Ambient Noise Multimode Rayleigh and Love Wave Tomography to Determine the Shear Velocity Structure Above the Groningen Gas Field

Geophysical Journal International

Ambient noise multimode Rayleigh and Love wave tomography to determine the shear velocity structure above the Groningen gas field

Journal: Geophysical Journal International

Manuscript ID GJI-18-1113.R1

Manuscript Type: Research Paper

Date Submitted by the n/a Author:

Complete List of Authors: Chmiel, Małgorzata; Sisprobe, ; Institut des Sciences de la Terre, Mordret, Aurélien; Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences Boué, Pierre; Université Joseph Fourier, ISTerre Brenguier, Florent ; Univ. Grenoble Alpes, ISTerre Lecocq, Thomas; Royal Observatory of Belgium, Seismology - Gravimetry Courbis, Roméo; Univ. Grenoble Alpes, ISTerre Hollis, Daniel; Sisprobe Campman, Xander; Shell International Exploration and Production B.V., ; MIT, ERL Romijn, Remco; Nederlandse Aardolie Maatschappij BV Van der Veen, Wim ; Nederlandse Aardolie Maatschappij BV

Surface waves and free oscillations < SEISMOLOGY, Tomography < Keywords: GEOPHYSICAL METHODS, Seismic noise < SEISMOLOGY, Wave propagation < SEISMOLOGY, Seismic interferometry < SEISMOLOGY

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1 2 3 Ambient noise multimode Rayleigh and Love wave tomography to determine 4 5 6 the shear velocity structure above the Groningen gas field 7 8 9 10 Authors: M. Chmiel1,2, A. Mordret3, P. Boué2, F. Brenguier2, T. Lecocq4, R. Courbis1,2, 11 12 D. Hollis1, X. Campman5, R. Romijn6, W. Van der Veen6 13 14

15 16 17 Abbreviated title: Ambient noise multimode Rayleigh and Love wave tomography 18 19 20 21 1 22 Sisprobe, 24 Allée des Vulpains, 38240 Meylan, France. 23 24 2 Institut des Science de la Terre, Univ. Grenoble Alpes, CNRS UMR 5275, Grenoble 25 26 France. 27 28 3 29 Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute 30 31 of Technology, Cambridge, MA, USA. 32 33 4 Royal Observatory of Belgium, Seismology-Gravimetry, Avenue circulaire 3, B1180, 34 35 Brussels, Belgium. 36 37 5 38 Shell International Exploration and Production, Rijswijk, The Netherlands. 39 40 6 Nederlandse Aardolie Maatschappij B.V., Assen, The Netherlands. 41 42 43 44 45 Corresponding author: Małgorzata Chmiel ([email protected]) 46 47 48 49 Dates of submission of the original paper: 10/12/2018 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 SUMMARY 4 5 6 The Groningen gas field is one of the largest gas fields in Europe. The continuous 7 8 gas extraction led to an induced seismic activity in the area. In order to monitor the 9 10 seismic activity and study the gas field many permanent and temporary seismic 11 12 arrays were deployed. In particular, the extraction of the shear wave velocity model is 13 14 15 crucial in seismic hazard assessment. Local S-wave velocity-depth profiles allow the 16 17 estimation of a potential amplification due to soft sediments. 18 19 Ambient seismic noise tomography is an interesting alternative to traditional 20 21 22 methods that were used in modelling the S-wave velocity. The ambient noise-field 23 24 consists mostly of surface waves, which are sensitive to the S-wave and if inverted, 25 26 they reveal the corresponding S-wave structures. 27 28 29 In this study, we present results of a depth inversion of surface waves obtained 30 31 from cross-correlation of 1 month of ambient noise data from four flexible networks 32 33 located in Groningen area. Each block consisted of 400 3C-stations. We compute 34 35 group velocity maps of Rayleigh and Love waves using a straight-ray surface wave 36 37 38 tomography. We also extract clear higher modes of Love and Rayleigh waves. 39 40 The S-wave velocity model is obtained with a joint inversion of Love and Rayleigh 41 42 waves using the Neighbourhood Algorithm. In order to improve the depth inversion, 43 44 45 we use the mean phase velocity curves and the higher modes of Rayleigh and Love 46 47 waves. Moreover, we use the depth of the base of the North Sea formation as a hard 48 49 constraint. This information provides an additional constraint for depth inversion, 50 51 52 which reduces the S-wave velocity uncertainties. 53 54 The final S-wave velocity models reflect the geological structures up to 1 km depth 55 56 and in perspective can be used in seismic risk modelling. 57 58 59 60

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1 2 3 1 INTRODUCTION 4 5 6 For many years it has been shown that seismic noise carries information about the 7 8 medium and can be used to image the Earth’s interior. Studying seismic noise 9 10 emerged with pioneering work of Aki (1967). 11 12 Ambient seismic noise consists of continuous vibrations in the Earth at different 13 14 15 frequencies in response to natural processes such as ocean waves and 16 17 anthropogenic activities such as road traffic. Ambient noise is not flat in the frequency 18 19 domain, but it peaks near the primary (around 15 s period) and secondary (around 20 21 22 7.5 s period) microseisms and rises at very long periods above 50 s to form a signal 23 24 now referred to as Earth ‘hum’ (Ekström, 2001, Rhie & Romanowicz 2004). 25 26 At microseismic periods (5-20) s seismic noise is mostly generated by ocean 27 28 29 waves and dominated by fundamental-mode Rayleigh and Love waves (Webb, 30 31 1998). However, at shorter periods the noise wavefield becomes more complex, with 32 33 higher modes of surface waves and body waves mixing with fundamental modes 34 35 (Bonnefoy-Claudet et al., 2006) 36 37 38 Numerous theoretical studies have proved that the cross-correlation of diffuse 39 40 wavefields (e.g. ambient noise, scattered coda waves) converges towards an 41 42 estimate of the Green function between the stations (e.g. Weaver & Lobkis 2001, 43 44 45 2004; Derode et al. 2003; Snieder 2004; Wapenaar 2004). In 2004, Campillo and 46 47 Shapiro demonstrated a practical study showing the possibility of correlating 48 49 continuous noise recordings to image the Earth’s interior. 50 51 52 Noise cross-correlation method has proved useful in imaging the subsurface at 53 54 local (e.g., Lin et al., 2013, Nakata et al., 2015) and crustal scales (Shapiro et al., 55 56 2005, Yang et al., 2007, Lin et al., 2008). Moreover, this method finds applications 57 58 also in monitoring volcanoes (Sens-Schönfelder and Wegler, 2006; Duputel et al., 59 60

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1 2 3 2009; Brenguier et al., 2011), landslides (Mainsant et al., 2013), underground mines 4 5 6 (Olivier et al., 2015), geothermal reservoirs (Obermann et al., 2015), and 7 8 hydrocarbon reservoirs (Mordret et al., 2013, Chmiel et al., 2016). 9 10 Surface waves are strongly present in the ambient noise recorded at the surface. 11 12 Dispersion of Rayleigh and Love waves can be used to obtain a S-wave velocity 13 14 15 model, which is essential for different near-surface applications, such as calculating 16 17 the statics for seismic reflection processing (Mari, 1984), or creating filters to remove 18 19 ground roll from seismic reflection trace (Halliday et al., 2010). Moreover, the S-wave 20 21 22 velocity model plays an important role in estimating seismic risk and site effects 23 24 related to the amplification of the S-waves (Boué et al., 2016). 25 26 In particular, the soft soil responds nonlinearly to the S-wave excitation and 27 28 29 develops an amplification of the S-wave (Singh et al., 1988, Beresnev et al., 1995). 30 31 The shallow sediments or soils can cause amplification and resonances when 32 33 seismic waves propagate near vertically up to the surface (e.g., Borcherdt and Gibbs, 34 35 1976). The S-wave velocity models are commonly used in site characterization for 36 37 38 earthquake engineering purposes, for example in the Groningen area (Bommer et al., 39 40 2017; Rodriguez-Marek et al., 2017). 41 42 The Groningen gas field is one of the largest gas fields in Europe. The long-term 43 44 45 gas extraction resulted in a reservoir compaction at depth, and the consequent 46 47 subsidence results in induced seismic activity (van Thienen-Visser and Breunese, 48 49 2015). Even though the magnitude of the earthquakes is quite modest (local 50 51 52 magnitudes reported up to ML = 3.6), some of them have strong influence on the 53 54 surface. It might be related to the complex geological structured of the Groningen 55 56 field, including the presence of shallow sediment that might amplify the wave motion 57 58 (Paap et al., 2018). 59 60

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1 2 3 Recent studies focused on the wave propagation at the Groningen production site 4 5 6 in order to assess a potential increase in peak ground acceleration (e.g., Kruiver et 7 8 al., 2017, Spica et al., 2017, Spica et al., 2018, Paap et al., 2018). 9 10 In order to quantify the seismic hazard in the Groningen field, a continuous and 11 12 temporary data acquisition is performed in the area. In particular, a set of temporary 13 14 15 nodal seismic arrays were deployed in the area by the Nederlandse Aardolie 16 17 Maatschappij (NAM). In this study, we present the results of surface wave inversion 18 19 to obtain a local S-wave velocity model up to 1000 m depth. We analyse four blocks 20 21 22 of around 400 3-components stations, which recorded continuously for one month 23 24 each. Each block is processed separately, using the same scheme: we first analyse 25 26 the quality of the recorded data and perform a beamforming analysis. Then, the 27 28 29 cross-correlation functions between all stations using all three components are 30 31 determined to produce nine correlation tensors (ZZ, ZR, ZT, RZ, RR, RT, TZ, TR, 32 33 and TT). Next, we perform both Love wave and Rayleigh wave tomography. 34 35 Furthermore, we extract mean phase dispersion curves of the fundamental modes 36 37 38 and higher modes of surface waves. Finally, we perform a joint depth inversion of 39 40 Rayleigh and Love waves with a geological constrain of the inversion (the base of the 41 42 North Sea formation). The S-wave velocity models in the area of Loppersum, 43 44 45 Borgsweer, Scheemda and Nieuw-Scheemda are presented, and finally compared to 46 47 existing S-wave velocity models of the Groningen field. 48 49 50 51 52 2 AMBIENT NOISE RECORD QUALITY 53 54 Four flexible VS800 (aiming at obtaining the S-wave velocity model for the first 800 55 56 m depth) networks were deployed in the Groningen field, in the northern part of the 57 58 Netherlands (Figure 1): Loppersum (presented in purple), Borgsweer (presented in 59 60

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1 2 3 blue), Nieuw-Scheemda (presented in pink) and Scheemda (presented in green). 4 5 6 Each data-block consists of around 400 3-C stations with 5 Hz geophones recording 7 8 ambient seismic noise for one month in 2016 (October-November, Loppersum), and 9 10 2017 (December 2016/January 2017, Borgsweer; April, Scheemda; November, 11 12 Nieuw-Scheemda). The nominal distance between the sensors is ~350m. The inset 13 14 15 of Figure 1 shows the geographical position of the Groningen field. In the following, 16 17 we present the processing workflow using the data from the Nieuw-Scheemda block. 18 19 The three remaining blocks were processed in the same way. 20 21 22 We first perform the quality check of the data and the analysis of the level of the 23 24 recorded seismic noise. The level of recorded ambient seismic noise is quite high 25 26 (Figure 2), similarly to other three blocks. 27 28 29 Figure 2 shows hourly maps (between 16:00h-17:00h, local time) of median noise 30 31 amplitude calculated over the time period 2017-04-01-2017-05-01 for low 32 33 frequencies: (1-10) s (a) and high-frequencies: (3-20) Hz (b). Stations 16.014 and 34 35 19.026 are indicated with red arrows. Stations located in the proximity of the roads 36 37 38 show higher noise levels (e.g., station 19.026). The high-frequency maps show bright 39 40 spots of high amplitude which aligned with the road position in the area. The low- 41 42 frequency maps show smaller variations in noise amplitude than the high-frequency 43 44 45 maps. 46 47 Spectrograms of Z component show the observed vertical seismic noise on raw 48 49 records throughout the seismic frequency band, and they are presented on Figure 2. 50 51 52 Both stations show high level of noise, with the values similar to the high-noise 53 54 models of Peterson, 1993, although station, 16.014 (Figure 2c) shows lower level of 55 56 noise and station 19.026 shows higher level of noise (Figure 2d) due to their 57 58 geographic location. Figure S1, in supplementary material, shows the probabilistic 59 60

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1 2 3 power spectral density for the station 16.014 (Figure S1a) and the station 19.026 4 5 6 (Figure S1b). It is interesting to notice that after the removal of the instrumental 7 8 response, we manage to retrieve the signal down to 7 s period, even for the data 9 10 recorded with 5 Hz nodes. 11 12 13 14 15 2.1 Ambient noise beamforming MBIENT NOISE BEAMFORMING 16 17 Next, we calculate a daily beamforming analysis by using the Z component of the 18 19 recorded noise wavefield to reveal the distribution and the dominant direction of the 20 21 22 noise source. The frequency-wave number analysis (FK analysis, Rost et al., 2002) 23 24 allows calculation of the power of the wavefield distributed among different apparent 25 26 slownesses and back azimuths. 27 28 29 The beamforming analysis computed on the vertical component (Figure 3) of the 30 31 Nieuw-Scheemda dataset shows a very powerful source of noise coming from the 32 33 North and the North-West with waves travelling with phase velocities from 500 m/s to 34 35 1 km/s or even higher at all frequency bands. Figure 3 also demonstrates the 36 37 38 complexity of the Rayleigh waves in the area. The fundamental model of Rayleigh 39 40 waves is visible at long periods (3-6) s, although as the periods decrease higher 41 42 modes of Rayleigh waves start to appear (2-4) s and their energy dominate the 43 44 45 fundamental mode (0.75-3) s. 46 47 The Groningen field lies in the Southern Permian Basin (Whaley, 2009, de Jager 48 49 and Visser, 2017). The presence of higher-order modes is common in basins (e.g., 50 51 52 Boué et al, 2016), and the higher modes are generated in the presence of a velocity 53 54 inversion in the vertically stratified media, e.g., the upper layer is low enough to 55 56 cause a complete reflection of both the P-waves and the SV-waves (Heaton 2005). If 57 58 properly identified, these overtones provide important information on shallow 59 60

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1 2 3 structures and on the uniqueness of the inverted models (van Heijst and Woodhouse 4 5 6 1997, Chmiel et al., 2018, Spica et al., 2018, Tomar et al., 2018). However, it might 7 8 be difficult to extract the higher modes of surface waves, due to their interference, 9 10 simultaneous arrivals, and overlap in the frequency domain. During our analysis of 11 12 the four data-blocks we observed higher modes of Rayleigh and Love waves at each 13 14 15 block, as did Spica et al., 2018 in the Loppersum area. We discuss the overtones 16 17 extraction in the following sections. 18 19 20 21 22 3 NOISE CROSS-CORRELATIONS 23 24 The pre-processing steps of obtaining noise-correlation functions and to retrieve 25 26 the Green’s function have become a common procedure in the recent year (e.g., 27 28 29 Bensen et al., 2007, Lecocq et al., 2014). Continuous data are first downsampled to 30 31 25Hz and then split in to 15 minute segments. Each segment is then spectrally 32 33 whitened to balance the contributions of all the different sources and to reduce the 34 35 influence of monochromatic sources. Moreover, each segment with strong energetic 36 37 38 patterns (i.e. standard deviation higher than 5 times the standard deviation of the 39 40 daily trace) is discarded. 41 42 The data sections are then cross-correlated with for all the stations pairs. For 403 43 44 45 stations, there are 81003 station pairs. Finally, for each station pair all the 15 minutes 46 47 cross-correlation are stacked to produce an estimate of the seismic the noise 48 49 correlation functions (turning all sensor into a virtual source). 50 51 52 The cross-correlation functions contain information at positive and negative lag- 53 54 times. If the conditions of the noise wavefield are ideal, these signals will be identical 55 56 due to reciprocity. In the following, we work with the symmetrized noise correlation 57 58 functions, the mean of the negative and positive lag-times. 59 60

