E.C. Slob
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27 records found
1
This thesis investigates the potential of BIOMASS P-band data for the detection and characterization of buried paleochannels in the Kufrah Basin (eastern Sahara) and the Paleo-Kuiseb system (Namib Desert). A multi-indicator workflow was developed, integrating polarimetric amplitude and phase observations, dielectric-roughness inversion, soil moisture estimation, interferometric coherence, and coherence-derived effective penetration depth retrievals.
The results show that BIOMASS observations reveal numerous paleochannel features consistent with known drainage networks and geomorphological structures. Dielectric contrasts, polarimetric responses, coherence variations, and localized penetration depth anomalies frequently coincide with independently identified paleochannels. While no single observable provides unambiguous detection, the combined interpretation of multiple indicators yields robust evidence for subsurface fluvial features. The study demonstrates the potential of BIOMASS P-band SAR for regional-scale paleohydrological investigations and highlights its capability to provide new insights into subsurface geomorphology in arid environments. ...
This thesis investigates the potential of BIOMASS P-band data for the detection and characterization of buried paleochannels in the Kufrah Basin (eastern Sahara) and the Paleo-Kuiseb system (Namib Desert). A multi-indicator workflow was developed, integrating polarimetric amplitude and phase observations, dielectric-roughness inversion, soil moisture estimation, interferometric coherence, and coherence-derived effective penetration depth retrievals.
The results show that BIOMASS observations reveal numerous paleochannel features consistent with known drainage networks and geomorphological structures. Dielectric contrasts, polarimetric responses, coherence variations, and localized penetration depth anomalies frequently coincide with independently identified paleochannels. While no single observable provides unambiguous detection, the combined interpretation of multiple indicators yields robust evidence for subsurface fluvial features. The study demonstrates the potential of BIOMASS P-band SAR for regional-scale paleohydrological investigations and highlights its capability to provide new insights into subsurface geomorphology in arid environments.
Inversion of electromagnetic induction measurements
Inversion of data from rigid boom single-frequency multi-configuration instruments
Near-surface applications of electromagnetic geophysical methods are crucial for society because they provide cost-effective non-invasive ways to investigate the subsurface for water management, natural resources exploration, and environmental hazards. Therefore, the appropriate use and accuracy of the estimations using this measurements is vital for resource assessment, effective hazard mitigation, and reliable engineering projects by providing a precise understanding of subsurface conditions. In general, this geophysical method has been studied exhaustively, but is still challenging to understand the uncertainties associated.
This thesis presents a comprehensive study of inversion methodologies for frequency domain electromagnetic induction surveys, addressing both computational efficiency and model reliability. We start by studying the sensitivity of the measurements to the electrical conductivity of the subsurface. We analyze the impact of using linear approximations and compare them to a global search strategy using precomputed lookup tables of semi-analytic forward responses. The global search method enables rapid electrical conductivity model estimation, without iterative forward modeling. This approach is validated with synthetic and field datasets, demonstrating fast convergence and reliable recovery of horizontally 2-layered earth models. Furthermore, the study demonstrates the added value of using both quadrature and in-phase components of the measurements. Numerical experiments demonstrate that incorporating the in-phase response enhances sensitivity to conductivity and, as a result, improves result accuracy compared with quadrature-only inversions, providing new insights into subsurface structure.
Moreover, this thesis investigates the well-posedness of frequency-domain electromagnetic induction data inversion. This analysis demonstrates that the uniqueness of one-dimensional subsurface models with two layers is ensured only when both quadrature and in-phase measurements are incorporated, for multi-coil single frequency configurations. On the contrary, for three-layer models the solution remains underdetermined without additional prior constraints. These findings highlight the fundamental limits of the inversion problem and the importance of exploiting the full information contained in the data.
Additionally, the limitations of piecewise inversion (subdividing the subsurface into one-dimensional datasets) and laterally constrained inversion are assessed under realistic three-dimensional conditions. Simulations reveal that lateral conductivity variations, topographic effects, and instrument tilt introduce significant distortions, even for small deviations from ideal assumptions. Numerical examples and field applications demonstrate that, while laterally constrained inversion provides smoother results than piecewise inversion, both methods remain vulnerable to error propagation in complex environments.
Finally, this thesis proposes a novel approach based on machine learning inversion via gradient boosted decision trees to estimate layered subsurface electrical conductivity distributions fromthe measurements. This prediction algorithm provides three clear advantages: Firstly, the ability of the decision trees to provide individual influence weights to different measurement parameters allows for a better understanding of the sensitivity of the individual data values to the subsurface properties. Secondly, this machine learning algorithm learns the physical system’s non-linearity by understanding how the different data parameters interact. Finally, predictions from the electrical conductivity model
are generated without prior knowledge of the subsurface. Compared to optimization based estimations, the machine learning resulting predictions more closely match the true models.
Together, these contributions advance the knowledge about the inversion of electromagnetic induction measurements by evaluating estimation methodologies, analyzing data utilization, and critically evaluating their application into electrical conductivity distribution of the subsurface. The results emphasize both the potential and the limitations of current inversion strategies, and they motivate the development of forward modelling and inversion methodologies for realistic frequency-domain electromagnetic induction survey conditions. The main contribution of this study is an easily applicable machine learning estimationmethod without additional information of the subsurface. ...
Near-surface applications of electromagnetic geophysical methods are crucial for society because they provide cost-effective non-invasive ways to investigate the subsurface for water management, natural resources exploration, and environmental hazards. Therefore, the appropriate use and accuracy of the estimations using this measurements is vital for resource assessment, effective hazard mitigation, and reliable engineering projects by providing a precise understanding of subsurface conditions. In general, this geophysical method has been studied exhaustively, but is still challenging to understand the uncertainties associated.
This thesis presents a comprehensive study of inversion methodologies for frequency domain electromagnetic induction surveys, addressing both computational efficiency and model reliability. We start by studying the sensitivity of the measurements to the electrical conductivity of the subsurface. We analyze the impact of using linear approximations and compare them to a global search strategy using precomputed lookup tables of semi-analytic forward responses. The global search method enables rapid electrical conductivity model estimation, without iterative forward modeling. This approach is validated with synthetic and field datasets, demonstrating fast convergence and reliable recovery of horizontally 2-layered earth models. Furthermore, the study demonstrates the added value of using both quadrature and in-phase components of the measurements. Numerical experiments demonstrate that incorporating the in-phase response enhances sensitivity to conductivity and, as a result, improves result accuracy compared with quadrature-only inversions, providing new insights into subsurface structure.
Moreover, this thesis investigates the well-posedness of frequency-domain electromagnetic induction data inversion. This analysis demonstrates that the uniqueness of one-dimensional subsurface models with two layers is ensured only when both quadrature and in-phase measurements are incorporated, for multi-coil single frequency configurations. On the contrary, for three-layer models the solution remains underdetermined without additional prior constraints. These findings highlight the fundamental limits of the inversion problem and the importance of exploiting the full information contained in the data.
Additionally, the limitations of piecewise inversion (subdividing the subsurface into one-dimensional datasets) and laterally constrained inversion are assessed under realistic three-dimensional conditions. Simulations reveal that lateral conductivity variations, topographic effects, and instrument tilt introduce significant distortions, even for small deviations from ideal assumptions. Numerical examples and field applications demonstrate that, while laterally constrained inversion provides smoother results than piecewise inversion, both methods remain vulnerable to error propagation in complex environments.
