Investigating the use of 3-D full-waveform inversion to characterize the host rock at a geological disposal site

The U.K. government has a policy to dispose of higher activity radioactive waste in a geological disposal facility (GDF), which will have multiple protective barriers to keep the waste isolated and to ensure no harmful quantities of radioactivity are able to reach the surface. Currently no specific GDF site in the United Kingdom has been chosen but, once it has, the site is likely to be investigated using seismic methods. In this study, we explore whether 3-D full-waveform inversion (FWI) of seismic data can be used to map changes in physical properties caused by the construction of the site, specifically tunnel-induced fracturing. We have built a synthetic model for a GDF located in granite at 1000 m depth below the surface, since granite is one of the candidate host rocks due to its high strength and low permeability and the GDF could be located at such a depth. We use an effective medium model to predict changes in P-wave velocity associated with tunnel-induced fracturing, within the spatial limits of an excavated disturbed zone (EdZ), modelled here as an increase in fracture density around the tunnel. We then generate synthetic seismic data using a number of different experimental geometries to investigate how they affect the performance of FWI in recovering subsurface P-wave velocity structure. We find that the location and velocity of the EdZ are recovered well, especially when data recorded on tunnel receivers are included in the inversion. Our findings show that 3-D FWI could be a useful tool for characterizing the subsurface and changes in fracture properties caused during construction, and make a suite of suggestions on how to proceed once a potential GDF site has been identified and the geological setting is known

Application of acoustic full waveform inversion to retrieve high-resolution temperature and salinity profiles from synthetic seismic data

14 pages, 14 figuresRecent works show that multichannel seismic (MCS) systems are able to provide detailed information on the oceans' fine structure. The aim of this paper is to analyze whether 1-D full waveform inversion algorithms are suitable to recover the extremely weak acoustic impedance contrasts associated to the oceans' fine structure, as well as their potential to image meso-scale objects such as meddies. We limited our analysis to synthetic, noise-free data, in order to identify some methodological issues related to this approach under idealistic conditions (e.g., 1-D wave propagation, noise-free data, known source wavelet). We first discuss the influence of the starting model in the context of the multi-scale strategy that we have implemented. Then we show that it is possible to retrieve not only sound speed but also salinity and temperature contrasts within reasonable bounds from the seismic data using Neural Network relationships trained with regional oceanographic data sets. Potentially, the vertical resolution of the obtained models, which depends on the maximum frequency inverted, is of the order of 5–10 m, whereas the root mean square error of the inverted properties is shown to be ∼0.5 m/s for sound speed, 0.1°C for temperature, and 0.06 for salinity. To conclude this study, we have inverted synthetic data simulated along an oceanographic transect acquired during the EU-funded Geophysical Oceanography (GO) project. The results demonstrate the applicability of the method for synthetic data, as well as its potential to define oceanographic features along 2-D transects at full ocean depth with excellent lateral resolutionThe authors are grateful to REPSOL for supporting this work through the Kaleidoscope project. Likewise, we also acknowledge the EU‐funded GO‐project (EC‐NEST‐15603) as well as German funded POSEIDON CRUISE (P350) for providing the experimental dataset. Part of the work has been done in the framework of projects MEDOC (Ref CTM2007‐66179‐C02‐02/MAR) and POSEIDON (Ref CTM2010‐21569), both funded by the Spanish MICINNPeer reviewe

Inversion de la forme d'onde dans le domaine fréquentiel de données sismiques grands offsets

L'approche standard en imagerie sismique repose sur une décomposition par échelle du modèle de vitesse: la détermination des bas nombres d'ondes est suivie par une reconstruction des hauts nombres d'ondes. Cependant, pour des modèles présentant une structure complexe, la détermination des hauts nombres d'ondes peut être améliorée de manière significative par l'apport des nombres d'ondes intermédiaires. Ces derniers peuvent être déterminés par l'inversion non-linéaire de la forme d'onde de données sismiques grands angles qui est, par ailleurs, limitée par la non-linéarité du problème inverse. La non-linéarité est gouvernée par la fréquence minimum dans les données et le modèle de vitesse initial. Pour les très basses fréquences, inférieures à 7 Hz, le problème est raisonnablement linéaire pour appliquer l'inversion de la forme d'onde à partir d'un modèle de départ déterminé par inversion tomographique des temps de trajets. Le domaine fréquentiel est alors très efficace pour inverser des basses vers les hautes fréquences. De plus, il est possible de discrétiser les fréquences avec un pas d'échantillonnage plus grand que celui dicté par le théorème d'échantillonnage. Une stratégie pour sélectionner les fréquences est développée qui réduit le nombre de fréquences nécessaire en imagerie lorsqu'une gamme d'offset est disponible: le nombre de fréquences diminue lorsque l'offset maximum augmente. Les donnés sismiques réelles ne contiennent malheureusement pas de très basses fréquences. Des techniques de pré-conditionnement doivent alors être appliquées afin d'améliorer l'efficacité de l'inversion à partir de fréquences réalistes. Le lissage du vecteur gradient ainsi que l'inversion des premières arrivées augmente les chances de convergence au minimum global. L'efficacité des méthodes de pré-conditionnement est tout de même limitée par le degré d'information contenu dans le modèle de départ.The standard imaging approach in exploration seismology relies on a decomposition of the velocity model by spatial scales: the determination of the low wavenumbers of the velocity field is followed by the reconstruction of the high wavenumbers. However, for models presenting a complex structure, the recovery of the high wavenumbers may be significantly improved by the determination of intermediate wavenumbers. These, can potentially be recovered by local, non-linear waveform inversion of wide-angle data. However, waveform inversion is limited by the non-linearity of the inverse problem, which is in turn governed by the minimum frequency in the data and the starting model. For very low frequencies, below 7 Hz, the problem is reasonably linear so that waveform inversion may be applied using a starting model obtained from traveltime tomography. The frequency domain is then particularly advantageous as the inversion from the low to the high frequencies is very efficient. Moreover, it is possible to discretise the frequencies with a much larger sampling interval than dictated by the sampling theorem and still obtain a good imaging result. A strategy for selecting frequencies is developed where the number of input frequencies can be reduced when a range of offsets is available: the larger the maximum offset is, the fewer frequencies are required. Real seismic data unfortunatly do not contain very low frequencies and waveform inversion at higher frequencies are likely to fail due to convergence into a local minimum. Preconditioning techniques must hence be applied on the gradient vector and the data residuals in order to enhance the efficacy of waveform inversion starting from realistic frequencies. The smoothing of the gradient vector and inversion of early arrivals significantly improve the chance of convergence into the global minimum. The efficacy of preconditioning methods are however limited by the accuracy of the starting model.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF