Maritime transport facilitates close to 80 % of global trade, standing as an unparalleled cornerstone in international commerce. Projections herald a further increase in maritime activity, due to the low carbon footprint and cost-effectiveness, elevating the need for ensuring navigational safety within ports and waterways. At the heart of safe maritime navigation lies the pivotal concept of maintaining adequate nautical-depth, a threshold where a ship’s keel encounters navigational constraints. However, this necessity inherently demands incessant monitoring and recurring dredging operations, resulting in high costs amounting to millions of euros and, contributing significantly to carbon emissions. These factors underscore an urgent call for optimization of monitoring the nautical-depth to reduce monitoring and dredging costs, and increase marine navigational safety.
Presently, common methods for nautical-depth monitoring rely heavily on acoustic echo sounders, rooted in dated methodologies with limited innovation over nearly a century. Acoustic echo sounders measure the two-way travel time of sound pulses, assuming a known propagation velocity of the acoustic energy. However, accurately approximating the pressure-wave velocity in shallow marine environments poses challenges due to variations in temperature and salinity among different water layers, leading to depth measurement inaccuracies. Furthermore, this method is limited by vessel availability and requires access to quay walls, often occupied by loading or unloading ships....@en

Glaciers’ mass balance and melting patterns can be monitored through the study of their facies. Whilst using Synthetic aperture radar (SAR) remote sensing data facilitates glaciology observations as it can be used under all weather conditions, the systemic backscatter intensity decay due to incidence angle (IA) variation makes classification even more challenging on such complex terrain. We investigate the classification accuracy of glacier facies using SAR data through a supervised learning algorithm that incorporates class-dependent local incidence angle correction. Focusing on the Holtedahlfonna and Kongsvegen glacier complexes in northeast Svalbard, we pre-process SAR data from Sentinel-1 using the SNAP toolbox. We then compare three Bayesian classifiers: one without any IA correction, one with a common IA slope correction, and the third incorporating a class-dependent IA slope correction. Our results show that per-class IA slope correction on training regions improves the models by around 30% compared to the naive one and around 10% from the common IA slope correction. When tested on the glaciers, their firn line could be mapped from 2017 to 2023 and a general retreat of 400-500 m is observed, changing to 3-4 km in some regions of Holtedalhfonna. However, when looking at regions of lower altitudes, regions with crevasses are largely misclassified. To aid crevasse classification, we finish this study by providing some insights on potential texture features, using either standard deviation or spatial Fourier transforms. In all, this work explores the extent to which class-dependent IA corrections should be included in SAR data analysis, contributing to enhanced glacier monitoring and climate research.

Multichannel Analysis of Surface Waves (MASW) is commonly used to determine the
shallow subsurface velocity structure. The obtained velocities provide valuable informa-
tion for foundation design and other geotechnical applications. The conventional method
of picking surface-wave dispersion curves, which are subsequently inverted to obtain the
desired velocity profiles, can be a labour-intensive task. In this thesis, we make use of
recent advances in Deep Learning (DL), utilizing Convolutional Neural Networks (CNNs)
to estimate dispersion curves directly from shot gathers or their corresponding dispersion
spectra. For this, we first test the proposed approach on various synthetic data, suc-
cessfully addressing challenges such as geological models with velocity inversion, different
noise levels, and the estimation of the first higher mode. When applied to field data,
our results indicate that training the CNN on dispersion spectra provides more accurate
and reliable predictions compared to training on shot gathers directly. We improve the
predictions using shot gathers as training data by modifying the original CNN architec-
ture and applying transfer learning techniques. The enhanced CNN models demonstrate
significant improvements, indicating that this approach could aid the analysis of MASW
data. To fully realize the potential of this method, future efforts should focus on increas-
ing the similarity between synthetic and field data, for example by incorporating realistic
noise.

