We present the status of ShakeMap-EU, an initiative initially proposed in 2018 to: (i) provide an integrated archive of ShakeMaps at the European level built on EPOS Seismology (www.epos-eu.org/tcs/seismology) services & data products and modern community software; (ii) serve as a backup to authoritative ShakeMap implementations; (iii) deliver ShakeMaps for Euro-Mediterranean regions where no local capability is yet available. ShakeMap-EU products are accessible since mid-2020 at the web portal shakemapeu.ingv.it. Jointly governed by the institutions participating in the initiative, ShakeMap-EU is founded on voluntary institutional contributions and EC-funded projects. ShakeMap-EU has become a reliable European seismological service that can easily and consistently integrate authoritative models and workflows. The system is based on:  (a) the latest version of ShakeMap® (usgs.github.io/shakemap); (b) the earthquake information delivered by the EMSC (www.emsc-csem.org); (c) the earthquake shaking data distributed by ORFEUS (orfeus-eu.org/data/strong); (d) the ground motion models adopted within EFEHR (www.efehr.org) for mapping seismic hazard across Europe; (d) the official ShakeMap configurations of some of the most hazardous countries in Europe. Configuration of, and input to the system are managed via a GitHub repository that allows automatic / manual triggering and interaction by authorized users. ShakeMap-EU provides a collaboration framework and laboratory for seismological agencies to address the challenges posed by the heterogeneity of ground shaking mapping strategies across Europe and the need to promote homogenization and best practices in this domain. ShakeMap-EU is used in research projects as the test platform for novel international collaborative research: among recent examples are the ongoing enhancements towards an evolutionary hazard information system including real-time seismicity characterisation and information on earthquake-induced phenomena.

How to cite: Michelini, A., Faenza, L., Cauzzi, C., Lauciani, V., Clinton, J., Kästli, P., Haslinger, F., Wiemer, S., Melis, N., Theodoulidis, N., Böse, M., Weatherill, G., Cotton, F., and Giardini, D.: ShakeMap-EU: an update on the shakemap service in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5937, https://doi.org/10.5194/egusphere-egu23-5937, 2023.

Shakemaps are important tools for characterizing and visualizing the geographic impact of seismic events. It is also a great support of communication and crisis management, especially in the early stage following an event. It is thus important to constrain at best the parameters controlling the calculation to have the most reliable shakemaps possible. 

Our study consists in optimizing the shakemaps in an application for moderate seismic activity in mainland France and French overseas territories. 

We studied the evolution from v3.5 to v4 of USGS ShakeMap^TM and compared the associated shakemaps for past events. An in-depth study of the core calculations of the v4 was necessary to take advantage of their new approaches and optimize the parameters and configuration for the French territories. 

Our goal was not only to implement the ShakeMap v4 as the new automatic functional version on www.franceseisme.fr, the BCSF-Rénass website (Résif-Epos), but also to evaluate the prospects and limitations for computing high-resolution shakemaps. We analyzed the uncertainties of shakemaps between rapid shaking assessment based on preliminary data and the late reference shakemap based on complete validated datasets for recent damaging or largely felt events in France.

What can we improve today to reach high-resolution? What might be possible in a few years? Finally, what are the limits that we may never be able to overcome in the ShakeMap^TM  program approach?

How to cite: Pellorce, L., Mendel, V., Schlupp, A., and Grunberg, M.: ShakeMapTM v4 study, analysis of rapid shaking assessment, optimization and evaluation of high-resolution prospects for mainland France and French overseas territories, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6302, https://doi.org/10.5194/egusphere-egu23-6302, 2023.

Building an earthquake early warning (EEW) network requires the installation of seismic instruments around the seismogenic zone. Using low-cost sensors to build a seismic network for EEW and to generate shakemaps is a cost-effective way in the field of seismology. The National Taiwan University (NTU) network employing 770 P-Alert low-cost sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years in Taiwan. This instrumentation is capable of recording the strong ground motions of up to ± 2g and is dense enough to record the near-field ground motion. The NTU system has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for onsite warning. Using real-time seismic signals, each P-Alert device provided a 2–8 s warning time for the near-source earthquake regions situated in the blind zone of the Central Weather Bureau (CWB) regional EEW system, during the 2016 Mw6.4 Meinong and 2018 Mw6.4 Hualien earthquakes. The shakemaps plotted by the P-Alert dense network help to assess the damage pattern and act as key features in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Currently, the P-Alert network can provide peak ground acceleration (PGA), peak ground velocity (PGV), spectral acceleration (Sa) at different periods, and CWB intensity shakemaps. Using shakemaps it is found that PGV is a better indicator of damage detection than PGA. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries.

How to cite: Wu, Y.-M.: Development of an earthquake early warning and shakemaps system using low-cost sensors in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2499, https://doi.org/10.5194/egusphere-egu23-2499, 2023.

ORFEUS (Observatories and Research Facilities for European Seismology, orfeus-eu.org) is a non-profit foundation that coordinates and promotes seismology in the Euro-Mediterranean area and beyond, through harmonized collection, archival and distribution of seismic waveform data, metadata, alongside offering services and products managed at national level by more than 60 participating seismological Institutions. ORFEUS is one of the founding members of EPOS Seismology (www.epos-eu.org/tcs/seismology) and a fundamental partner of EC-funded projects. ORFEUS services are largely integrated in the EPOS Data Access Portal (www.ics-c.epos-eu.org). The key goals of ORFEUS (Bylaws, Article I.3: orfeus-eu.org/documents//ORFEUS_Bylaws_September_2022.pdf) are achieved through the development and maintenance of data services targeted to a broad community of seismological data users. ORFEUS comprises: (i) the European Integrated waveform Data Archive (EIDA; orfeus-eu.org/data/eida); (ii) the European Strong-Motion databases (orfeus-eu.org/data/strong); and iii) the recently established group representing the community of European mobile pools, including amphibian instrumentation (orfeus-eu.org/data/mobile). Products and services for computational seismology are also considered for integration in the ORFEUS domain. Currently, ORFEUS services  provide access to the waveforms acquired by ~18,000 stations in the Euro-Mediterranean region, including dense temporary experiments (e.g., AlpArray, AdriaArray), with strong emphasis on open, high-quality data. Access to data and products is ensured through state-of-the-art information and communication technologies, with strong emphasis on federated web services, clear policies and licenses, and acknowledging the crucial role played by data providers. Significant efforts are underway, by ORFEUS participating institutions,  to enhance the existing services to tackle the challenges posed by the Big Data Era, and to actively encourage interoperability and integration of multidisciplinary datasets in seismological and Earth Science workflows.  ORFEUS also implements Community services that include software and travel grants, webinars, workshops and editorial initiatives. ORFEUS data and services are assessed and improved through the technical and scientific feedback of a User Advisory Group, which comprises European Earth scientists with expertise on a broad range of disciplines. 

How to cite: Cauzzi, C., Bieńkowski, J., Crawford, W., Custódio, S., D'Amico, S., Evangelidis, C., Haberland, C., Haslinger, F., Kiratzi, A., Kolínský, P., Lanzano, G., Roumelioti, Z., Sigloch, K., Sleeman, R., and Strollo, A.: ORFEUS Data Services, Products and Actions to Coordinate Access to Seismic Waveform Data in the Euro-Mediterranean Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9051, https://doi.org/10.5194/egusphere-egu23-9051, 2023.

Deployment of the mobile seismic network around Petrinja in the aftermath of the Petrinja earthquake

After the devastating Petrinja earthquake (ML 6.2) in December 2020, the Croatian Seismological Survey acquired 20 sets (seismographs and accelerographs ) to use as a mobile pool of seismic stations. From January 2021, the installation of more than 20 stations in the wider Petrinja area began. All stations were operational in less than two weeks. Initially, the mobile pool worked offline until a working data transmission solution was found, as DynDNS didn't work with the current cellular network operator. Finally, a VPN solution was applied by installing an OpenVPN server and creating a VPN network that was proven to work properly. Since April 2021, all stations have been online and transmitting data to the CSS headquarters, where the SeisComP3 machine is responsible for data acquisition and processing. After the Ljubinje earthquake in April 2022, some of the stations of the Petrinja mobile pool were relocated to the wider Dubrovnik area. Both sites use accelerographs and seismographs for earthquake detection purposes, with 100 Hz and 200 Hz sampling for seismographs and 200 Hz for accelerographs.The Petrinja Mobile Network is still active today as the aftershock sequence is still active in that area. Details of the equipment purchased, problems encountered and solutions found for data transmission, site selection procedures applied and results achieved are presented.

How to cite: Fiket, T.: Deployment of the mobile seismic network around Petrinja in the aftermath of the Petrinja ML 6.2 earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2142, https://doi.org/10.5194/egusphere-egu23-2142, 2023.

In recent years the Swedish National Seismic Network (SNSN) made an increased effort to modernize station and communication equipment, and thereby has significantly improved continuous real-time data availability and data quality.  Currently, the SNSN is operating 67 permanent broadband seismic stations evenly distributed in the South, along the Eastern shore and the North of Sweden. In addition, a temporay network of 13 stations was deployed in 2021 for a 3-year period to monitor small earthquakes associated with a linear cluster of events at the Western cost of the Gulf of Bothnia. SNSN transmits continuous real-time data to networks in the neighboring countries (Norway, Denmark, Finland, Germany) and in turn receives and processes data from about 120 stations abroad.

Compared to many other countries, Sweden has a relatively low seismicity. This makes it all the more important to focus on small seismic events in order to map crustal structures and processes and to provide a data basis for reasonable long-term seismic hazard assessments. Turning to small events means to deal with many events and most of them being man-made seismic events (blasts related to quarries, underground mines, road/tunnel constructions, etc). Within the last 22 years SNSN has recorded and analyzed about 170,000 seismic events out of which only 11,000 (~6.5%) were classified as natural events. Automatic event processing and event type classification are of the essence in order to cope with the amount of data and to decrease the workload of the analysts.

SNSN is running four different and independent automatic processing routines in parallel: SeisComp (SC), Earthworm (EW), MSIL and a migration stack algorithm (MS). The main purpose of SC and EW is to detect and locate events in realtime. Both systems are set up to be very sensitive in order to detect as small events as possible, which on the other side also increases the probability to generate spurious events. To counterbalance that we generate a common event catalogue (i.e. events that were located both by SC as well as EW) which turnes out to be very reliable. The common event bulletin captures events as small as about ML1 and contains almost all events ML > 1.5.  MSIL and MS are running in offline and delayed mode which allows the backfilling of potential data gaps, before processing. These systems are catching events down to about magnitude ML0. All events of the common bulletin and the MSIL bulletin are subject to an automatic Neural Network event typ classification into earthquakes, blasts and mining-induced events. In a final step all events classified as earthquakes, significant blasts (felt events or events of special interest) or events with unclear cassification are reviewed by SNSN analysts and are being made available on the SNSN web page. In the framework of EPOS-Sweden, SNSN will make available the waveform and metadata data of the permanent network via FDSN-services.

How to cite: Roth, M., Lund, B., Eggertsson, G., Schmidt, P., and Shomali, H.: The Swedish National Seismic Network - Infrastructure, Data Processing and Products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6914, https://doi.org/10.5194/egusphere-egu23-6914, 2023.

As part of the “Deutsches Seismologisches Breitband Array” (DSEBRA), a mobile station array of 100 identical seismological broadband stations, the remote monitoring devices “PowBox” were developed by the Ludwig-Maximilians-Universität München.

The PowBoxes log and report the health status of the autonomously operating seismological stations, such as 12V/230V availability or battery temperature. With their various fail-safes the most common issues such as router and battery charger problems are fixed by automatic power resets and efforts and costs on unnecessary maintenance trips can be reduced.

More than 80 PowBoxes are now in operation for the first time with the deployment of the DSEBRA stations in South-East Europe as part of the AdriaArray project, a follow-up to the successful AlpArray project.

To ensure a good overview for the network operator, the modular Python software tool “survBot” was developed at the Ruhr-Universität Bochum. It analyses the different state-of-health channels of the PowBox and the Datalogger, displays them in a graphical user interface or as a html-webpage and informs about emerging problems via e-mail. It is openly available on github.

How to cite: Paffrath, M., Schlömer, A., Terpoorten, M., Egdorf, S., Schwab, A., and Friederich, W.: Enhancing remote monitoring of autonomous seismic stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15147, https://doi.org/10.5194/egusphere-egu23-15147, 2023.

The Guralp Minimus broadband digitizer has led the way with introducing a number of innovative features to broadband digitizers including easy network configuration, compact form-factor, extensive State of Health (SOH) monitoring and low latency digitization. Since its introduction, there have been major technological advances in processing chips resulting in the power consumption of seismic digitizers decreasing drastically in the last few years. The next iteration of Minimus, Minimus2, takes advantage of modern chip power consumption to reduce overall nominal power consumption by over 50% whilst maintaining high functionality. This significant decrease in power consumption will facilitate far more simplified field deployments for offline deployments.

The Minimus platform also provides a high level of functionality for online stations, including the industry unique option of sending State of Health (SOH) data via the SEEDlink protocol. This makes SOH monitoring far simpler for larger networks as SOH data be managed using similar methods as waveform data. This also allows for time-series analysis of SOH data to be able to proactively maintain stations and advance diagnose any issues before they result in any loss of data. The Minimus platform seamlessly interfaces the Discovery software to seamlessly integrate new stations into existing networks. The management of large numbers of real-time seismic stations is further enhanced with Guralp Data Centre (“GDC”) a cloud-based software package to build on the Discovery tool set.

The Minimus platform was built from the ground up to provide one of the lowest latency digitizers available with digitization latencies down to 40ms, making it well suited to Earthquake Early Warning applications. This is achieved with the use of causal decimation filters, high sample rates and Guralp’s proprietary GDI protocol. The Minimus platform is built as a modular digitizer platform that is available within a number of different packages to suit a range of applications, including as a standalone digitizer or built within Broadband seismometer and Force Balance Accelerometer systems. 

How to cite: Lindsey, J., Watkiss, N., Reis, W., and Whealing, D.: The Minimus digitizer platform: a user-friendly ecosystem for efficient network management and seismic station configuration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6725, https://doi.org/10.5194/egusphere-egu23-6725, 2023.

More science, particularly related to hazard reduction and earthquake forecasting, is enabled via the availability of rich seismic datasets and event catalogs. Deployment of high performing monitoring networks, which produce high quality datasets, is an investment that enables ongoing and future science advancements.

One measure of network performance, magnitude of completeness (Mc), is determined by a number of factors including station density, network geometry, self-noise and passband of the system used, ambient noise environment and sensor installation method and depth.  Sensor installation techniques related to depth are of particular importance due to their impact on deployment cost and station performance. It is well established that deploying seismic sensors at greater depths reduces their exposure to cultural and environmental noise, improving seismic signal detection. When extended to overall network performance, this noise suppression results in improved (decreased) magnitude of completeness for the network. Using modeling tools, we assess the theoretical improvement in performance associated with an upgrade to borehole installations, increasing sensor depth, for a real world network in the Puget Sound area of Washington State.

The goals of monitoring networks and science are overlapping and dependent. Establishing measurable and achievable performance metrics for these supported networks helps the community understand the present distribution of performance and converge on recommendations for government agencies that will benefit both science and monitoring. For example, datasets from monitoring networks with reduced Mc are likely to inform and enable earthquake forecasting research with the potential to benefit hazard reduction for the general population.

How to cite: Laporte, M., Parker, T., Hamilton, V., and McNamara, D.: Designing or Upgrading a Seismic Network To Meet Specific Performance Criteria Using Array Modeling, a Case Study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16691, https://doi.org/10.5194/egusphere-egu23-16691, 2023.

The Republic of Croatia is a seismically highly active area and is currently insufficiently covered by seismological stations. Due to the shape of Croatia, a dense network of seismological stations is needed for more accurate determination of the magnitude and location of earthquakes. One of the main goals of the CROSSNET Project, which the Croatian Seismological Survey carries out, is to install 95 new seismological stations across the Republic of Croatia.

Selection of a site for a seismological station is a complex process that requires careful consideration of a range of factors to ensure that the station is at an optimal location for collecting accurate and reliable data on seismic activity. Field surveys will be required to verify methods used for spatial analysis. The location of the seismological station was chosen based on several factor maps, including natural and anthropogenic noise sources (such as roads, railways, energy infrastructure, industry, and surface waters), geology, and geomorphological criteria (such as slope and aspect).

This study aims to find the most appropriate locations for installing a network of seismological stations in Osječko-baranjska and Vukovarsko-srijemska counties in Croatia. It is necessary to set up seismological instruments at locations with reduced seismic noise conditions and favourable geological conditions to improve the quality of the data acquired.

Given the large scope of the project and the area it covers, an adequate approach is necessary to plan field investigations; therefore, the Suitability modeller in ESRI software ArcGIS Pro was used to evaluate locations for future field surveys. The suitability modelling identifies relevant criteria (factor maps), prepares criteria data, transforms values for each criterion to a standard suitability scale, weights criteria relative to one another according to their importance, combines the criteria into a suitability map, and finally locates the most suitable areas for field investigations.

This work has been fully supported by Next Generation EU and National Recovery and Resilience Plan under project C6.1. R4-I1.

How to cite: Brcković, A., Damjanović, V., Gašo, V., Grljević, S., Kapelj, M., Kostanjšek, I., Milec, V., Pervan, M., Tremljan, A., and Fiket, T.: Seismological station placement in Vukovarsko-srijemska and Osječko-baranjska counties, Croatia: a GIS analysis of environmental noise effects for the CROSSNET project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5389, https://doi.org/10.5194/egusphere-egu23-5389, 2023.

Earthquake early warning (EEW) is a technology that aims to reduce damage by notifying an alarm message before a large shaking due to an earthquake. The EEW currently adopted by most national or local governments is a network-based method. Network-based EEW produces seismic source information based on seismic wave detection from at least three observation stations, so the density of the observation network is a very important factor in shortening the warning time. However, a huge budget and space are required to construct a dense seismic observation network. In order to compensate for such limitations, all sensors capable of detecting vibration are being expanded and applied to seismometers. The Korea Meteorological Administration (KMA) signed an MOU with a private telecommunication service provider and installed about 6,700 MEMS sensors across the country. This is about 22 times more sensors than the existing KMA seismic observation network. In this study, the seismic detection performance of MEMS sensors and the KMA seismometers installed across the country was analyzed from the perspective of time. Since it is difficult to apply the existing seismic wave detection technology to the MEMS sensor as it is, an artificial intelligence-based seismic detection technology was applied. We compared the analysis results of the KMA observation network and the MEMS observation network in real-time for earthquakes of M 3.5 or greater. As a result of real-time detection, it was found that the high-density of observation network was more effective in detecting earthquakes than the performance of the sensor.

How to cite: Park, E., Ahn, J.-K., Hwang, E.-H., Lee, H.-W., and Park, S.-C.: A Study on Earthquake Detection Performance of MEMS Sensors According to Seismic Observation Network Density, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3032, https://doi.org/10.5194/egusphere-egu23-3032, 2023.

Recent years have seen the development of several very powerful machine learning pickers for P and S waves. The recent development of the SeisBench platform (https://github.com/seisbench/seisbench) in combination with mixed regional teleseismic benchmark datasets published by the NEIC (USGS) and GEOFON (GFZ Potsdam) enabled the retraining of the most popular picker neural network models (PhaseNet and EQTransformer) optimised for global monitoring applications in the benchmark study of Münchmeyer et al (2021, J. Geophys. Res.).  

In this contribution we introduce a module scdlpicker, which connects SeisBench to SeisComP (https://www.seiscomp.de/)  through a client submodule, which listens to new event detections from the regular SeisComP automatic detection system and triggers repicking of those events with any picker implemented in SeisBench, using the improved picks to trigger a relocation. The machine learning picks are subsequently available within the SeisComP GUI in case further manual refinement or checking is desired. 
We demonstrate application of this system with the GEOFON global earthquake monitoring service (https://geofon.gfz-potsdam.de/eqexplorer), evaluating the benefits of using the machine learning picker with respect to the conventional workflow relying on traditional pickers with respect to timeliness of reporting earthquakes and reduction of manual work load, and improvement in the number of high quality picks available for each event. 
The quantification of the uncertainty of machine learning picks is important when weighing the contribution of different picks in many location algorithms, yet this information is not readily available from machine learning pickers. They do return, however, a characteristic function (nominally the confidence in the pick), whose properties might correlate with the uncertainty of the pick. We will show whether and how the picking uncertainty correlates with properties of the characteristic function. 

How to cite: Tilmann, F., Bornstein, T., Saul, J., Münchmeyer, J., and Beutel, M.: Employing machine learning pickers for routine global earthquake monitoring with SeisComP: What are the benefits and how can we quantify the uncertainty of picks?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10046, https://doi.org/10.5194/egusphere-egu23-10046, 2023.