Enter Zoom Meeting


International Monitoring System and On-site Verification for the CTBT, disaster risk reduction and Earth sciences

The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) senses the solid Earth, the oceans and the atmosphere with a global network of seismic, infrasound, and hydroacoustic sensors as well as detectors for atmospheric radioactivity. The primary purpose of the IMS data is for nuclear explosion monitoring regarding all aspects of detecting, locating and characterizing nuclear explosions and their radioactivity releases. On-site verification technologies apply similar methods on smaller scales as well as geophysical methods such as ground penetrating radar and geomagnetic surveying with the goal of identifying evidence for a nuclear explosion close to ground zero. Papers in this session address advances in the sensor technologies, new and historic data, data collection, data processing and analysis methods and algorithms, uncertainty analysis, machine learning and data mining, experiments and simulations including atmospheric transport modelling. This session also welcomes papers on applications of the IMS and OSI instrumentation data. This covers the use of IMS data for disaster risk reduction such as tsunami early warning, earthquake hazard assessment, volcano ash plume warning, radiological emergencies and climate change related monitoring. The scientific applications of IMS data establish another large range of topics, including acoustic wave propagation in the Earth crust, stratospheric wind fields and gravity waves, global atmospheric circulation patterns, deep ocean temperature profiles and whale migration. The use of IMS data for such purposes returns a benefit with regard to calibration, data analysis methods and performance of the primary mission of monitoring for nuclear explosions.

Co-organized by OS4/SM2
Convener: Martin Kalinowski | Co-conveners: Gérard Rambolamanana, Yan Jia, Christoph Pilger, Ole Ross
| Mon, 23 May, 15:10–18:30 (CEST)
Room C

Mon, 23 May, 15:10–16:40

Chairpersons: Martin Kalinowski, Ole Ross

Introduction to the CTBT monitoring system

Pierrick Mialle and the PTS colleagues

In 2001, when the first data from an International Monitoring System infrasound station started to arrive in near real-time at the International Data Centre (IDC), its infrasound processing system was in a premature state. The IDC embarked for a multi-year design and development of its dedicated processing system, which led to operational IDC automatic processing and interactive analysis systems in 2010. In the next twelve years the IDC produced over 40,000 infrasound events reviewed by expert analysts.
In an effort to continue advancing its methods, improving its automatic system and providing software packages to Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) users, the IDC focused on several projects. First, the automatic system for the identification of valid signals was redesigned with the development of DTK-(G)PMCC (Progressive Multi-Channel Correlation), which is in IDC Operations and made available to CTBTO users within NDC-in-a-Box. And second, an infrasound model was developed for automatic waveform network processing software NET-VISA with an emphasis on the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria.
Ongoing and future improvements of the IDC processing system are planned to further reduce analyst workload and improve the quality of IDC products.

How to cite: Mialle, P. and the PTS colleagues: The International Data Centre infrasound processing system, a 25 years travel, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7943, https://doi.org/10.5194/egusphere-egu22-7943, 2022.

Benoît Doury and Ichrak Ketata

The International Monitoring Systems (IMS) operational manuals for waveform stations require that IMS stations be calibrated regularly. Since 2012, the Provisional Technical Secretariat (PTS) had relied mostly on electrical calibration to meet that requirement. However electrical calibration has inherent challenges (no traceability, integration and sustainment issues, high operating costs…).

A part of the geophysical community, including Station Operators, has started performing regular calibrations by comparison against a co-located reference. This method allows a more systematic and centralized approach to calibration. Over the past few years, it has been increasingly used at IMS stations, particularly infrasound ones. In this context, the PTS is developing tools to support this alternative approach.

We present CalxPy, a web-application developed at the PTS for the calibration of geophysical systems by comparison. With CalxPy, one can calculate, store, and display the response of a system for a given period, or track the evolution of the response against time or environmental variables. CalxPy also allows the refinement and evaluation of the measured response against a baseline, and the reporting of calibration results.

CalxPy supports the Initial calibration and on-site yearly calibration processes, as well as data quality control. CalxPy can be deployed in the IDC pipeline and in NDC-in-a-box.

How to cite: Doury, B. and Ketata, I.: CalxPy: a software for the calibration of geophysical systems against a reference , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9074, https://doi.org/10.5194/egusphere-egu22-9074, 2022.

Michaela Schwardt et al.

In the low-frequency range down to 0.1 Hz suitable and reliable calibration procedures, which include traceability to SI, for seismic and infrasonic sensors are currently missing. Although many events occur whose evaluation is of global interest, much of the low frequency range relevant to these applications is not yet covered by primary measurement standards. A laboratory calibration of sensors results in an interruption of the measurements, just as the use of built-in calibration coils disturbs the measurements. Therefore, with regard to the design goal of the Comprehensive Nuclear-Test-Ban Treaty Organization’s (CTBTO) International Monitoring System (IMS), which requires the stations to be operational 100 % of the time, on-site calibration during operation with a reference sensor previously calibrated in the laboratory is of special interest.

We have assembled sets of both natural and anthropogenic sources of seismic, infrasonic, and hydroacoustic waves with respect to their individual signal characteristics and, as part of the joint research project "Metrology for low-frequency sound and vibration - 19ENV03 Infra-AUV", evaluated their potential use as excitation signals for on-site calibration regarding aspects that include knowledge about the source characteristics, the frequency content, reproducible and stable properties as well as the applicability in terms of cost-benefit. With the aid of these sources, procedures are to be established which will allow permanent on-site calibration without any interruptions of the recordings, thereby improving data quality and consequently the identification of treaty-relevant events.

