Pure and Applied Geophysics

Mialle, Pierrick; Kalinowski, Martin B.

doi: 10.1007/s00024-025-03890-0pmid: N/A

Simon, Rapelang E.; King, J. G.; Moffat, L. C.; Moidaki, M. D.; Kwadiba, M. T. O.; Jackson, K. G.; Ntibinyane, O.; Ranganai, R. T.

doi: 10.1007/s00024-024-03435-xpmid: N/A

On the 3rd April 2017 a widely felt Moiyabana earthquake shook Botswana and the rest of southern Africa. Previous Moiyabana earthquake locations used mainly teleseismic or regional seismograms; and/or non-seismic methods which include Synthetic Aperture Radar (InSAR), and magnetotelluric (MT). These results did not agree, as evidenced by the depth of the earthquake that ranged from zero to 30 km (i.e. indicating either a man-made event or a natural event); thus motivating us to re-assess the location parameters. Unfiltered seismic waveform data from the recent project of the Network of Autonomously Recording Seismographs (NARS) in Botswana was complimented with stations from the International Monitoring System (IMS) to relocate the event. Relocated parameters are origin time, epicentre, focal depth, and magnitude. Geotool software from the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO), and the Regional Seismic Travel Time model (RSTT) were used to process vertical components waveforms from 9 NARS and 32 IMS stations. Geotool results are: earthquake epicentre (22.645 °S: 25.220 °E); origin time of 17:40:16.9 (UTC); hypocentral depth range of 22 to 24 km; body magnitude (mb) and local magnitude (ml) of 6.3 ± 0.6 and 6.0 ± 0.8, respectively. RSTT results are: earthquake epicentre (22.667 °S: 25.257 °E); origin time of 17:40:16.95 (UTC); hypocentral depth of 25 km; and mb of 6.65 ± 0.03. The seismological location parameters from Geotool and RSTT, agree very well within experimental uncertainties with the non-seismic geophysical methods.

Mulwa, Josphat K.

doi: 10.1007/s00024-024-03458-4pmid: N/A

The International Data Centre (IDC) routinely applies event screening using a multi-technology approach in order to enable member states to characterize events as either natural or anthropogenic. Various event discriminants are presented in literature. At the Kenya National Data Centre (KE-NDC or N090), a systematic and step-by-step procedure of SEISMIC events discrimination is applied. Results from the discriminants adopted are obtained within a short time and the discriminants are relatively easy and fast to use. The discriminants used at KE-NDC (N090) are ranked in a hierarchy based on results obtained from one discriminant being applied in subsequent discriminants and ease of returning results within the shortest time possible to allow for events discrimination and dissemination of results. The discriminants applied and their hierarchy at KE-NDC include: (i) event location (epicenter/hypocenter parameters) (ii) hypocenter parameters based on events relocation using HYPOCENTER, (iii) magnitude determination, (iii) mb:Ms criteria and (iv) focal mechanism determination. Two seismic events are used as case examples to demonstrate how event discrimination is achieved based on the discriminants presented herein. The two seismic events are the 20190324 and 20200503 seismic events in southwestern and northern Kenya respectively. The choice of these two events is based on the fact that they were strong enough to be recorded by a number of global seismic stations and their magnitudes are comparable to the 2009, 2013 and 2016, but slightly lower than the 20170903 DPRK announced tests. Based on the discriminants used and presented herein, the two seismic events were categorized as being due to natural earthquakes.

Elkhouly, Shimaa. H.; Ali, Ghada

doi: 10.1007/s00024-024-03463-7pmid: N/A

In the field of seismic signal analysis, it is of utmost importance to accurately differentiate between earthquakes and underground nuclear explosions. As a contribution for the verification regime of the Comprehensive Nuclear Test Ban Treaty (CTBT), Various methods have been employed for this purpose, including Complexity, Spectral ratio, mb—Ms (body wave and surface wave magnitudes), and corner frequency of P and S waves. These discrimination techniques have been examined to manually identify natural seismic events from nuclear explosions across different regions worldwide, such as China, India, Pakistan, North Korea, and the United States. To gather the necessary data, a comprehensive dataset comprising nuclear explosions and earthquakes of the same magnitude range (4 ≤ mb ≤ 6.5) of 35 seismic events from 1945 to 2017 has been compiled from the International Research Institute for Seismology (IRIS) using broadband and long period seismic stations. The objective of this study is to employ a range of linear and nonlinear Machine Learning (ML) models with the aim of automatically distinguishing between underground nuclear explosions and large earthquakes to enhance the accuracy of manual feature extraction. For this purpose, time domain waveforms and different classifier techniques focused on feature extraction have been used. The ML models employed include logistic regression, K-nearest neighbours classifier, decision tree classifier, random forest classifier, voting classifier, and Naive Bayes. The outcomes of the ROC and AUC analyses were employed to validate the validity of our proposed discrimination algorithm. The results show that the Random Forest Classifier is the most effective model, obtaining 100% accuracy in the case of feature extraction, while the best model for the time domain waveform classifier that achieved 75.5% accuracy is the voting classifier.

