly, given that the system’s mode is not directly measurable and could potentially differ from the observer’s and controller’s mode, the paper constructs an asynchronous observer employing a hidden Markov model. Secondly, a sliding surface is designed to correspond with the asynchronous observer. Moreover, a multi-parameter event-triggered mechanism is incorporated into the observer design to alleviate bandwidth strain. Thirdly, by applying the integrated sliding mode control law, we ensure that the system state trajectories will reach the sliding surface within a finite time. Fourthly, the achievement of H∞ stability is realized by making use of the Lyapunov function. Lastly, a practical-oriented example is presented to illustrate the efficiency of the established method.

Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of documents. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

Adaptive Event-Triggered Consensus Control of Nonlinear Multi-Agent Systems via Output Feedback Methodology: An Application to Energy Efficient Consensus of AUVs

For dealing with the energy consumption in multi-agent systems (MASs), an event-triggered (ET) methodology is promising, which relies on the activation of communication devices only when communication of data is needed. This paper explores the leaderless consensus for nonlinear MASs using an adaptive ET approach via an output feedback methodology. This adaptive ET scheme is preferred as it can adapt to the environment through setting a communication threshold. The proposed approach renders the observed states of agents by use of nonlinear observers in an output feedback control dilemma, making it more practical. Simple Luenberger observers are developed to avoid the problem of always measuring agents’ states. The strategy of adaptive ET-based control is employed to minimize resource use and information transmission. Design conditions for the observer-based adaptive ET consensus control of nonlinear MASs have been derived via a Lyapunov function, containing state estimation error, consensus error, adaptation term, and nonlinearity bounds. In contrast to the existing methods, the present approach applies a more practical output feedback schema, uses adaptive ET proficiency, and deals with nonlinear agents. An example of a formation of autonomous underwater vehicles achieving the basic consensus realization between displacement and velocity is included to illustrate the viability of the resultant approach.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

Grid-search method for short-term over long-term average parameter tuning: an application to Stromboli explosion quakes

The collection of a significant catalogue of seismo-volcanic data involves the selection of relevant parts of raw signals, which can be automatised by using the short-term over long-term average (STA/LTA) method. The STA/LTA method employs the “Characteristic Function” to describe a section of a seismic record in terms of trace amplitude and first-time difference. This function is calculated in a short-term and long-term window; the ratio between the two windows defines a quantity that is controlled through threshold values, i.e., trigger on and trigger off. These threshold values indicate whether there is an increase in the energy in the seismic signal compared to the background noise. The common approach to the selection of the STA/LTA values is the adoption of literature-suggested ones. This could be a limitation as there may be cases in which a choice adapted to a specific raw signal may significantly help in the extraction of the relevant parts. To overcome the possible drawbacks of a non-adaptive choice imposed by such standard literature values, in this study, we propose a methodology for the automatic selection of STA/LTA values that can optimise the extraction of explosion quakes (EQs) from a seismo-volcanic raw signal. The values are obtained through a grid search over an index named quality–numerosity index (QNI) that measures the accordance in the automatic cuts and the consequent number of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain for the specific application of the explosion quake signal extraction at Stromboli volcano. The experiments were conducted by selecting a subset of the dataset as training where to search for the best values, which were subsequently adopted in a test set. The results prove that the values suggested by our approach significantly improve the quality of the relevant part compared to the one extracted by adopting the values indicated in the literature. The methodology presented in this study can be applied to a wider typology of signals of volcanic, seismic, and other origin, potentially becoming a widely used approach in parameter optimisation processes.

A Perspective on the Rupture States of Complex Earthquake Scenarios in the 2023 USGS Seismicity Model for the Western U.S. Faults

Abstract The 2023 U.S. Geological Survey (USGS) seismicity model for faults in the western United States (USGS-SMF-WUS) includes many earthquake scenarios (EQS) that rupture multifault sections (MFS), reflecting decades of observations that large crustal earthquakes often rupture sections of different neighboring faults in complex ways. Many of the complex MFS scenarios include rupture sections, on different faults, that are not contiguous, with spacing up to 15 km. The USGS-SMF-WUS uses an inversion methodology, referred to as magnitude-rate optimization, to optimally formulate the occurrence rates of the EQS in such a way that they collectively release the accumulating seismic moment rates on faults, at subsection level, defined by various types of geologic, geodetic, and seismologic data and constraints. This raises questions about the treatment of the MFS scenarios in the USGS-SMF-WUS regarding their rupture states and how that could potentially impact the magnitude-rate optimization and ground motions (GMs) for hazard analysis. Among many different possible rupture states for the MFS earthquakes, the two extreme scenarios could be visualized as (1) a series of noncontiguous sections rupturing in such a way that their total seismic moment and GM footprints are scaled with the sum of their rupture areas and (2) a series of noncontiguous triggered events, grouped together based on the spacing between the participating fault sections and other criteria, each releasing seismic moment and generating GM footprints that are scaled with its own rupture area. It will be shown later that the average slips on the participating sections for these two extreme rupture states would not be the same. In addition, the GM footprints from these two rupture states would be different and the differences are frequency dependent. This is especially the case when considering the complexity of rupture directivity on the participating fault sections in the USGS-SMF-WUS. It is the objective of this study to explore and discuss the implications of the triggered rupture state assumption in estimating the average slips and GM footprints of the MFS earthquakes in the USGS-SMF-WUS.