The GEOFON program consists of a global seismic network, a seismological data center, and a global earthquake monitoring system. The seismic network has regional focus in Europe and North Africa as well as throughout the Indian Ocean, but it operates stations on all continents, including Greenland on the North American continental plate and Antarctica. The data center provides real-time seismic data through the SeedLink protocol and historical data from its large archive that currently comprises 120 TB of temporary and permanent seismic network data from GeoForschungsZentrums and third-party partners made available via standard services as part of the European Integrated Data Archive and within the International Federation of Digital Seismograph Networks. GEOFON also provides global and rapid earthquake information. The rapid earthquake information service prioritizes fast information dissemination globally after moderate and large earthquakes based on automatic processing. Most operations are carried out using the SeisComP system. GEOFON distributes findable, accessible, interoperable, reusable data, services, products, and software free of charge, and it is used worldwide by hundreds of users and other data centers.

Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering. Most current inference methods rely on time series data recorded from all dynamical variables in the system. In applications, often only some of these time series are accessible, while other units or variables of all units are hidden, i.e. inaccessible or unobserved. For instance, in AC power grids, frequency measurements often are easily available whereas determining the phase relations among the oscillatory units requires much more effort. Here, we propose a network inference method that allows to reconstruct the full network topology even if all units exhibit hidden variables. We illustrate the approach in terms of a basic AC power grid model with two variables per node, the local phase angle and the local instantaneous frequency. Based solely on frequency measurements, we infer the underlying network topology as well as the relative phases that are inaccessible to measurement. The presented method may be enhanced to include systems with more complex coupling functions and additional parameters such as losses in power grid models. These results may thus contribute towards developing and applying novel network inference approaches in engineering, biology and beyond.INDEX TERMS complex networks, inverse problems, nonlinear dynamics, network inference, power grids I. INTRODUCTION

<p>Data from Ocean Bottom Seismometer (OBS) deployments nowadays are routinely integrated in federated seismological data centers, but often without following standardized procedures as recommended by the OBS community. This makes it difficult to locate and exploit these data mainly due to the restrictions imposed by the standard metadata format (stationXML) and the services.</p> <p align="justify">In the context of the Helmholtz Metadata Collaboration (HMC), supported by OBS experts, we selected AWI and GEOMAR datasets to be archived at the GEOFON data center with standardized procedures according to the FDSN straw man proposal of W. Crawford for OBS data and metadata using also the OBSinfo tools developed at IPGP. In addition, a special emphasis was put on enhancing FAIRness for the selected datasets, for example identifiers of individual instruments were included, which are linked to instrument databases. Keywords were also added to make the data more easily findable and interoperable. From these experiences we formulated guidelines for OBS data management, which should help researchers to archive their OBS data consistently throughout EIDA data centers.</p>

Superficial geological layers can strongly modify the surface ground motion induced by an earthquake. These so-called site effects are highly variable from one site to another and still difficult to quantify for complex geological configurations. That is why site-specific studies can greatly contribute to improve the hazard prediction at a specific site. However, site-specific studies have historically been considered difficult to carry out in low-to-moderate seismicity regions. We present here seismological datasets acquired in the framework of the French–German dense array for seismic site effect estimation project in the heavily industrialized area surrounding the French Tricastin Nuclear Site (TNS). TNS is located above an ancient canyon dug by the Rhône River during the Messinian period. The strong lithological contrast between the sedimentary fill of the canyon and the substratum, as well as its expected confined geometry make this canyon a good candidate for generating site effects that are variable on short spatial scales. To investigate the impact of this geological structure on the seismic motion, we conducted complementary seismic campaigns in the area. The first main campaign consisted of deploying 400 nodes over a 10 × 10 km area for one month and aimed at recording the seismic ambient noise. A second seismic campaign involved the deployment of 49 broadband stations over the same area for more than eight months. This complementary campaign aimed at recording the seismicity (including local, regional, and teleseismic events). These different designs allowed us to target a variety of seismic data at different spatial and temporal scales. Beyond the interest for local operational seismic hazard applications, these datasets may be valuable for studying seismic wave propagation within complex kilometer-scale sedimentary structures. In this article, we present the deployment designs as well as initial analyses to provide information on the characteristics and the overall quality of the data acquired to future users.

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