In that context, man-made controlled sources such as drop weights or loudspeakers exhibit properties that make them an interesting source signal for the calibration of seismometers and infrasound sensors. Among the natural sources, earthquake generated signals in particular stand out because of their highly suitable signal and spectral properties. In addition, microbaroms and microseisms also play an important role for calibration, since they cover the lowest frequency range of interest. In particular, we focus here on sources that may generate both seismic and infrasonic signals. By means of a joint review of the waves’ sources in the solid earth and the atmosphere, parallels and differences are highlighted. Preliminary comparisons performed with IMS stations PS19 and IS26 in Germany show that the frequency response of different excitation sources can be determined using spectral methods and correlation analyses.

How to cite: Schwardt, M., Gaebler, P., Hupe, P., and Pilger, C.: Natural and anthropogenic excitation sources for seismic and infrasonic on-site calibration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3620, https://doi.org/10.5194/egusphere-egu22-3620, 2022.

Christoph Pilger et al.

We report on a review of multiple sources and source characteristics of hydroacoustic signals recorded at the six hydrophone stations of the International Monitoring System for verifying compliance with the Comprehensive Nuclear-Test-Ban Treaty.  We present a comprehensive list of hydroacoustic sources as well as their general waveform shape and individual spectral source characteristic, i.e. the time duration, source intensity, frequency content and signal variation.

We identify and investigate numerous natural sources like earthquakes, volcanoes, icebergs and marine mammals as well as anthropogenic sources like explosions, airgun surveys and shipping activity. We show selected example events and associated references, collected in the course of the joint research project "Metrology for low frequency sound and vibration - 19ENV03 Infra-AUV". We further use freely available recordings from e.g. seismic stations for cross-validation purposes.

This overview provides the basis for an open-access systematic source classification, where only few, fragmentary event catalogues are available up to now and in situ identification of sources and calibration of instruments are difficult and complex. This work is applicable to future activities in automatic source detectors and event catalogs, sensor calibration activities using remote excitation sources and data comparison with other hydroacoustic measurements. We invite the scientific community to discuss useful source labels for such a compilation and useful datasets for comparison and validation.



How to cite: Pilger, C., Steinberg, A., Gaebler, P., and Schwardt, M.: Characteristics of hydroacoustic sources of natural and anthropogenic origin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3710, https://doi.org/10.5194/egusphere-egu22-3710, 2022.

Ben Dando et al.

While seismic arrays have been in use since the 1950s and are currently a vital part of the IMS, they have fundamentally consisted of single or 3-component seismometers to measure the ground motion at a discrete set of locations known as the array elements. With the advent of Distributed Acoustic Sensing (DAS) within the last two decades, there is currently great interest in exploring the potential seismological applications. In contrast to traditional seismometers, DAS measures the deformation (e.g. strain-rate) along the length of a fibre optic cable with great flexibility in the number of measurements that can be taken and where they are taken along a given cable layout. Applying such technology to seismic arrays offers an exciting opportunity to design array configurations that were previously impractical with individual seismometers. However, the use of DAS requires special consideration of its unique signal characteristics, which include insensitivity of P-waves arriving broadside to the fibre optic cable.

In this paper we present a design study for the installation of a new fibre optical cable at the site of the existing NORES seismic array in Norway – a 1.4 km aperture array located within a subarray of IMS station PS27 (NOA). We demonstrate through the modelling of DAS-specific array response functions how to optimize a new seismic array for regional seismic monitoring, highlighting the importance of incorporating DAS directivity effects. The final design will be installed in 2022 supplementing the current NORES array and will provide a unique data set that could lead to a new generation of DAS seismic arrays for both regional and global seismic monitoring.

How to cite: Dando, B., Iranpour, K., Wuestefeld, A., Näsholm, S. P., Baird, A., and Oye, V.: Designing the next generation of seismic arrays using fibre optic DAS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6408, https://doi.org/10.5194/egusphere-egu22-6408, 2022.

Mikhail Rozhkov et al.

The Preparatory Commission for the CTBTO routinely process data from the International Monitoring System, IMS – a global network of seismic, hydro-acoustic, and infrasound stations. The data are processed to detect, locate, and screen events that may have characterization parameters similar to those from nuclear explosions. The observation and processing systems are required to be sensitive to low-magnitude events, especially in unusual locations (e.g., aseismic regions). A promising way to improve the system sensitivity is by refining the receiver velocity models underneath IMS stations by incorporating a number of ambient noise processing techniques into the International Data Center (IDC) practice. In particular, this approach should lead to reduction of the arrival time residuals between empirical and observed onset times of seismic waves. The Big Data basis for this approach is using a vast amount of seismic noise data acquired in the IDC for more than 20 years. It would also allow to shed a light on the existence of seismic velocity evolution at least for unstable crustal regions applying a time-lapse ambient noise tomography (ANT) method (4D high resolution passive seismic). A lack of reference models can be partially overcome and examining the models within the seismic array aperture can be performed by the convergence of the spatial seismic correlation methods and the local single station measurements - seismic impedance and the direct Rayleigh ellipticity estimations by the H/V ratio and random decrement techniques

We conducted a case study for ARCES IMS array-station in Northern Norway, which consists of 4 rings of all 3C broadband (120s shallow vault   seismometers. Besides building an averaged uppermost ARCES velocity model, we demonstrate the trial application of the ANT methods for the individual model retrieval at different flanks of spatially distributed sensors comprising seismic arrays as a generalized way to aggregating the block velocity models.  Modified spatial autocorrelation (MSPAC) has been applied for ARCES data both for the whole set of elements as well as for four geographically symmetrical sub-groups relative to the array center. Spatial correlation patterns demonstrate the Bessel function (relative to the ground motion frequency) behavior as predicted by Aki (1957). The cross-correlation analysis of the background noise at ARCES was carried out in the wide frequency range because of the broadband hybrid channel frequency response at each array element.  Revealed models demonstrate considerable difference and thus could be further utilized for improvement of event location and as a station specific correction instrument.

Also, we provide an example with the spiral geometry but smaller aperture seismic array in Norcia intermountain basin, Northern Italy. The model estimation based on MSPAC conducted with the medium range sensors provides the results consistent with the well and gravity study conducted in Italy (2019).

For enhancement of CTBTO OSI aftershock monitoring system, the same approach can be utilized by retrofitting velocity models produced with the noise data collected from the temporarily OSI array. The same method could be also implemented in hydrofracking and induced seismicity monitoring.

How to cite: Rozhkov, M., Starovoyt, Y., and Kitov, I.: IMS location capability improvement with the ambient noise tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2906, https://doi.org/10.5194/egusphere-egu22-2906, 2022.

Christos Saragiotis et al.

Prediction of seismic travel times at the International Data Centre (IDC) of the Comprehensive Nuclear-Test Ban Treaty Organization (CTBTO) has been based until recently on the one-dimensional IASPEI91 travel-time curves for teleseismic and regional phases, with the addition of some local or regional models for regional and local phases in some areas (North America and Eurasia). Since IASPEI91 is not universally applicable in a heterogeneous Earth, travel-time predictions are further corrected to account for, among others, the Earth’s ellipticity, station elevation, and source-specific effects, including regional geology.

In order to improve travel time predictions, especially for regional phases for which the prediction error is most prominent, the IDC is now using travel time corrections based on the Regional Seismic Travel Time (RSTT) velocity model first introduced by Lawrence Livermore National Labs to account for the source-specific effects. The RSTT velocity model is a global model that approximates a 3D crust and upper mantle and is based on ground truth (GT) events recorded globally.

Examination of one year (August 2020 until August 2021) of the Reviewed Event Bulletin (REB) shows that the use of these RSTT-based travel time corrections has improved the precision of event location as measured by a) travel time residuals of regional phases, b) the number of defining regional phases according to the stringent IDC event definition criteria and c) comparison of events similar in magnitude and location in the periods before and after the application of the RSTT-based corrections. Although the improvement is seen worldwide, it is more prominent for stations in areas such as Australia and Africa, where previously the travel time corrections were based only on the IASPEI91 curves, that is, there were no local or regional velocity models available.

How to cite: Saragiotis, C., Le Bras, R., and Kasmi, A.: Improving event location accuracy at the IDC using RSTT-based travel time corrections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10097, https://doi.org/10.5194/egusphere-egu22-10097, 2022.

John Condon et al.

When monitoring for possible underground nuclear tests,
identifying shallow earthquakes from explosive sources can
be achieved using the ratio of the body-wave magnitude to
the surface-wave magnitude (mb:Ms criterion), with explosive
sources producing less energetic surface wave excitation.
Current methods for automated surface-wave detection at the
International Data Centre (IDC) rely on a dispersion test - a
global group-velocity model is used to predict a time window
based on event origins in the IDC Reviewed Event Bulletin
(REB). The data in the predicted time window are narrowband
filtered into eight frequency bands - if the time of the maximum
energy of at least 6/8 of the bands sits within a specified error
of the expected dispersion curves, a surface wave is said to be
detected. Stevens et al. (2001) added phase match filtering to
the process to improve the signal-to-noise ratio, and this was
implemented into provisional operations at the IDC in 2010,
under the name Maxpmf.

A number of issues can potentially arise with this automatic
detection technique, leading to false detections and mis-associations, these include:
• local noise passing the dispersion test and being erroneously associated;
• surface waves detected at close-to-regional distances
experience little dispersion and hence impulsive signals
can pass the dispersion test;
• since automatic detection is only attempted for REB
events, some surface waves may be missed entirely, as
they lack an origin from which to calculate an arrival-time

Assuming random noise and that the signals are independent,
Stevens (2007) defined parameters that determine the false
alarm rate, determined empirically from the network as it was
in 2007. Stevens (2007) recommended that these parameters be
continually reviewed. Since automated surface wave processing
at the IDC was implemented, the number of International
Monitoring System (IMS) seismic stations with at least one
surface-wave detection in the REB has significantly increased
(from around 50 stations in 2002, to around 145 in 2020)
without review of the false alarm rate parameters.
We have designed interactive software to manually review
stages of the IDC automatic surface-wave detection algorithm.
We will use this to investigate the false-detection rate and
how it has changed over time, and interrogate whether the
independence and random noise assumptions this prediction is
predicated on are still valid for a larger network.

Stevens, J. L., 2007. Automatic surface wave processing support
and documentation, Tech. rep., CTBTO Vienna International
Stevens, J. L., Adams, D. A., & Baker, G. E., 2001. Improved
surface wave detection and measurement using phasematched filtering with a global one-degree dispersion model,
Tech. rep., Science Applications International Corp San
Diego CA.

How to cite: Condon, J., Selby, N., and Keeble, J.: Towards Assessing the Quality of Surface Wave Associations in the Reviewed Event Bulletin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2581, https://doi.org/10.5194/egusphere-egu22-2581, 2022.

Ehsan Qorbani et al.

Data from the stations of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) organization are being processed by automatic processing, Global Association (GA), and interactively analyzed and reviewed by analysts, resulting in the International Data Centre (IDC) bulletins. The Network Processing Vertically Integrated Seismic Analysis (NET-VISA) is a Bayesian seismic monitoring system designed to process data from the IMS to reduce the number of missed and false events in the automatic processing stage. NET-VISA has been implemented in the automatic process as an additional event scanner in operation at the IDC since January 15, 2018. In this study we assess the influence of NET-VISA automatic scanner on the number of events in the IDC bulletins, LEB (Late Event Bulletin) and REB (Reviewed Event Bulletin). In particular, the impact of NET-VISA scanner on the number of scanned events during the interactive analysis is assessed. We use three distinct time periods, each including 1200 days, two before and one after the NET-VISA implementation to evaluate the NET-VISA influence as well as the effect of the other possible factors such as global seismicity and network performance. The results show a 4.6% increase in the number of LEB events after including the NET-VISA scanner in operation, with an average of 7 events per day, and a notable increase of 17.90% in the number of scanned events. We also discuss the effect of other possible factors on such increase and conclude it can be attributed to the implementation of the NET-VISA scanner.

How to cite: Qorbani, E., Ali, S. M., Le Bras, R., and Rambolamanana, G.: Interactive analysis prospective on implementation of the NET-VISA in the IDC bulletin production, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13165, https://doi.org/10.5194/egusphere-egu22-13165, 2022.

Alessandro Pignatelli et al.

Analyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists.
Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data
“grow up” makes “human possibility” of data processing more complex in terms of required efforts and time demanding. That is why new technological
approaches such as artificial intelligence are becoming very popular and more and more exploited. In this work, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is “included” in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.

How to cite: Pignatelli, A., D'Ajello Caracciolo, F., and Console, R.: Automatic inspection and analysis of digital waveform images by means of convolutional neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2864, https://doi.org/10.5194/egusphere-egu22-2864, 2022.

Andreas Köhler et al.

Array processing is routinely used to measure apparent velocity and back-azimuth of seismic arrivals. Being an integral part of automatic processing pipelines for seismic event monitoring at the IDC and NDCs, this processing step usually follows seismic phase detection in continuous data and precedes event association and location. The apparent velocity is used to classify the type of the detected phase, while the measured back-azimuth is assumed to point towards the event epicentre. Phase type and back-azimuth are usually determined under the plane wave assumption using Frequency-Wavenumber (FK) analysis or other wave front fitting algorithms such as Progressive Multi-Channel Correlation (PMCC). However, local inhomogeneities below the seismic array as well as regional sub-surface structures can lead to deviations from the plane wave character and to differences between the measured back-azimuth and the actual source direction. This can also affect the slowness estimates and, thus, the accuracy of phase type classification. Previous attempts to take these issues into account were based for example on empirical array-dependent slowness vector corrections.

Here, we suggest a neural network architecture to learn from past observations and to determine the seismic phase type and back-azimuth directly from the arrival time differences between all combinations of stations of a given array (the co-array), without assuming a certain wavefield geometry. In particular, input data are phase differences measured for multiple frequencies from the cross-spectrum of each co-array element. The neural network is a combined classification (phase type) and regression (back-azimuth) network and is trained using P and S arrivals of over 30,000 seismic events from the reviewed regional bulletins in Scandinavia of the past three decades and seismic noise examples. Hence, phase types are classified without first measuring the apparent velocity and without using pre-set velocity thresholds, and an unbiased back-azimuth is determined pointing directly towards the source. Training data are selected based on coherency thresholds to avoid training with too noisy arrivals included in the bulletins where for example the analysist placed a pick based on additional information. Furthermore, we test augmenting training data with time differences corresponding to plane waves to add source directions which are underrepresented in the bulletins. Models are trained and evaluated for regional seismic phase observations at the ARCES, NORES and SPITS arrays. Very good performance for seismic phase type classification (97% accuracy) and low source back-azimuth misfits were obtained. A systematic and careful test of the performance compared to FK analysis in NORSAR’s automatic processing (FKX) was conducted to evaluate potential improvements for event association and location. Taking the reviewed bulletins as reference, our first results suggest that the machine learning phase classifier performs equally well as FKX processing when it comes to phase classification and better for source back-azimuth estimation.

How to cite: Köhler, A., Myklebust, E., and Stangeland, T.: A combined seismic phase classification and back-azimuth regression neural network for array processing pipelines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3543, https://doi.org/10.5194/egusphere-egu22-3543, 2022.

Jinyin Hu et al.

A seismic moment tensor (MT) consisting of 6 independent components is widely used to parameterise a seismic point-source by assuming no net torque. However, there are well-documented seismic sources for which net torques are significant, and single force (SF) components are necessary to describe the physics of the problem, e.g., the collapse of cavities, landslides, and glacier earthquakes. Therefore, combining MT and SF components can explore a broader range of source representation in seismic source inversion. In addition, rigorous uncertainty estimate has been a leading-edge topic in seismic source inversion. A complete uncertainty treatment should consider both data noise involved in the acquisition process and theoretical error primarily due to imperfect knowledge of Earth structure. Recent advancements jointly treating data noise and theoretical errors have been made for the MT representation within the hierarchical Bayesian framework, where noise is treated as a free parameter. However, to our best knowledge, a decomposition of the seismic source to MT and SF, including a rigorous treatment of uncertainty, remains an unaddressed problem. Here, we propose a joint inversion scheme of MT and SF within the hierarchical Bayesian framework that accounts for both data and structural (theory) uncertainties. Several carefully designed synthetic experiments modelling underground explosions demonstrate the feasibility of this method. Our current focus is on practical applications. We are hopeful that our approach will provide further insights into the physics of seismic sources for underground nuclear explosions, thus helping verify compliance with the CTBT.

How to cite: Hu, J., Phạm, T.-S., and Tkalčić, H.: A Joint Point-source Moment Tensor and a Single Force Inversion Within Hierarchical Bayesian Inference for Revealing the Source Mechanism of Underground Nuclear Explosions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3244, https://doi.org/10.5194/egusphere-egu22-3244, 2022.

Itahisa Gonzalez Alvarez et al.

P waves are often used to calculate the yield of chemical or nuclear explosions in forensic seismology. These estimations often rely on amplitude measurements affected by seismic scattering and attenuation caused by the presence of heterogeneities on the scale of the seismic wavelength and seismic energy conversion into heat, both on the source and receiver side. It is therefore important to accurately characterize the effect of these phenomena on the recorded wavefields so that any source size (and type) obtained from them are not under or overestimated.  
In our previous study (González Alvarez et al., 2021), we combined single layer and multi-layer energy flux modeling with a Bayesian inference algorithm to characterize lithospheric small-scale heterogeneities beneath seismic stations or arrays by calculating the characteristic scale length and fractional velocity fluctuations of the crust and lithospheric mantle beneath them. Here, we take this approach further and remove the dependence on the less realistic, single layer energy flux model by including the intrinsic quality factor and its frequency dependence as free parameters into our Bayesian inference algorithm. We use the multi-layer energy flux model to produce synthetic envelopes for 2-layer models of the lithosphere for different values of the scattering and intrinsic attenuation parameters. We then use our improved Bayesian inference algorithm to sample the likelihood space by means of the Metropolis-Hastings algorithm and obtain posterior probability distributions for all parameters and layers in the model. To our knowledge, such an approach has not been attempted before. We thoroughly tested this inversion algorithm and its sensitivity to four different levels of crustal and lithospheric mantle intrinsic attenuation settings using 18 synthetic datasets. Our results from these tests, while showing complex trade-offs between the parameters, show that scattering parameters can be recovered accurately in most cases. Intrinsic attenuation shows higher variability and non-uniqueness in our inversions, but can generally be recovered for over half of the synthetic models. To further test the accuracy of the results obtained from this Bayesian algorithm, we applied this technique to the large, high-quality dataset from PSAR and IMS arrays ASAR and WRA used in our previous study and found excellent agreement between both approaches in all cases. 
Finally, we applied this technique to datasets of teleseismic earthquakes from several arrays part of the IMS (YKA, ILAR, TXAR, PDAR, BOSA and KURK) to characterize the lithospheric scattering and attenuation structure beneath them and relate our findings to the tectonic setting and history of the regions they are installed on.  

González Álvarez, I.N., Rost, S., Nowacki, A. and Selby, N.D., 2021. Small-scale lithospheric heterogeneity characterization using Bayesian inference and energy flux models. Geophysical Journal International, 227(3), pp.1682-1699.

How to cite: Gonzalez Alvarez, I., Rost, S., Nowacki, A., and Selby, N.: Lithospheric scattering and intrinsic attenuation characterization from a Bayesian energy flux model , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5770, https://doi.org/10.5194/egusphere-egu22-5770, 2022.


Mon, 23 May, 17:00–18:30

Chairpersons: Christoph Pilger, Gérard Rambolamanana

Yuichi Kijima et al.

For enhancement of the International Data Centre (IDC) products such as the Standard Screened Radionuclide Event Bulletin (SSREB), there is a need to associate the detections of CTBT relevant isotopes in samples at International Monitoring System (IMS) radionuclide stations with the same release to characterize its source for the purpose of nuclear explosion monitoring. Episodes of anomalous concentrations at the stations are the best first guess for being related to the same event. For multiple isotope observations, the consistency of their isotopic ratios in subsequent samples with radioactive decay is another plausible hint at coming from the same source. Moreover, atmospheric transport modelling (ATM) will help to get further evidence and gain confidence in sample associations by identifying the air masses that link the release to multiple samples. We focused on the basic approach as well as the criteria for automatic sample association for the SSREB.

How to cite: Kijima, Y., Kalinowski, M., Liu, B., Kuśmierczyk-Michulec, J., Schoemaker, R., and Tipka, A.: Sample Association by using Anomalous Concentration Episodes and Decay-Consistent Isotopic Ratios at IMS Radionuclide Stations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2246, https://doi.org/10.5194/egusphere-egu22-2246, 2022.

Martin Kalinowski

The frequency of radionuclides in remote atmospheric observations of historic nuclear test explosions is established from a collection of papers. These report on tests conducted between 1964 and 1996. Most of these tests occurred in the atmosphere but observation of nuclear debris from venting of underground nuclear tests were also found. The review is limited to off-site monitoring and many observations were done at large distances including several tests that were detected on multiple locations on the same hemisphere. The isotope frequency is compared to several radionuclide lists considered for nuclear explosion monitoring to explore whether these lists match the historic evidence. The objective is to identify opportunities for further studies on validating monitoring methods, including atmospheric transport simulations with the objective of identifying the source of an event that is of relevance for atmospheric radioactivity monitoring for the Comprehensive-Nuclear-Test Ban Treaty (CTBT).

How to cite: Kalinowski, M.: Frequency of radionuclides in remote atmospheric observations of historic nuclear test explosions compared to lists of radionuclides considered for nuclear explosion monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1490, https://doi.org/10.5194/egusphere-egu22-1490, 2022.

Seyed Omid Nabavi et al.

We intercompare simulations of the dispersion of aerosol and gaseous radionuclides (137Cs and 131I) driven by a four-member ensemble of (re-)analysis and forecast datasets to quantify statistical and systematic uncertainties. The Lagrangian particle dispersion model FLEXPART 10.4 and FLEXPART-WRF are driven by 6-hourly data from NCEP Global Forecast System (GFS) and Final Analysis (FNL), at spatial resolutions of 0.5 and 0.25 degrees. In addition, for running FLEXPART-WRF, the FNL and ECMWF Reanalysis v5 (ERA5) were first downscaled, to the finer resolutions of 10 km and 1 hour, using the Weather Research and Forecasting (WRF) model. A total of 365 experiments (each day of 2019) were conducted to produce hourly simulations at the spatial resolution of 10 km in 14 vertical levels through 96 hours after a fictitious nuclear power plant accident at Barakah, UAE, in an effort to study the potential risks to the population in the state of Qatar. The source term was scaled to the maximum estimates of the radioactive materials from the Fukushima accident in 2011 (0.042 kg of 131I and 7 kg of 137Cs), released within 24 hours after the accident. We intercompare radionuclide age spectra, cumulative deposition, and population exposure, seasonal variance, and investigate the degree of variability and correlation between ensemble members. Results show that the computational particles corresponded to dense 131I clouds enter Qatar more frequently within 10 to 20 hours after the accident. The cumulative distribution of simulated 137Cs depositions indicates that more than 80% of 137Cs depositions occurs within 75 hours after the accident, with a hotspot in the southeast of Qatar. GFS and ERA-5 show a high degree of correlation, whereas FNL is different. We also observe seasonal variation due to deposition and boundary layer development.

How to cite: Nabavi, S. O., Christoudias, T., Fountoukis, C., Al-Sulaiti, H., and Lelieveld, J.: Ensemble modeling of radionuclide dispersion over the Arabian Peninsula from nuclear power plant accidents using FLEXPART, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-860, https://doi.org/10.5194/egusphere-egu22-860, 2022.

Jolanta Kusmierczyk-Michulec et al.

The operational Atmospheric Transport Modelling (ATM) system deployed and used at CTBTO produces source receptor sensitivity (SRS) fields, which specify the location of the air masses prior to their arrival at any radionuclide station of the International Monitoring System (IMS) network. The ATM computations support the radionuclide technology by providing a link between radionuclide detections and the regions of their possible source. If an IMS station detects an elevated level of radionuclide, the ATM in a backward mode is used to identify the origin of air masses. In the case of a single detection, the FOR (Field of Regard) is computed, which denotes the possible source region for a material detected within one single sample. On some occasions, multiple detections occur at one or more IMS stations. Depending on the nature of these detections and on prevailing meteorological conditions, it is possible that all these detections may come from a unique source. For this case, the PSR (Possible Source Region) is computed for each grid point in space and time by calculating the correlation coefficients between the measured and simulated activity concentration values (SRS fields). Obviously, the result will depend on the algorithms used for that purpose. Currently, in the WEB-connected GRAPhics Engine (WEB-GRAPE) software, designed and developed by the International Data Centre (IDC) to visualize and post-process of the ATM results, three different PSR algorithms are implemented: two based on the Pearson’s correlation coefficient and one based on the Spearman’s rank correlation coefficient. 


For the quality assessment of these PSR algorithms, subsets of datasets developed in the framework of the 2nd and 3rd ATM Challenge will be used, which satisfy the condition that the agreement between Xe-133 measured and simulated values is very good. In this sense, the selected samples will represent “ground truth” data, where the contribution from all dominated sources (e.g. Isotope Production Facilities or Nuclear Power Plants) is included. For these selected samples, the results produced by the different PSRs algorithms will be assessed, taking into account both spatial and temporal variations.  


How to cite: Kusmierczyk-Michulec, J., Tipka, A., Schoemaker, R., and Kalinowski, M.: Quality assessment of the different Possible Source Region (PSR) algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8689, https://doi.org/10.5194/egusphere-egu22-8689, 2022.

