The characterization of the megaregolith on the Moon has been investigated in various ways including analysis of lunar meteorites, remote sensing of mineralogy and gravity, and deriving a seismic velocity profile. In this study, we propose a method for analyzing azimuthal anisotropy of the megaregolith. We call this method deep-moonquake seismic interferometry applied to S-wave coda (DMSI-S). DMSI-S can turn the records of deep moonquakes into recordings from virtual active sources. The retrieved virtual sources coincide with the station positions, and thus, we obtain virtual zero-offset (pulse-echo) measurements. DMSI-S is applied to seven clusters of deep moonquakes recorded at the Apollo 14 landing site, resulting in virtual zero-offset measurements at the Apollo station 14. We use the S-wave recordings retrieved from DMSI-S to analyze azimuthal anisotropy. We find weak anisotropy at the layer where the megaregolith is assumed to be present. We interpret our result to show that the megaregolith at this site is characterized by a layer (or layers) of impact material, following the Imbrium impact, with internal alignment of the crushed material.

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Tomographic imaging based on ambient seismic noise measurements has shown to be a powerful tool, especially in areas like Iceland, where the microseism illumination is excellent. In this paper, we produce a 3D S-wave tomographic image over the western Reykjanes Peninsula high-enthalpy geothermal fields and evaluate the reliability of the tomographic results for different resolutions through simulated and real data. We use 30 broadband stations operating for approximately one-and-a-half year and apply ambient noise seismic interferometry for each station-pair. This results in empirical Green's functions in which especially the ballistic surface waves (BSW) are well resolved. The retrieved BSW exhibit a high signal-to-noise ratio between 0.1 and 0.5 Hz, and the beamforming analysis indicates an apparent surface-wave velocity of 3 km/s over a broad azimuthal range. For the tomographic inversion, we invert the estimated phase velocities between all station pairs to frequency-dependent phase velocity maps in four different resolutions (1, 2, 3, and 4 km) using a Tikhonov regularisation. With the estimated regularisation parameter per frequency per resolution, we invert simulated data for checkerboard sensitivity tests per frequency for different combinations of velocity anomaly sizes and resolutions.

Finally, after the inversion to depth, we detect S-wave velocity anomalies with variations between −15% and 15% with reference to an estimated average velocity using 1 km and 3 km of lateral resolutions and 1 km of vertical resolution. This study shows the potential of ambient noise tomography as complementary seismological tool for reservoir characterization.@en

Torfajökull volcano, Iceland, has not erupted since 1477. However, intense geothermal activity, deformation, and seismicity suggest a long‐lasting magmatic system. In this paper, we use ambient noise tomography to image the magmatic system beneath Torfajökull volcano. One hundred days of ambient noise data from 23 broadband seismometers show the consistent presence of double‐frequency microseism noise with significant power between ∼0.1 and 0.5 Hz. Beamforming results indicate microseism noise with persistent higher energy propagating from west and SE directions and apparent velocities below 3 km/s. We use ambient noise seismic interferometry to retrieve Rayleigh waves, and we introduce a method to estimate the reliability of the retrieved surface waves. We find stable estimation of surface wave phase velocities between 0.16 and 0.38 Hz. Azimuthal velocity variations show a trend of higher velocities in the NE/SW direction, the strike of the rift zone intersecting Torfajökull, and orientation of erupted lavas on a NE‐SW fissure swarm. Tomographic results indicate low‐velocity anomalies beneath the volcano caldera (between −5% and −10%) and even lower velocity variations in the southeast and southwest study area (below −10%), outside the volcano caldera. Low anomalies may indicate the existence of hot material, more prominent outside the caldera outskirts. High‐velocity variations (between 5% and 10%) outline the volcano caldera between 4‐ and 5‐km depth and more pronounced velocities (between 10% and 15%) up to 5‐km depth in the north of the volcano caldera. We interpret the former as possible caldera collapse structure and the latest as solidified intrusive magma from the old preferred magma paths.@en