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1 2 3 4 5 6 4 AVERAGE PHASE VELOCITY DISPERSION CURVES 7 8 Figure 4 presents stacked sections of noise cross-correlations filtered in the 9 10 frequency band (0.5-5) Hz for ZZ (a), RR (b) and TT(c) components. These average 11 12 seismic sections are constructed by binning the correlations in fixed distance 13 14 15 intervals (every 50 m). This procedure is commonly used in order to reconstruct and 16 17 enhance body waves from correlations (Boué et al., 2013, Nakata et al., 2015). 18 19 However, this allows us to also explore the multi-modal nature of the surface waves 20 21 22 in the Groningen area and to have a better understanding of the general surface 23 24 wave propagation in the basin. Doing so, we assume that the underlying velocity 25 26 model is mostly 1D. By stacking the large amount of data in each distance bin, the 27 28 29 signal-to-noise ratio (SNR) is highly increased, which permits the extraction of the 30 31 fundamental mode average propagation characteristics as well as several overtones 32 33 (when present). 34 35 The clear Love wave travels on average with a very slow group velocity of about 36 37 38 300 m/s (Figure 4c). The Rayleigh (Figure 4a and 4b) wave propagation is more 39 40 complex and shows a high-frequency branch travelling at about 600 m/s and a lower 41 42 frequency branch travelling on average at 350 m/s. 43 44 45 Next, we perform a frequency-wavenumber (FK) analysis of the stacked sections, 46 47 to accurately pick phase velocity dispersion curves for the different modes and the 48 49 different components (Figure 5c). In order to clean up the FK plot and to enhance the 50 51 52 maximum of the FK plot, we use multiscale vessel enhancement filtering (Frangi et 53 54 al., 1998). The dispersion curves are pointed semi-automatically by specifying the 55 56 control points. 57 58 59 60

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1 2 3 These 1D-averaged phase velocity dispersion curves are presented on 5d 4 5 6 (Rayleigh and Love waves). Note that this approach allows a simultaneous 7 8 measurement of the mean dispersion curves for the fundamental and the 1st higher 9 10 mode for the Rayleigh waves. The mean group velocity dispersion curves can be 11 12 calculated as a derivate of the mean phase velocity dispersion curves. We verified 13 14 15 that the mean group velocity dispersion curves of calculated from FK plots are 16 17 coherent with the mean group velocity dispersion curves from frequency time 18 19 analysis (FTAN) picking. The FTAN measurements of the group velocity are 20 21 22 described in the following section. 23 24 25 26 5 RAYLEIGH AND LOVE GROUP VELOCITY DISPERSION CURVE PICKING 27 28 29 AND QUALITY CONTROL 30 31 We pick the group velocity dispersion curves for the Love and Rayleigh waves 32 33 automatically by using the FTAN (Dziewonski et al., 1969) algorithm. 34 35 The Love wave group velocity dispersion curves for the 81 003 possible TT 36 37 38 correlations have been automatically picked using the FTAN algorithm. We then 39 40 rejected all the dispersion curves for station pairs separated by less than 1 km to 41 42 avoid larger uncertainty measurements due to near source effects. Finally, after 43 44 45 computing the statistics of all remaining dispersion curves we rejected only the 46 47 individual points of the dispersion curves larger (or smaller) than the most probable 48 49 velocity value (called mode) ± 50 m/s. In Figure 5a, the mode value is shown by the 50 51 52 blue curve and the 50 m/s limits by the dashed white curves. The background of the 53 54 figure shows the probability density function of the Love wave group velocity 55 56 dispersion curves, after the quality control. The extraction was done automatically 57 58 between 0.5 s and 4.5 s period. 59 60

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1 2 3 An adequate approach has been applied to ZZ correlations to measure the 4 5 6 fundamental mode group velocity dispersion curves. The extraction was done 7 8 automatically between 1.1 s and 2.9 s of period and 0.2 to 0.5 km/s group velocity. 9 10 We then rejected all the dispersion curves for station pairs separated by less than 2 11 12 km to avoid larger uncertainty measurements due to near source effects and mixing 13 14 15 of different modes. In Figure 5a, the mode value is shown by the blue curve and the 16 17 50 m/s limits by the dashed white curves. Similarly, to the Love waves, the 18 19 background of the figure shows the probability density function of the Rayleigh wave 20 21 22 fundamental mode group velocity dispersion curves and after the quality control. 23 24 We verified the number of dispersion curves left at each period which is used for 25 26 the surface wave tomography. It goes from 60 000 dispersion curves between at 3.5 27 28 29 s period to about 1000 measurements at 4 s and 30 000 measurements at 0.5 s for 30 31 Love waves. For Rayleigh waves, the number of dispersion curves left at each period 32 33 and used for the tomography goes up to measured 3000 dispersion curves for the 34 35 periods < 3 s. The number of pairs is limited due to the complicated multi-modal 36 37 38 nature of Rayleigh waves in the area and complicated shape of the dispersion 39 40 relation. 41 42 The azimuthal distribution of the paths at each inverted period mostly shows the 43 44 45 azimuths around North-West are dominating (similarly to the results from the 46 47 beamforming analysis), but there are still quite a large number of paths in the East- 48 49 West direction preventing smearing of the seismic anomalies in the tomography. This 50 51 52 trend is related to both the noise directivity and the geometry of the array. 53 54 55 56 6 SURFACE WAVE TOMOGRAPHY 57 58 59 60

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1 2 3 For each period (0.5 to 4.4 s with 0.1 s steps for Love waves, and 1.1 to 2.9 s with 4 5 6 0.1 s step for Rayleigh waves), the dispersion curves are inverted and regionalized 7 8 into a regular grid of 0.005° by 0.0025° rectangular cells (in longitude and latitude 9 10 directions) using Mordret et al. (2013) approach. Figure 6 shows the results the 11 12 Rayleigh wave group velocity maps; the velocities are in km/s. Figure 7 shows the 13 14 15 results for the Love wave group velocities. 16 17 The density of rays in each cell of the model at each period is of more than 800 18 19 rays per cell for Love waves, which permits to achieve a nominal lateral resolution of 20 21 22 about 1000 m for all periods for Love waves. The density of rays for Rayleigh waves 23 24 tomography is lower (200 rays per cell). However, this is still sufficient to ensure an 25 26 average lateral resolution of about 1000 m for most of the area with a minimum 27 28 29 resolution of about 1500 m in the South-West part of the Nieuw-Scheemda area. 30 31 Moreover, maps for Love and Rayleigh wave show a similar structure at depth. In 32 33 particular, the maps for Love waves at 2.1 s and Rayleigh waves at 2.5 s show a 34 35 clear structure from the South to the North, which possibly corresponds to a 36 37 38 paleochannel. 39 40 41 42 7 DEPTH INVERSION AND ESTIMATION OF THE MODEL ROBUSTNESS 43 44 45 The full set of group velocity maps describes local dispersion curves at each 46 47 single cell of the map. These local dispersion curves can be inverted at depth to 48 49 obtain local 1D Vs profiles. The ensemble of every best local 1D model will constitute 50 51 52 of the final 3D Vs model. We jointly inverted both Love and Rayleigh dispersion 53 54 curves for robustness. Moreover, we used the mean dispersion curves to complete 55 56 and improve the inversion at depth. In total, we invert 5 dispersion curves for each 57 58 single cell. These curves are defined in the Table 1: 59 60