Anne Tipka et al.

Detection of radionuclides released from a nuclear explosion is an essential task mandated by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Atmospheric transport modelling (ATM) identifies either possible source regions for relevant radionuclide observations at anomalous concentrations through the so-called International Monitoring System (IMS) or potential stations for measuring releases from known source locations. This is a well-known methodology for connecting sources and receptors of any substance in the atmosphere. The Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) currently investigates the potential advantages of using high-resolution ATM. Past announced underground nuclear tests at the Punggye-ri Nuclear Test Site from the Democratic People’s Republic of Korea (DPRK) are used in this study to scale the CTBTO’s capability to identify IMS stations that might detect a hypothetical release. These events are also used to identify the capability to locate Punggye-ri as the possible source location.

A sensitivity study is presented that demonstrates the CTBTO’s capability to identify Punggye-ri as a possible source region for the relevant radionuclide measurements at IMS stations. The aim is to find the best model set-up from varying combinations of meteorological resolution, regional domain set-up, and physical parameterization. Variations in resolution are accomplished by using first the Lagrangian Particle Dispersion Model FLEXPART, which will be driven by meteorological fields from the European Centre for Medium-Range Weather Forecast (ECMWF) with either 0.5° or 0.1° spatial and 1 h temporal resolution; and second, by using a combination of the Weather Research and Forecasting Model (WRF) and FLEXPART-WRF to scale down to 1 km spatial resolution. The potential accuracy increase is evaluated by using metrics from previous ATM challenges.

How to cite: Tipka, A., Kuśmierczyk-Michulec, J., Schoemaker, R., and Kalinowski, M.: A demonstration of CTBTO’s capability to identify the possible source region of the specific case of DPRK announced tests by conducting a sensitivity study using high-resolution ATM , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8514, https://doi.org/10.5194/egusphere-egu22-8514, 2022.

Tormod Kvaerna et al.

The permanent seismic stations of the European Arctic maintain a detection threshold of around magnitude 2 for events on and around Novaya Zemlya. Events above magnitude 3 are clearly observed by multiple stations at regional and far-regional distances and, with improved traveltime models, can be located with high accuracy. The monitoring capability for smaller magnitude events is dominated by the small aperture seismic arrays ARCES and SPITS. We review the properties of Novaya Zemlya seismic signals on key stations and discuss how empirical signal processing may enhance detection and interpretation of future events in the region. We present a joint probabilistic location for 21 low magnitude events between 1986 and 2020 in which the joint probability distribution for all events simultaneously exploits both constraints on earlier events from stations no longer in operation and constraints on newer events from more recently deployed stations. Advances in signal processing, enhanced exploitation of archive data, new permanent stations, and comparative multiple event analysis will all contribute both to a more robust and sensitive detection capability and higher confidence in signal interpretation.

How to cite: Kvaerna, T., Dando, B., and Gibbons, S.: Seismic Monitoring of Novaya Zemlya: Progress, Challenges, and Prospects, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2971, https://doi.org/10.5194/egusphere-egu22-2971, 2022.

Ivan Kitov

The first aftershock of the announced nuclear test conducted by the DPRK on 09.09.2016 was found by the detection method based on waveform cross correlation on September 11, 2016. This was the only aftershock which was found during the period between the first (09.10.2006) and the sixth (03.09.2017) DPRK tests, using the signals of the DPRK tests as waveform templates. The DPRK6 underground test with mb=6.1 generated a significant aftershock sequence, with some events detected at teleseismic distances. The aftershocks with the best signal quality were used as master events in the multi-master method, working as an active radar focused on the aftershock area. The multi-master method allowed to find more than 100 aftershocks, including 7 aftershocks of the DPRK3 and DPRK4. The aftershock sequence is still active, with 25 aftershocks detected between January 1 and December 10, 2021. The mutual cross correlation of the DPRK aftershocks revealed the presence of two sequences generated by the DPRK5 and DPRK6 cavity collapse. The length, intensity, and alternating character of these two sequences suggest specific mechanisms of energy release. Such a mechanism can be associated with the interaction of the damaged zones of the DPRK5 and DPRK6 and the collapse of their cavities with progressive propagation of the collapsing chimneys towards the free surface. The higher activity in 2021 indicates that the chimney collapse is not finished. We expect more aftershocks, possibly ended with the chimney reaching the free surface.

How to cite: Kitov, I.: Evolution of the DPRK5 and DPRK6 aftershock sequences: 2016 to 2022, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2230, https://doi.org/10.5194/egusphere-egu22-2230, 2022.

Haijun Wang and Ivan Kitov

There was more than a dozen of aftershocks generated by the announced nuclear test conducted by the DPRK on September 3, 2017 (DPRK6) which were found in routine interactive analysis conducted by the International Data Centre. The first DPRK aftershock was found by the method of waveform cross correlation (WCC) on September 11, 2016 after the DPRK5. Dozens of aftershocks were found by cross correlation after the DPRK6 in addition to those found in the routine processing and then confirmed by IDC analysts. The set of robust aftershocks allowed to develop, test, and apply in the routine WCC processing the multi-master method. This method was consistently applied to seismic data at IMS stations KSRS and USRK collected since 2009. Many new aftershocks were found after the third (DPRK3) and the fourth (DPRK4) announced underground nuclear tests conducted by the DPRK on 12.02.2013, and 06.01.2016, respectively. The second DPRK test (25.05.2009) had no reliable aftershock hypotheses at the level of the method sensitivity and resolution. The largest aftershocks of the DPRK3 and DPRK4 could be interpreted as related to the cavity collapse process possibly followed by a chimney collapse, not reaching the free surface. The DPRK3 and DPRK4 aftershocks were confirmed by interactive analysis.