Finally, this thesis proposes a novel approach based on machine learning inversion via gradient boosted decision trees to estimate layered subsurface electrical conductivity distributions fromthe measurements. This prediction algorithm provides three clear advantages: Firstly, the ability of the decision trees to provide individual influence weights to different measurement parameters allows for a better understanding of the sensitivity of the individual data values to the subsurface properties. Secondly, this machine learning algorithm learns the physical system’s non-linearity by understanding how the different data parameters interact. Finally, predictions from the electrical conductivity model
are generated without prior knowledge of the subsurface. Compared to optimization based estimations, the machine learning resulting predictions more closely match the true models.
Together, these contributions advance the knowledge about the inversion of electromagnetic induction measurements by evaluating estimation methodologies, analyzing data utilization, and critically evaluating their application into electrical conductivity distribution of the subsurface. The results emphasize both the potential and the limitations of current inversion strategies, and they motivate the development of forward modelling and inversion methodologies for realistic frequency-domain electromagnetic induction survey conditions. The main contribution of this study is an easily applicable machine learning estimationmethod without additional information of the subsurface.
Frequency-Domain Wideband Ground Penetrating Radar Modelling
Using Finite Elements and Perfectly Matched Layers
This thesis evaluates the feasibility and advances the methodology of two key geophysical approaches for reservoir monitoring: the controlled-source electromagnetic (CSEM) method and the full waveform inversion (FWI) of seismic data. The research is grounded in two study areas: the Delft campus geothermal project in the Netherlands and the Munich geothermal project in Germany.
A feasibility study of CSEM monitoring was carried out on the Delft site to assess its sensitivity to subtle resistivity variations corresponding to temperature changes in the reservoir. Surface-to-borehole CSEM survey configuration was modeled to optimize source frequency and offset, with results demonstrating the detectability of a 4 Ω・m resistivity increase calculated for a 25 ◦C temperature drop in the Delft Sandstone reservoir. The study systematically analyzed the impacts of environmental disturbances—random noise, repeatability errors, seasonal near-surface temperature fluctuations, and the presence of steel-cased wells—on the performance of CSEM monitoring data. It was shown that a careful survey design and adequate source parameters allow CSEM monitoring, which is robust against most undesired effects, although steel casings require careful consideration due to their strong field attenuation within a radius of 100 m for a frequency of 1 Hz.
For high-resolution seismic characterization, the thesis develops and validates a novel sequential FWI approach for reconstructing high-resolution models of P-wave velocity and impedance from vertical seismic profiling (VSP) data. The method incorporates traveltime tomography for starting models and introduces a temporal phase resemblance step to improve convergence and mitigate phase error propagation in impedance inversion. Inversion experiments of synthetic data demonstrate that this approach enables the detection of impedance variations greater than 2 %, directly linked to temperature-driven reservoir changes. Field application to baseline VSP data at the Munich geothermal site confirms the robustness of the approach. A comparative analysis of distributed acoustic sensing (DAS) and conventional geophone-based FWI of P-wave velocity further elucidates the operational benefits and challenges of fiber-optic deployments inside the casing for characterization of geothermal reservoirs.
The results presented in this thesis establish CSEM and advanced seismic FWI as promising and complementary tools for noninvasive monitoring of low-enthalpy geothermal reservoirs. The work concludes with a discussion of current limitations, practical considerations for field deployment, and recommendations for future research.
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This thesis evaluates the feasibility and advances the methodology of two key geophysical approaches for reservoir monitoring: the controlled-source electromagnetic (CSEM) method and the full waveform inversion (FWI) of seismic data. The research is grounded in two study areas: the Delft campus geothermal project in the Netherlands and the Munich geothermal project in Germany.
A feasibility study of CSEM monitoring was carried out on the Delft site to assess its sensitivity to subtle resistivity variations corresponding to temperature changes in the reservoir. Surface-to-borehole CSEM survey configuration was modeled to optimize source frequency and offset, with results demonstrating the detectability of a 4 Ω・m resistivity increase calculated for a 25 ◦C temperature drop in the Delft Sandstone reservoir. The study systematically analyzed the impacts of environmental disturbances—random noise, repeatability errors, seasonal near-surface temperature fluctuations, and the presence of steel-cased wells—on the performance of CSEM monitoring data. It was shown that a careful survey design and adequate source parameters allow CSEM monitoring, which is robust against most undesired effects, although steel casings require careful consideration due to their strong field attenuation within a radius of 100 m for a frequency of 1 Hz.
For high-resolution seismic characterization, the thesis develops and validates a novel sequential FWI approach for reconstructing high-resolution models of P-wave velocity and impedance from vertical seismic profiling (VSP) data. The method incorporates traveltime tomography for starting models and introduces a temporal phase resemblance step to improve convergence and mitigate phase error propagation in impedance inversion. Inversion experiments of synthetic data demonstrate that this approach enables the detection of impedance variations greater than 2 %, directly linked to temperature-driven reservoir changes. Field application to baseline VSP data at the Munich geothermal site confirms the robustness of the approach. A comparative analysis of distributed acoustic sensing (DAS) and conventional geophone-based FWI of P-wave velocity further elucidates the operational benefits and challenges of fiber-optic deployments inside the casing for characterization of geothermal reservoirs.
The results presented in this thesis establish CSEM and advanced seismic FWI as promising and complementary tools for noninvasive monitoring of low-enthalpy geothermal reservoirs. The work concludes with a discussion of current limitations, practical considerations for field deployment, and recommendations for future research.
Reverse-Time Migration (RTM) is a common depth migration technique that migrates recorded wavefields from the space-time domain to the space-depth domain. It utilizes the Born approximation and the adjoint of the Born operator to produce an RTM image. However, RTM can suffer from errors, such as noise, temporal and spatial limitations, and multiple reflections.
Least-Squares Reverse-Time Migration (LSRTM) is used to overcome some of these errors. LSRTM involves resolving the reflectivity model by least-squares inversion, which is computationally expensive. Gradient-based optimization algorithms are often employed to reduce the computational burden, but they still require solving the wave equation and its adjoint for a large model in multiple iterations. One way to reduce the computational cost is by limiting the computational domain to a target region of interest.
Target-oriented LSRTM, known as TOLSRTM, focuses on the wavefield just above the target by bypassing the overburden. This approach proves beneficial when the overburden generates strong internal multiple reflections that obscure the reflections from the target area. However, a redatuming method is required to predict all orders of multiples. Marchenko redatuming is a data-driven technique that predicts the Green's functions at the boundary of the target region, incorporating all orders of internal multiples. It allows for double-sided redatuming, considering both the source and receiver perspectives. By combining the LSRTM algorithm and Marchenko double-focusing, a target-oriented LSRTM algorithm is devised that can predict interactions between the target and overburden and remove the effects of the overburden in the image. Predicting these interactions results in an artifact-free image, a better convergence rate, and a high-resolution image of the target.
Target-oriented migration algorithms typically consider only the upper horizontal boundary of the region of interest (ROI), neglecting wavefields entering the ROI from the medium beneath the lower boundary. To address this, a target-enclosed LSRTM algorithm is proposed, including both the ROI's upper and lower boundaries. Including the lower boundary provides transmission information and can improve inversion convergence. In addition, this algorithm is adopted for virtual receivers created by Marchenko redatuming. In the case of physical receivers at the boundaries of the target zone, the target-enclosed algorithm can incorporate the transmission information emanating from the lower boundary to the upper one. Consequently, when the initial model is far from the actual model, the resulting image partly recovers the long wavelength part of the model in agreement with the Born approximation criteria. Moreover, when an initial model closer to the actual model is used, the algorithm can partially recover the vertical interfaces of the perturbation. In the case of virtual receivers at the boundaries of the target zone, since the Marchenko redatuming is performed in the initial background model, the redatumed wavefields at the lower boundary suffer from kinematic errors. Therefore, the algorithm can not recover the long wavelength part of the model.
The thesis concludes with a discussion of the results obtained from applying the algorithms to marine datasets. The images resulting from the Marchenko double-focusing based target-oriented LSRTM algorithm show improvements in both resolution and artifact reduction by suppressing the overburden generated internal multiple effects. Moreover, the double-focusing enables the user to reduce the computational costs of the LSRTM algorithm and choose finer spatial sampling for the image.
An appendix proposes a formulation for integrating the target-oriented algorithms with non-linear inversion like Full Waveform Inversion (FWI). The results of this proposed algorithm show its effectiveness by reducing the internal multiple related artifacts and increasing resolution and faster convergence. ...
Reverse-Time Migration (RTM) is a common depth migration technique that migrates recorded wavefields from the space-time domain to the space-depth domain. It utilizes the Born approximation and the adjoint of the Born operator to produce an RTM image. However, RTM can suffer from errors, such as noise, temporal and spatial limitations, and multiple reflections.
Least-Squares Reverse-Time Migration (LSRTM) is used to overcome some of these errors. LSRTM involves resolving the reflectivity model by least-squares inversion, which is computationally expensive. Gradient-based optimization algorithms are often employed to reduce the computational burden, but they still require solving the wave equation and its adjoint for a large model in multiple iterations. One way to reduce the computational cost is by limiting the computational domain to a target region of interest.
Target-oriented LSRTM, known as TOLSRTM, focuses on the wavefield just above the target by bypassing the overburden. This approach proves beneficial when the overburden generates strong internal multiple reflections that obscure the reflections from the target area. However, a redatuming method is required to predict all orders of multiples. Marchenko redatuming is a data-driven technique that predicts the Green's functions at the boundary of the target region, incorporating all orders of internal multiples. It allows for double-sided redatuming, considering both the source and receiver perspectives. By combining the LSRTM algorithm and Marchenko double-focusing, a target-oriented LSRTM algorithm is devised that can predict interactions between the target and overburden and remove the effects of the overburden in the image. Predicting these interactions results in an artifact-free image, a better convergence rate, and a high-resolution image of the target.
Target-oriented migration algorithms typically consider only the upper horizontal boundary of the region of interest (ROI), neglecting wavefields entering the ROI from the medium beneath the lower boundary. To address this, a target-enclosed LSRTM algorithm is proposed, including both the ROI's upper and lower boundaries. Including the lower boundary provides transmission information and can improve inversion convergence. In addition, this algorithm is adopted for virtual receivers created by Marchenko redatuming. In the case of physical receivers at the boundaries of the target zone, the target-enclosed algorithm can incorporate the transmission information emanating from the lower boundary to the upper one. Consequently, when the initial model is far from the actual model, the resulting image partly recovers the long wavelength part of the model in agreement with the Born approximation criteria. Moreover, when an initial model closer to the actual model is used, the algorithm can partially recover the vertical interfaces of the perturbation. In the case of virtual receivers at the boundaries of the target zone, since the Marchenko redatuming is performed in the initial background model, the redatumed wavefields at the lower boundary suffer from kinematic errors. Therefore, the algorithm can not recover the long wavelength part of the model.
The thesis concludes with a discussion of the results obtained from applying the algorithms to marine datasets. The images resulting from the Marchenko double-focusing based target-oriented LSRTM algorithm show improvements in both resolution and artifact reduction by suppressing the overburden generated internal multiple effects. Moreover, the double-focusing enables the user to reduce the computational costs of the LSRTM algorithm and choose finer spatial sampling for the image.
An appendix proposes a formulation for integrating the target-oriented algorithms with non-linear inversion like Full Waveform Inversion (FWI). The results of this proposed algorithm show its effectiveness by reducing the internal multiple related artifacts and increasing resolution and faster convergence.
Time-lapse monitoring with virtual seismology
Applications of the Marchenko method for observing time-lapse changes in subsurface reservoirs
...
For delaminations, machine learning algorithms, first the EM algorithm and then the YOLO v3 algorithm, were used as an attempt to highlight and detect them. Though the results still need improving, it is still valuable that the workload for human intervention can be alleviated with the help of these algorithms and that better performance can be expected based on current work, with the increasing amount of data with high quality achieved in the future. ...
For delaminations, machine learning algorithms, first the EM algorithm and then the YOLO v3 algorithm, were used as an attempt to highlight and detect them. Though the results still need improving, it is still valuable that the workload for human intervention can be alleviated with the help of these algorithms and that better performance can be expected based on current work, with the increasing amount of data with high quality achieved in the future.
Vector and total field data inversions for magnetometer UXO surveys in the North Sea
An alternative to the industry standard
First, both the TMI and vector data inversion are tested on 3000 noise-free synthetic datasets located in the North Sea where a prolate spheroid with varying shape, orientation and magnetic susceptibility creates an anomaly field. The largest global root mean square (GNRMS) error for the point-source inversion of the Gauss-Newton vector data inversion has a value in the order 10−9 whereas the Basin-hopping inversion for the TMI data returns for only 61% of the cases a GNRMS error in the order 10−7 or lower.
The result of the point-source inversion serves as a plateau for the prolate spheroid inversion; when the point-source inversion does not reach the set level of accuracy, it cannot be determined if the prolate spheroid model describes the true object well. For both the TMI and vector data, the UXO was correctly classified in 72% of the 3000 datasets.
Second, comparing the current industry standard to the Basin-hopping point-source inversion only gave inconclusive results. The estimated centre location of both methods are within a 0.15 m radius but the disagreement in magnetic moment leaves an advice on which technique would work better indecipherable. ...
First, both the TMI and vector data inversion are tested on 3000 noise-free synthetic datasets located in the North Sea where a prolate spheroid with varying shape, orientation and magnetic susceptibility creates an anomaly field. The largest global root mean square (GNRMS) error for the point-source inversion of the Gauss-Newton vector data inversion has a value in the order 10−9 whereas the Basin-hopping inversion for the TMI data returns for only 61% of the cases a GNRMS error in the order 10−7 or lower.
The result of the point-source inversion serves as a plateau for the prolate spheroid inversion; when the point-source inversion does not reach the set level of accuracy, it cannot be determined if the prolate spheroid model describes the true object well. For both the TMI and vector data, the UXO was correctly classified in 72% of the 3000 datasets.
Second, comparing the current industry standard to the Basin-hopping point-source inversion only gave inconclusive results. The estimated centre location of both methods are within a 0.15 m radius but the disagreement in magnetic moment leaves an advice on which technique would work better indecipherable.
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. ...
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.
Forward modeling for electromagnetic subsea cable tracking
A better understanding of offshore wind turbine power cables
provide information about the shape of the object. This thesis focuses on modeling a prolate spheroid, that not only recovers the position and the magnetic moment moment, but also the orientation and the dimensions of body. The results were fairly similar, though the residuals in the case of the sphere were less than the ones of the spheroid. In addition, measurements of real UXOS were used to verify the code. The inversion yielded reasonable models in four cases, but did not manage to recover an accurate model for one them. Lastly, the octupole moment was modeled as well, though its contribution to the results was deemed negligible. ...
provide information about the shape of the object. This thesis focuses on modeling a prolate spheroid, that not only recovers the position and the magnetic moment moment, but also the orientation and the dimensions of body. The results were fairly similar, though the residuals in the case of the sphere were less than the ones of the spheroid. In addition, measurements of real UXOS were used to verify the code. The inversion yielded reasonable models in four cases, but did not manage to recover an accurate model for one them. Lastly, the octupole moment was modeled as well, though its contribution to the results was deemed negligible.
Marine Unexploded Ordnance Detection with the Transient Electromagnetic Method
A Numerical Feasibility Study
Inversion algorithm development for passive electromagnetic detection of line sources
Prototyping a submarine power cable tracking system