Check out our project log on Research Gate: https://www.researchgate.net/project/PACIFIC-H2020
Executive summary: This deliverable gathers risks related to safety or environmental issues relevant for PACIFIC activities. For each type of issue, the risks/hazards are listed with their score before and after mitigation and the corresponding control measures. A safe working procedure is also described. This database will serve as reference for the ESMC – Environmental and Safety Risk Management Committee – for follow-up during the course of the project.
Download the PACIFIC project presentation https://www.pacific-h2020.eu/wp-content/uploads/PACIFIC-Project-presentation.pdf
Executive summary: A key goal of the PACIFIC project is to develop methodologies for the extraction of body waves from passive seismic data, for use in the environmentally sustainable environments. Recovering body waves from ambient noise data has proved to be challenging as they are usually weak and ambient noise fields are rich in surface waves. Here we propose and test a method, based on the Radon Transformation, that helps suppress surface waves and enhance reflected body waves. The method exploits the ‘moveout’ differences between reflected body (hyperbolic) and surface waves (linear) and is tested on synthetic 2D & 3D model data prior to its application to ambient noise field data. We refer to it as Radon Correlation. Synthetic tests are very encouraging, showing clear body wave recovery that cannot be seen in raw cross-correlated data. Using these synthetics to have a choice of parameters, we then move to field passive data from the Marathon site within PACIFIC. We generate virtual shot gathers by applying Radon Correlation to single virtual sources into a linear array of receivers. Again, results are very encouraging with clear reflected body wave recovery from the ambient noise data and determined by clear hyperbolic arrivals on the virtual shot gathers. There is a hint that using time windows that contain active blast seismic coda possibly further enhances body wave recovery. Finally, velocity analysis on these virtual shot gathers leads to a P-wave velocity model that compares well with models derived from surface wave dispersion analysis of the same ambient noise data. However, these models are not currently publicly available and hence are not shown here, in this report.
Ambient noise multimode Rayleigh and Love wave tomography to determine the shear velocity structure above the Groningen gas field
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 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 S-wave velocity. The ambient noise field consists mostly of surface waves, which are sensitive to the Swave 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 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 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.
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