Distributed Acoustic Sensing (DAS) is of critical value for the offshore expansion of seismological networks. The work presented here is part of the 5-years ERC ABYSS project, which aims at building a permanent seafloor seismic observatory leveraging offshore telecommunication cables along the central coast of Chile. 

In preparation for this project, a first experiment named POST was conducted from October to December 2021 on a submarine fiber-optic cable connecting the city of Concón to La Serena. DAS data were recorded continuously for 38 days over a distance of 150 km, constituting more than 37,500 virtual sensors sampled at 125 Hz. We develop a workflow to detect more than 3500 local, regional and teleseismic events with local magnitudes down to ML = 0.5, automatically processing over 72 TB of data. We show that applying those methods to DAS data combined with data from the national onland seismic network greatly increases the accuracy of the earthquake hypocenter localizations. As a first step, we perform automatic seismic phase arrival picking using PhaseNet pretrained on conventional seismological stations, followed by phase association with GaMMA. We then apply a correction of the phase picks to account for shallow sedimentary layers and invert for the event hypocenter with VELEST. Finally, we estimate a local magnitude based on peak ground displacements.  

The ABYSS project near-real time data collection started the 30th of September 2023 using three DAS units to sense two offshore telecommunications cables connecting the cities of Concón to La Serena and La Serena to Caldera. The DAS data covers over 500 km of cable, comprising 30,000 virtual sensors sampled at 62.5 Hz. These data are synchronized once a day with a storage server located in France, the volume of which is anticipated to reach an estimated 608 TB by the end of the project. By applying our workflow, tested and validated on the POST experiment, to our daily data, we are able to process data in near-real time to build a catalog that will span 5 years, and that will be used as a reference for subsequent studies within the framework of the ABYSS project. Furthermore, the size of our catalog, enriched with numerous offshore events is a significant improvement over the existing regional catalogs, which may aid future studies of the Chilean margin subduction zone seismicity. 

How to cite: Baillet, M., Trabattoni, A., van den Ende, M., Vernet, C., and Rivet, D.: A workflow for building an automatic earthquake catalog from near-real time DAS data recorded on offshore telecommunications cable in central Chile., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10668, https://doi.org/10.5194/egusphere-egu24-10668, 2024.

Distributed Acoustic Sensing (DAS) is an emerging data acquisition technology that utilises an optical fiber to measure dynamic strain along its axis. Composed by an optical fiber and an interrogator unit (IU), the system emits laser pulses into the fiber and detects phase shifts in the backscattered light, converting them into strain or strain rate measurements. DAS is becoming popular in many seismological applications and, in particular, for logistically challenging environments such as offshore areas, boreholes, glaciers, and volcanic settings, where deploying conventional monitoring is challenging. Spatial and temporal sampling of DAS systems is much higher than traditional seismological instruments, offering a detailed picture of the recorded seismic wave field. This high spatial and temporal sampling of DAS systems results in massive data generation, especially over extended acquisition periods. For instance, a single day's data collected with a 1 km fiber, featuring inter-channel distances of approximately 1m and a temporal sampling rate of 0.5 ms, can easily reach 2 TB. This highlights the need for efficient data analysis procedures in Distributed Acoustic Sensing (DAS) with methods that are both computationally fast and capable of exploiting the extensive information embedded in such data. As DAS data acquisition experiments are still few in numbers, generating and using synthetic data becomes essential for evaluating performance across diverse DAS acquisition geometries and testing new data analysis techniques. Despite the constant growth of DAS systems, there is a lack of standard modelling and analysis tools that can be used within routine procedures. To address this issues, we formulated a versatile workflow designed to generate synthetic DAS data based on the convolutional model. A central component of this workflow is a travel-time calculator based on the solution of the Eikonal equation, accommodating various data acquisition geometries, including scenarios involving optical fibers deployed in deep boreholes—whether vertical or oblique. Synthetic DAS seismograms are subsequently generated by using the computed travel times, for both P and S phases, with the convolutional model. These seismograms contain several information, such as the radiation pattern of the source and the directivity of the fiber, with the possibility of selecting an arbitrary wavelet. While DAS synthetics computed using the convolutional model may be less realistic than those generated with methods like the reflectivity or the spectral element method, their computational speed is much higher. This efficiency becomes particularly crucial when dealing with the generation of extensive DAS synthetic datasets. The synthetic generation workflow can be used for 1) testing new seismic event detection and location methods for DAS data and 2) training machine learning models. Lastly, this work includes a comparative analysis of synthetics obtained through our workflow against those generated using the spectral element method, followed by an application with a waveform-based DAS event detector.

How to cite: Rapagnani, G., Gaviano, S., Pecci, D., Carelli, G., Saccorotti, G., and Grigoli, F.: A workflow for synthetic DAS data generation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5936, https://doi.org/10.5194/egusphere-egu24-5936, 2024.

Machine Learning (ML) applications in geoscience are growing exponentially, particularly in the field of seismology. ML has significantly impacted traditional seismological observatory tasks, such as phase picking and association, earthquake detection and location, and magnitude estimation. However, despite promising results, ML-based classical workflows still face challenges in analyzing microseismic data

Leveraging recent advances in Deep Learning (DL) methods, we present HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity. This tool utilizes an attention-based, spatiotemporal graph-neural network for seismic event detection and employs a waveform-stacking approach for event location, using output probability functions over a dense regular grid.

We applied HEIMDALL to a one-month dataset (December 2018) from the publicly available Hengill Geothermal Field in Iceland, collected during the COSEIMIQ project (active from December 2018 to August 2021). This dataset is ideal for testing seismic event detection and location algorithms due to its high seismicity rate (over 12,000 events in about two years) and the presence of burst sequences with very short interevent times (e.g., less than 5 seconds).

We assessed the methodology's performance by comparing our catalog with those obtained by two recent DL methods and one manually compiled by ISOR for the same period. The DL algorithms we considered are: (i) MALMI, a waveform-based location algorithm, and (ii) the recent GENIE graph-neural-network algorithm. For GENIE, we conducted a full repicking of continuous waveforms using the PhaseNet picking algorithm and subsequent retraining of its model to adapt it to the new seismic network.

Finally, we highlight the pros and cons of each method and discuss potential improvements for microseismic event detection and location, with a particular focus on induced seismicity monitoring operations at EGS sites.

How to cite: Bagagli, M., Grigoli, F., and Bacciu, D.: HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6552, https://doi.org/10.5194/egusphere-egu24-6552, 2024.

Distributed Acoustic Sensing (DAS) has emerged as a powerful tool in seismological applications, transforming fiber-optic cables into dense arrays of geophones that can continuously sample seismic wavefields across several kilometers. DAS data acquisition presents a versatile approach, utilizing either ad hoc installations with specific cables or leveraging existing telecommunication optical fiber-network infrastructure. Its adaptability makes DAS particularly advantageous for seismic monitoring in logistically challenging environments like volcanoes or offshore areas, where traditional seismometers may face limitations.

Conventional seismological techniques struggle to effectively process DAS data due to its unique characteristics—typically, wavefields are sampled at 1 m spacing with frequencies exceeding 1 kHz. As a result, this technology provides a detailed mapping of the seismic wavefield across the length of the fiber, and it also generates a significant amount of data compared to the sparse seismometer installations. In order to efficiently analyze these data, we introduced HECTOR, a waveform-based detection method designed specifically for DAS data (Porras et al. 2024).

In this study, we investigate the capabilities of HECTOR following preprocessing of DAS data using various characteristic functions (CF). We explore non-negative functions, including Short Term Average to Long Term Average (STALTA), Energy, and Envelope, whose peculiarity is to preserve noise. Conversely, zero-mean characteristic functions such as Short Term Average to Long Term Average derivative (STALTA derivative), Kurtosis, and Kurtosis derivative enhance signals and mitigate noise. Our objective is to assess HECTOR's performance when analyzing preprocessed data compared to raw data.

To validate our findings, we initially test the detector on synthetic data. These simulations encompass diverse optical fiber geometries, source configurations, and locations. Subsequently, we apply the algorithm to real data collected in two distinct scenarios. The first scenario involves the FORGE experiment situated in Utah, US, which entails a borehole installation of 1 km optical fiber deployed above a geothermal reservoir characterized by induced seismic activity. The second scenario involves a 90 km horizontal optical fiber deployed in the Pyrenees region. The area is characterized by natural earthquake activity with magnitudes (2.01≥ML≥0.02), alongside anthropic events due to quarry blasts. 

Our evaluation focuses on quantifying the enhancement in HECTOR's performance following the application of CFs compared to analyzing raw data.

Through this comprehensive exploration, we aim to advance the understanding of DAS data processing, demonstrating the efficacy of HECTOR across diverse scenarios. 

We would like to thank TotalEnergies for sharing this data set with us as well as Febus Optic for providing the DAS interrogator used for the data acquisition.

References: 

A Semblance-based Microseismic Event Detector for DAS Data.

• Porras, D. Pecci, G. Bocchini, S. Gaviano, M. De Solda, K. Tuinstra, F. Lanza, A. Tognarelli, E. Stucchi, F. Grigoli. Geophysical Journal International (GJI) 2024 (Accepted)

How to cite: Gaviano, S., Rapagnani, G., Pecci, D., Porras, J., Rebel, E., and Grigoli, F.: Exploring the application of Characteristic Functions on DAS data and their influence in event detection performance., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10893, https://doi.org/10.5194/egusphere-egu24-10893, 2024.

The electrical network frequency (ENF) of the alternating current operated on the power grid is a well-known source of noise in digital recordings. The noise (i.e., signal) is widespread and appears not just in close proximity to high-voltage power lines, but also in instruments simply connected to the mains powers grid. This omnipresent, anthropogenic signal is generally perceived as a nuisance in the processing of geophysical data. Research has therefore been mainly focused on its elimination from data, while its benefits have gone largely unexplored. It is shown that mHz fluctuations in the nominal ENF (50 - 60Hz) induced by variations in power usage can be accurately extracted from geophysical data. This information represents a persistent time-calibration signal that is coherent between instruments over national scales. Cross-correlation of reliable reference ENF data published by electrical grid operators with estimated ENF data from geophysical recordings allows timing errors to be resolved at the 1s level. Furthermore, it is shown that a polarization analysis of particle motion at the ENF may assist in the detection of instrument orientation anomalies at the surface. Furthermore, it is explored whether this method can be applied to determine orientations of geophones inside seismic boreholes.

How to cite: Koymans, M., de Zeeuw-van Dalfsen, E., Evers, L., and Assink, J.: Passive Assessment of Geophysical Instruments Performance using Electrical Network Frequency Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15414, https://doi.org/10.5194/egusphere-egu24-15414, 2024.

Seismic ground motions in the near-fault region produce strong pulses in the velocity-time history, resulting in severe damage to structures. To accurately and effectively monitor these ground motion signals with strong pulses, Shock-Waveform (SW) method is introduced to quantitatively extract the largest velocity pulse from a given ground motion. SW method is an energy-based and adaptive signal analysis method, which has proven capability of analyzing different physical and engineering signals initiated by sudden actions. It is suitable to identify pulse components in the signal with low error and high efficiency. Three variables are proposed to classify ground motions, which is combined with the Principal Component Analysis (PCA) for data dimensionality reduction and subsequent analysis. In addition, an optimum classification standard on pulse-like and non-pulse-like ground motion is established. To avoid the subjective judgement induced by manual selection, unsupervised machine learning classification method and Support Vector Machine (SVM) are used successively to find the decision boundary. In this study, about 100 pulse-like ground motions with large-velocity pulses are identified from approximately 1000 near-fault ground motion recorded in PEER Next Generation Attenuation-West2 database. It shows that most of the pulse-like ground motions are caused by the directivity effect. Based on the proposed classification approach, new models are developed to forecast the possibility of a single pulse, multi-pulses, and pulse period for a given earthquake event. 

How to cite: Wang, J. and Li, Q.: Identification of strong-velocity pulses in seismic ground motion signals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3453, https://doi.org/10.5194/egusphere-egu24-3453, 2024.