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1 2 3 Periods 4 Wave type Velocity Mode Measurement 5 6 (s) 7 8 Love Phase Fundamental 0.8-3.5 FK 9 10 Love Group Fundamental 0.6-4.2 FTAN 11 12 13 Rayleigh Phase Fundamental 0.8-2.9 FK 14 15 Rayleigh Phase 1st Higher 0.7-2.9 FK 16 17 1.1-2.9 FTAN 18 Rayleigh Group Fundamental 19 20 0.8-1.0 FK 21 22 23 24 Table 1: Dispersion curves used in depth inversion for each point of the grid. 25 26 27 28 29 The use of phase velocities for both Love and Rayleigh waves and the 1st higher 30 31 mode of Rayleigh waves in the inversion improves the resolution at depth and lower 32 33 the uncertainties. Figure S2, in supplementary material, shows sensitivity kernels for 34 35 36 group velocities of fundamental mode of Rayleigh and Love waves, Figure S3 shows 37 38 sensitivity kernels of phase velocities of fundamental mode of Rayleigh and Love 39 40 waves together with the 1st higher mode for Rayleigh waves. One can see that 41 42 43 Rayleigh waves provide the information about the deeper parts of the model, in 44 45 particular the velocity anomaly at ~ 500 m. Moreover the 1st higher mode of Rayleigh 46 47 waves adds additional constraint for shallow layers of the model. 48 49 50 For each dispersion curve presented in Table 1, we also define error bars. The 51 52 error bars of the mean dispersion curves calculated with FK analysis are twice as 53 54 high as the error bars calculated with FTAN analysis (FK: +/- 100 m/s and FTAN: +/- 55 56 50 m/s). This equalizes the contribution of the mean dispersion curves in the 57 58 59 inversion. 60

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1 2 3 The depth inversion of surface waves constrains sufficiently the velocity 4 5 6 anomalies, although the depth and the topography of different horizons are less well 7 8 constrained, due to an assumption of the 1D velocity model in the surface wave 9 10 tomography. This might cause an issue in geologically complex areas, such as the 11 12 Groningen field, where the structure at depth can be inclined. 13 14 15 In order to constrain better the depth inversion of the surface waves, NAM 16 17 provided us with a reference of the rock horizon: base of the North Sea (NSB) or Top 18 19 Chalk formation, which adds an additional constrain to our depth inversion workflow. 20 21 22 The NSB horizon was imaged with an active reflection seismic study that was 23 24 performed in the area Groningen area (e.g., Kuriver, 2017). 25 26 First, we extracted the depth of the NSB for the analysed areas. Next, the depth of 27 28 29 the NSB was interpolated onto the grid used in depth inversion. The topography of 30 31 the depth of the NSB is quite flat for the Loppersum and the Nieuw-Scheemda area, 32 33 although in the Borgsweer area the NSB has a strong topography, showing depth 34 35 variations between 550-850 m, and in the Scheemda area NSB also varies very 36 37 38 strongly, between 400 m and 850 m. 39 40 This kind of strong topography occurring at large depths, is hard to resolve with 41 42 the surface wave inversion, without using any a priori information. We also performed 43 44 45 the depth inversion without adding the NSB constrain, and the resulting models 46 47 shown flat structures at depth, which can be expected with the surface wave 48 49 inversion, due to the 1D assumption that is used in the group velocity tomography. 50 51 52 We used a Monte-Carlo approach to invert the dispersion curves at depth because 53 54 of the strong non-linearity of the problem and the absence of accurate a priori starting 55 56 velocity model. This Monte-Carlo approach is based on a Neighbourhood Algorithm 57 58 and is described in detail by Mordret et al. (2014). The depth inversion looks for the 59 60

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1 2 3 optimum parameters at each point of the geographical grid that minimizes the 4 5 6 difference between the synthetic dispersion curves and the observed ones. The 1D 7 8 model is discretized with 32 homogeneous layers with constant thicknesses. During 9 10 the inversion, the P-wave velocity is scaled to Vs using the empirical relationships of 11 12 Kruiver et al. (2017) and the density is scaled to Vp using the empirical relationship of 13 14 15 Brocher (2005). 16 17 The general 1D velocity profile is parameterized by a power-law profile backbone 18 19 modified by a linear combination of 6 cubic splines for the North Sea. The base of the 20 21 22 North Sea formation has a varying depth of +/- 5 m from the provided NSB depth, 23 24 and has a variable velocity. The top of the Cretaceous has a varying depth and 25 26 velocity and the velocity profile within the top Cretaceous follows a varying gradient. 27 28 29 In total, we invert for 11 parameters: the 6 cubic splines amplitudes and the five 30 31 previously described parameters. The fifth spline is allowed to have larger amplitude 32 33 to potentially take into account the presence of the Brussel sands layer (a layer of 34 35 faster S-wave velocity, e.g., Kuriver et al., 2017). All four data blocks are 36 37 38 parametrized in a uniform way. 39 40 We sampled a total of 5201 models at each geographical point of our grid. The 41 42 final model is the average of the 300 best models (with the lowest misfits). This 43 44 45 Monte-Carlo technique also allows us to estimate uncertainties for our model as the 46 47 standard deviation of the distribution of the 300 best velocity models at each depth. 48 49 Even if the regionalization phase of the tomography (the group velocity maps 50 51 52 computation) involves some lateral smoothing and imposes a minimum correlation 53 54 length in the maps, the shear-wave versus depth inversion is performed at each pixel 55 56 independently. Therefore, by using our Monte-Carlo approach for the inversion, we 57 58 lose the correlation information between neighbouring cells. To overcome this, we 59 60

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1 2 3 post-process the velocity model by smoothing each layer by a Gaussian kernel the 4 5 6 same size as the one used during the group velocity maps computation. This 7 8 operation induces a damping of the velocity anomaly amplitudes. To correct for that, 9 10 we measured the standard deviation of the velocity anomalies before and after 11 12 smoothing and we scale the smooth velocities so that their new standard deviation 13 14 15 equates the one of the non-smoothed velocities. The following sections show the 16 17 final scaled model. 18 19 Figure 8a shows the resulting S-wave velocity map for the Nieuw-Scheemda data 20 21 22 block (horizontal slice taken at 60 m depth). Figure 8 b-g shows the results of the 23 24 inversion at depth for one point of the grid (white star on Figure 8a). For each plot, 25 26 300 best velocity models are represented in the shade of gray, the best model is 27 28 29 represented in color. In Figure 8b-f the error bars are presented for the dispersion 30 31 curves and in Figure 8g – the final S-wave velocity model is presented in red. We 32 33 observe that our model achieves a good fit for the five dispersion curves. 34 35 The misfit between the measured dispersion curve and the theoretical one is the 36 37 38 area of the theoretical dispersion curve outside the area defined by the measured 39 40 dispersion curve and its uncertainties, normalized by the area of the measured 41 42 dispersion curve. The details can be found in Mordret et al., 2014. 43 44 45 Figure 9 presents vertical profiles through the 3D S-wave model in the Nieuw- 46 47 Scheemda area. The location of the profiles is shown in Figure 8a (the black lines). 48 49 The S-wave velocity model shows an increase of velocity with the depth down to 400 50 51 52 m depth. Below 400 m, the inversion exhibits a high velocity anomaly between 500 53 54 and 700 m followed by a low velocity anomaly below 700 m. This anomaly can be 55 56 identified as, previously mentioned, high velocity layer of Brussel sands. Moreover, 57 58 59 60

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1 2 3 we observe a strong velocity contrast at around 800 m depth related to the presence 4 5 6 of the bedrock. 7 8 The a priori information about NSB helps to constrain better the depth inversion. 9 10 The use of NSB depth in inversion strongly improves the resolution and lower the 11 12 uncertainties of the final model. The velocity uncertainties (calculated as the standard 13 14 15 deviation of the best 300 models) of the final model are on the order of 20 m/s above 16 17 the Brussel sands layer, increasing from surface to depth. The uncertainty is slightly 18 19 higher in the layer related to the fast velocity layer of the Brussel Sands (Figure S5). 20 21 22 23 24 8 DISCUSSION AND COMPARISION WITH EXISITNG MODELS 25 26 We now compare our results with an integrated shear-wave velocity model for the 27 28 29 Groningen gas field from Kruiver et al. (2017), referred in the following as “Deltares- 30 31 NAM”. Their Vs model was obtained by integrating high-resolution geological model 32 33 (first 50 m), field measurements from seismic reflection study and Modal Elastic 34 35 Inversion (from 50 m to 120 m) and sonic logging (the deepest parts). 36 37 38 Figure 10 shows the comparison between depth slices through the Vs models 39 40 from Kruiver et al. (2017) with positioned depth slices (at a depth of 65 m) of 41 42 Loppersum, Borgsweer, Scheemda and Nieuw-Scheemda. Both models are 43 44 45 represented in the same colorscale, but slightly different colormaps (see presented 46 47 colorbars on Figure 10). The models show similar patterns of low and high Vs 48 49 values, that reveals geological structures (such as paleochannel). 50 51 52 One can see that ambient-noise imaging completes the Vs map from Kuriver et al. 53 54 (2017) in the areas of Borgsweer and Scheemda, where the active-seismic survey 55 56 was not available or the Modal Elastic Inversion did not perform well due to the high 57 58 ambient noise levels. Contrary to this active seismic survey, by using the ANSWT we 59 60

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1 2 3 are not limited by the source frequency range and we are able to retrieve deeper 4 5 6 layers of the area with surface waves propagating at frequencies lower than 1 Hz. 7 8 Moreover, the dense acquisition allows us to use high-frequency surface wave 9 10 propagation and in consequence to obtain the shallow layers of the Vs model. 11 12 However, we also observe some discrepancies between the two models. In 13 14 15 particular, in the eastern corner of the Scheemda data-block, where the topography 16 17 of NSB becomes strong, we observe an artefact in the S-wave model (Figure S4). 18 19 This might be related also to a smaller number of dispersion measurements at the 20 21 22 edge of the Scheemda data block. 23 24 We now compare the 1D average velocity models from all the studied blocks, the 25 26 “Deltares-Nam” average Vs model (from Kruiver et al., 2017), and the sonic log Vs 27 28 29 model provided by NAM for Loppersum and Borgsweer area. It is fair to make this 30 31 comparison because on the first order, the velocity structures are mainly horizontal 32 33 with small lateral variations (Figure 11 and Figure 12). Figure 11 shows the 34 35 comparison between the 1D average velocity models from the depth inversion in all 36 37 38 four blocks. Figure 12 shows the comparison between our 1D average velocity 39 40 models and the “Delaters-Nam” average model for the Loppersum (Figure 12a), 41 42 Borgsweer (Fibure 12b), Scheemda (Fibure 12c) and (Figure 12d) Nieuw-Scheemda 43 44 45 arrays. Our model is presented in colour together with its uncertainty (in grey), Nam- 46 47 Deltares model is presented in black, and the sonic-log models are presented in red. 48 49 The near surface between 50 and 200 m depth is similar for Loppersum, 50 51 52 Borgsweer and Nieuw-Scheemda models, and slightly faster (up to 50 m/s) than in 53 54 Scheemda. Deeper, between 400 m and 700 m depth, all the areas exhibit high 55 56 velocities due to the Brussel sands. However, the models from Scheemda, 57 58 Borgsweern Nieuw-Scheemda show higher velocities than the model from 59 60

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1 2 3 Loppersum (up to 60 m/s). All the models show the strong velocity contrast between 4 5 6 700 m and 800 m depth. For the deep layers, Borgsweer model shows significantly 7 8 higher velocities, although note that this part of the model has higher uncertainties. 9 10 These four models are rather consistent with the 1D Vs models from Deltares- 11 12 NAM and from the sonic log provided by NAM. The 1D Vs model from the sonic log 13 14 15 shows stronger velocity variation (in particular the layer of Brussel sands is shallower 16 17 and has higher velocities than our 1D models). However, the sonic log is a local 18 19 measurement, our 1D Vs models are averaged over the whole area. 20 21 22 The discrepancy between the Deltares-NAM models and our models can be 23 24 spotted for the shallow subsurface. 25 26 Deltares-NAM model reveals a low S-wave velocity (Holocene) layer, while our 27 28 29 models show higher velocities in the near-surface. It might be related to the limited 30 31 dispersion measurements in the frequency domain, the parametrization of our depth 32 33 inversion, and the geometry of the acquisition which limits the measurements of short 34 35 wavelengths. Also, the discrepancy between the two models grows stronger with 36 37 38 depth, which might be related to a lower sensitivity of surface waves to the deeper 39 40 structures. Moreover, our model consequently shows lower depths of the Brussel 41 42 sand layer, although the velocities of the Brussel sand layer are in agreement for 43 44 45 both models. Finally, the difference between the Deltares model and our 1D average 46 47 model is within our model’s uncertainty range (in grey). Both models show the same 48 49 depth of the North Sea base at depth 700-800 m, due to the use of a hard constraint 50 51 52 in our depth inversion. Finally, the Deltares-NAM model and our VS model show 53 54 fairly similar crucial features of the model. 55 56 Other works also focused on the use of the ambient noise in the Groningen 57 58 field area. In particular, Spica et al. (2018) used a joint inversion surface waves and 59 60

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1 2 3 H/V spectral ratio using the Loppersum passive seismic data. Our model shows 4 5 6 similar characteristics to Spica’s et al. (2018) average 1D velocity model, especially 7 8 in the first ∼500 m. However, their results did not retrieve the Brussel’s sands layer 9 10 due to the weak sensitivity of surface waves at these depths. In this study, we 11 12 managed to overcome this limitation due to an accurate depth inversion 13 14 15 parametrization. 16 17 18 19 9 CONCLUSIONS 20 21 22 We presented results of a depth inversion of surface waves obtained from cross- 23 24 correlation of 1 month of ambient noise data from four flexible networks located in 25 26 Groningen area. Using ANSWT and a joint inversion of Love and Rayleigh waves, we 27 28 29 computed high-resolution S-wave velocity models for Loppersumm, Borgsweer, 30 31 Nieuw-Scheemda and Scheemda area in the Groningen gas field province. Our 32 33 results prove that with an appropriate processing and parameterization, the surface 34 35 waves depth inversion can provide detailed S-wave models up to 1 km depth. 36 37 38 The use of phase velocities for both Love and Rayleigh waves, and the 1st higher 39 40 mode of Rayleigh waves in the inversion lowers the degree of nonuniqueness of 41 42 depth inversion. Moreover, we were able to image the layer of Brussel sand due to 43 44 45 an appropriate parameterization of the depth inversion. The use of NSB as a hard 46 47 constraint strongly improves the resolution at depth and lowers the uncertainties of 48 49 the final S-wave model. These improvements allow us to obtain the S-wave velocity 50 51 52 models that reflect local geological structures. Final results of the passive survey in 53 54 the Groningen field area are consistent and complementary with the existing S-wave 55 56 models. 57 58 59 60

