Abstract. Geothermal systems in the Hengill volcanic area, SW Iceland, started to be exploited for electrical power and heat production since the late 1960s. Today the two largest operating geothermal power plants are located at Nesjavellir and Hellisheiði. This area is a complex tectonic and geothermal site, located at the triple junction between the Reykjanes Peninsula (RP), the Western Volcanic Zone (WVZ), and the South Iceland Seismic Zone (SISZ). The region is seismically highly active with several thousand earthquakes located yearly. The origin of such earthquakes may be either natural or anthropogenic. The analysis of microseismicity can provide useful information on natural active processes in tectonic, geothermal and volcanic environments as well as on physical mechanisms governing induced events. Here, we investigate the microseismicity occurring in Hengill area, using a very dense broadband seismic monitoring network deployed in Hellisheiði since November 2018, and apply sophisticated full-waveform based method for detection and location. Improved locations and first characterization indicate that it is possible to identify different types of microseismic clusters, which are associated with either production/injection or the tectonic setting of the geothermal area.
<p>Despite advanced seismological methods, source characterization for micro-seismic events remains challenging since current inversion and modelling of high-frequency waveforms are complex and time consuming. For a real-time application like induced-seismicity monitoring, these methods are slow for true real-time information because they require repeated evaluation of the often computationally expensive forward operation. Moreover, because of the low amplitude and high-frequency content of the recorded micro-seismic signals, routine inversion procedure can become unstable and manual parameter tuning is often required. Therefore, real-time and automatic source inversion procedures are difficult and not standard. A more promising alternative to the current inversion methods for rapid source parameter inversion is to use a deep-learning neural network model that is calibrated on a data set of past and/or possible future observations. Such data-driven model, once trained, offers the potential for rapid real-time information on seismic sources in a monitoring context.</p><p>In this study, we investigate how a supervised deep-learning model trained on a data set of synthetic seismograms can be used to rapidly invert for source parameters. The inversion is represented in compact form by a convolutional neural network which yields seismic moment tensor. In other words, a neural-network algorithm is trained to encapsulate the information about the relationship between observations and underlying point-source models. The learning-based model allows rapid inversion once seismic waveforms are available. Moreover, we find that the method is robust with respect to perturbations such as observational noise and missing data. In this study, we seek to demonstrate that this approach is viable for micro-seismicity real-time estimation of source parameters. As a demonstration test, we plan to apply the new approach to data collected at the geothermal field system in the Hengill area, Iceland, within the framework of the COSEISMIQ project funded through the EU GEOTHERMICA programme.</p>
<p>In May 2020 an earthquake with Mw 5.0 struck at ~40 km east of Tehran metropolis and ~15 km south of the Damavand stratovolcano. It was responsible for 2 casualties and 23 injured. The mainshock was preceded by a foreshock with Ml 2.9 and followed by a significant aftershock sequence, including ten events with Ml 3+. The occurrence of this event raised the question of its relation with volcanic activities and/or concern about the occurrence of larger future earthquakes in the capital of Iran. Tehran megacity is surrounded by several inner-city and adjacent active faults that correspond to high-risk seismic sources in the area. The Mosha fault with ~150 km long is one of the major active faults in central Alborz and east of Tehran. It has hosted several historical earthquakes (i.e. 1665 Mw 6.5 and 1830 Mw 7.1 earthquakes) in the vicinity of the 2020 Mw 5.0 Tehran earthquake&#8217;s hypocenter. In this study, we evaluate the seismic sequence of the Tehran earthquake and obtain the full moment tensor inversion of this event and its larger aftershocks, which is a key tool to discriminate between tectonic and volcanic earthquakes. Furthermore, we obtain a robust characterization of the finite fault model of this event applying probabilistic earthquake source inversion framework using near-field strong-motion records and broadband seismograms, with an estimation of the uncertainties of source parameters. Due to the relatively weak magnitude and deeper centroid depth (~12 km), no static surface displacement was observed in the coseismic interferograms, and modeling performed by seismic records. Focal mechanism solution from waveform inversion, with a significant double-couple component, is compatible with the orientation of the sinistral north-dipping Mosha fault at the centroid location. The finite fault model suggests that the mainshock rupture propagated towards the northwest. This directivity enhanced the peak acceleration in the direction of rupture propagation, observed in strong-motion records. The 2020 moderate magnitude earthquake with 2 casualties, highlights the necessity of high-resolution seismic monitoring in the capital of Iran, which is exposed to a risk of destructive earthquakes with more than 10 million population. Our results are important for the hazard and risk assessment, and the forthcoming earthquake early warning system development in Tehran metropolis.</p>
Since its first applications in the past decade, the use of fiber optic cables as ground motion sensors has become a central topic for seismologists, with successful applications of Distributed Acoustic Sensing (DAS) in various key fields such as seismic monitoring, structural imaging and source characterisation.The instrument response of DAS cables however is largely unknown. Instrument response is a combination of instrument design, local site effects and ground coupling, and for DAS, the latter ones are believed to have a strong, spatially variable, but yet largely unquantified effect. This limits the application of a large number of staple seismological techniques (e.g. earthquake magnitude estimation, waveform tomography) that can require accurate knowledge of a signal’s amplitude and frequency content.Here we present a method for accurately simulating a DAS cable and its response. The scheme is based on molecular dynamic-like particle-based numerical modelling, allowing the investigation of the effect of varying DAS-ground coupling scenarios. At first, we compute the full strain field directly, for each pair of neighbouring particles in the model. We then define a virtual DAS cable, embedded within the model and formed by a single string of interconnected particles. This allows us to control all aspects of the cable-ground coupling and their properties at an effective granular level through changing the bond strengths and bond types (e.g. nonlinearity) for both the cable and the surrounding medium. Arbitrary cable geometries and heterogeneous materials can be accommodated at the desired scale of investigation.We observe that at the meter scale, realistic DAS materials, cable-ground coupling and the presence of unconsolidated trench materials around it dramatically affect wave propagation, each change affecting the synthetic DAS record, with differences exceeding at times the magnitude of the recorded signal. These differences show that cable coupling and local site effects have to be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes are considered.
The 2021 Fagradalsfjall eruption in the Reykjanes peninsula, Iceland, was marked by episodes with varying volcanic activity. Our study focuses on the period from eruption start on the 19th March 2021 until the 2nd May 2021. This phase was marked by relatively continuous lava flows and non-periodic lava fountaining observed at up to 12 different vents, increasing in intensity throughout the observation period. Seismic tremor emanating from co-eruptive processes like for example lava fountaining, collapse of crater walls and magma and lava migration is non-impulsive, often with emergent onsets and no defined phase arrivals. Thus it is difficult to locate the tremor sources with traditional network based methods. We show that using small aperture arrays it is possible to locate and monitor several tremor sources that were active simultaneously, providing good spatial resolution on the details of the eruptive fissure. We investigate how array processing of 3-component data can assist with the determination of different seismic wave types and lead to a better understanding of the underlying volcanic processes. We find that seismic arrays are well suited to monitor the location, type and strength of volcanic processes that are active simultaneously. This can have important implications for volcanic hazard monitoring, especially when visual monitoring with webcams is difficult for example due to remoteness or poor visibility.
Abstract. On 3 September 2017 official channels of the Democratic People's Republic of Korea announced the successful test of a thermonuclear device. Only seconds to minutes after the alleged nuclear explosion at the Punggye-ri nuclear test site in the mountainous region in the country's northeast at 03:30:02 (UTC), hundreds of seismic stations distributed all around the globe picked up strong and distinct signals associated with an explosion. Different seismological agencies reported body wave magnitudes of well above 6.0, consequently estimating the explosive yield of the device on the order of hundreds of kT TNT equivalent. The 2017 event can therefore be assessed as being multiple times larger in energy than the two preceding North Korean events in January and September 2016. This study provides a multi-technology analysis of the 2017 North Korean event and its aftermath using a wide array of geophysical methods. Seismological investigations locate the event within the test site at a depth of approximately 0.6 km below the surface. The radiation and generation of P- and S-wave energy in the source region are significantly influenced by the topography of the Mt. Mantap massif. Inversions for the full moment tensor of the main event reveal a dominant isotropic component accompanied by significant amounts of double couple and compensated linear vector dipole terms, confirming the explosive character of the event. The analysis of the source mechanism of an aftershock that occurred around 8 min after the test in the direct vicinity suggest a cavity collapse. Measurements at seismic stations of the International Monitoring System result in a body wave magnitude of 6.2, which translates to an yield estimate of around 400 kT TNT equivalent. The explosive yield is possibly overestimated, since topography and depth phases both tend to enhance the peak amplitudes of teleseismic P waves. Interferometric synthetic aperture radar analysis using data from the ALOS-2 satellite reveal strong surface deformations in the epicenter region. Additional multispectral optical data from the Pleiades satellite show clear landslide activity at the test site. The strong surface deformations generated large acoustic pressure peaks, which were observed as infrasound signals with distinctive waveforms even at distances of 401 km. In the aftermath of the 2017 event, atmospheric traces of the fission product 133Xe were detected at various locations in the wider region. While for 133Xe measurements in September 2017, the Punggye-ri test site is disfavored as a source by means of atmospheric transport modeling, detections in October 2017 at the International Monitoring System station RN58 in Russia indicate a potential delayed leakage of 133Xe at the test site from the 2017 North Korean nuclear test.
Global earthquake locations are often associated with very large systematic travel-time residuals even for clear arrivals, especially for regional and near-regional stations in subduction zones because of their strongly heterogeneous velocity structure. Travel-time corrections can drastically reduce travel-time residuals at regional stations and, in consequence, improve the relative location accuracy. We have extended the shrinking-box source-specific station terms technique to regional and teleseismic distances and adopted the algorithm for probabilistic, nonlinear, global-search location. We evaluated the potential of the method to compute precise relative hypocentre locations on a global scale. The method has been applied to two specific test regions using existing P- and pP-phase picks. The first data set consists of 3103 events along the Chilean margin and the second one comprises 1680 earthquakes in the Tonga-Fiji subduction zone. Pick data were obtained from the GEOFON earthquake bulletin, produced using data from all available, global station networks. A set of timing corrections varying as a function of source position was calculated for each seismic station. In this way, we could correct the systematic errors introduced into the locations by the inaccuracies in the assumed velocity structure without explicitly solving for a velocity model. Residual statistics show that the median absolute deviation of the travel-time residuals is reduced by 40–60 per cent at regional distances, where the velocity anomalies are strong. Moreover, the spread of the travel-time residuals decreased by ∼20 per cent at teleseismic distances (>28°). Furthermore, strong variations in initial residuals as a function of recording distance are smoothed out in the final residuals. The relocated catalogues exhibit less scattered locations in depth and sharper images of the seismicity associated with the subducting slabs. Comparison with a high-resolution local catalogue reveals that our relocation process significantly improves the hypocentre locations compared to standard locations.
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