The current limited knowledge about Earth system prevents deterministic earthquake prediction. This will probably continue for the foreseeable future. However, the improved capability of identifying reliable precursory phenomena can allow geoscientists to comprehend if the monitored system is evolving toward an unstable state. Among the premonitory phenomena preceding earthquakes, foreshocks represent the most promising candidate. Physically, two hand-member mechanisms have been proposed to interpret foreshocks. The first considers the failing of populations of small patches of fault that eventually but not necessarily become large earthquakes whereas the second assumes that foreshocks are a part of the nucleation process which ultimately leads to the mainshock. The prompt identification of foreshocks with respect to background seismicity is an issue and the task is worsened when dealing with low-magnitude earthquakes. However, the use of Artificial Intelligence (AI) can help real-time seismology to effectively discriminate precursory signals.

In the present study, we propose a deep learning method named PreD-Net (Precursor Detection Network) to address the precursory signal identification of induced earthquakes through the analysis of several statistical features. PreD-Net has been trained on data related to two induced seismicity areas, namely The Geysers, located in California, USA, and Hengill in Iceland. Notably, the network shows a suitable model generalization, providing considerable results on samples that were excluded from the training dataset of all the sites. The performed tests on related samples of induced relatively large events demonstrate the possibility of setting up a real-time warning strategy to be used to avoid adverse consequences during field operations.

This work is supported by project D.I.R.E.C.T.I.O.N.S. - Deep learning aIded foReshock deteCTIOn Of iNduced mainShocks, project code: P20229KB4F - - Next Generation EU (PRIN-PNRR 2022, CUP D53D23022800001)

How to cite: Convertito, V., Giampaolo, F., Amoroso, O., and Piccialli, F.: Deep learning forecasting of induced earthquakes through the analysis of precursory signals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4268, https://doi.org/10.5194/egusphere-egu24-4268, 2024.

Subduction zones are among the most active tectonic areas on the planet. Their primary characteristic is the enormous amount of stress accumulated at the interface between the subducting oceanic plate and the overriding plate. The release of this stress is accommodated by a wide range of behaviours, ranging from aseismic slip (slip at speeds too slow to radiate seismic energy), through the spectrum of slow slip and tremor, to seismic slip capable of generating major earthquakes. The main investigative tools for subduction zones to map out this range of behaviour, and to assess the coupling properties of the subduction interface, involve the direct observation of ground movements through geodesy (either terrestrial or satellite-based) or through local seismic surveillance using near-field instrumentation, all of which are logistically complex, and typically only feasible on land.

Utilizing the recent expansion of seismic arrays in continental regions, we propose an alternative approach for the study of subduction zones that bypasses the aforementioned limitations through the use of teleseismic waves—recorded at a distance between 30º and 90º from the epicenter—based on the identification of the presence (or absence) of highly reflective layers at the megathrust interface. Previous studies using local seismic data have observed the presence of highly reflective layers, characterized by strong impedance contrasts, located at the megathrust interface, capable of producing a reflection in the wavefield that results into the presence of precursors of depth phases. Since impedance contrasts in the solid Earth are linked to variations in the elastic properties of the medium, reflectivity offers a window into the rheology of the plate interface. Understanding the reasons behind such strong impedance contrasts, their potential variability over time and space, could pave the way for understanding why the degree of coupling of subduction interfaces varies, whether it is related to transient processes, or if it is stable over time.

Here, we present an automated waveform processing approach designed to detect such reflections in remote seismic data, and illustrate this with a test region from the Central America subduction zone.  We analyse waveforms produced by seismic events with magnitudes ranging from 4.5 to 5.5 occurring at different times and recorded by small aperture seismic arrays. Our observations in Central America prove to be an excellent tool for studying the coupling properties of the megathrust interface. This work represents a first attempt, with the ultimate goal of mapping subduction zones and their coupling properties, even in currently inaccessible submarine areas, allowing for a better understanding of the seismic risk that subduction zones represent.

How to cite: Rappisi, F., Craig, T., and Rost, S.: Unveiling coupling properties of subduction zones with novel telesismic waveform approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12443, https://doi.org/10.5194/egusphere-egu24-12443, 2024.

Focal depth of earthquakes is essential for studies of seismogenic processes and seismic hazards. Surface waves are usually the strongest seismic phases at local and regional distances, and its excitation is sensitive to source depth. We observe that the optimal period (the period corresponding to the maximum amplitude) of Rayleigh waves at local distances shows an almost linear correlation with focal depth, based on which we propose a method for resolving the focal depth of local earthquakes. We propose an automated data processing workflow, and applications to earthquakes in diverse tectonic settings demonstrate that reliable focal depth with uncertainty of 1~2 km can be determined even with one or a few seismic stations. Then, we use the Longmenshan region as a case study to systematically assess the impact of the 3D velocity model on the results through forward simulation. A total of 191 events at depths ranging from 5 to 20 km are simulated. The standard deviation between the focal depths determined by this method and the input values is approximately 1.5 km, with 95% events having errors within 2 times the standard deviation. This indicates that the method exhibits good applicability even in regions with complex velocity structures, and highlights the applicability of the method in scenarios characterized by sparse network coverage or historical events.

How to cite: He, X., Zhang, P., Ni, S., Wu, W., Chu, R., Wang, Y., and Zheng, K.: Automatic determination of focal depth with the optimal period of Rayleigh wave amplitude spectra and uncertainty assessment in 3D velocity model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13591, https://doi.org/10.5194/egusphere-egu24-13591, 2024.

PrESENCE ANR project (2022-2025) focuses on seismic hazards induced by deep geothermal operations in northern Alsace, France, and their associated societal perception. Seismological observations are obtained using a large number of low cost internet-connected equipment (Raspberry Shake seismic station and associated open access data). The breakthrough strategy of the project relies on the deployment of the stations in residences or administrative buildings of non-seismologist volunteer citizens or authorities. The aim is to use those stations to densify the french permanent seismic network, and to improve the detection and location of seismic events, in particularly small ones. Our presentation will be focused on the Soultz-sous-Forêts and Rittershoffen areas (northern Alsace, France), which are sites of deep geothermal operations. 

The topology of the seismological network was determined by the location of permanent stations, from Epos-France permanent network (4) and public stations belonging to geothermal operators (2), the number of low-cost stations (35) to be deployed in the region, the location of deep geothermal power plants (Soultz and Rittershoffen) and the location of volunteer citizen hosts. Volunteer citizens were selected initially by word of mouth, then by a call for applications (through social networks, flyers, local newspapers). Twenty-one stations are currently (end of 2023) hosted in the area. About ten additional stations are planned to be deployed early 2024 in the area.

Based on our past experience in deploying similar networks in other contexts and regions (Mayotte, Vosges massif, Mulhouse, etc.), we have consolidated the installation of these stations to ensure reliable data acquisition and, in particular, to achieve better data completeness (acquisition directly at the station using the Seedlink protocol via a VPN, hardware watchdog). We use Ansible (an open source IT automation platform) to facilitate the deployment of Raspberry Shake stations configuration and management tasks, ensuring rapid and consistent production deployment.

The workflow for building the seismicity catalog benefits from our advances in the use of new artificial intelligence tools, such as PhaseNet, a deep learning automatic picking method, as well as in the development of a deep learning method for discrimination between earthquakes, quarry blasts and explosions. Our tests over the year 2023 show that even if the stations are installed in urban areas (and therefore in a noisy environment), the network is able to automatically detect and locate many small induced earthquakes, including around 250 with a high level of confidence, compared with the ten detected or so by the standard procedure of BCSF-Renass, the French National Observation Service.

How to cite: Turlure, M., Grunberg, M., Engels, F., Jund, H., Schlupp, A., and Schmittbuhl, J.: Contribution of SeismoCitizen Raspberry Shake dense network in monitoring induced seismicity in northern Alsace (France), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10651, https://doi.org/10.5194/egusphere-egu24-10651, 2024.