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1 2 3 Our models reveal low and high Vs patterns up to a depth of 1 km and they could 4 5 6 be used to model seismic ground motion in order to mitigate seismic risks on the 7 8 Groningen local population. ANSWT is an interesting alternative to existing methods 9 10 that are used in modelling shear-wave velocity. 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 ACKNOWLEDGMENTS 4 5 6 The data were provided by NAM (Nederlandse Aardolie Maatschappij). We thank P. 7 8 Kruiver from Deltares for providing us with their velocity models. We acknowledge P. 9 10 Roux, and M. Campillo for useful discussions. The authors thank NAM and Shell for 11 12 permission to publish. This project received funding from the Shell Game Changer 13 14 15 project HiProbe. We also acknowledge support from the European Union’s Horizon 16 17 2020 research and innovation program under grant agreement No 776622 18 19 (PACIFIC). AM acknowledges support from the National Science Foundation grants 20 21 22 PLR-1643761. 23 24 The processing was done with the help of ObsPy (Krischer et al, 2015) and MATLAB 25 26 and Statistics Toolbox Release 2017a, The MathWorks, Inc., Natick, Massachusetts, 27 28 29 United States. 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 REFERENCES 4 5 6 Aki, K., 1967. Scaling law of seismic spectrum. J. Geophys. Res., 72(4), 1217– 7 8 1231, doi: 10.1029/JZ072i004p01217 9 10 Bensen, G. D., Ritzwoller, M.H., Barmin, M.P., Levshin, A.L, Lin, F., Moschetti, 11 12 M.P., Shapiro, N.M., and Yang, Y., 2008. Processing seismic ambient noise data to 13 14 15 obtain reliable broad-band surface wave dispersion measurements. Geophysical 16 17 Journal International, 169(3):1239–1260. 18 19 Borcherdt, R. D., Gibbs, J.F., 1976. Effect of local geological conditions in the San 20 21 22 Francisco bay region on ground motions and the intensities of the 1906 earthquake. 23 24 Bulletin of the Seismological Society of America. 66(2), 467-500. 25 26 Beresnev, I.A., and Wen, K.L. 1995. P-Wave Amplification by Near Surface 27 28 29 Deposits at Different Excitation Levels. Journal of BSSA, 95(5), 1490-1494. 30 31 Bommer, J.J., Stafford, P. J., Edwards, B., Dost, B., van Dedem, E., Rodriguez- 32 33 Marek, A., Kruiver, P.P., van Elk, J., Doornhof, D., and Ntinalexis, M., 2017. 34 35 Framework for a ground-motion model for induced seismic hazard and risk analysis 36 37 38 in the Groningen gas field, the Netherlands, Earthq. Spectra, 33(2), doi: 39 40 10.1193/082916EQS138M. 41 42 Bonnefoy-Claudet, S., Cotton, F., & Bard, P.-Y., 2006. The nature of noise 43 44 45 wavefield and its applications for site effects studies: a literature review, Earth- 46 47 Science Reviews, 79(3), 205–227. 48 49 Boué, P., Poli, P., Campillo, M., Pedersen, H., Briand, X., Roux, P., 2013, 50 51 52 Teleseismic correlations of ambient seismic noise for deep global imaging of the 53 54 Earth, Geophysical Journal International, 194(2), 844–848, 55 56 https://doi.org/10.1093/gji/ggt160 57 58 59 60

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1 2 3 Ekström, G., 2001. Time domain analysis of earth’s long-period background 4 5 6 seismic radiation, Journal of Geophysical Research: Solid Earth (1978–2012), 7 8 106(B11), 26483–26493. 9 10 Frangi, A.F., Niessen, W. J, Vincken, K.L., & Viergever, M. A., 1998. Multiscale 11 12 vessel enhancement filtering. In International Conference on Medical Image 13 14 15 Computing and Computer-Assisted Intervention (pp. 130-137). Springer, Berlin, 16 17 Heidelberg. 18 19 Halliday, D., Curtis, A., Vermeer, P., Strobbia, C., Glushchenko, A., van Manen, 20 21 22 D.-J., and Robertsson, J.O., 2010. Interferometric ground-roll removal: Attenuation of 23 24 scattered surface waves in single-sensor data, GEOPHYSICS, 75(2), SA15- 25 26 SA25.https://doi.org/10.1190/1.3360948 27 28 29 Heaton, T.H., 2005. Engineering Seismology Notes. http://www.its.caltech.edu/ 30 31 ~heatont/Eng_Seism_Notes/ch5_surfacewaves.pdf. 32 33 de Jager, J., Visser, C., 2017. Geology of the Groningen field – an overview, 34 35 Netherlands Journal of Geosciences —doi:10.1017/njg.2017.22. 36 37 38 Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., 39 40 Wassermann, J., 2015. ObsPy: a bridge for seismology into the scientific Python 41 42 ecosystem. Comput. Sci. Disc. 8, 014003. https://doi.org/10.1088/1749- 43 44 45 4699/8/1/014003 46 47 Kruiver, P. P., van Dedem, E., Romijn, R., de Lange, G., Korff, M., Stafleu, J., 48 49 Gunnink, J. L., Rodriguez-Marek, A., Bommer, J. J., van Elk, J., et al., 2017. An 50 51 52 integrated shear-wave velocity model for the groningen gas field, the netherlands, 53 54 Bulletin of Earthquake Engineering, 15(9), 3555–3580. 55 56 57 58 59 60

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1 2 3 Lecocq, T., Caudron, C., Brenguier, F., 2014. MSNoise, a Python Package for 4 5 6 Monitoring Seismic Velocity Changes Using Ambient Seismic Noise, Seismological 7 8 Research Letters, 85 (3): 715-726. https://doi.org/10.1785/0220130073 9 10 Lin, F.-C., Moschetti, M. P., & Ritzwoller, M. H., 2008. Surface wave tomography 11 12 of the western United States from ambient seismic noise: Rayleigh and love wave 13 14 15 phase velocity maps, Geophysical Journal International, 173(1), 281–298. 16 17 Lin, F.-C., Li, D., Clayton, R.W., Hollis, D. , 2013. High-resolution 3D shallow 18 19 crustal structure in Long Beach, California: application of ambient noise tomography 20 21 22 on a dense seismic array, Geophysics, 78, Q45-Q56, 10.1190/GEO2012-0453.1 23 24 Mainsant, G., Larose, E., Bronnimann, C., Jongmans, D., Michoud, C., & 25 26 Jaboyedoff, M., 2013. Ambient seismic noise monitoring of a clay landslide: toward 27 28 29 failure prediction, Journal of Geophysical Research, 117, F01030. 30 31 Mari, J.L., 1984. Estimation of static corrections for shear-wave profiling using the 32 33 dispersion properties of Love waves: Geophysics, 49, 1169–1179. 34 35 Nakata, N., Chang, J.P., Lawrence, J.F., and Boué, P., 2015. Body wave 36 37 38 extraction and tomography at Long Beach, California, with ambient-noise 39 40 interferometry, J. Geophys. Res. Solid Earth, 120, 1159–1173, 41 42 doi:10.1002/2015JB011870. 43 44 45 Mordret, A., Landes, M., Shapiro, N. M., Singh, S. C., Roux, P., and Barkved, O. I., 46 47 2013. Near-surface study at the Valhall oil field from ambient noise surface wave 48 49 tomography. Geophysical Journal International, 61. doi: 10.1093/gji/ggt061. 50 51 52 Mordret, A., Landes, M., Shapiro, N., Singh, S., and Roux, P. 2014. Ambient noise 53 54 surface wave tomography to determine the shallow shear velocity structure at 55 56 Valhall: depth inversion with a Neighbourhood Algorithm. Geophysical Journal 57 58 International, 198(3), 1514{1525. 59 60

