In 3D seismic data acquisition, sufficiently dense spatial sampling is most often not possible, because of physical, financial, and temporal constraints. The resulting aliased data is a major obstacle for accurate subsurface images. Therefore, methods are needed to reconstruct these sparse data to an adequate sampling. We develop a novel kinematic wavefront-based seismic data reconstruction method that uses the existing nonlinear beamforming (NLBF) framework, which allows for building a more detailed sampling from a sparse input. We present the theory and methodology of our NLBF reconstruction algorithm and test it on a synthetic Society of Exploration Geophysicists Advanced Modeling (SEAM) Arid dataset and a field dataset. We attempt to answer the following question: can the NLBF framework be used for seismic data reconstruction, and how do these results compare to some conventional reconstruction methods? Control parameter tests are performed to find the optimal NLBF reconstruction, and results are compared to those from several control methods including the convergent alternating projection onto convex sets (POCS) and bootstrap POCS methods, which were also developed for this study. Our NLBF reconstruction method successfully reconstructs high-quality data on both datasets. We find that the optimal NLBF control parameters are ultimately dataset-dependent as the concrete data acquisition geometry plays a central role. Specifically for our datasets, optimal parameters include a time window of 12∆ts, an operator aperture of 600 m x 600 m, and a parameter trace interval of ∆x = ∆y = 60 m. The synthetic SEAM Arid NLBF reconstruction results show high trace fidelity to the ground truth. The field data NLBF results show better-reconstructed gaps, and wavefield variations closer to those of physical propagating waves in comparison to our control methods. These results show the potential of the NLBF reconstruction method to become a common data reconstruction tool used in the seismic industry.

The seismic method is an important indirect method to investigate the subsurface of the earth. By analyzing how the earth affects the propagation of mechanical waves, the structure of the earth and its seismic properties can be inferred. The seismic vibrator is the most commonly used land source in active-source exploration and monitoring to generate these waves and is the subject of this thesis. The goal of a seismic vibrator is to produce seismic waves with a known signal signature. Commonly sinusoidal signals whose frequency varies over time, called sweeps, are used for this purpose. These signals are typically quite lengthy to compensate for the fact that the instantaneous amplitudes of the vibrator are relatively weak compared to the ones from impulsive sources and the target depths faced. Via the processing step of correlation, the lengthy source signature is collapsed and virtual records are generated as if the vibrator would have released all energy at once. The quality of these virtual records depends on the ability of the vibrator engines to generate the force signature wanted and the ability of the vibrator mechanics and the vibrator-ground interaction to successfully transform the driving force to a seismic wave. In this thesis we investigate the feasibility of driving a vibrator with linear synchronous motors, the influence of drive level on the signals a vibrator generates, the effect of the vibrator-ground coupling, and the possibilities to design more robust source signals.@en

During a seismic experiment, mechanical waves are usually generated by various manmade sources. These waves propagate in the subsurface and are recorded at receivers. Modern seismic exploration methods analyze them to infer the mechanical properties of the subsurface; this is commonly referred as quantitative imaging. These properties assist in the determination of the subsurface rock type and structure. Exploration methods are not only useful while looking for the deposits such as crude oil, natural gas and minerals but also for near-surface geotechnical investigation. A motive of this thesis is to adopt these methods to image the subsurface ahead of a tunnel-boring machine for hazard assessment during excavation. Full-waveform inversion (FWI) is a gradient-based optimization problem that is employed in seismic exploration for quantitative imaging of the recorded waves. During FWI, seismic waves are simulated in a computer by using certain physical laws that govern the wave propagation. After inversion, output subsurface properties simulate waves that fit the recorded waves in a least-squares sense. In other words, the gradient-based optimization aims to find the minimum of the least-squares misfit between the simulated and the recorded waves. Finding such a minimum is not straight forward due to the existence of multiple local minima when using the least-squares objective. As a result, it might often happen that the optimizer converges to local minima, where the simulated waves only partially explain the recorded waves. The presence of local minima is associated to the strong non-linear dependence of the recorded waves on the subsurface properties. In this thesis, we attempt to overcome this difficulty. We propose a new measure of misfit between the recorded and the simulated waves. This measure compares the waveforms in a simplified form after taking the absolute value and blurring. We show that the new misfit measure suffers less from the local-minima problem. For robust inversion, we use a multi-objective inversion scheme, where the new measure is used as an auxiliary objective to pull the trapped solution out of the least-squares local minimum whenever necessary. In multi-parameter FWI, more than one kind of subsurface properties are simultaneously estimated. When only the first-order derivatives of the misfit are used during minimization, different choices of subsurface parameterization are not equivalent; they can be interpreted as different preconditioners. Therefore, the choice of parametrization will affect the rate of convergence in multi-parameter FWI and the best choice of parameterization is the one with the highest rate. In this thesis, we also analyse various choices of subsurface parameterization in search of the best one. It is well known that the local-minima problem in FWI can easily be resolved by reliably generating and recording low-frequency waves in the subsurface. Recently, a seismic source capable of generating such low frequencies is developed based on linear synchronous motors technology. Finally, we demonstrated a shear-wave seismic ground prediction system using these sources to enable imaging ahead of a tunnel boring machine (TBM). @en

