Observational Seismology Research Articles

Undergraduate persistence in an open-access, online, scientific computing training is influenced by expectancy, value, and cost

During 2020 a free, 3-month long, certificate-granting, online workshop was offered to increase undergraduates’ knowledge, skills, self-efficacy, and interest in observational seismology and scientific computing. Course registrants were comprised of 760 upper-level undergraduates from across science and math majors, representing 60 countries. Performance data revealed 58% of registrants initiated the workshop by completing at least one assignment, and 30% successfully completed the full workshop. These rates were higher than most comparable large-scale, open-access courses. This study explores factors associated with persistence by examining registration, performance, and pre/post survey data through the lens of the expectancy-value theory (EVT). Results indicate that EVT constructs show moderate to weak associations with initiation, including intrinsic (p<.001), utility (p<.001), and attainment value (p=.24), and expectancy (e.g., completion intentions [p<.001], prior computing experience [p<.001], seismology research experience [p=.01], seismology self-efficacy [p=.84]). Two expectancy factors, academic major (p=.050) and prior geophysics coursework (p<.001), showed weak associations with completion. Demographic factors such as race, ethnicity, and gender were not associated with initiation or completion, but primary language showed an association with attainment and intrinsic values. Dissuading factors or costs were also captured through surveys. The few responses from non-completers limited the analysis. However, an examination of acute and accumulated costs suggests they may influence persistence, especially at completion. Based on these results, interventions are proposed to enhance persistence in future workshops. These include increasing motivational messaging, tailoring course structure to mitigate avoidable costs, and where possible, increasing communication to set time and effort expectations and showcase prior participants overcoming costs.

Locating clustered seismicity using Distance Geometry Solvers: applications for sparse and single-borehole DAS networks

Summary The determination of seismic event locations with sparse networks or single-borehole systems remains a significant challenge in observational seismology. Leveraging the advantages of the location approach HADES (eartHquake locAtion via Distance gEometry Solvers), which was initially developed for locating clustered seismicity recorded at two stations, through the solution of a Distance Geometry Problem, we present here an improved version of the methodology: HADES-R (HADES-Relative). Where HADES previously needed a minimum of 4 absolutely located master events, HADES-R solves a least-squares problem to find the relative inter-event distances in the cluster, and uses only a single master event to find the locations of all events, and subsequently applies rotational optimizer to find the cluster orientation. It can leverage iterative station combinations if multiple receivers are available, to describe the cluster shape and orientation uncertainty with a bootstrap approach. The improved method requires P- and S-phase arrival picks, a homogeneous velocity model, a single master event with a known location, and an estimate of the cluster width. The approach is benchmarked on the 2019 Ridgecrest sequence recorded at two stations, and applied to two seismic clusters at the FORGE geothermal test site in Utah, USA, with a microseismic monitoring scenario with a DAS in a vertical borehole. Traditional procedures struggle in these settings due to the ill-posed network configuration. The azimuthal ambiguity in such a scenario is partially overcome by the assumption that all events belong to the same cluster around the master event and a cluster width estimate. We are able to find the cluster shape in both cases, although the orientation remains uncertain. HADES-R contributes to an efficient way to locate multiple events simultaneously with minimal prior information. The method’s ability to constrain the cluster shape and location with only one well-located event offers promising implications, especially for environments where limited or specialised instrumentation is in use.

Advanced Technology and Data Analysis of Monitoring Observations in Seismology

Over more than a century of systematic observational seismology, the instrumental capabilities to detect, record, and analyze elastic waves and other physical variables associated with the source, medium, and recording site of earthquakes have evolved substantially [...]

