Apart from classical earthquake monitoring, seismological data can also be used to detect explosions in near-real-time on both regional and global scales. We demonstrate how seismic and infrasound data can provide more comprehensive and objective information about conflict-related explosions or suspicious events that might be the result of targeted attacks. We can identify the underwater explosions at the Nord Stream pipeline infrastructure in the Baltic Sea in September 2022. Cross-correlation analysis allowed us to identify sub-events several seconds apart which can be associate with specific locations along the pipelines. Furthermore, we detect a signal at the Finish seismic array in October 2023 which may be associated with the damage along the Balticconnector. The other example is from Ukraine, where we present the ability to automatically identify and locate ground explosions related to the Russia-Ukraine conflict with data from the Malin array (AKASG). Between February and November 2022, we observe more than 1,200 explosions from the Kyiv, Zhytomyr, and Chernihiv provinces. Both seismic and infrasound detections can be used to verify and improve accurate reporting of military attacks and help to provide an unprecedented view of an active conflict zone. We analyze events with a variety of seismo-acoustic signatures and significant differences in explosive yield. These can be associated with various types of military attacks, including artillery shelling, cruise missile attacks, airstrikes, or the destruction of the Kakhovka dam NE of Cherson.

Seismometers are generally used by the research community to study local or distant earthquakes, but seismograms also contain critical observations from regional1,2 and global explosions3, which can be used to better understand conflicts and identify potential breaches of international law. Although seismic, infrasound and hydroacoustic technology is used by the International Monitoring System4 to monitor nuclear explosions as part of the Comprehensive Nuclear-Test-Ban Treaty, the detection and location of lower-yield military attacks requires a network of sensors much closer to the source of the explosions. Obtaining comprehensive and objective data that can be used to effectively monitor an active conflict zone therefore remains a substantial challenge. Here we show how seismic waves generated by explosions in northern Ukraine and recorded by a local network of seismometers can be used to automatically identify individual attacks in close to real time, providing an unprecedented view of an active conflict zone. Between February and November 2022, we observed more than 1,200 explosions from the Kyiv, Zhytomyr and Chernihiv provinces, providing accurate origin times, locations and magnitudes. We identify a range of seismoacoustic signals associated with various types of military attack, with the resulting catalogue of explosions far exceeding the number of publicly reported attacks. Our results demonstrate that seismic data can be an effective tool for objective monitoring of a continuing conflict, providing invaluable information about potential breaches of international law.

<p>Since the invasion of Ukraine in February 2022, daily media reports have shown the shocking effects of fighting and the inevitable devastation associated with war. However, getting a comprehensive and unbiased overview of the ongoing military attacks remains a challenge. The availability of geophysical data that can identify individual attacks provides much needed objectivity to this problem. The pressure waves generated by an explosion travel through the atmosphere and subsurface as sound and seismic waves, and their signature can be recorded by arrays of seismometers for ground motion or microbarometers for sound propagation. In this work, we demonstrate the first known case of using seismological data to detect conflict-related explosions in near-real-time. Using the Ukrainian primary station of the International Monitoring System (IMS), the Malin array (AKASG), we automatically locate explosions around the Kyiv and Zhytomyr provinces. We show how our resulting catalogue of explosions correlates with key events in the Ukraine conflict and how these data can be used to both verify and improve accurate reporting of military attacks. We analyze events with a variety of seismo-acoustic signatures and significant differences in explosive yield. These can be associated with various types of military attacks, including artillery shelling, cruise missile attacks and airstrikes. This work opens-up the possibility for future conflict monitoring using geophysical data.</p>

Seismic data have been used to continuously identify individual military explosions in Ukraine. Such conflict monitoring provides unprecedented details of these attacks and an objective data source that is essential for accurate war reporting and for identifying potential breaches of international law.

Distributed Acoustic Sensing (DAS) involves the transmission of laser pulses along a fiber‐optic cable. These pulses are backscattered at fiber inhomogeneities and again detected by the same interrogator unit that emits the pulses. Elastic deformation along the fiber causes phase shifts in the backscattered laser pulses which are converted to spatially averaged strain measurements, typically at regular fiber intervals. DAS systems provide the potential to employ array processing algorithms. However, there are certain differences between DAS and conventional sensors. While seismic sensors typically record the directional particle displacement, velocity, or acceleration, the DAS axial strain is inherently proportional to the spatial gradient of the axial cable displacement. DAS is therefore insensitive to broadside displacement, for example, broadside P‐waves. In classical delay‐and‐sum beamforming, the array response function is the far‐field response on a horizontal slowness (or wavenumber) grid. However, for geometrically non‐linear DAS layouts, the angle between wavefront and cable varies, requiring the analysis of a steered response that varies with the direction of arrival. This contrasts with the traditional array response function which is given in terms of slowness difference between arrival and steering. This paper provides a framework for DAS steered response estimation accounting also for cable directivity and gauge‐length averaging – hereby demonstrating the applicability of DAS in array seismology and to assess DAS design aspects. It bridges a gap between DAS and array theory frameworks and communities, facilitating increased employment of DAS as a seismic array, while providing building blocks for the development of DAS array design tools.