GeoSphere Austria (GA, formerly ZAMG), the Italian National Institute of Oceanography and Applied Geophysics (OGS) and the Slovenian Environment Agency (ARSO) are the agencies dedicated to real-time seismological monitoring of Austria, north-eastern Italy and Slovenia, in cooperation with the respective civil protection authorities. In 2014, GA (then ZAMG), OGS and ARSO founded the “Central and Eastern Europe Earthquake Research Network” (CE3RN, http://www.ce3rn.eu/) to 1) formally establish the cross-border network, 2) define the rules of conduct for the management, improvement, extension and expansion of the network, 3) improve seismological research in the region and 4) support civil protection activities. As part of CE3RN, GA, OGS and ARSO have adoptd the “Antelope” software package for collecting, archiving, analysing and sharing seismological data.
In 2022, the international AdriaArray experiment was launched, following on from the previously successful AlpArray experiment. AdriaArray is a multinational effort to map the Adriatic plate and its active margins in the central Mediterranean with a dense regional array of seismic stations to understand the causes of active tectonics and volcanic fields in the region. GA, OGS and ARSO are actively involved in the AdriaArray experiment by providing data from their seismic monitoring networks and - in the case of OGS - also by installing and managing dedicated seismic stations. As part of the AdriaArray experiment, several additional seismic stations have been set up in Austria and north-eastern Italy. It is therefore to be expected that the additional seismic stations installed will improve the earthquake localization capabilities of GA, OGS and ARSO. This certainly applies to Austria and north-eastern Italy, but also to Slovenia, as a large part of its seismicity lies on the border with Italy.
The GOAT-CASE experiment aims to quantify the improvement in earthquake localization capability across the entire area. The underlying methodology is to locate earthquakes also using the additional seismic stations and to compare the results. The workload for the detections is distributed among the three partners, while the mapping is done centrally. An attempt will be made to use artificial intelligence to detect earthquakes and compare the results with the standard routines of the agencies.
The AdriaArray experiment is planned for a duration of 3 years starting around mid-2022. In this presentation we will illustrate the results of the first year of the experiment, from 01/07/2022 to 30/06/2023.

How to cite: Pesaresi, D., Horn, N., and Pahor, J.: GA-OGS-ARSO Transfrontier CE3RN AdriaArray Seismicity Experiment (GOAT-CASE): results of the first year of data collection and analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5459, https://doi.org/10.5194/egusphere-egu24-5459, 2024.

GEObit Instruments are proud to announce the market release of a new low-cost and low -latency seismic accelerograph, the GEO-T200, for earthquake monitoring, early waring applications, and structural monitoring. The device mainly consists of two sections, the triaxial sensor sensor and the digitizer. The architecture and the hardware is based on the GEObit GEOtiny platform.

The sensing elements are based in a re-designed previous generation GEObit force balance acceleration sensor unit [1], providing very high dynamic range 160+dB, and wide bandwidth, flat response DC to 260Hz. The acceleration range is user configurable and can be set between +/-4g to +/-0.5g but other ranges are also available upon request.

The digitizer is based on a 24bit ADC and provides high effective dynamic range 140dB, high sampling rate up to 4000sps, integrates seedlink server and the earthworm chain. The device is based on a locally running open-source components ported on ARM Linux board. It is able to apply local signal processing and trigger detection based on multiple schemes (amplitude, STA/LTA etc.) through open-source components ported from the Earthworm toolchain and transmit pick times over MQTT with ultra-low latency based signaling for trigger event distribution supporting multiple centralized or distributed schemes.

It Supports ethernet port and Wi-Fi. Also supports continuous data stream, triggered data stream (level, LTA/LTA, both) or both.

The device is housed into a small cylindrical enclosure, aluminum made, IP68 with dimensions 120mm diameter and 143mm height. Three leveling legs are provided along with a central bolt for proper mounting of the device. An bright OLED lcd screen reports the user about the instrument operation and state of health. The SOH stream is also transmitted in real time over TCP.

References:

[1]: Design, Modeling, and Evaluation of a Class-A Triaxial Force-Balance Accelerometer of Linear Based Geometry” N. Germenis, G. Dimitrakakis, E. Sokos, and P. Nikolakopoulos Seismol. Res. Lett. 93, 2138–2146, doi: 10.1785/0220210102

How to cite: Germenis, N.: A new low latency and low-cost force-balance accelerograph for earthquake and structural monitoring and for early waring applications., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5299, https://doi.org/10.5194/egusphere-egu24-5299, 2024.