Improved Seismic Monitoring with OBS Deployment in the Arctic: A Pilot Study from Offshore Western Svalbard

Jeddi, Zeinab; Ottemöller, Lars; Sørensen, Mathilde B.; Rezaei, Sara; Gibbons, Steven J.; Strømme, Marte Louise; Voß, Peter; Dahl‐Jensen, Trine

doi:10.1785/0220200471

Cited by 6 publications

(2 citation statements)

References 40 publications

(42 reference statements)

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“…The lower velocities at the northern end of our study area (anomaly 10) correspond to lower velocities that are detected in full waveform inversion (Rickers et al., 2013) and in delays of phase arrivals at OBS stations positioned to the west of the ridge axis (Jeddi et al., 2021). These studies resolve the velocity structure at greater depths than we do, but may both point to an area with different lithospheric architecture and properties, such as elevated temperatures or different composition.…”

Section: Discussion and Interpretationsupporting

confidence: 69%

Magmatic Activity and Dynamics of Melt Supply of Volcanic Centers of Ultraslow Spreading Ridges: Hints From Local Earthquake Tomography at the Knipovich Ridge

Meier

Schlindwein

Schmid

2022

Geochem Geophys Geosyst

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Ultraslow spreading ridges with spreading rates below 20 mm/yr exhibit different characteristics and behavior than any faster spreading ridges (Dick et al., 2003). At ultraslow pace, melt supply is not high enough to sustain a spatially continuous shallow melt reservoir underneath the rift valley and therefore volcanic activity and magmatism do not occur along the entire rift axis. Instead, they are bound to distinct regions along the ridge that receive more melt than the ridge on average does (Cannat et al., 2003;Standish et al., 2008). Those magmatic segments build volcanic centers that are widely spaced, often by several tens of kilometers (Cannat et al., 2003;Curewitz et al., 2010;Standish et al., 2008). Volcanic centers receive enough melt to build volcanic edifices in the rift valley. If the melt supply persists over time, seamount chains or a line of elevations form in spreading direction (Jokat et al., 2003;Mendel et al., 2003) (e.g., Figure 1b). The regions in between volcanic centers are dominated

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Section: Discussion and Interpretationsupporting

confidence: 69%

Magmatic Activity and Dynamics of Melt Supply of Volcanic Centers of Ultraslow Spreading Ridges: Hints From Local Earthquake Tomography at the Knipovich Ridge

Meier

Schlindwein

Schmid

2022

Geochem Geophys Geosyst

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“…For this reason, there is a large uncertainty in the location of earthquakes and their hypocentral depths, leading to an imprecise seismological image of the region (Figure 1; Bondár & Storchak, 2011). Temporary deployments of ocean bottom seismometers (OBS) allow the detection of lower magnitude earthquakes in targeted investigations along Arctic ridge boundaries (e.g., Jeddi et al., 2021; Meier et al., 2021; Schlindwein et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Local Seismicity and Sediment Deformation in the West Svalbard Margin: Implications of Neotectonics for Seafloor Seepage

Domel,

Plaza‐Faverola,

Schlindwein

et al. 2023

Geochem Geophys Geosyst

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In the Fram Strait, mid‐ocean ridge spreading is represented by the ultra‐slow system of the Molloy Ridge, the Molloy Transform Fault and the Knipovich Ridge. Sediments on oceanic and continental crust are gas charged and there are several locations with documented seafloor seepage. Sedimentary faulting shows recent stress release in the sub‐surface, but the drivers of stress change and its influence on fluid flow are not entirely understood. We present here the results of an 11‐month‐long ocean bottom seismometer survey conducted over the highly faulted sediment drift northwards from the Knipovich Ridge to monitor seismicity and infer the regional state of stress. We obtain a detailed earthquake catalog that improves the spatial resolution of mid‐ocean ridge seismicity compared with published data. Seismicity at the Molloy Transform Fault is occurring southwards from the bathymetric imprint of the fault, as supported by a seismic profile. Earthquakes in the northern termination of the Knipovich Ridge extend eastwards from the ridge valley, which together with syn‐rift faulting identified in seismic reflection data, suggests that a portion of the currently active spreading center is buried under sediments away from the bathymetric expression of the rift valley. This hints at the direct link between crustal rifting processes and faulting in shallow sediments. Two earthquakes occur close to the seepage system of the Vestnesa Ridge further north from the network. We suggest that deeper rift structures, reactivated by gravity and/or post‐glacial subsidence, may lead to accommodation of stress through shallow extensional faults, therefore impacting seepage dynamics.

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Event recognition in marine seismological data using Random Forest machine learning classifier

Domel

Schlindwein

Plaza‐Faverola

2023

Geophysical Journal International

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Summary Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogs containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalog screening and event recognition among different signals (i.e., earthquakes, short duration events (SDE) and marine noise sources). The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalog of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogs. Our final model reaches an average accuracy of 87% in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2015 and 2016 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analyzing SDE intensity, periodicity, and distance to seepage sites in relation to other published studies on SDEs.

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Improved Seismic Monitoring with OBS Deployment in the Arctic: A Pilot Study from Offshore Western Svalbard

Cited by 6 publications

References 40 publications

Magmatic Activity and Dynamics of Melt Supply of Volcanic Centers of Ultraslow Spreading Ridges: Hints From Local Earthquake Tomography at the Knipovich Ridge

Magmatic Activity and Dynamics of Melt Supply of Volcanic Centers of Ultraslow Spreading Ridges: Hints From Local Earthquake Tomography at the Knipovich Ridge

Local Seismicity and Sediment Deformation in the West Svalbard Margin: Implications of Neotectonics for Seafloor Seepage

Event recognition in marine seismological data using Random Forest machine learning classifier

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