How to cite: Wang, H. and Kitov, I.: Aftershocks of the announced underground nuclear tests conducted by the DPRK on 12.02.2013 and 06.01.2016 found by waveform cross correlation and confirmed by interactive analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3164, https://doi.org/10.5194/egusphere-egu22-3164, 2022.

Volcano monitoring

Pierrick Mialle et al.

Almost 20 years ago, the first infrasound event built only from infrasound arrivals was reported in the Reviewed Event Bulletin (REB) of the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Over the last 25 years, 53 infrasound stations from the International Monitoring System (IMS) have been installed and are transmitting data to the IDC for the purpose of detecting any nuclear explosions in the atmosphere. The infrasound component of the IMS daily registers infragenic signals originating from various sources such as volcanic eruptions, earthquakes, microbaroms, meteorite entering the atmosphere or explosions. The IDC routinely and automatically processes infrasound data with the objective to detect and locate events then reviewed by interactive analysis.

As the IDC advances its methods and continuously improves its automatic system for the infrasound technology, several events received global interest from the scientific community and the public. On 15 February 2013 the Chelyabinsk meteor entered the atmosphere over Ural region (Russian Federation) and generated infrasound waves that were recorded by 20 of the 42 infrasound IMS stations operating at the time. Almost 9 years later, on 15 January 2022 the Hunga Tonga–Hunga Haʻapai eruption reached a climax around 04:15 UTC, which generated acoustic waves circumnavigating the Earth for several days. In addition to seismic and hydro-acoustic recordings, all 53 IMS infrasound stations registered signals from this eruption. This event is the largest ever recorded by the infrasound component of the IMS network.

How to cite: Mialle, P., Le Bras, R., and Bittner, P. and the CTBTO Colleagues: CTBTO International Data Centre analysis of the Hunga Tonga–Hunga Haʻapai eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13421, https://doi.org/10.5194/egusphere-egu22-13421, 2022.

J. Ole Ross et al.

The Comprehensive Nuclear-Test-Ban Treaty prohibits all nuclear explosions. For detection of potential non-compliance, the International Monitoring System with 321 stations is being installed and largely completed. Seismic, hydroacoustic and infrasound stations detect, localize and characterize explosions. Highly sensitive radionuclide stations sniff for radioactive traces potentially released from nuclear explosions. The International Data Centre (IDC) in Vienna processes the IMS data and generates several standard data analysis products for distribution to the member states. However, the judgement on the character of potentially treaty relevant events it is the sole responsibility of the State Signatories. Therefore, National Data Centres (NDC) are established in many states. The German National Data Centre is hosted by BGR and supported by BfS (Federal Office for Radiation Protection) with radionuclide expertise. Furthermore NDCs can use additional observation data sources other than recorded by the IMS like national stations or remote sensing data. There have been several larger test cases for the verification system as the announced nuclear tests in the DPRK 2006-2017, the Fukushima-Daiichi radionuclide emissions 2011, the Chelyabinsk meteorite 2013 or the accidental explosion in Beirut 2020.

Recently, the very large eruption of the Hunga Tonga Hunga Ha’apai volcano occurred on January 15th 2022 in the South Pacific Ocean and turned out to be a strong source of waveform phenomena in solid earth, water and atmosphere.

Seismic PKP phases travelling through the core of the Earth were the first seismic signal of the event registered at German IMS station PS19 and the national Gräfenberg array. A preliminary moment tensor inversion analysis for P- and S-Phases shows the mainly explosive character of the event. Sensors of the hydro-acoustic component of the IMS also recorded the main eruption as well as ancillary volcanic activity at the two hydrophone arrays in the Pacific Ocean up to nearly 10000 km distance. The eruption caused a long period atmospheric pressure wave even measurable with classical barometers and pressure sensors in smartphones around the globe. Consequently, all 53 certified IMS infrasound stations detected signals from the event. Recurrent infrasonic signatures travelled around the globe several times and were recorded by IMS stations in the following days. The eruption was presumably the strongest infrasound source since installation of the IMS started.

Finally, the atmospheric sensitivity of the IMS radionuclide stations to hypothetical releases connected with the eruption is investigated by means of Atmospheric Transport Modelling. The results show threshold values for detectable releases of radioactive fission and activation products.

Overall, the very huge volcanic eruption can serve as upper benchmark event for the CTBT compliance monitoring capability using cross-technology analysis of IMS data.

How to cite: Ross, J. O., Ceranna, L., Donner, S., Gaebler, P., Hupe, P., Plenefisch, T., Pilger, C., Schwardt, M., and Steinberg, A.: Global cross-technology analysis of the Hunga Tonga-Hunga Ha’apai explosive eruption from the perspective of CTBT monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13389, https://doi.org/10.5194/egusphere-egu22-13389, 2022.

Discussion with authors