CTBT seismic monitoring using coherent and incoherent array processing

Kværna, Tormod; Gibbons, Steven J.; Näsholm, Sven Peter

doi:10.1007/s10950-021-10026-z

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Incoherent processing approaches can to some extent mitigate limitations coming from decorrelation of higher‐frequency arrivals (Gibbons, 2014; Gibbons et al., 2008; Kvaerna et al., 2021). Recently, efforts also have been made to mitigate slowness estimation uncertainties due to, for example, array geometry and noise structure using a sensor bootstrapping approach (Ward et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Array Signal Processing on Distributed Acoustic Sensing Data: Directivity Effects in Slowness Space

et al. 2022

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This paper considers directional sensitivity aspects of distributed fiber-optic acoustic sensing (DAS) cables employed to probe seismic waves. We provide a unified framework that bridges classical array seismology with DAS array processing to be applied by experts as well as novices from these disciplines.Array-based methods have been used in seismology since the late 1950s and were adapted from radio astronomy, radar, acoustics, and sonar (Schweitzer, 2014). Seismic arrays (e.g., Rost & Thomas, 2002Schweitzer et al., 2021) have the benefit of both improving the signal-to-noise ratio (SNR) of a seismic phase onset, whilst also providing information on the direction-of-arrival for an incoming signal. Applications of seismic arrays are wide ranging and include the monitoring of nuclear explosions, analyzing volcanic tremors, determining earth structure, and more recently, monitoring of induced seismicity (Oye et al., 2021).

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Section: Introductionmentioning

confidence: 99%

Array Signal Processing on Distributed Acoustic Sensing Data: Directivity Effects in Slowness Space

et al. 2022

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“…Incoherent processing approaches can to some extent mitigate limitations coming from decorrelation of higher-frequency arrivals (Gibbons, 2014;Gibbons et al, 2008;Kvaerna et al, 2021). Recently, efforts also have been made to mitigate slowness estimation uncertainties due to, for example, array geometry and noise structure using a sensor bootstrapping approach (Ward et al, 2021).…”

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confidence: 99%

Array signal processing on distributed acoustic sensing data: directivity effects in slowness space

Näsholm

Iranpour²,

Wuestefeld³

et al. 2022

Preprint

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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. The current paper is focused on taking these differences into account. 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, e.g., 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 &#8211; 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.

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Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning

Köhler

Myklebust

Maeland

2022

Geophysical Journal International

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SUMMARY Seismic signals generated by iceberg calving can be used to monitor ice loss at tidewater glaciers with high temporal resolution and independent of visibility. We combine the Empirical Matched Field (EMF) method and machine learning using Convolutional Neural Networks (CNNs) for calving event detection at the SPITS seismic array and the single broadband station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals generated by events in a confined target region similar to single P and/or S phase templates by assessing the beam power obtained using empirical phase delays between the array stations. The false detection rate depends on threshold settings and therefore needs appropriate tuning or, alternatively, post-processing. We combine the EMF detector at the SPITS array, as well as an STA/LTA detector at the KBS station, with a post-detection classification step using CNNs. The CNN classifier uses waveforms of the three-component record at KBS as input. We apply the methodology to detect and classify calving events at tidewater glaciers close to the KBS station in the Kongsfjord region in Northwestern Svalbard. In a previous study, a simpler method was implemented to find these calving events in KBS data, and we use it as the baseline in our attempt to improve the detection and classification performance. The CNN classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples, and regional tectonic seismic events. Subsequently, we process continuous data of 6 months in 2016. We test different CNN architectures and data augmentations to deal with the limited training data set available. Targeting Kronebreen, one of the most active glaciers in the Kongsfjord region, we show that the best performing models significantly improve the baseline classifier. This result is achieved for both the STA/LTA detection at KBS followed by CNN classification, as well as EMF detection at SPITS combined with a CNN classifier at KBS, despite of SPITS being located at 100 km distance from the target glacier in contrast to KBS at 15 km distance. Our results will further increase confidence in estimates of ice loss at Kronebreen derived from seismic observations which in turn can help to better understand the impact of climate change in Svalbard.

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CTBT seismic monitoring using coherent and incoherent array processing

Cited by 7 publications

References 27 publications

Array Signal Processing on Distributed Acoustic Sensing Data: Directivity Effects in Slowness Space

Array Signal Processing on Distributed Acoustic Sensing Data: Directivity Effects in Slowness Space

Array signal processing on distributed acoustic sensing data: directivity effects in slowness space

Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning

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