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This thesis presents the experimental and numerical analysis of seismic waves that are produced by wind farms. With the aim to develop renewable energies rapidly, the number of wind turbines has been increased in recent years. Ground motions induced by their operation can be observed by seismometers several kilometers away. Hence, the seismic noise level can be significantly increased at the seismic station. Therefore, this study combines long-term experiments and numerical simulations to improve the understanding of the seismic wavefields emitted by complete wind farms and to advance the prediction of signal amplitudes.
Firstly, wind-turbine induced signals that are measured at a small wind farm close to Würzburg (Germany) are correlated with the operational data of the turbines. The frequency-dependent decay of signal amplitudes with distance from the wind farm is modeled using an analytical method including the complex effects of interferences of the wavefields produced by the multiple wind turbines. Specific interference patterns significantly affect the wave propagation and therefore the signal amplitude in the far field of a wind farm. Since measurements inside the wind turbines show that the assumption of in-phase vibrating wind turbines is inappropriate, an approach to calculate representative seismic radiation patterns from multiple wind turbines, which allows the prediction of amplitudes in the far field of a complete wind farm, is proposed.
In a second study, signals with a frequency of 1.15 Hz, produced by the Weilrod wind farm (north of Frankfurt, Germany) are observed at the seismological observatory TNS (Taunus), which is located at a distance of 11 km from the wind farm. The propagation of the wavefield emitted by the wind farm is numerically modeled in 3D, using the spectral element method. It is shown that topographic effects can cause local signal amplitude reductions, but also signal amplification along the travel path of the seismic wave. The comparison of simulations with and without topography reveals that the reduction and amplification are spatially linked to the shape of the topography, which could be an explanation for the relatively high signal amplitude observed at TNS.
Finally, the reduction of the impact of wind turbines on seismic measurements using borehole installations is studied using 2D numerical models. Possible effects of the seismic velocity, attenuation, and layering of the subsurface are demonstrated. Results show that a borehole can be very effective in reducing the observed high-frequency signals emitted by wind turbines. However, a borehole might not be beneficial if signals with frequencies of about 1 Hz (or lower) are of interest, due significant wavelength-dependent effects. The estimations of depth-dependent amplitudes with a layered subsurface are validated with existing data from wind-turbine-induced signals measured at the top and bottom of two boreholes.
The experimental analysis of measurements conducted at wind farms and the advances of modeling such signals improve the understanding of the propagation of wind-farm induced seismic wave fields. Furthermore, the methods developed in this work have a high potential of universal application to the prediction of signal amplitudes at seismometers close to wind farms with arbitrary layout and geographic location.
Indian Ocean came into existence with the breakup of Gondwana in the Mesozoic era. The presence of complex aseismic ridges and plateaus in the Indian Ocean makes it the least-understood of all the oceans. Mascarene Plateau, apart from Central Indian Ridge (CIR) running north-south between 2◦N and 25◦S in the Indian Ocean, is one such complex feature in the Indian Ocean that consists of Seychelles microcontinent in the north and the volcanic islands of Mauritius, La Réunion and Rodrigues in the south.
Most of the previous seismological studies on the islands of Mauritius, Rodrigues and Seychelles are restricted as each of them has only one operational permanent station. In the current study, I present the results obtained from the investigations of the seismological data obtained from the deployment of temporary seismic network on Mauritius (November, 2012–August, 2014) and Seychelles (March, 2013–March, 2015) under Réunion Hotspot and Upper Mantle–Réunions Unterer Mantel (RHUM–RUM) project and later in Rodrigues (September, 2014–June, 2016) under a collaborative project between Goethe-Universität, Frankfurt, Germany and Mauritius Oceanography Institute (MOI), Mauritius. Additional data from the permanent stations were also used in this study. The investigations and results are presented under three themes, namely: (1) crustal structure beneath Mauritius, (2) upper mantle anisotropy below Mauritius, Rodrigues and Seychelles and (3) intraplate seismicity in the Rodrigues–CIR region.
Upper mantle anisotropy in south-west Indian Ocean region are very limited, especially from the islands of Mauritius and Rodrigues. With the new data from the seismic stations deployed in Mauritius and Seychelles, under RHUM–RUM, and permanent stations in Rodrigues, I constrain the upper mantle flow pattern beneath these islands. From the joint-splitting analysis, I obtain fast-polarisation direction (φ) dominant in N80◦E and delay time (δt) of ≈0.85 s for Mauritius and φ tending east–west in Rodrigues with δt of ≈1.1 s. Parabolic asthenospheric flow model explains the orientation of the fast-polarisation direction beneath Mauritius, whereas deep mantle circulation patterns best explain the horizontal alignment of the fast-polarisation direction in Rodrigues. From Seychelles data, the results show φ trending NE and δt ≈0.74 s, even for the island close to Amirante Ridge, suggesting an asthenospheric deformation induced by relative motion between the plate and the deep mantle flow.
It has recently been suggested that the volcanic island of Mauritius may be underlain by a remnant of continental origin termed “Mauritia.” To constrain the crustal thickness beneathMauritius, I analysed data from 11 land stations, 10 of which were deployed recently under the RHUM–RUM project. From the recordings, I obtained 382 P-receiver functions. On the obtained receiver functions, I applied the H–κ stacking technique and derived the crustal thickness of ≈10–15 km. I observe a considerable variation in the VP/VS ratio caused by a lack of clear multiples. Using forward modelling of receiver functions, I show that the lack of clear multiples can be explained by a transitional Moho, where the velocity increases gradually. The modelling further indicates that the thickness of this gradient zone is estimated to be ≈10 km. I argue that my findings suggest oceanic crust thickened by crustal underplating due to the mantle plume currently located beneath La Réunion.
Seismicity around Rodrigues Island is generally associated with events recorded by the global networks along the CIR. Using seismological array techniques on the data collected by the temporary deployment of seismic array on Rodrigues Island for a period of 22 months (September, 2014–June 2016), 62 new events were located, which were not reported by any global network. Determination of backazimuth and apparent velocity were performed by applying array methods in the time-domain instead of the more conventional frequency-domain analysis. Event distances were calculated using a 1-D velocity model and the measured travel-time differences between S- and P-wave arrivals. Local magnitudes of the events were obtained by removing the velocity response from the seismographs and then convolving with Wood–Anderson transfer function to obtain ground motion in nanometers. Most of the newly-detected events are located off the ridge axis and can be classified as intraplate events. Three different seismic clusters were observed around the island. Most of the events were localised in the north-east of Rodrigues at a distance of ≈138 km from the reference station. A distinguishable swarm of earthquakes was observed on the west of the spreading segment from March to April 2015. The local magnitudes (ML) of the events varied between 1.6 and 3.7.