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1 2 3 Nakata, N., Chang, J. P., Lawrence, J.F., & Boué, P., 2015. Body wave extraction 4 5 6 and tomography at Long Beach, california, with ambient-noise interferometry, Journal 7 8 of Geophysical Research: Solid Earth, 120(2), 1159–1173. 9 10 Obermann, A., Kraft, T., Larose, E., & Wiemer, S., 2015. Potential of ambient 11 12 seismic noise techniques to monitor the St. Gallen geothermal site (Switzerland), 13 14 15 Journal of Geophysical Research: Solid Earth, 120(6). 16 17 Olivier, G., Brenguier, F., Campillo, M., Roux, P., Shapiro, N., & Lynch, R., 2015. 18 19 Investigation of coseismic and postseismic processes using in situ measurements of 20 21 22 seismic velocity variations in an underground mine, Geophysical Research Letters, 23 24 42(21), 9261–9269. 25 26 Paap., B., Kraaijpoel, D., Bakker, M., and Nath Gharti, H., 2018, Wave 27 28 29 propagation modelling of induced earthquakes at the Groningen gas production site; 30 31 Geophys. J. Int., 214, 1947–1960 doi: 10.1093/gji/ggy225 32 33 Peterson, J., 1993. Observations and modeling of seismic background noise. U.S. 34 35 Geol. Survey Open-File Report 93-322, 95 pp. 36 37 38 Rhie, J. and Romanowicz, B., 2004. Excitation of earth’s incessant free oscillations 39 40 by Atmosphere-Ocean-Seafloor coupling, Nature, 431, 552–556. 41 42 Rodriguez-Marek, A., Kruiver, P.P., Meijers, P., Bommer, J. J., Dost, B., van Elk, 43 44 45 J., and Doornhof, D., 2017. A regional site-response model for the Groningen gas 46 47 field, Bull. Seismol. Soc. Am. 107, no. 5, 2067–2077. 48 49 Rost, S., Thomas, C., 2002, Array seismology: methods and applications, Reviews 50 51 52 of Geophysics, 40(3), 8755-1209/02/2000RG000100 1008, 53 54 doi:10.1029/2000RG000100 55 56 57 58 59 60

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1 2 3 Sens-Schönfelder, C., & Wegler, U., 2006. Passive image interferometry and 4 5 6 seasonal variations of seismic velocities at Merapi volcano, Indonesia, Geophysical 7 8 research letters, 33(21). 9 10 Shapiro, N., and Campillo, M., 2004. Emergence of broadband Rayleigh waves 11 12 from correlations of the ambient seismic noise, Geophysical Research Letters, 31(7), 13 14 15 L07614. 16 17 Shapiro, N., Campillo, M., Stehly, L., and Ritzwoller, M., 2005. High resolution 18 19 surface-wave tomography from ambient seismic noise, Science, 307, 615–618. 20 21 22 Singh, S. K., Lermo, J., Dominguez, T., Ordaz, M., Espinosa, J. M., Mena, E., and 23 24 Quaas, R., 1988. The Mexico earthquake of September 19, 1985--a study of 25 26 amplification of seismic waves in the Valley of Mexico with respect to a hill zone site, 27 28 29 Earthquake Spectra, 4, 653- 673. 30 31 Snieder, R., 2004. Extracting the Green’s function from the correlation of coda 32 33 waves: a derivation based on stationary phase, Phys. Rev. E, 69, 046610. 34 35 Spica, Z.J., Nakata, N., Liu, X., Zijian, T., Campman, X., and Beroza, G. C., 2018. 36 37 38 The ambient seismic field at Groningen gas field: An overview from the surface to 39 40 reservoir depth, Seismol. Res. Lett. doi: 10.1785/0220170256 41 42 Spica, Z. J., Perton, M., Nakata, N., Liu, X., and Beroza, G. C., 2017. Site 43 44 45 characterization at Groningen gas field area through joint surfaceborehole H/V 46 47 analysis, Geophys. J. Int., 212(1), 412–421, doi: 10.1093/gji/ggx426. 48 49 Tomar, G., Stutzmann, E., Mordret, A., Montagner, J. P., Singh, S. C., & Shapiro, 50 51 52 N. M., 2018. Joint inversion of the first overtone and fundamental mode for deep 53 54 imaging at the Valhall oil field using ambient noise. Geophysical Journal 55 56 International, 214(1), 122-132. 57 58 59 60