ABSTRACT The precision of P‐ and S‐wave phase picking strongly determines the precision of earthquake locations, but such picking can be challenging in the case of emergent signals, large data sets or temporally varying seismic networks. To overcome these challenges, we have developed the concept of an aggregated template to perform automatic picking of the P‐ and S‐wave phases. An aggregated template is defined as a representative event for a small area, built by aggregating the best signal‐to‐noise‐ratio seismic traces from events with similar waveforms (i.e. multiplet events). A template matching procedure, based on the cross‐correlation between an aggregated template and an unpicked event, automatically determines the unpicked event P‐ and S‐wave phases. This method enables (1) consistent and accurate P‐ and S‐wave phase picking and (2) reduces processing time relative to traditional template matching by using a clustering method that finds the most representative templates for a region, and thus limiting the required number of templates. We established two parameters to weight the picking precision: (1) the cross‐correlation between the aggregated template and the unpicked event and (2) the number of P‐ and S‐wave picks determined per event. We tested this method on 2100 events recorded in the south‐west of Iceland. Nineteen aggregated templates have been defined and used to automatically pick ∼65% of the complete event catalogue with an accuracy within the range of the manual picking uncertainty. These automatically picked events can then be used for event location, even when characterized by low magnitude, low signal to noise ratios and with emergent P‐wave signals.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2014A robust method for determining moment tensors from surface microseismic dataAuthors: Ben D. E. Dando*Kit ChambersRaquel VelascoBen D. E. Dando*Pinnacle – a Halliburton serviceSearch for more papers by this author, Kit ChambersPinnacle – a Halliburton serviceSearch for more papers by this author, and Raquel VelascoPinnacle – a Halliburton serviceSearch for more papers by this authorhttps://doi.org/10.1190/segam2014-1481.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract We present a new method to determine moment tensors using surface microseismic imaging. The method involves the projection of data onto each moment tensor component, and using the amplitudes at the source origin time and location as an estimate of the seismic moment tensor. The procedure combines both precise source location and source characterization, and is known as Moment Tensor Microseismic Imaging (MTMI) (Chambers et al., in press). We compare the moment tensor results obtained using this method with those from a standard least squares inversion. We investigate the relative performance of the two approaches using synthetic data computed for two commonly observed microseismic source types, and a variety of signal to noise ratios (SNRs). MTMI proves to be significantly more robust at low SNRs. Moreover, we demonstrate a simple denoising technique that further increases our ability to determine the moment tensor in the presence of high noise. Keywords: imaging, monitoring, microseismic, unconventional, shale gasPermalink: https://doi.org/10.1190/segam2014-1481.1FiguresReferencesRelatedDetailsCited ByReferences20 August 2020A Validation Assessment of Microseismic Monitoring1 February 2016Full waveform microseismic inversion using differential evolution algorithm SEG Technical Program Expanded Abstracts 2014ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2014 Pages: 5183 publication data© 2014 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 05 Aug 2014 CITATION INFORMATION Ben D. E.Dando*, KitChambers, and RaquelVelasco, (2014), "A robust method for determining moment tensors from surface microseismic data," SEG Technical Program Expanded Abstracts : 2261-2266. https://doi.org/10.1190/segam2014-1481.1 Plain-Language Summary Keywordsimagingmonitoringmicroseismicunconventionalshale gasPDF DownloadLoading ...

ABSTRACTWe develop and apply an imaging procedure for simultaneous location and characterization of seismic source properties called Moment Tensor Migration Imaging. The procedure constructs images for moment tensor components using a weighted diffraction stack migration, and combines ray‐theoretical Green's functions with a reverse time moment tensor imaging methodology. By applying an approximation we term the ‘ray‐angles only approximation’, we form an expression for Moment Tensor Migration Imaging where the migration weights depend only on the take‐off and arrival angles for rays leaving receiver positions and incident upon the image points. Moment Tensor Migration Imaging retains the benefits of diffraction stack procedures for source location and characterization, namely speed, flexibility, and the potential for incorporating non‐linear stacking procedures, whilst also providing the benefits of moment tensor imaging such as: the inclusion of multiple phase and multiple component data; the collapsing of the source radiation pattern; estimation of the moment tensor.We examine variations of the imaging procedure through a synthetic test. We show that although the assumptions required for the imaging and ray‐angles only approximation may not be strictly valid for realistic survey geometries, a simple weight adjustment can be used to obtain more accurate and stable results in these situations. In our synthetic example we find that the use of a P‐wave only migration without this reweighting structure produces poor results, whereby the resulting images show activity upon incorrect moment tensor components. However, many of these effects are mitigated by use of the reweighting scheme and the results are further improved through the introduction of non‐linear stacking operators such as semblance weighted stacks. The highest quality moment tensor images (for the synthetic test examined here) are obtained through the use of both P‐wave and S‐wave wave fields. This highlights the importance of multicomponent data and multiphase modelling when characterizing seismic sources. We also find that the imaged moment tensor components vary proportionately when the input velocities are perturbed by a scale factor. This suggests, for the geometry investigated here, derived source properties such as fault‐plane solutions and shear‐tensile components will not be influenced by bulk changes in seismic velocities. Finally, we show the application to a real microseismic event observed using a surface array during hydraulic fracturing. We find that the procedure collapses the seismic radiation pattern into an anomaly with a maximum at the hypocentre and our derived mechanism is consistent with the observed radiation pattern from the source.

Earthquake early warning systems must save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with great accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave.