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Misinformation spreads fast in times of crises, corroding public trust and causing further harm to already vulnerable communities. In earthquake seismology, the most common misinformation and misleading popular beliefs generally relate to earthquake prediction, earthquake genesis, and potential causal relations between climate, weather and earthquake occurrence. As a public earthquake information and dissemination center, the Euro-Mediterranean Seismological Center (EMSC) has been confronted many times with this issue over the years. In this paper we describe several types of earthquake misinformation that the EMSC had to deal with during the 2018 Mayotte earthquake crisis and the 2021 La Palma seismic swarm. We present frequent misinformation topics such as earthquake predictions seen on our communication channels. Finally, we expose how, based on desk studies and users' surveys, the EMSC has progressively improved its communication strategy and tools to fight earthquake misinformation and restore trust in science. In this paper we elaborate on the observed temporality patterns for earthquake misinformation and the implications this may have to limit the magnitude of the phenomenon. We also discuss the importance of social, psychological and cultural factors in the appearance and therefore in the fight against misinformation. Finally, we emphasize the need to constantly adapt to new platforms, new beliefs, and advances in science to stay relevant and not allow misinformation to take hold.

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Social media such as Facebook or Twitter are at present considered part of the communication systems of many seismological institutes, including the European–Mediterranean Seismological Center (EMSC). Since 2012, the EMSC has been operating a hybrid Twitter system named @LastQuake comprising a bot for rapid information on global felt earthquakes and their effects, which is complemented by manual moderation that provides quasi-systematic and rapid answers to users' questions, especially after damaging earthquakes and earthquake sequences. The 2022 release of @LastQuake transcends a mere alert service and possessess additional capabilities, including fighting against misinformation and enhancing earthquake risk awareness and preparedness by exploiting the teachable moments opened by widely felt but non-damaging earthquakes. @LastQuake significantly increases the visibility and audience of the European–Mediterranean Seismological Center services, even in regions where its smartphone application (app) and websites are well known. It also contributes to increasing the volume of crowdsourced eyewitness observations that are collected, notably through the publication of rapid non-seismic-wave-based detections, as well as by reaching out to Twitter users who post about felt experiences through individual invitation messages. Although its impact, especially in raising awareness and preparedness is difficult to evaluate, @LastQuake efficiently supports crisis communication after large earthquakes and receives positive feedback from users for satisfying identified information needs of eyewitnesses automatically and in a timely manner. This study shares the experience gained over the last 10 years of operating the bot, presents the impact of users' feedback on empirically driving its evolution, and discusses the ways by which we can move toward a more data-driven assessment of its impact.

Summary The retrieval of earthquake finite-fault kinematic parameters after the occurrence of an earthquake is a crucial task in observational seismology. Routinely-used source inversion techniques are challenged by limited data coverage and computational effort, and are subject to a variety of assumptions and constraints that restrict the range of possible solutions. Back-projection (BP) imaging techniques do not need prior knowledge of the rupture extent and propagation, and can track the high-frequency (HF) radiation emitted during the rupture process. While classic source inversion methods work at lower frequencies and return an image of the slip over the fault, the BP method highlights fault areas radiating HF seismic energy. Patterns in the HF radiation are attributable to the spatial and temporal complexity of the rupture process (e.g. slip heterogeneities, changes in rupture speed and in slip velocity). However, the quantitative link between the BP image of an earthquake and its rupture kinematics remains unclear. Our work aims at reducing the gap between the theoretical studies on the generation of HF radiation due to earthquake complexity and the observation of HF emissions in BP images. To do so, we proceed in two stages, in each case analyzing synthetic rupture scenarios where the rupture process is fully known. We first investigate the influence that spatial heterogeneities in slip and rupture velocity have on the rupture process and its radiated wave field using the BP technique. We simulate two different rupture processes using a 1D line source model: a homogeneous process, where the kinematic parameters are constant along the line, and a heterogeneous process, where we introduce a central segment along the line that has a step change in kinematics. For each rupture model, we calculate synthetic seismograms at three teleseismic arrays and apply the BP technique to reveal how HF emissions are influenced by the three kinematic parameters controlling the synthetic model: the rise time, final slip, and rupture velocity. Our results show that the HF peaks retrieved from BP analysis are better associated with space-time heterogeneities of slip acceleration. We then build on these findings by testing whether one can retrieve the kinematic rupture parameters along the fault using information from the BP image alone. We apply a machine learning, convolutional neural network (CNN) approach to the BP images of a large set of simulated 1D rupture processes to assess the ability of the network to retrieve, from the progression of HF emissions in space and time, the kinematic parameters of the rupture. These rupture simulations include along-strike heterogeneities whose size is variable and within which the parameters of rise-time, final slip, and rupture velocity change from the surrounding rupture. We show that the CNN trained on 40 000 pairs of BP images and kinematic parameters returns excellent predictions of the rise time and the rupture velocity along the fault, as well as good predictions of the central location and length of the heterogeneous segment. Our results also show that the network is insensitive towards the final slip value, as expected from theoretical results.