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1 2 3 Wapenaar, K., 2004. Retrieving the elastodynamic Green’s function of an arbitrary 4 5 6 inhomogeneous medium by cross correlation, Phys. Rev. Lett., 93, 254301, 7 8 doi:10.1103/PhysRevLett.93.254301. 9 10 Weaver, R.L., & Lobkis, O.I., 2001. On the emergence of the Green’s function in 11 12 the correlations of a diffuse field, J. acoust. Soc. Am., 110, 3011–3017. 13 14 15 Webb, S., 1998. Broadband seismology and noise under the ocean, Reviews of 16 17 Geophysics, 36, 105–142. 18 19 Whaley, J, 2009. The Groningen Gas Field, GeoExPro, 6(4). 20 21 22 van Thienen-Visser, K., and Breunese, J., 2015. Induced seismicity of the 23 24 Groningen gas field: History and recent developments, The Leading Edge, 34(6), 25 26 664–671. 27 28 29 van Heijst, H.J., & Woodhouse, J., 1997. Measuring surface-wave overtone phase 30 31 velocities using a mode branch stripping technique, Geophys. J. Int., 131, 209–230. 32 33 Yang, Y.J., Ritzwoller, M.H., Levshin, A., & Shapiro, N.M., 2007. Ambient noise 34 35 Rayleigh wave tomography across Europe, Geophysical Journal International, 36 37 38 168(1), 259–274. 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 FIGURES 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Figure 1: Location of four flexible VS800 networks in the Groningen field, in the 41 42 northern part of the Netherlands. Four VS800 networks are used in this study: 43 44 45 Loppersum (presented in purple), Borgsweer (presented in blue), Nieuw-Scheemda 46 47 (presented in pink) and Scheemda (presented in green). Each data-block consists of 48 49 around 400 3-C stations recording ambient seismic noise for a month, and the 50 51 52 nominal distance between the sensors is ~350m. The inset shows the geographical 53 54 position of the Groningen field. In the following, we present the processing workflow 55 56 using the data from the Nieuw-Scheemda block. 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Figure 2: Spectrograms of Z component raw records for stations 16.014 (c) and 27 28 19.026 (d), and hourly maps (between 16:00h-17:00h, local time) of median noise 29 30 31 amplitude calculated over the time period 2017-04-01-2017-05-01 for low frequencies 32 33 (a) and high-frequencies (b). Stations 16.014 and 19.026 are pointed with red arrows. 34 35 Stations located in the proximity of the roads show higher noise levels (e.g., station 36 37 38 19.026). The high-frequency maps show bright spots of high amplitude which aligned 39 40 with the road position in the area. The low-frequency maps show smaller variations in 41 42 noise amplitude than the high-frequency maps. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Figure 3: Beamforming results averaged over the monthly acquisition period. 36 37 38 Similarly, to the results from other datasets, all frequency bands show a very 39 40 powerful source of noise coming from the North and the North-West with waves 41 42 travelling with phase velocities from 500 m/s to 1 km/s or even higher. The 43 44 45 fundamental model of Rayleigh waves is visible at long periods (3-6) s in (a), 46 47 although as the periods decrease higher modes of Rayleigh waves start to appear (2- 48 49 4) s in (b), and their energy dominate over the fundamental mode (0.75-3) s, in (c) 50 51 and (d). 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Figure 4: Stacked sections of noise cross-correlations filtered in the frequency 19 20 21 band (0.5-5) Hz for ZZ (a), RR (b) and TT(c) components. The fundamental modes 22 23 and overtones are clearly visible. We build an average seismic section by binning the 24 25 correlations in fixed distance intervals (every 50 m). By doing so, we assume that the 26 27 28 underlying velocity model is mostly 1D. By stacking the large amount of data in each 29 30 distance bin, the signal-to-noise ratio (SNR) is highly increased, which permits the 31 32 extraction of the average fundamental mode propagation characteristics as well as 33 34 several overtones (when present). A frequency-wavenumber (FK) analysis of the 35 36 37 stack section allows us to accurately pick phase velocity dispersion curves for the 38 39 different modes and the different components. 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 Figure 5: Group velocity and phase velocity dispersion measurements. Probability 39 40 41 density distribution of all Rayleigh (a) and Love wave group velocity dispersion 42 43 curves (b). The red, black and blue curves show the mean, median and most 44 45 probable dispersion curve, respectively. The dashed white curves are the limits used 46 47 to reject outlier dispersion curves. (c) FK plot calculated on the stacked sections of 48 49 50 noise cross-correlations for TT components. The picked phase dispersion relation of 51 52 the fundamental mode of the Love waves is presented with orange dots. (d)Mean 53 54 dispersion curves calculated from F-K plots of Rayleigh waves (fundamental mode in 55 56 st 57 yellow and the 1 higher mode in red), and Love waves (fundamental mode in blue). 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Figure 6: Rayleigh wave group velocities at (a) 1.1 s, (b) 1.6 s, (c) 2.1 s, (d) 2.6 s. 29 30 For each period (from 1.1 to 2.9 s with 0.1 s step), the dispersion curves are inverted 31 32 and regionalized into a regular grid of grid 500 m by 250 m rectangular cells (in 33 34 35 longitude and latitude directions) using Mordret et al. (2013) approach. The density of 36 37 ray for Rayleigh waves is of 200 rays per cell. This density is sufficient to ensure an 38 39 average lateral resolution of about 1000 m for most of the area with a minimum 40 41 42 resolution of about 1500 m in the South-West part of the Nieuw-Scheemda block. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Figure 7: Love wave group velocities at (a) 0.5 s, (b) 1.5 s, (c) 2.5 s, (d) 3.5 s. The 27 28 dispersion curves were inverted for each period from 0.5 s to 4.4 s with 0.1 s step. 29 30 The high density of rays of more than 800 rays per cell for Love waves permits to 31 32 33 achieve a nominal lateral resolution of about 500 m for all periods for Love waves. 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Figure 8: Depth inversion. (a) S-wave velocity map at 60 m with point 930 marked 32 33 34 with a white star. Local inversion result. For each plot, 1000 best models are 35 36 presented in the shade of gray (the scale corresponds to the common logarithm of 37 38 misfit values), the best model is represented in color. The error bars (dispersion 39 40 41 measurements) are presented in the same color. Our model achieves a good fit for 42 43 the five dispersion curves. The large error bars (100 m/s) are for the average 44 45 dispersion curves, the small error bars (50 m/s for TT and for ZZ) are for the local 46 47 dispersion curves. (b-f) Inverted dispersion curves with error bars (all five dispersion 48 49 50 curves specified in Table 1 are presented). (g) S-wave velocity model: result of joint 51 52 inversion of Love and Rayleigh waves. The final VS model is presented in red. 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Figure 9: (a-d) Vertical profiles through the 3D model Nieuw-Scheemda. The 35 36 37 location of the profiles is shown in Figure 8a. 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Figure 10: (a) Shear-wave velocity model for the Groningen gas field from Kruiver 34 35 et al. (2017) with the contours of the four analysed blogs marked in black. (b) 36 37 38 Comparison between depth slices through the Vs models from all the data-blocks at 39 40 a depth of 65 m with the Vs model from Kruiver et al. (2017). The same color scale 41 42 was used for our results and Kruiver et al. (2017) results. Both maps are presented 43 44 used the same colorscale, but slightly different colormaps (see presented colorbars). 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 0 8 Vs mean Nieuw-Scheemda 9 10 100 Vs mean Borgsweeer 11 12 200 Vs mean Loppersum 13 14 Vs mean Scheemda 15 300 16 17 400 18 19 20 500 21 22 Depth(m) 600 23 24 25 700 26 27 800 28 29 30 900 31 32 1000 33 02004006008001000 1200 1400 1600 34 35 Vs (m/s) 36 37 Figure 11: Comparison between the 1D average velocity model from the 38 39 Loppersum Borgsweer, Scheemda and Nieuw-Scheemda arrays. 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 a) b) 4 0 0 Vs mean std 100 100 Vs mean std 5 Vs mean Vs mean 6 200 Deltares-NAM 200 Sonic Log (ZRP-2/3) Deltares-NAM 7 Sonic Log (BRW-5)

8 300 300 )

9 400 ) 400

(

h t

10 500 t 500

e D 11 D 600 600 12 13 700 700 14 800 800

15 900 900 16 1000 1000 17 0 200 400 600 800 1000 1200 1400 1600 0 200 400 600 800 1000 1200 1400 1600 18 Vs (m/s) Vs (m/s) 19 c) d) 20 0 0 21 100 Vs mean std 100 Vs mean std 22 200 Vs mean 200 Vs mean 23 Deltares-NAM Deltares-NAM 300 300

24 )

400 ) 400 m

25 m

(

h t

26 500 t 500

e D 27 600 D 600 28 29 700 700 30 800 800 31 900 900 32 1000 1000 33 0 200 400 600 800 1000 1200 1400 1600 0 200 400 600 800 1000 1200 1400 1600 34 Vs (m/s) Vs (m/s) 35 36 Figure 12: Comparison between the 1D average velocity models from the depth 37 38 inversion and Deltares-Nam average model for the Loppersum (a), Borgsweer (b), 39 40 Scheemda (c) and Nieuw-Scheemda arrays (d). For the Loppersum and Borgsweer 41 42 43 data-block local sonic-log models are also presented. 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 SUPPLEMENTARY MATERIAL 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Figure S1: Probabilistic Power Spectral Density (High Noise Model and Low Noise 24 25 26 Model are represented in grey lines) for the station 16.014 (a), and the station 19.026 27 28 (b). The level of the recorded ambient seismic noise is quite high, and after the 29 30 removal of the instrumental response, we manage to retrieve the signal down to 7 s 31 32 period. 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Figure S2: (a) Depth sensitivity kernels for Love wave group velocities, fundamental 26 27 28 mode, as a function of depth and period. The black line shows the S-wave velocity 29 30 model used for the computation and is the sonic log S-wave velocity profile from 31 32 Loppersum. (b) Depth sensitivity kernels for Rayleigh wave group velocities, 33 34 35 fundamental mode, as a function of depth and period. 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Figure S3: (a) Depth sensitivity kernels for Love wave phase velocities, fundamental 35 36 mode, as a function of depth and period. The black line shows the S-wave velocity 37 38 model used for the computation and is the sonic log S-wave velocity profile from 39 40 41 Loppersum. (b) Depth sensitivity kernels for Rayleigh wave phase velocities, 42 43 fundamental mode, as a function of depth and period. (c) Depth sensitivity kernels for 44 45 Rayleigh wave phase velocities, 1st higher mode, as a function of depth and period. 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Figure S4: 3D Vs uncertainty model. The locations of the profiles are shown in 34 35 36 Figure 8. 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Figure S5: Depth inversion in Scheemda block: (a) the map of the common 44 45 logarithm of the best misfit. Most of the area exhibits reasonably low misfit 46 47 (log10(misfit)<0), indicating that the chosen parametrization gives results that fit well 48 49 50 the data. However, in the eastern corner of the field, the misfits are anomalously 51 52 high, demonstrating that the dispersion curves from this area are less well inverted. 53 54 In this area, the topography of NSB strongly increases. The current parametrization 55 56 57 of the depth inversion might be insufficient for this area and the 1D approximation 58 59 used in surface wave inversion is no longer valid. Moreover, the higher misfit might 60

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1 2 3 be due to a smaller number of dispersion measurements at the edge of the 4 5 6 Scheemda block. (b) Vertical profiles through the Scheemda 3D model (marked in 7 8 dashed line in a). 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60