ALBERT

Description: 〈span〉〈div〉SUMMARY〈/div〉The Groningen gas field is one of the largest gas fields in Europe. The continuous gas extraction led to an induced seismic activity in the area. In order to monitor the seismic activity and study the gas field many permanent and temporary seismic arrays were deployed. In particular, the extraction of the shear wave velocity model is crucial in seismic hazard assessment. Local 〈span〉S〈/span〉-wave velocity-depth profiles allow us the estimation of a potential amplification due to soft sediments.Ambient seismic noise tomography is an interesting alternative to traditional methods that were used in modelling the 〈span〉S〈/span〉-wave velocity. The ambient noise field consists mostly of surface waves, which are sensitive to the 〈span〉S〈/span〉wave and if inverted, they reveal the corresponding 〈span〉S〈/span〉-wave structures.In this study, we present results of a depth inversion of surface waves obtained from the cross-correlation of 1 month of ambient noise data from four flexible networks located in the Groningen area. Each block consisted of about 400 3-C stations. We compute group velocity maps of Rayleigh and Love waves using a straight-ray surface wave tomography. We also extract clear higher modes of Love and Rayleigh waves.The 〈span〉S〈/span〉-wave velocity model is obtained with a joint inversion of Love and Rayleigh waves using the Neighbourhood Algorithm. In order to improve the depth inversion, we use the mean phase velocity curves and the higher modes of Rayleigh and Love waves. Moreover, we use the depth of the base of the North Sea formation as a hard constraint. This information provides an additional constraint for depth inversion, which reduces the 〈span〉S〈/span〉-wave velocity uncertainties.The final 〈span〉S〈/span〉-wave velocity models reflect the geological structures up to 1 km depth and in perspective can be used in seismic risk modelling.〈/span〉

Description: 〈span〉〈div〉Summary〈/div〉The Groningen gas field is one of the largest gas fields in Europe. The continuous gas extraction led to an induced seismic activity in the area. In order to monitor the seismic activity and study the gas field many permanent and temporary seismic arrays were deployed. In particular, the extraction of the shear wave velocity model is crucial in seismic hazard assessment. Local S-wave velocity-depth profiles allow the estimation of a potential amplification due to soft sediments.Ambient seismic noise tomography is an interesting alternative to traditional methods that were used in modelling the S-wave velocity. The ambient noise-field consists mostly of surface waves, which are sensitive to the S-wave and if inverted, they reveal the corresponding S-wave structures.In this study, we present results of a depth inversion of surface waves obtained from cross-correlation of 1 month of ambient noise data from four flexible networks located in Groningen area. Each block consisted of about 400 3C-stations. We compute group velocity maps of Rayleigh and Love waves using a straight-ray surface wave tomography. We also extract clear higher modes of Love and Rayleigh waves.The S-wave velocity model is obtained with a joint inversion of Love and Rayleigh waves using the Neighbourhood Algorithm. In order to improve the depth inversion, we use the mean phase velocity curves and the higher modes of Rayleigh and Love waves. Moreover, we use the depth of the base of the North Sea formation as a hard constraint. This information provides an additional constraint for depth inversion, which reduces the S-wave velocity uncertainties.The final S-wave velocity models reflect the geological structures up to 1 km depth and in perspective can be used in seismic risk modelling.〈/span〉

Description: This study presents a depth inversion of Scholte wave group and phase velocity maps obtained from cross-correlation of 6.5 hr of noise data from the Valhall Life of Field Seismic network. More than 2 600 000 vertical–vertical component cross-correlations are computed from the 2320 available sensors, turning each sensor into a virtual source emitting Scholte waves. We used a traditional straight-ray surface wave tomography to compute the group velocity map. The phase velocity maps have been computed using the Eikonal tomography method. The inversion of these maps in depth are done with the Neighbourhood Algorithm. To reduce the number of free parameters to invert, geological a priori information are used to propose a power-law 1-D velocity profile parametrization extended with a gaussian high-velocity layer where needed. These parametrizations allowed us to create a high-resolution 3-D S -wave model of the first 600 m of the Valhall subsurface and to precise the locations of geological structures at depth. These results would have important implication for shear wave statics and monitoring of seafloor subsidence due to oil extraction. The 3-D model could also be a good candidate for a starting model used in full-waveform inversions.

Description: 〈span〉〈div〉Summary〈/div〉We use one month of continuous seismic waveforms from a very dense seismic network to image with unprecedented resolution the shallow damage structure of the San Jacinto fault zone across the Clark fault strand. After calculating noise correlations, high apparent-velocity arrivals coming from below the array are removed using a frequency-wavenumber filter. This is followed by a double-beamforming analysis on multiple pairs of subarrays to extract phase and group velocity information across the study area. The phase and group velocity dispersion curves are regionalized into phase and group velocity maps at different frequencies, and these maps are inverted using a neighbourhood algorithm to build a 3D shear wave velocity model around the Clark fault down to ∼500 m depth. The model reveals strong lateral variations across the fault strike with pronounced low velocity zones corresponding to a local sedimentary basin and a fault zone trapping structure. The results complement previous earthquake- and seismic noise-based imaging of the fault zone at greater depth and clarify properties of structural features near the surface.〈/span〉

Description: The Greenland ice sheet presently accounts for ~70% of global ice sheet mass loss. Because this mass loss is associated with sea-level rise at a rate of 0.7 mm/year, the development of improved monitoring techniques to observe ongoing changes in ice sheet mass balance is of paramount concern. Spaceborne mass balance techniques are commonly used; however, they are inadequate for many purposes because of their low spatial and/or temporal resolution. We demonstrate that small variations in seismic wave speed in Earth’s crust, as measured with the correlation of seismic noise, may be used to infer seasonal ice sheet mass balance. Seasonal loading and unloading of glacial mass induces strain in the crust, and these strains then result in seismic velocity changes due to poroelastic processes. Our method provides a new and independent way of monitoring (in near real time) ice sheet mass balance, yielding new constraints on ice sheet evolution and its contribution to global sea-level changes. An increased number of seismic stations in the vicinity of ice sheets will enhance our ability to create detailed space-time records of ice mass variations.

Description: We used 6 hr of continuous seismic noise records from 2320 four-component sensors of the Valhall ‘Life of Field Seismic’ network to compute cross-correlations (CCs) of ambient seismic noise. A beamforming analysis showed that at low frequencies (below 2 Hz) the seismic noise sources were spatially homogeneously distributed, whereas at higher frequencies (2–30 Hz), the dominant noise source was the oil platform at the centre of the network. Here, we performed an ambient noise surface wave tomography at frequencies below 2 Hz. We used vertical-component geophones CCs to extract and measure the Scholte waves group velocities dispersion curves that were then processed with a set of quality criteria and inverted to build group velocity maps of the Valhall area. Although Scholte wave group velocity depends on S wave, our group velocity maps show features similar to that was previously obtained from P -wave velocity full-waveform inversion of an active seismic data set. Since the dominant noise source at high frequency (above 3 Hz) was the oil platform, we determined a 2-D S -wave velocity model along a profile aligned with the platform by inverting group velocity dispersion curves of Love waves from transverse-component geophones CCs. We found that S -wave velocity down to 20 m was low and varied along the profile, and could be used to estimate S -wave static.

Description: We applied the Helmholtz tomography technique to 6.5 hours of continuous seismic noise record data set of the Valhall Life of Field network. This network, that has 2320 receivers, allows us to perform a multifrequency, high-resolution, ambient-noise Scholte wave phase velocity tomography at Valhall. First, we computed crosscorrelations between all possible pairs of receivers to convert every station into a virtual source recorded by all other receivers. Our next step was to measure phase traveltimes and spectral amplitudes at different periods from crosscorrelations between stations separated by distances between two and six wavelengths. This is done in a straightforward fashion in the Fourier domain. Then, we interpolated these measurements onto a regular grid and computed local gradients of traveltimes and local Laplacians of the amplitude to infer local phase velocities using a frequency dependent Eikonal equation. This procedure was repeated for all 2320 virtual sources and final phase velocities were estimated as statistical average from all these measurements at each grid points. The resulting phase velocities for periods between 0.65 and 1.6 s demonstrate a significant dispersion with an increase of the phase velocities at longer periods. Their lateral distribution is found in very good agreement with previous ambient noise tomography done at Valhall as well as with a full waveform inversion P-wave model computed from an active seismic data set. We put effort into assessing the spatial resolution of our tomography with checkerboard tests, and we discuss the influence of the interpolation methods on the quality of our final models.

Description: 〈span〉〈div〉Summary〈/div〉The spatial distribution of temporal variations in seismic wavespeed is key to understanding the sources and physical mechanisms of various geophysical processes. The imaging of wavespeed changes requires accurate measurements of travel-time delays with both high lapse-time and frequency resolutions. However, traditional methods for time-shift estimation suffer from their limited resolutions. In this paper we propose a new approach, the wavelet method, to measure the travel-time changes in the time-frequency domain. This method is based on wavelet cross-spectrum analysis, and can provide optimal time-frequency joint resolution while being computationally efficient. It can deal not only with coda but also dispersive surface waves even in the presence of cycle skipping. Using synthetic coda, we show that the wavelet method can retrieve travel-time shifts more stably and accurately than traditional methods. An application at Salton Sea Geothermal Field indicates that the wavelet method is less affected by spectral smearing and better discriminates 〈span〉dv/v〈/span〉 variations at different frequencies. Furthermore, upon investigations on synthetic coda, we illustrate that the bias on 〈span〉dv/v〈/span〉 measurements due to changes in source frequency content is likely to be negligible, either with traditional methods or with the new wavelet method. The wavelet method sheds lights on applications of seismic interferometry that aim to locate changes in space.〈/span〉