In the Netherlands a seismic network is in place to monitor both induced and natural seismicity. Most natural seismicity occurs in the south, over an extensional tectonic regime that can be seen as an extension of the Rhine Graben. Most induced seismic activity occurs in the north of the country and is primarily related to gas extraction and reactivation of existing faults at reservoir level (Spetzler and Dost, 2017; Willacy et al., 2019). In Groningen, in the north east of the Netherlands, an especially dense network is in place. The network is operated by the Royal Netherlands Meteorological Institute (KNMI). Both event data and continuous recordings are publicly available (KNMI, 1993). In the Nineties, a seismic network has been installed to monitor seismicity from the Groningen field and a string of surrounding gas fields (Dost et al., 2017). Since 2014 this network has been expanded with a dedicated network to monitor seismicity from the Groningen field (the G-network, Figure 1), and two gas storage plants (the N- and GK- networks, Figure 1). The area has soft soil and high seismic noise conditions. As a remedy, most of the seismic sensors have been installed in boreholes - in a set-up shown on Figure 1(c). This set-up yields a seismic power reduction up till about 30 dB in the relevant bandwidth (Ruigrok and Dost, 2019). Besides induced seismicity and natural seismicity, these networks pick up arrivals from all kinds of other seismic sources: sonic booms, explosions, piling works, etc. Events that are detect-ed at multiple stations are analysed. All non-earthquake events end up at a separate list. A subset of the non-earthquake sources are the controlled explosions. These events are compiled into the Groningen explosion database. In Groningen and surroundings, KNMI has detected three types of explosions (Figure 2): 1. Most of the onshore explosions are part of seismic surveys. Buried dynamite charges are used to illuminate subsurface targets as part of seismic acquisition. In recent years, a sur-vey was done to improve the model for the unconsolidated sediments, which make up about the first 800 metres of the subsurface below Groningen. Seismic characteristics of these sediments are relevant for assessing the seismic wave amplification in the near surface, which is one ingredient of the seismic hazard model for the region (Rodriguez Marek et al., 2017; Bommer et al., 2017). 2. In the Dutch subsurface, remnants from the Second World War ordnance are still present. Some of the explosives that were released from bombers did not detonate when hitting SPECIAL TOPIC: NEAR SURFACE GEOSCIENCE the Dutch soft soil. These unexploded ordnance (UXO) are actively sought prior to construction works, if there are indications that there have been bombings in the area. Also they are found by chance, e.g., by farmers ploughing their fields. A division of the Dutch army (EOD) is mobilized whenever an UXO is found. What follows is a controlled explosion by adding an additional explosive charge. The controlled explosion is typically done at the spot where the UXO is found. When this yields potential damage, the UXO is first moved to a place with more favourable near-surface and infrastructure conditions. 3. Also on the sea bottom, a large amount of UXOs exist, in both Dutch and German territorial waters close to Groningen. For example, sea mines that were not cleaned up, torpedoes and aircraft bombs that missed their target and lodged in the sea bed and ammunition that was dumped at sea. In recent years, many offshore construction works have taken place. Electricity cables have been placed to connect the Dutch grid with the Norwegian grid (NorNed) and with the Danish grid (COBRA). Wind turbine parks have been constructed north of Groningen (e.g., Riffgat, Riffgrund and Gemini) and many new offshore wind farms are under construction or on the drawing board. Prior to all this activity on the sea bed, geophysical surveys are carried out to find UXOs (e.g., van der Baan, 2019). When found, also these UXOs are typically detonated at, or close to, the place where they are found. Figure 2 shows locations of controlled detonations. For the KNMI, these explosions are part of the ambient field. Nevertheless, they have found their way in various work flows. Their accurate location makes them suitable for different kinds of studies. We will show how the explosions are distinguished from local earthquakes. Moreover, we will exemplify the use of ‘ambient’ explosions for sensor orientation, deep crustal imaging and near-surface tomography.@en

Global phases, viz. seismic phases that travel through the Earth?s core, can be used to locally image the crust by means of seismic interferometry. This method is known as Global Phase Seismic Interferometry (GloPSI). Traditionally, GloPSI retrieves low-frequency information (up to 1 Hz). Recent studies, however, suggest that there is high-frequency signal present in the coda of strong, distant earthquakes. This research quantifies the potential of these high-frequency signals, by analysing recordings of a multitude of high-magnitude earthquakes (≥6.4M_w) and their coda on a selection of permanent USArray stations. Nearly half of the P, PKP and PKIKP phases are recorded with a signal-to-noise ratio of at least 5 dB at 3 Hz. To assess the viability of using the high-frequency signal, the second half of the paper highlights two case studies. First, a known sedimentary structure is imaged in Malargue, Argentina. Secondly, the method is used to reveal the structure of the Midcontinent Rift below the SPREE array in Minnesota, USA. Both studies demonstrate that structural information of the shallow crust (≤5 km) below the arrays can be retrieved. In particular, the interpreted thickness of the sedimentary layer below the Malargue array is in agreement with earlier studies in the same area. Being able to use global phases and direct P-phases with large epicentral distances (>80°) to recover the Earth?s sedimentary structure suggests that GloPSI can be applied in an industrial context.

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Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, these seismic interferometric responses are retrieved by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as a 'virtual source' whose response is retrieved at the other receivers. When surface waves are retrieved, the newly retrieved responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals (the coda). For all applications, however, the accuracy of the retrieved responses is paramount. In practice, this accuracy is often degraded by irregularities in the illumination pattern: correct response retrieval relies on a uniform illumination of the receivers. Reformulating the theory underlying seismic interferometry by crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of these non-uniform illumination patterns by means of a so-called point-spread function (PSF). We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just east of the Andean mountain range. The array has a T-shape, which makes it very well suited for the application of SI by MDD. We select time windows dominated by surface-wave noise traveling in a favorable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve the virtual-source responses. These time windows are selected based upon the slownesses along the two receiver lines. From the selected time windows, virtual-source responses are retrieved by computation of ensemble-averaged crosscorrelations. Similarly, ensemble-averaged crosscorrelations between the positions of the virtual sources are computed: the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface-wave responses.@en