Berezina, Anna; Sokolova, Inna; Kopnichev, Yuri; Pershina, Elena; Nikitenko, Tatiana

doi: 10.1007/s00024-024-03482-4pmid: N/A

Based on digitized and digital records of events at the Lop Nor test site, recorded by seismic stations installed in Central Asia, the dynamic parameters of nuclear explosions carried out in different mediums (in the air and underground) were studied, the characteristic features of the waveform of each type of events were found, and a comparison with records of earthquakes, occurred in the area of the Lop Nor test site was done. Temporal variations in the attenuation field of short-period shear (S-) waves in the area of the Lop Nor test site, associated with the migration of deep fluids as a result of long-term intensive induced impact on the geological environment, have been identified.

Ziolkowski, Anton

doi: 10.1007/s00024-024-03454-8pmid: N/A

The Democratic People’s Republic of Korea (DPRK) conducted six underground nuclear explosions at the Punggye-ri nuclear test site at Mount Mantap, a granite peak. Test 1 was separate from tests 2 to 6, which were within about 1 km of each other. Using seismograms recorded at Mudanjiang (MDJ) seismic station in China, I propose a new approach to obtain source parameters, source time functions and yields of events 2 to 6, assuming they share the same Green’s function from Punggye-ri to MDJ. Each source is modelled as a spherical cavity in a homogeneous isotropic elastic full space, with four independent parameters constrained by published data on the properties of granite and analysis of the recorded MDJ seismograms. The effect of the ground surface is included as a planar reflection that modifies the pressure at the cavity boundary. The Green’s function for each event is estimated by deconvolving the seismogram for the estimated source time function. Very fast simulated annealing (VFSA) is used to search the parameter space to minimise the root-mean square difference among the estimated Green’s functions and their mean. The estimated Green’s functions are similar and differ in amplitude by less than a factor of 2. Green’s functions from Punggye-ri to other seismic stations may be obtained by deconvolving the seismograms for the corresponding source time functions. Mapping the nonlinear zones surrounding each explosion indicates that this part of the site was destroyed by the underground nuclear tests before its official destruction on 24 May 2018.

Guilhem Trilla, Aurelie; Cano, Yoann

doi: 10.1007/s00024-024-03455-7pmid: N/A

The rapid detection and source characterization of any type of seismic events including earthquakes and nuclear explosions is one of the missions of many seismological laboratories. Most often, the techniques used are based on phase picking and amplitude measurements for detecting and locating events, and for estimating magnitudes. From these parameters, especially for anthropogenic sources, event screening is then done empirically, and this may lead to misinterpretations of the source nature. However, it is known that seismic waveform inversion for the determination of the moment tensor has proven to be a reliable source physics-based method for event characterization. Here, we present a technique already used for earthquake monitoring in tectonically active regions, and we test it on very shallow natural and anthropogenic events recorded in the vicinity of the Democratic People’s Republic of Korea (DPRK). From a grid of potential locations and by scanning continuous seismic waveforms, it is possible to implement a rapid detector of seismic events providing the full information of the sources (origin time, location, magnitude, mechanism, and source decomposition). We show its overall performances on all past DPRK nuclear tests and regional earthquakes. From such an approach fast complete event screening is achieved. Source uncertainties can also be estimated. This stand-alone detector and identifier of seismic events may help monitoring seismological agencies to provide a rapid and complete alert for any events within a region of interest before more in-depth discrimination analysis can be run.

Berg, Elizabeth M.; Carmichael, Joshua D.; Young, Christopher J.; Eras, Stephanie J.; Hodgkinson, Kathleen M.

doi: 10.1007/s00024-025-03808-wpmid: N/A

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

Steinberg, Andreas; Gaebler, Peter; Hartmann, Gernot; Lehr, Johanna; Pilger, Christoph

doi: 10.1007/s00024-024-03491-3pmid: N/A

We test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Arora, Nimar S.; Ali, Sherif Mohamed; Shashkin, Aleksandr; Tamarit, Vera Miljanovic; Khukhuudei, Urtnasan

doi: 10.1007/s00024-024-03574-1pmid: N/A

Large seismic events often trigger a wave train of slow decaying energy known as the coda that can mislead signal detectors into forming coda detections that appear to look like regular phase detections. These coda detections can confuse event formation algorithms into building false events known as coda events. Naive solutions to this problem by dropping any detection that looks like a coda detection can have the negative consequence of missing real events. We propose to address this issue by extending an existing Bayesian approach, NET-VISA that has been designed to build event bulletins using a generative model of global-scale seismology. Our extensions significantly boost the existing work by reducing the total number of false events by nearly half and virtually eliminating coda events without changing the number of real events.