When humans started started exploiting the abundant underground natural resources the Earth has to offer such as hydrocarbons, minerals and heat, we started to experience earthquakes that are related to this exploitation, so called induced earthquakes. Under certain conditions those can damage local infrastructure. However, most events are weak and only sensed by seismic sensors. Microseismic monitoring plays a vital role to optimize and insure the safety of these underground activities and new technologies such as carbon capture and storage. One key task besides the detection of microseimsic events is to determine the source location of these events using data recorded at the surface. In this thesis we investigate a method to localize weak microseismic events, using a deterministic approach, assuming a dense network of sensors. In simple words this method takes the seismic signals recorded at the Earth’s surface and sends them back into the Earth, where the signals start to focus at the point they originated from. This focusing method uses one-way wavefield extrapolation with an estimate of the background velocity model. The advantage of this method is that the weak signals recorded by the different sensors at the surface are amplified as they approach the location of the event that emitted the signal due to constructive interference. However, this is not enough to reliably recover the source location because typically earthquakes do not radiate seismic waves evenly; complex radiation patterns are typically observed depending on the mechanical properties of the rupture. To obtain a strong focused signal at the optimal source location we therefore perform a grid search over possible source mechanisms and increase the strength of the signal by deconvolution. Without taking the source mechanism into account we are not able to obtain accurate source locations, especially at low signal-to-noise ratios. However, by taking the source mechanism into account we are able to retrieve accurate source locations while also retrieving information about the source mechanism. Good results were obtained for 2D synthetic data for both a simple subsurface model as well as the realistic Annerveen salt model even when realistic noise was added... @en

Geophysical methods are widely used for hydrocarbon exploration and time-lapse measurements. One method that can be applied in place of or in addition to the routinely used seismic method, is the Controlled-Source ElectroMagnetic (CSEM) method. The work described in this thesis explores various aspects to improve the land CSEM method for exploration and monitoring purposes. Therefore, three separate active land CSEM field experiments, a baseline and two repeat surveys, were carried out over a period of nearly two years at the Dutch Schoonebeek oil field, where heavy oil is produced by injecting steam to reduce the viscosity of the oil. Steam injection and oil production have to be closely monitored to prevent breakthrough of steam to production wells and to detect possible unwanted leakage or contamination of groundwater such that further measures can be taken.@en

In this thesis, we discuss two pattern recognition techniques, template matching, and neural networks. We discuss how these techniques have been used for the development of two earthquake detection algorithms. The first algorithm is based on template matching and the second is based on deep learning. The algorithms are designed for the detection of small <0.5M events in the subsurface of Zeerijp, Groningen. These two algorithms have been compared to assess their earthquake detectability and practicality. The systems have been compared using field data from Zeerijp. The algorithm based on deep learning (a neural network) produced too many false positives considering the amount of seismic data we would like to use it for. The algorithm based on template matching did not produce any false positives during testing. The template matching system has been fed six months of continuous seismic data fromZeerijp. This resulted in the detection of at least 22 new events.

Failure of subsea wind turbine cables are the main cause for wind farm downtimes. Furthermore, 80% of insurance payouts to wind farms come from cable repairs and maintenance. Cable tracking is part of the wind farm cables maintenance scheme. Surveys are required for localisation and determining the depth of burial of the cables. Conventional surveying methods have shortcomings when it comes to localising offshore buried power cables, which can be resolved by using electromagnetic methods. From the measured magnetic field created by the cable, its position and burial depth can be inverted. Forward modelling the magnetic response of cables improves the understanding of their electromagnetic behaviour in one dimensional complex submarine environments. Maxwell's equations are the cornerstones of understanding the behaviour of electromagnetic fields, forming the basis of the models. Implementing numerical modelling packages like Empymod accommodate for boundary conditions in one-dimensional layered media to more accurately investigate the magnetic response of electric cables. Land measurements show that Empymod's numerical models can model cables in the accuracy range of 2-15% depending on the distance. New numerical model analyses show the influence of water depth, underlying layers, depth of burial, geometrical complexities and adjacent cables on the magnetic field. Surrounding resistivity contrasts together with changes in water depth change the measured magnitude of the magnetic field by up to 10%. Geometrical complexities have large effects on the magnetic field, sometimes completely changing the expected response. Therefore, twisted and irregularly shaped cables can pose problems when inverting for their position during surveys.