False positives are common in single-station template matching

Template matching has become a cornerstone technique of observational seismology. By taking known events, and scanning them against a continuous record, new events smaller than the signal-to-noise ratio can be found, substantially improving the magnitude of completeness of earthquake catalogues. Template matching is normally used in an array setting, however as we move into the era of planetary seismology, we are likely to apply template matching for very small arrays or even single stations. Given the high impact of planetary seismology studies on our understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reliability of template matching in the small-n setting. Towards this goal, we estimate a lower bound on the rate of false positives for single-station template matching by examining the behaviour of correlations of totally uncorrelated white noise. We find that, for typical processing regimes and match thresholds, false positives are likely quite common. We must therefore be exceptionally careful when considering the output of template matching in the small-n setting.

Observations of Mantle Seismic Anisotropy Using Array Techniques: Shear‐Wave Splitting of Beamformed SmKS Phases

AbstractShear‐wave splitting measurements are commonly used to resolve seismic anisotropy in both the upper and lowermost mantle. Typically, such techniques are applied to SmKS phases that have reflected (m‐1) times off the underside of the core‐mantle boundary before being recorded. Practical constraints for shear‐wave splitting studies include the limited number of suitable phases as well as the large fraction of available data discarded because of poor signal‐to‐noise ratios (SNRs) or large measurement uncertainties. Array techniques such as beamforming are commonly used in observational seismology to enhance SNRs, but have not been applied before to improve SmKS signal strength and coherency for shear wave splitting studies. Here, we investigate how a beamforming methodology, based on slowness and backazimuth vespagrams to determine the most coherent incoming wave direction, can improve shear‐wave splitting measurement confidence intervals. Through the analysis of real and synthetic seismograms, we show that (a) the splitting measurements obtained from the beamformed seismograms (beams) reflect an average of the single‐station splitting parameters that contribute to the beam; (b) the beams have (on average) more than twice as large SNRs than the single‐station seismograms that contribute to the beam; (c) the increased SNRs allow the reliable measurement of shear wave splitting parameters from beams down to average single‐station SNRs of 1.3. Beamforming may thus be helpful to more reliably measure splitting due to upper mantle anisotropy. Moreover, we show that beamforming holds potential to greatly improve detection of lowermost mantle anisotropy by demonstrating differential SKS–SKKS splitting analysis using beamformed USArray data.

Basin-scale subsurface characterization using single-station teleseismic receiver function analysis

Teleseismic receiver function (RF) analysis traditionally has targeted deep earth structures in the context of observational seismology, allowing the detection of converted seismic phases originating from major boundaries of impedance. Recent efforts to adapt this passive-source seismic imaging technique to basin-scale subsurface characterization have retrieved primary basin geometries and key stratigraphic boundaries by relying on densely deployed broadband seismometer networks. While accurate interpretation of RF time series on Earth benefitted from complementary subsurface constraints (e.g., core and well logs), lack of such resources and instrumental limitations must be considered in the context of planetary geophysics, where passive-source imaging may emerge as a key frontier exploration technique. Thus, it is important to establish an analysis framework in which observed RF signals are interpreted by relying on first-order suppositions about the physical properties of the underlying subsurface. In this work, we seek to simulate this approach in single-station scenarios and qualitatively examine the baseline information inferable from the RF time series. Our results suggest that signals observed in the sedimentary interval can be reasonably attributed to major impedance changes expected from the generalized lithostratigraphy of the local subsurface, including the transition to the underlying crust. We also find patterns of anisotropy-related directional variations in high-frequency signals as well as unique frequency-dependent responses likely associated with the depth and vertical dimension of converting boundaries. Together, these seismic observables enable inference of various geologic attributes within the underlying sedimentary unit, proving it to be a critical tool in future efforts of planetary exploration.

Automatic earthquake detection and phase picking in Muong Te, Lai Chau region: an application of machine learning in observational seismology in Vietnam

We applied the automatic detection and picking of P- and S-wave to one-year continuous raw seismic data from 17 seismic stations in the Muong Te area, northwestern Vietnam. The deep learning picker, Earthquake Transformer, has performed automatic picking of P- and S-waves, and phase association, then we located the earthquakes using Hypoinverse and NonLinLoc programs. The newly derived catalog consisted of 893 events, which is significantly higher than the number of events in the manual catalog. From this new catalog, we can observe more earthquakes related to the Muong Te ML 4.9 earthquake on June 16, 2020, and the earthquake activity in other faults such as the Dien Bien Phu and Muong Nhe faults. The extended catalog can further study the seismogenesis and the seismic velocity structure of the crust beneath northwestern Vietnam.

A machine-learning-based method of detecting and picking the first P-wave arrivals of acoustic emission events in laboratory experiments

SUMMARY Detecting and picking the first P-wave arrivals of seismic events in seismograms is fundamental in observational seismology. Recently, several machine-learning-based algorithms have been developed to incorporate human expertise for picking P-wave arrival times automatically. One shortcoming of these models is that they pick arrival times at individual seismic stations separately, which need to be sorted and associated to identify the seismic event. Also, most of them rely on existence of P-wave arrivals in the seismograms to be picked. Here, we developed a machine-learning-based seismic event detection and P-wave arrival time picking method called MultiNet and applied it to acoustic emission (AE) waveform data recorded in laboratory experiments. The MultiNet uses 2-D waveform images from multichannel AE recordings as the input to a convolutional neural network (CNN) to detect whether there is an AE event in an image and, if so, uses a fully convolutional neural network (FCN) to pick the P-wave arrival time at each channel in the image. We tested the MultiNet using 550 known AE events recorded during syn-deformational phase transformation from olivine to spinel in Mg2GeO4 (an analogue to Mg2SiO4) in a high-pressure experiment. Waveform data of 50 events were used to train the neural networks and the rest of data were used to validate the method. At the optimal image length and detection threshold, the CNN was able to detect all 500 known events plus 48 more events missed previously. Overall, 98.7 per cent of P-wave arrival times picked by the FCN were within 0.5 $\mu$s from the manually picked times. The average picking errors at different channels range from 0.01 ± 0.05 to −0.06 ± 0.22 $\mu$s. Our method greatly reduces the amount of human labour in picking P-wave arrival times for event location and source moment tensor inversion. It can be easily adapted to process continuous waveform data of a seismic network for earthquake detection and location in real time.

Correcting the Background Tilt Signal of the Horizontal Seismometer Using a Rotation Sensor

Abstract In observational seismology, the long-period background noise level in horizontal seismometer components is always higher than the vertical component at the same location. It is believed that the horizontal long-period noise is mostly attributed to ground tilt, yet the authentic tilt recordings of background noise in the seismic frequency band (0.01–20 Hz) have not been recorded successfully. We demonstrate the use of a newly developed optical-based rotation sensor, blueSeis-3A, to correct for the tilt effect in a horizontal seismometer. A station with a horizontal long-period (1 &amp;lt; T &amp;lt; 100 s) background noise level higher than the global new high-noise model was chosen, where a seismometer, a tiltmeter, and a rotation sensor were collocated. We first confirmed that the horizontal recordings from a tiltmeter and a seismometer were visually consistent, although it was unclear if tilt contamination existed in both recordings. It became evident until the ground tilt recorded by a rotation sensor was found to be waveform-matched with them, indicating that both sensors were contaminated by tilt. The spectrum of tilt records showed that ground tilts were mostly at period bands &amp;gt;1 s. By subtracting tilt from seismometer records, the horizontal background noise levels could be improved, approximately to the resolution level of the rotation sensors. Our results demonstrate that collocating rotation sensors with seismometers is a feasible way to improve the quality of the long-period background noise level in seismometer horizontal components when rotation sensors are sufficiently sensitive.

One hundred plus years of recomputed surface wave magnitude of shallow global earthquakes

Abstract. Among the multitude of magnitude scales developed to measure the size of an earthquake, the surface wave magnitude Ms is the only magnitude type that can be computed since the dawn of modern observational seismology (beginning of the 20th century) for most shallow earthquakes worldwide. This is possible thanks to the work of station operators, analysts and researchers that performed measurements of surface wave amplitudes and periods on analogue instruments well before the development of recent digital seismological practice. As a result of a monumental undertaking to digitize such pre-1971 measurements from printed bulletins and integrate them in parametric data form into the database of the International Seismological Centre (ISC, http://www.isc.ac.uk, last access: August 2021), we are able to recompute Ms using a large set of stations and obtain it for the first time for several hundred earthquakes. We summarize the work started at the ISC in 2010 which aims to provide the seismological and broader geoscience community with a revised Ms dataset (i.e., catalogue as well as the underlying station data) starting from December 1904 up to the last complete year reviewed by the ISC (currently 2018). This Ms dataset is available at the ISC Dataset Repository at https://doi.org/10.31905/0N4HOS2D (International Seismological Centre, 2021d).

Investigating the influence of earthquake source complexity on back-projection images using convolutional neural networks

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). 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 analysing 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 1-D 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 1-D 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.

CCFE: A Few-Shot Learning Model for Earthquake Detection and Phase Identification

Earthquake detection and phase identification are fundamental and challenging tasks in observational seismology. Deep learning has achieved many achievements in these two tasks. To overcome the limitations of existing methods, mainly becauseof the lack oflarge labeled seismic datasets and the separation of detection and identification tasks, we introducedthe CWT-CNN Few-shot learning Earthquake model (CCFE), a deep learning model for simultaneous earthquake detection and phase identification. CCFE can performfew-shot learning with minimal labeled seismic data by utilizing continuous wavelet transform and lightweight convolutional neural networks with fewer layers. We tested our model in the Huoshan area of southern Chinaand foundthat CCFE outperformed both traditional and published representative deep learning models for detection and identification in this area and that combining detection and identification tasks enhances the performance of each task separately. We found 76% more earthquakes using CCFE than the manual catalog across 15 days of continuous data from the Huoshan region. In regions with low seismicity, CCFE can aid in enhancing earthquake monitoring capacity.

Wavefield-based evaluation of DAS instrument response and array design

SUMMARY Distributed acoustic sensing (DAS) networks promise to revolutionize observational seismology by providing cost-effective, highly dense spatial sampling of the seismic wavefield, especially by utilizing pre-deployed telecomm fibre in urban settings for which dense seismic network deployments are difficult to construct. However, each DAS channel is sensitive only to one projection of the horizontal strain tensor and therefore gives an incomplete picture of the horizontal seismic wavefield, limiting our ability to make a holistic analysis of instrument response. This analysis has therefore been largely restricted to pointwise comparisons where a fortuitious coincidence of reference three-component seismometers and colocated DAS cable allows. We evaluate DAS instrument response by comparing DAS measurements from the PoroTomo experiment with strain-rate wavefield reconstructed from the nodal seismic array deployed in the same experiment, allowing us to treat the entire DAS array in a systematic fashion irrespective of cable geometry relative to the location of nodes. We found that, while the phase differences are in general small, the amplitude differences between predicted and observed DAS strain rates average a factor of 2 across the array and correlate with near-surface geology, suggesting that careful assessment of DAS deployments is essential for applications that require reliable assessments of amplitude. We further discuss strategies for empirical gain corrections and optimal placement of point sensor deployments to generate the best combined sensitivity with an already deployed DAS cable, from a wavefield reconstruction perspective.

An ObsPy Library for Event Detection and Seismic Attribute Calculation: Preparing Waveforms for Automated Analysis

We have implemented an extension for the observational seismology <em>obspy</em> software package to provide a streamlined tool tailored to the processing of seismic signals from non-earthquake sources, in particular those from deforming systems such as glaciers and landslides. This <em>seismic attributes</em> library provides functionality to: (1) download and/or pre-process seismic waveform data; (2) detect and catalogue seismic events using multi-component signals from one or more seismometers; and (3) calculate characteristics (‘attributes’/‘features’) of the identified events. The workflow is controlled by three main functions that have been tested for the breadth of data types expected from permanent and campaign-deployed seismic instrumentation. A selected STA/LTA-type (short-term average/long-term average), or other, event detection algorithm can be applied to the waveforms and user-defined functions implemented to calculate any required characteristics of the detected events. The code is written in Python 2/3 and is available on GitHub together with detailed documentation and worked examples.

An ObsPy library for event detection and seismic attribute calculation: preparing waveforms for automated analysis

We have implemented an extension for the observational seismology obspy software package to provide a streamlined tool tailored to the processing of seismic signals from non-earthquake sources, in particular those from deforming systems such as glaciers and landslides. This seismic attributes library provides functionality to: (1) download and/or pre-process seismic waveform data; (2) detect and catalogue seismic events using multi-component signals from one or more seismometers; and (3) calculate characteristics ('attributes'/'features') of the identified events. The workflow is controlled by three main functions that have been tested for the breadth of data types expected from permanent and campaign-deployed seismic instrumentation. A selected STA/LTA-type (short-term average/long-term average), or other, event detection algorithm can be applied to the waveforms and user-defined functions implemented to calculate any required characteristics of the detected events. The code is written in Python 2/3 and is available on GitHub together with detailed documentation and worked examples.

Instrument Response Removal and the 2020 MLg 3.1 Marlboro, New Jersey, Earthquake

Abstract To better understand earthquakes as a hazard and to better understand the interior structure of the Earth, we often want to measure the physical displacement, velocity, or acceleration at locations on the Earth’s surface. To this end, a routine step in an observational seismology workflow is the removal of the instrument response, required to convert the digital counts recorded by a seismometer to physical displacement, velocity, or acceleration. The conceptual framework, which we briefly review for students and researchers of seismology, is that of the seismometer as a linear time-invariant system, which records a convolution of ground motion via a transfer function that gain scales and phase shifts the incoming signal. In practice, numerous software packages are widely used to undo this convolution via deconvolution of the instrument’s transfer function. Here, to allow the reader to understand this process, we start by taking a step back to fully explore the choices made during this routine step and the reasons for making them. In addition, we introduce open-source routines in Python and MATLAB as part of our rflexa package, which identically reproduce the results of the Seismic Analysis Code, a ubiquitous and trusted reference. The entire workflow is illustrated on data recorded by several instruments on Princeton University campus in Princeton, New Jersey, of the 9 September 2020 magnitude 3.1 earthquake in Marlboro, New Jersey.

Learning in a Crisis: Online Skill Building Workshop Addresses Immediate Pandemic Needs and Offers Possibilities for Future Trainings

Abstract The COVID-19 pandemic led to the suspension of many summer research opportunities for science, technology, engineering and mathematics students. In response, the Incorporated Research Institutions for Seismology Education and Outreach program, in collaboration with Miami University, offered a free online Seismology Skill Building Workshop to increase undergraduates’ knowledge, skills, self-efficacy, and interest in observational seismology and scientific computing. Registrations were received from 760 undergraduates representing 60 different countries. U.S. participants consisted of 59% women and 29% from populations traditionally underrepresented in the geosciences. The workshop design consisted of a tailored Linux virtual machine, regular webinars, a Slack workspace, tutorial-style active e-learning assignments, and an optional final project. Every other week for 12 weeks, a module with ∼6 assignments was released to build skills with Linux, Generic Mapping Tools, Seismic Analysis Code, webservices, seismic network processing, Python, ObsPy, and Jupyter notebooks. A final module focused on competitiveness for graduate school, summer internships, and professional jobs. Evaluation of the workshop relied on registration data, pre- and post-workshop surveys, and performance data from the learning management system. 440 participants completed at least one assignment, 224 completed at least 80% of the assignments, and 191 completed all 35 assignments, significantly higher than most comparable large-scale, open-access courses. Participants invested ∼6 hrs per week and averaged a score of 88% on assignments. We identified &amp;gt;60% normalized gain in scientific computing skills. There is evidence that the inclusive design of the workshop was able to attract and retain a diverse population. However, some additional investigation is needed to ensure that benefits were evenly experienced. Regardless of the degree of completion, participants perceived the workshop quite positively: on average 96% described it as high to very high quality, 83% satisfied to very satisfied with their experience, and 70% very likely to recommend to peers. We identify future directions for running a second iteration of the workshop, including strategies to continue broadening participation and improving retention.

SCALODEEP: A Highly Generalized Deep Learning Framework for Real‐Time Earthquake Detection

AbstractThe detection of earthquake signals is a fundamental yet challenging task in observational seismology. A robust automatic earthquake detection algorithm is strongly demanded in view of the ever‐growing global seismic dataset. Here, we develop an automatic earthquake detection framework based on a deep learning approach (SCALODEEP). It extracts high‐order features embedded in three‐component seismograms by encoding a time‐frequency representation of the data (scalogram) into a deep network with skip connections. The SCALODEEP is trained and validated on an open‐source dataset from North California, and then employed to seismicity detection in four areas, including Arkansas, Japan, Texas, and Egypt. Despite vastly varying characteristics of regional earthquakes (e.g., focal mechanism, duration, and noise level), SCALODEEP successfully detects seismic signals over a broad range of local magnitudes (as low as −1.3) and outperforms conventional algorithms such as STA/LTA, FAST, and template matching. Compared to recently proposed deep learning based frameworks (e.g., CRED and Earthquake transformer), SCALODEEP achieves a superior generalization ability via a sophisticated network architecture. In summary, our study offers a promising new tool to improve existing earthquake detection systems and, as importantly, sheds light on designing an effective deep learning network for generalized earthquake detection.

Recent advances in earthquake monitoring I: Ongoing revolution of seismic instrumentation

Seismic networks have significantly improved in the last decade in terms of coverage density, data quality, and instrumental diversity. Moreover, revolutionary advances in ultra-dense seismic instruments, such as nodes and fiber-optic sensing technologies, have recently provided unprecedented high-resolution data for regional and local earthquake monitoring. Nodal arrays have characteristics such as easy installation and flexible apertures, but are limited in power efficiency and data storage and thus most suitable as temporary networks. Fiber-optic sensing techniques, including distributed acoustic sensing, can be operated in real time with an in-house power supply and connected data storage, thereby exhibiting the potential of becoming next-generation permanent networks. Fiber-optic sensing techniques offer a powerful way of filling the observation gap particularly in submarine environments. Despite these technological advancements, various challenges remain. First, the data characteristics of fiber-optic sensing are still unclear. Second, it is challenging to construct software infrastructures to store, transfer, visualize, and process large amount of seismic data. Finally, innovative detection methods are required to exploit the potential of numerous channels. With improved knowledge about data characteristics, enhanced software infrastructures, and suitable data processing techniques, these innovations in seismic instrumentation could profoundly impact observational seismology.

From National to Transnational Seismic Monitoring Products and Services in the Republic of Bulgaria, Republic of Moldova, Romania, and Ukraine

Abstract The aim of this article is to analyze the background, current status, and outlook of seismic monitoring products and services in Bulgaria, Moldova, Romania, and Ukraine. These countries manage seismic networks that contribute to the European Integrated Data Archive node in the framework of the Observatories and Research Facilities for European Seismology, which represents a collaborative effort in coordinating observational seismology across the European region through the collection, archiving, and dissemination of seismic waveform data, metadata, and related products. All of the aforementioned countries share a common threat: strong earthquakes occurring in the Vrancea area located in central-eastern Romania at intermediate depths (usually in the 60–180 km interval). Events such as the ones on 10 November 1940 and 4 March 1977 generated high damage in Romania, northern Bulgaria, and Moldova. In addition to Vrancea, crustal earthquakes in areas such as Shabla or Dulovo can lead to cross-border damage. Therefore, understanding the way national seismic networks are distributed, how they cooperate, and the products and services that they provide in (near) real time and their terms is of significant interest in the context of necessary hazard harmonization and joint emergency intervention and risk mitigation actions.