Fiber-optic distributed acoustic sensing (DAS) excels in high-quality seismic signal acquisition and detection but is often hindered by noise, significantly reducing signal-to-noise ratio (SNR) and impeding microseismic event detection. Moreover, continuous seismic monitoring campaigns generates huge data volumes. While numerous denoising approaches exist, they often demand substantial computational resources, hindering real-time implementation. We propose an unsupervised neural network to suppress random noise without requiring noisefree ground truth or prior noise characteristics. The network learns to extract features by masking random input traces and reconstructing the target using long-term coherence from neighboring traces. We explore hyperparameter optimization by varying input sample generation, activation functions, scaling methods, and the number of input traces. We evaluate the model by running a detection algorithm on FORGE data and achieve a 43% increase in event detection. We further exploit our algorithm in real-time experiments and achieved within a 90% processing time compared to the data acquisition rate with denoising implemented. Our approaches can be incorporated real-time data acquisition, effectively facilitate the screening and storing the data timeframe with useful information.

Seismic imaging and monitoring with reflected waves, originally used in the oil and gas industry to identify and assess potential hydrocarbon reservoirs and later monitor their exploitation, also have diverse applications in near-surface geophysics, mineral exploration, geothermal energy, and CO2 or H2 storage. Beyond revealing subsurface structures, these techniques enhance our understanding of how the subsurface responds to human activities, such as induced seismicity due to extraction processes. Seismic imaging and monitoring often focus on specific target layers within the subsurface, but challenges from interferences with surrounding layers and small changes within the specific layer(s) can distort the specific signals and lower the accuracy. Our research aims to address these challenges and provide practical solutions for more accurate and reliable seismic imaging and monitoring with reflected waves.
In this thesis, we aim to develop seismic data-driven methods for layer-specific imaging and monitoring, with a primary focus on advancing the technique of ghost-reflection retrieval using seismic interferometry (SI) and showing how the Marchenko method could be used with land seismic data.
SI often involves the cross-correlation of seismic observations at different receiver locations and the consecutive summation over the available sources, allowing the retrieval of new seismic responses from virtual sources located at the receiver positions. When using sources and receivers only at the surface, the virtual-source gathers retrieved by SI contain not only pseudo-physical reflections but also ghost (non-physical) reflections. These ghost reflections result mainly from the cross-correlation (CC) or auto-correlation (AC) of primary reflections from two different depths, representing reflections from inside specific subsurface layer(s), as measured with a virtual ghost source and a virtual ghost receiver positioned directly on top of the specific layer(s). Consequently, the ghost reflections can provide information about the specific layer(s) without the effects of the overburden and underburden layers.
We first explore the use of ghost reflections for layer-specific characterisation of the shallow subsurface using SI by AC, utilising numerically modelled data for a layered subsurface model down to 30 m depth, incorporating a lateral change in velocity, a velocity gradient with depth, a thickness change, and a velocity change in the target layer. Additionally, we present the first application of ghost reflections to shallow subsurface field data. Ghost reflections typically exhibit similar characteristics to other reflection events, appear close to or interfere with other events with only slight temporal differences. This makes their identification a significant challenge. To address this, we eliminate surface-related multiples and demonstrate how specific ghost reflections can be more efficiently retrieved by muting undesired reflections in the dataset before applying SI.
To extend the application of ghost reflections to deep structures, we focus on the feasibility of monitoring pore-pressure changes in the Groningen gas field in the Netherlands. We utilise numerical modelling to simulate scalar reflection data, deploying sources and receivers at the surface. We conduct an ultrasonic transmission laboratory experiment to measure S-wave velocities at different pore pressures. This data is used to create subsurface models, which are then utilized to simulate scalar reflection seismic data for monitoring purposes. We retrieve zero-offset ghost reflections by applying SI by AC to the modelled datasets. We then use a correlation operator to determine time differences between a baseline survey and monitoring surveys. Additionally, we investigate the effects of the sources and receivers' geometry and spacing, as well as the number of virtual sources and receivers, on retrieving ghost reflections with high interpretability and resolution. Besides observing time shifts in the ghost reflections, we also explore the feasibility of using the amplitude of ghost reflections for reservoir monitoring.
Having clear reflections from both the top and bottom of the specific layer(s) is crucial for retrieving ghost reflections, which can be challenging when using land seismic datasets due to the usual presence of strong surface waves. Conventionally, surface waves are suppressed during data processing using frequency-offset, frequency-wavenumber, or bandpass filters. However, these approaches can prove ineffective when the surface waves are scattered and/or overlap with the frequency regions of the reflected body waves that we intend to preserve. To overcome some of these challenges, we show the efficacy of the interferometric surface-wave suppression using a 2D seismic reflection dataset from Scheemda, Groningen province, the Netherlands. Interferometric surface-wave suppression can be used to effectively suppress surface waves by applying SI to first estimate the surface waves and second followed by their adaptive subtraction from the original data. We propose to apply these two steps recursively, i.e., several times, which yields better results than a single application in terms of clearer and more continuous reflections. This technique can function as a standalone technique or as part of a pre-processing flow.
When applying the seismic reflection method for monitoring purposes, specific reflections, e.g., from the top and bottom of the reservoir, are of interest. The reflections from both the top and bottom of the specific layer(s) can also be distorted by other events from the surrounding layers. To eliminate such distortions, the Marchenko-redatuming method was introduced. Several Marchenko-redatuming methods have been applied successfully to marine field data. We demonstrate, for the first time, the application of the Marchenko-based isolation technique to field land seismic data to isolate the target response by removing the overburden and underburden. Land data are intrinsically elastic, known for dominant surface waves and a low signal-to-noise ratio, posing a challenge for the Marchenko method, which requires high-quality reflection data. After we carefully apply several pre-processing steps, including recursive interferometric surface-wave suppression, we apply the Marchenko method twice: first, to remove the overburden effects by choosing a focal depth of 30 m, and then to remove the underburden effects by choosing a focal depth of 270 m. This process generates a new reflection response from the target area, providing clearer subsurface responses. The Marchenko method is particularly beneficial for data-driven techniques such as ghost-reflection retrieval, seismic imaging, and time-lapse studies using land seismic datasets.@en

With a rapid increase in computational resources two dimensional full-waveform inversion is evolving into a promising tool for near-surface geophysics. However, near-surface applications suffer from local minima due to amplitude errors associated with two dimensional full-waveform inversion. By clever definition of the misfit function, the influence of the amplitude errors can be mitigated. Here, I use a recently proposed misfit function based on the instantaneous-phase coherency. The instantaneous-phase coherency misfit function uses complex trace analysis to create an amplitude unbiased misfit function. First, I compare the new misfit function to a traditional least-squares misfit function by inverting synthetic models where noise is added, a field dataset containing Rayleigh waves and a field dataset containing Love waves. Next, I perform inversions on layer cake models to investigate the accuracy of the full-waveform inversion using the new misfit function and finally, I test the robustness of the inversion by using a complex subsurface model. The inversions performed on the synthetic models show that the instantaneous-phase coherency misfit is more robust when noise is introduced to the data compared to the least-squares misfit. Furthermore the two field datasets, demonstrate the ability of the instantaneous-phase to deliver accurate near-surface results when used on field data. The results from the layer cake inversions where inconclusive, however I did demonstrate that a better selection of the bandwidth did improve the result. Finally, the results from the complex subsurface model show that the instantaneous-phase coherency is able to resolve parts of the complex subsurface model.

CO2 and H2S are reinjected at Hellisheiði, Iceland, to reduce the emissions of greenhouse gases. An active-source seismic campaign was done in July, 2021, to image the reinjection reservoir with various seismic methods for consecutive monitoring. At the same time, passive-source seismic data was recorded for imaging.
We process this passive data with seismic interferometry. For this, an illumination analysis is performed to filter noise panels that are dominated by surface-wave noise and to keep panels dominated by body-wave noise. Afterwards, panels with near-vertical events are autocorrelated to retrieve a zero-offset section. The full set of selected panels is crosscorrelated to retrieve virtual shot records. These are processed with a simple reflection seismological processing workflow to obtain a stacked section.
The results show that the autocorrelated zero-offset sections appear more noisy, but are characterised by higher frequencies, while the crosscorrelated stacked sections are characterised by a lower-frequency content and contain more dipping reflectors. The major, horizontal reflectors correspond between the two types of sections.
A rudimentary interpretation of reflectors is done, based on the two types of sections and compared with a local geological model. This shows that the major lithological differences and the base of the Hengill volcano can be distinguished in the interpreted section.

In this thesis I conducted ground penetrating radar (GPR) and ground conductivity meter (GCM) surveys to detect the presence of simulated clandestine burials at the Amsterdam Research Initiative for Subsurface Taphonomy and Anthropology (ARISTA) test facility, and determine their characteristic response in this environment; providing valuable insights and recommendations for forensic investigations. I performed four days of GPR and GCM surveys over three simulated clandestine burials at ARISTA. I collected common-offset GPR data to investigate changes to burial detectability due to different central antenna frequencies (250 MHz and 500 MHz), different GPR instruments (NOGGIN or pulseEKKO), changes to survey grid orientation relative to burials, and increased soil moisture content in the survey area. Additionally, I acquired common-source GPR data to examine the efficacy of electromagnetic interferometry (EI) and adaptive subtraction (AS) methods in improving burial detectability. I conducted GCM surveys with two coil configurations, (horizontal co-planar (HCP) and vertical co-planar (VCP)), three intercoil spacings (0.32 m, 0.71 m, 1.18 m), two different line spacings (0.5 m, 0.25 m) and in the presence of variable soil moisture content. I also performed low induction number (LIN) correction and elevation correction procedures on GCM data to determine the extent to which these influence the detectability of clandestine burials in this environment.
In common-offset radargrams characteristic burial anomalies take on many forms, appearing as disruptions to existing features (direct-wave arrivals and soil horizons) and as isolated reflection events (hyperbolic events and burial length horizontal anomalies). In timeslices, burials are characterized by high or low amplitude rectangular anomalies. When used in conjunction, radargrams and timeslices produced characteristic responses regardless of survey grid orientation, consistent with the locations of the burials. Increased soil moisture at the site improved the detectability of burials and the 250 MHz antenna was found to be superior to the 500 MHz antenna in obtaining a characteristic burial response, though both were successful to a large extent. EI and AS processing techniques were successful in removing direct-wave contributions in radargrams, though detectability was not significantly improved when compared to raw data. Overall, the three burials were detected using GPR to various extents, and in future work thorough historical data in addition to zero-measurements should be obtained for all burials in order to investigate the source of these differences. GCM surveys conducted in this work were largely unsuccessful in detecting simulated clandestine burials due to significant conductive noise sources (metal fence, sensors, etc.) and the limited conductivity contrast in the soil. Low conductivity zones were detected over some burials using HCP at an intercoil spacing of 1.18 m, however, confidence in the validity of these responses is low due to the dominating noise sources.

The dolmens erected in the province of Drenthe between 3350-2700 BC are the most ancient monuments of the Netherlands. They consist of a long assemblage of rocks capped with boulders which served as burial chambers for the Funnel Beaker population. Despite their robust look, dolmens are vulnerable. For example, one of the caprocks of dolmen D14 fell off in 2019, once again. The method currently used to repair megaliths is not optimal: a crane is mobilized and the rocks are repositioned using a trial and error strategy. The reconstruction scenario should be selected and fine-tuned digitally beforehand. Moreover, the structural stability of the new assemblage of rocks should also be checked numerically. This necessitates a digital model of all the rocks that have to be rearranged. Not only the visible part of these rocks has to be digitized. The buried parts of the support rocks have to be modelled too as these rocks might have to be displaced and tilted to obtain a more compact and interlocked structure. Since dolmens occupy a prominent position in the Dutch heritage, only non-destructive see-through techniques have to be used for imaging the hidden contours of the bearing rocks. In addition, the bearing stones have complex geometries and are not isolated, which increases the complexity of the problem.

Two suitable geophysical methods are the Ground Penetrating Radar (GPR) and seismic techniques with a focus on reflection measurements. For the GPR specifically, we choose a Common Offset Survey, which can map reflections from the subsurface. For the seismic techniques, we choose a line array measurement, among others. We use the GPR to estimate the buried rock contour of the keystone Sl2 of megalith D14, which is a bearing stone formerly supporting capstone D9. We perform several reflection tests on various rocks unrelated to D14 using different seismic sources and receivers to estimate the reflection depths. We follow a proposed approach for both methods.

To evaluate the GPR data from the field, we assume a simplified GPR with zero-dimensional antennas (GPR point model). Subsequently, we develop two mathematical models (GPR point-to-GEO and GEO-to-GPR point model), based on this conceptual model in order to I) calculate the (buried) rock surfaces from field data and II) model field data from estimated buried rock contours.

We first perform the Common Offset Survey on a non-buried boulder on the campus of the TU Delft to evaluate the accuracy of the developed GPR point-to-GEO model and to optimise the second survey on keystone Sl2. We first perform the seismic reflection measurements on several rock samples to determine the best seismic source. Finally, we perform a line array measurement on a cylindrical basalt column using 300 kHz transducers.

We calculate rock contour coordinates from the GPR data and these show a reasonable fit with the contour of the TU Delft boulder, with an accuracy of 5-10 cm. For the keystone Sl2, the maximum burial depth is determined to be 80 cm at the southern side. The bottom of the keystone is sloping downward starting from ground level at the northern side. The southern, eastern and western rock faces are steep, almost vertical, which is confirmed by historic photographs. However, the calculated (buried) rock surface coordinates consist of an incoherent set of coordinates with locally a lack of data or blind-spots. Estimating a coherent buried rock contour, therefore, requires shortcuts and a decrease of accuracy is to be expected especially for rock surfaces near blind-spots in the GPR data. Furthermore, the identification of relevant reflection surfaces is rather subjective and combined with blind spots in the acquired GPR data, this can lead to wrongful interpretations of the buried rock contour.

The seismic reflection measurements we perform give clear reflections for the 300 kHz transducers on rocks of limited size with simple geometries. However, the transducers should first be applied on rocks with increasingly more complex geometries before being applied in the field. The accuracy in the order of 1 cm can be considered promising, but its applicability for complex geometries and reflection depths larger than 0.5 m remains unknown.

Geophysical monitoring is a popular tool in aquifer characterization and groundwater flow. To address this objective at groundwater extraction site ‘t Klooster, an ERT dataset was analyzed to identify groundwater flow patterns resulting from the injection of warm oxygenated water. Using a petrophysical model, changes in resistivity were converted to estimated temperature changes to visualize the spread of warm oxygenated water. Multi-dimensional analysis of the resistivity response of the subsurface was carried out. This allowed for the division of the subsurface into 4 depth regimes according to their response to well activity. It is shown that wells up to 100m removed from the ERT set-up influenced the temperature distribution. Furthermore, injected oxygenated water highlighted a preferential flow path between the depths of 20 and 35m in a north-west direction. This is in line with global groundwater flow in the area. Groundwater flow effects could not be reliably separated from the effect of well activity, however its effect is recognized both during extraction of groundwater and injection of warm water.

Regular Cartesian grid models provide satisfactory numeric results when a numerical scheme for reservoir flow simulation is applied. However, they cannot recreate complex geological features existing in realistic reservoir models such as faults and irregular reservoir boundaries. Corner point grids can represent these geological characteristics and can be adapted and represent any reservoir. In the subsurface reservoirs is usually typical to find fractures networks, and it is necessary to simulate the effect of them in reservoir models based on corner point grids. Although several works validate the precision of embedded Discrete Fracture Model (EDFM) for representing fractures in cartesian grids, very few studies have been presented to examine the accuracy of fracture modeling in geologically complex reservoir models. In this work, the novel discrete fracture model, the Projection-based Embedded Discrete Fracture Model (pEDFM), is implemented to represent fractures in reservoir models based on corner point grids. pEDFM provides additional features to the EDFM and is applied to explicitly and consistently define fractures. It implements independent grid sets for the fractures (described as lower-dimensional domains) and the rock matrix irrespective of the grid domains’ complex geometrical shapes. The suitability of the original pEDFM method has been expanded to a fully generic 3D geometry, and it lets on including fractures with any orientation on the corner point grid cells, an important development for the method’s viability in field-scale applications. Further to the geometrical flexibility of EDFM, matrix-matrix and fracture matrix connectivities are readapted to account for the projection of fracture plates on the interfaces. This allows for consistent modeling of fractures with generic conductivity values, from high conductive networks to impermeable flow barriers. A fully implicit scheme is used to get a discrete system with two main unknowns (i.e., pressure and phase saturation) on both matrix and fracture networks. Several 3D test cases of reservoirs models with complex corner point grids and fracture networks arbitrary designed in them are presented to demonstrate the devised method’s accuracy and applicability. The results show that the pEDFM implementation for two-phase flow is highly successful for modeling fractures with a broad range of conductivity on field-scale reservoir models.

In this research, a new measuring method to detect shear parameters in fluid mud is proposed, namely using shear waves, or S-waves opposed to the conventional pressure waves, or P-waves. Currently, detecting fluid mud is done by the use of P-waves. This paper shows that conventional P-waves velocity measurements are unsuitable for linking velocities to the yield stress, which is likely caused due to gas in the mud. Opposed to this, S-wave velocities do show an logarithmic increase, which most likely can be linked to the yield stress development of fluid mud. Both S-wave velocities and yield-point measurements show an exponential increase over time and, because of this, it could be that there is a linear relation between them. The difference why S-waves are more suitable for shear-strength measuring is due to their nature of propagation. S-waves do not propagate through gas and are therefore hardly effected by gas production, opposed to P-waves. For this research, both P-and S-wave velocities are estimated from a transmission seismic experiment. Also, a frequency analysis has been conducted showing large similarities over time and for dissimilarities between different mud samples. The yield-point measurements are derived from a rheometer with the same mud sample used for the seismic experiments. Besides a velocity analysis from the transmission measurements, also a reflection measurement has been conducted. The aim for this is to detect converted P-to S-waves and to detect the Scholte wave. Furthermore, a dispersion analysis has been conducted, which is likely important since the relative change in S-wave velocities is small and the velocities are frequency-dependent.

Coda wave interferometry is a technique used in the field of seismology, which utilizes the later part of the signal (coda) to detect subtle changes in a medium. In recent years, the application of coda wave interferometry to concrete structure has been assessed for structural health monitoring purposes. Smart aggregate is a sensor which consists of a piezoelectric sheet which is sandwiched between two marble layers which are meant to be used for structural health monitoring purposes by embedding it into concrete. However, its implementation for coda wave interferometry applications had not been attempted previously. In this research, the application of coda wave interferometry in concrete structures is explored further. The aim of this research is to assess the possibility of implementing coda wave interferometry to monitor the hydration process and the evolution of elasticity-modulus of concrete, as well as to learn how the wavespeed changes in concrete specimens subjected to cyclic loading in compression and bending. Additionally, seismic interferometry is also attempted to retrieve virtual impulse response to be used for coda wave interferometry. All experiments in this research utilize smart aggregates as transducers. By implementing coda wave interferometry, it is found that wavespeed does increase as concrete ages. This wavespeed increase can be linked to the evolution of elasticity-modulus of concrete, which enables its value to be monitored through the utilization of coda wave interferometry. It is also found that the use of embedded smart aggregate yields excellent reciprocity and stable correlation coefficient throughout the recording, while attached smart aggregates do not perform as well as the embedded ones in terms of reciprocity and correlation coefficient. Positive linear wavespeed change vs. stress and strain relationships in compressive samples are observed in lower stresses. In higher stresses, both wavespeed change vs. stress and strain display gradient reductions. Under repeated cyclic loadings, the loading phase of the first load cycle tend to have lower initial wavespeed change vs. stress and strain gradients compared to the following load steps, and the wavespeed change vs. stress and strain paths of reloading phases tend to follow the paths of their previous unloading phases. Wavespeed change vs. strain is more representative compared to wavespeed change vs. stress in depicting the compressive specimens’ condition due to the occurrence of permanent deformation during loadings. In a 10m-long beam specimen subjected to bending and shear, coda wave interferometry of later arrivals reveal decrease in wavespeed in the first loading phase of the test, while earlier arrivals show increase in wavespeed in the same phase. Moreover, it is possible to detect major crack formations by utilizing coda wave interferometry, which sensitivity is determined by the location of the cracks relative to the source-receiver sensors’ proximity. By assessing earlier arrivals of the signals recorded by smart aggregate implanted in the compression zone, the shift from uncracked to cracked section is observed through changes in wavespeed change vs load gradients. Seismic interferometry attempt was unsuccessful due to poor repeatability of the hammer hits and insufficient illumination to create diffuse wavefield.

Current Port of Rotterdam procedure is to define the nautical depth based on the density of a fluid-mud layer, that settles at the bottom of the port. Rheological parameters could be an improved indicator for nautical depth and decrease the frequency and cost of dredging. I investigate the use of reflected seismic waves to derive in situ rheological parameters. I model a simplified port profile to investigate the effects of o_set on the types of seismic reflections acquired. Four models of how the density of the fluid-mud layer varies with depth are proposed. I undertake simple laboratory experiments to measure the effects of consolidation time, density and frequency has on the seismic velocities of the fluid-mud layer. From these laboratory experiments I calculate the shear modulus, bulk modulus, Young's modulus and Poisson's ratio for the fluid-mud layer of varying densities. Reflected seismic-wave velocities prove to be promising in deriving in situ rheological parameters, elastic constants and, potentially, viscosity.

Aftercare of sanitary landfills represents a burden for future generations, for emission potential of leachate and gases remains for hundred of years. Treatment methods have to be developed in order to accelerate waste degradation and reduce emission potential preferably within the time-span of one generation. Aeration seems a promising treatment method but as yet has to be proven effective as a methodology to enhance waste degradation at full scale. Water content plays a crucial role in evaluating aeration, but the highly heterogeneous nature of a landfill body poses a big uncertainty in quantifying it and therefore also quantifying the effectiveness of aeration in reducing emission potential. To improve understanding of water within a waste body, Electrical Resistivity Tomography ERT is to be used to indirectly measure water content by obtaining electric resistivity information. However, full scale landfills have large areas and therefore a protocol needs to be developed for generating an optimum survey strategy, so that high resolution information is obtained while covering a large area. This thesis presents such a protocol consisting of four parts. First, optimum spread and spacing are defined by building a Pareto front with resolution and covered area as objective criteria. Second, array is designed in the previously defined grid, with standard and non-standard four-electrode configurations, by using a goodness function applied to multiple channel acquisition systems. Third, array design is tested with synthetic models showing that smooth resistivity models are well captured by data inversion, but array design performs poorly in a sharp resistivity model. Finally, practical aspects namely injection time, polarization effects and unstable configurations which are usually overlooked, are shown to have significant influence in data quality. This protocol is intended as a systematic approach to generate an optimum ERT survey strategy which could be extended to other geophysical methods.