K. Löer
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1
Ambient noise seismic interferometry oers a cost-eective and flexible approach to subsurface imaging, but the standard method relies on an equipartitioned wavefield assumption that is frequently overlooked. In real data, directional source biases systematically distort retrieved Green’s functions, leading to apparent velocities many times higher than physically expected values. This systematic error represents a critical limitation that remains under-explored in the literature.
We present a novel solution leveraging three-component beamforming to identify and correct directional wavefield bias in noise cross-correlation interferometry. We develop a method that uses careful time window selection based o of beamforming results to synthetically create a source distribution favourable for Green’s function retrieval. This hybrid approach leverages the complementary strengths of beamforming and interferometric methods.
Applied to real seismic data, our time window selection method significantly improves Green’s function retrieval without additional computational overhead. We establish a framework for systematic refinement of this technique and demonstrate that accounting for directional bias is essential for reliable ambient noise imaging. ...
We present a novel solution leveraging three-component beamforming to identify and correct directional wavefield bias in noise cross-correlation interferometry. We develop a method that uses careful time window selection based o of beamforming results to synthetically create a source distribution favourable for Green’s function retrieval. This hybrid approach leverages the complementary strengths of beamforming and interferometric methods.
Applied to real seismic data, our time window selection method significantly improves Green’s function retrieval without additional computational overhead. We establish a framework for systematic refinement of this technique and demonstrate that accounting for directional bias is essential for reliable ambient noise imaging. ...
Ambient noise seismic interferometry oers a cost-eective and flexible approach to subsurface imaging, but the standard method relies on an equipartitioned wavefield assumption that is frequently overlooked. In real data, directional source biases systematically distort retrieved Green’s functions, leading to apparent velocities many times higher than physically expected values. This systematic error represents a critical limitation that remains under-explored in the literature.
We present a novel solution leveraging three-component beamforming to identify and correct directional wavefield bias in noise cross-correlation interferometry. We develop a method that uses careful time window selection based o of beamforming results to synthetically create a source distribution favourable for Green’s function retrieval. This hybrid approach leverages the complementary strengths of beamforming and interferometric methods.
Applied to real seismic data, our time window selection method significantly improves Green’s function retrieval without additional computational overhead. We establish a framework for systematic refinement of this technique and demonstrate that accounting for directional bias is essential for reliable ambient noise imaging.
We present a novel solution leveraging three-component beamforming to identify and correct directional wavefield bias in noise cross-correlation interferometry. We develop a method that uses careful time window selection based o of beamforming results to synthetically create a source distribution favourable for Green’s function retrieval. This hybrid approach leverages the complementary strengths of beamforming and interferometric methods.
Applied to real seismic data, our time window selection method significantly improves Green’s function retrieval without additional computational overhead. We establish a framework for systematic refinement of this technique and demonstrate that accounting for directional bias is essential for reliable ambient noise imaging.
A significant share of Europe's traffic infrastructure was constructed during the economic boom of the 1950s and 1960s. Since then, traffic volumes and axle loads have increased substantially, while many of these structures are now approaching or exceeding their original design life. Due to outdated design codes, vintage detailing, and uncertainties in construction execution, their remaining load-bearing capacity is often unclear. Given the limited resources available for structural renewal, reliable and cost-effective methods for Structural Health Monitoring (SHM) are vital.
This thesis investigates the feasibility of using traffic noise interferometry for the structural assessment of existing concrete structures. The approach relies on established methods from seismology, where cross-correlation of ambient noise signals recorded at two receiver locations are used to estimate Green's function, the transfer function between the two locations, and create an image of the subsurface.
Two measurement campaigns were conducted in the Maastunnel in Rotterdam, using piezoelectric sensors attached to the bottom surface of the concrete road slab. The first campaign focused on characterising the nature of the traffic noise. It was found that the recorded signals feature long periods of instrument noise and short regions of elevated amplitude corresponding to passing vehicle axles. These axle passings last approximately 0.1–0.15 s and show a high response in a frequency range of 20–50 kHz. The transient rather than ambient nature of the noise source is an important distinction from seismic applications and requires a dedicated signal extraction algorithm, which was developed as part of this work.
The second campaign focused on Green's function estimation and the investigation of influential factors. For this purpose, the extracted axle passings were pre-processed by a range of schemes for temporal and spectral normalization and subsequently cross-correlated and stacked. Results showed that coherent Green's function estimates can be obtained at a sensor spacing of 0.25 m with a stacking duration of approximately 1 second of axle passings. Spectral whitening was found to be beneficial but not strictly necessary. Apart from that, it was found that excluding the central 0.01 s of each axle passing and stacking the early and late parts yields an improved estimation quality and clearer time lag peaks.
Furthermore, the sensitivity of the estimated Green's function to structural damage was explored. In a region with a prominent crack oriented perpendicular to the wave propagation direction, an increase in wave travel time of 20–30% was observed. While this observation requires validation, it suggests that the method can detect structural damage. The influence of vehicle type and size was also investigated, though no significant differences were observed. Environmental factors, e.g., temperature, were found to be stable within the tunnel environment and did not significantly affect the results.
Overall, this research provides a first proof of concept for the use of traffic noise interferometry as a passive SHM tool for concrete structures. While the estimation of Green's function and an influence of cracks could be demonstrated, aspects such as stress and strain state estimation and the influence of varying environmental conditions remain open for future investigation. ...
This thesis investigates the feasibility of using traffic noise interferometry for the structural assessment of existing concrete structures. The approach relies on established methods from seismology, where cross-correlation of ambient noise signals recorded at two receiver locations are used to estimate Green's function, the transfer function between the two locations, and create an image of the subsurface.
Two measurement campaigns were conducted in the Maastunnel in Rotterdam, using piezoelectric sensors attached to the bottom surface of the concrete road slab. The first campaign focused on characterising the nature of the traffic noise. It was found that the recorded signals feature long periods of instrument noise and short regions of elevated amplitude corresponding to passing vehicle axles. These axle passings last approximately 0.1–0.15 s and show a high response in a frequency range of 20–50 kHz. The transient rather than ambient nature of the noise source is an important distinction from seismic applications and requires a dedicated signal extraction algorithm, which was developed as part of this work.
The second campaign focused on Green's function estimation and the investigation of influential factors. For this purpose, the extracted axle passings were pre-processed by a range of schemes for temporal and spectral normalization and subsequently cross-correlated and stacked. Results showed that coherent Green's function estimates can be obtained at a sensor spacing of 0.25 m with a stacking duration of approximately 1 second of axle passings. Spectral whitening was found to be beneficial but not strictly necessary. Apart from that, it was found that excluding the central 0.01 s of each axle passing and stacking the early and late parts yields an improved estimation quality and clearer time lag peaks.
Furthermore, the sensitivity of the estimated Green's function to structural damage was explored. In a region with a prominent crack oriented perpendicular to the wave propagation direction, an increase in wave travel time of 20–30% was observed. While this observation requires validation, it suggests that the method can detect structural damage. The influence of vehicle type and size was also investigated, though no significant differences were observed. Environmental factors, e.g., temperature, were found to be stable within the tunnel environment and did not significantly affect the results.
Overall, this research provides a first proof of concept for the use of traffic noise interferometry as a passive SHM tool for concrete structures. While the estimation of Green's function and an influence of cracks could be demonstrated, aspects such as stress and strain state estimation and the influence of varying environmental conditions remain open for future investigation. ...
A significant share of Europe's traffic infrastructure was constructed during the economic boom of the 1950s and 1960s. Since then, traffic volumes and axle loads have increased substantially, while many of these structures are now approaching or exceeding their original design life. Due to outdated design codes, vintage detailing, and uncertainties in construction execution, their remaining load-bearing capacity is often unclear. Given the limited resources available for structural renewal, reliable and cost-effective methods for Structural Health Monitoring (SHM) are vital.
This thesis investigates the feasibility of using traffic noise interferometry for the structural assessment of existing concrete structures. The approach relies on established methods from seismology, where cross-correlation of ambient noise signals recorded at two receiver locations are used to estimate Green's function, the transfer function between the two locations, and create an image of the subsurface.
Two measurement campaigns were conducted in the Maastunnel in Rotterdam, using piezoelectric sensors attached to the bottom surface of the concrete road slab. The first campaign focused on characterising the nature of the traffic noise. It was found that the recorded signals feature long periods of instrument noise and short regions of elevated amplitude corresponding to passing vehicle axles. These axle passings last approximately 0.1–0.15 s and show a high response in a frequency range of 20–50 kHz. The transient rather than ambient nature of the noise source is an important distinction from seismic applications and requires a dedicated signal extraction algorithm, which was developed as part of this work.
The second campaign focused on Green's function estimation and the investigation of influential factors. For this purpose, the extracted axle passings were pre-processed by a range of schemes for temporal and spectral normalization and subsequently cross-correlated and stacked. Results showed that coherent Green's function estimates can be obtained at a sensor spacing of 0.25 m with a stacking duration of approximately 1 second of axle passings. Spectral whitening was found to be beneficial but not strictly necessary. Apart from that, it was found that excluding the central 0.01 s of each axle passing and stacking the early and late parts yields an improved estimation quality and clearer time lag peaks.
Furthermore, the sensitivity of the estimated Green's function to structural damage was explored. In a region with a prominent crack oriented perpendicular to the wave propagation direction, an increase in wave travel time of 20–30% was observed. While this observation requires validation, it suggests that the method can detect structural damage. The influence of vehicle type and size was also investigated, though no significant differences were observed. Environmental factors, e.g., temperature, were found to be stable within the tunnel environment and did not significantly affect the results.
Overall, this research provides a first proof of concept for the use of traffic noise interferometry as a passive SHM tool for concrete structures. While the estimation of Green's function and an influence of cracks could be demonstrated, aspects such as stress and strain state estimation and the influence of varying environmental conditions remain open for future investigation.
This thesis investigates the feasibility of using traffic noise interferometry for the structural assessment of existing concrete structures. The approach relies on established methods from seismology, where cross-correlation of ambient noise signals recorded at two receiver locations are used to estimate Green's function, the transfer function between the two locations, and create an image of the subsurface.
Two measurement campaigns were conducted in the Maastunnel in Rotterdam, using piezoelectric sensors attached to the bottom surface of the concrete road slab. The first campaign focused on characterising the nature of the traffic noise. It was found that the recorded signals feature long periods of instrument noise and short regions of elevated amplitude corresponding to passing vehicle axles. These axle passings last approximately 0.1–0.15 s and show a high response in a frequency range of 20–50 kHz. The transient rather than ambient nature of the noise source is an important distinction from seismic applications and requires a dedicated signal extraction algorithm, which was developed as part of this work.
The second campaign focused on Green's function estimation and the investigation of influential factors. For this purpose, the extracted axle passings were pre-processed by a range of schemes for temporal and spectral normalization and subsequently cross-correlated and stacked. Results showed that coherent Green's function estimates can be obtained at a sensor spacing of 0.25 m with a stacking duration of approximately 1 second of axle passings. Spectral whitening was found to be beneficial but not strictly necessary. Apart from that, it was found that excluding the central 0.01 s of each axle passing and stacking the early and late parts yields an improved estimation quality and clearer time lag peaks.
Furthermore, the sensitivity of the estimated Green's function to structural damage was explored. In a region with a prominent crack oriented perpendicular to the wave propagation direction, an increase in wave travel time of 20–30% was observed. While this observation requires validation, it suggests that the method can detect structural damage. The influence of vehicle type and size was also investigated, though no significant differences were observed. Environmental factors, e.g., temperature, were found to be stable within the tunnel environment and did not significantly affect the results.
Overall, this research provides a first proof of concept for the use of traffic noise interferometry as a passive SHM tool for concrete structures. While the estimation of Green's function and an influence of cracks could be demonstrated, aspects such as stress and strain state estimation and the influence of varying environmental conditions remain open for future investigation.
Characterization of mine tailings using seismic ambient-noise methods
A case study of the Bäckegruvan tailings in central Sweden
Master thesis
(2025)
-
J.T.J. van Meulebrouck, Ayse Kaslilar, D.S. Draganov, K. Löer, Florian Wagner
With an increasing worldwide demand for critical raw materials, among which Rare Earth Elements (REE), mine-waste deposits in Sweden are considered as secondary sources of critical minerals. These so-called mine tailings are abundant, and located close to the surface, facilitating their excavation. To assess the economic potential of these tailings as a source of critical minerals, their volume must be estimated. The horizontal-to-vertical spectral ratio (HVSR) method, which estimates the fundamental frequency of sedimentary sites, is particularly suited for the purpose of mapping the depth-to-bedrock. We apply this method to three-component ambient-noise recordings that were recently collected over the Bäckegruvan mine tailings in the Bergslagen province, central Sweden, to delineate their depth.
Additionally, we employ 3C beamforming to characterize the ambient-noise wavefield, with the aim of supporting HVSR curve interpretation. However, the results of the 3C beamforming prove unreliable, which we attribute primarily to the array's suboptimal geometry - specifically, its sparse station spacing and highly anisotropic wavenumber resolution.
We estimate the thickness of tailings at each station by combining the fundamental site frequency with a constant S-wave velocity. We then interpolate these individual measurements to construct a three-dimensional model of the tailings-bedrock interface, from which we derive the total volume of the tailings deposit. To assess the reliability of our model, we compare the interpolated depths with findings from a recent investigation in the same region, which utilized geoelectrics and electromagnetic geophysical methods. Comparing the results, we find strong concordance between the two approaches.
Our case study highlights the HVSR method’s potential as a cost-effective, fast, and robust approach for obtaining a preliminary estimate of mine-tailings depth. ...
Additionally, we employ 3C beamforming to characterize the ambient-noise wavefield, with the aim of supporting HVSR curve interpretation. However, the results of the 3C beamforming prove unreliable, which we attribute primarily to the array's suboptimal geometry - specifically, its sparse station spacing and highly anisotropic wavenumber resolution.
We estimate the thickness of tailings at each station by combining the fundamental site frequency with a constant S-wave velocity. We then interpolate these individual measurements to construct a three-dimensional model of the tailings-bedrock interface, from which we derive the total volume of the tailings deposit. To assess the reliability of our model, we compare the interpolated depths with findings from a recent investigation in the same region, which utilized geoelectrics and electromagnetic geophysical methods. Comparing the results, we find strong concordance between the two approaches.
Our case study highlights the HVSR method’s potential as a cost-effective, fast, and robust approach for obtaining a preliminary estimate of mine-tailings depth. ...
With an increasing worldwide demand for critical raw materials, among which Rare Earth Elements (REE), mine-waste deposits in Sweden are considered as secondary sources of critical minerals. These so-called mine tailings are abundant, and located close to the surface, facilitating their excavation. To assess the economic potential of these tailings as a source of critical minerals, their volume must be estimated. The horizontal-to-vertical spectral ratio (HVSR) method, which estimates the fundamental frequency of sedimentary sites, is particularly suited for the purpose of mapping the depth-to-bedrock. We apply this method to three-component ambient-noise recordings that were recently collected over the Bäckegruvan mine tailings in the Bergslagen province, central Sweden, to delineate their depth.
Additionally, we employ 3C beamforming to characterize the ambient-noise wavefield, with the aim of supporting HVSR curve interpretation. However, the results of the 3C beamforming prove unreliable, which we attribute primarily to the array's suboptimal geometry - specifically, its sparse station spacing and highly anisotropic wavenumber resolution.
We estimate the thickness of tailings at each station by combining the fundamental site frequency with a constant S-wave velocity. We then interpolate these individual measurements to construct a three-dimensional model of the tailings-bedrock interface, from which we derive the total volume of the tailings deposit. To assess the reliability of our model, we compare the interpolated depths with findings from a recent investigation in the same region, which utilized geoelectrics and electromagnetic geophysical methods. Comparing the results, we find strong concordance between the two approaches.
Our case study highlights the HVSR method’s potential as a cost-effective, fast, and robust approach for obtaining a preliminary estimate of mine-tailings depth.
Additionally, we employ 3C beamforming to characterize the ambient-noise wavefield, with the aim of supporting HVSR curve interpretation. However, the results of the 3C beamforming prove unreliable, which we attribute primarily to the array's suboptimal geometry - specifically, its sparse station spacing and highly anisotropic wavenumber resolution.
We estimate the thickness of tailings at each station by combining the fundamental site frequency with a constant S-wave velocity. We then interpolate these individual measurements to construct a three-dimensional model of the tailings-bedrock interface, from which we derive the total volume of the tailings deposit. To assess the reliability of our model, we compare the interpolated depths with findings from a recent investigation in the same region, which utilized geoelectrics and electromagnetic geophysical methods. Comparing the results, we find strong concordance between the two approaches.
Our case study highlights the HVSR method’s potential as a cost-effective, fast, and robust approach for obtaining a preliminary estimate of mine-tailings depth.
With the growing concern of aging infrastructures, the need for effective and non-intrusive monitoring techniques has become increasingly important. While most current methods rely on active testing, this study explores the potential of using ambient noise interferometry as a passive method for Structural Health Monitoring (SHM) of concrete structures. It investigates whether the Green's function (GF) of concrete medium can be adequately estimated from traffic noise to assess its health condition.
Two datasets are examined: a validation dataset from a laboratory experiment simulating ambient noise on a pre-stressed concrete girder, and real-world traffic noise data from the Maastunnel in Rotterdam. For each dataset, the following aspects are analyzed: (1) signal characteristics, including amplitudes and frequency distributions; (2) the optimal pre-processing scheme, incorporating temporal and spectral normalization, along with frequency filtering; and (3) the coherence of the resulting GF estimation from interferometry, particularly time of wave arrivals.
The results from the validation dataset demonstrate that ambient noise interferometry can reliably reconstruct the GF for concrete medium, indicating its effectiveness for monitoring changes such as crack formation and strain changes. However, the analysis of actual traffic noise data did not provide sufficient evidence to support its use for SHM with the current setup. Although a coherent and usable frequency range for traffic noise was identified, the limited amount of data led to a low signal-to-noise ratio (SNR), which made it challenging to highlight relevant features.
Moving forward, future researchers are encouraged to collect sufficient amount of data for analysis to better determine the feasibility of reconstructing the GF with ambient traffic noise. Additionally, exploring alternative sampling methods like continuous recording could address one of the limitations of this research. Finally, employing decomposition methods may help in increasing the SNR. ...
Two datasets are examined: a validation dataset from a laboratory experiment simulating ambient noise on a pre-stressed concrete girder, and real-world traffic noise data from the Maastunnel in Rotterdam. For each dataset, the following aspects are analyzed: (1) signal characteristics, including amplitudes and frequency distributions; (2) the optimal pre-processing scheme, incorporating temporal and spectral normalization, along with frequency filtering; and (3) the coherence of the resulting GF estimation from interferometry, particularly time of wave arrivals.
The results from the validation dataset demonstrate that ambient noise interferometry can reliably reconstruct the GF for concrete medium, indicating its effectiveness for monitoring changes such as crack formation and strain changes. However, the analysis of actual traffic noise data did not provide sufficient evidence to support its use for SHM with the current setup. Although a coherent and usable frequency range for traffic noise was identified, the limited amount of data led to a low signal-to-noise ratio (SNR), which made it challenging to highlight relevant features.
Moving forward, future researchers are encouraged to collect sufficient amount of data for analysis to better determine the feasibility of reconstructing the GF with ambient traffic noise. Additionally, exploring alternative sampling methods like continuous recording could address one of the limitations of this research. Finally, employing decomposition methods may help in increasing the SNR. ...
With the growing concern of aging infrastructures, the need for effective and non-intrusive monitoring techniques has become increasingly important. While most current methods rely on active testing, this study explores the potential of using ambient noise interferometry as a passive method for Structural Health Monitoring (SHM) of concrete structures. It investigates whether the Green's function (GF) of concrete medium can be adequately estimated from traffic noise to assess its health condition.
Two datasets are examined: a validation dataset from a laboratory experiment simulating ambient noise on a pre-stressed concrete girder, and real-world traffic noise data from the Maastunnel in Rotterdam. For each dataset, the following aspects are analyzed: (1) signal characteristics, including amplitudes and frequency distributions; (2) the optimal pre-processing scheme, incorporating temporal and spectral normalization, along with frequency filtering; and (3) the coherence of the resulting GF estimation from interferometry, particularly time of wave arrivals.
The results from the validation dataset demonstrate that ambient noise interferometry can reliably reconstruct the GF for concrete medium, indicating its effectiveness for monitoring changes such as crack formation and strain changes. However, the analysis of actual traffic noise data did not provide sufficient evidence to support its use for SHM with the current setup. Although a coherent and usable frequency range for traffic noise was identified, the limited amount of data led to a low signal-to-noise ratio (SNR), which made it challenging to highlight relevant features.
Moving forward, future researchers are encouraged to collect sufficient amount of data for analysis to better determine the feasibility of reconstructing the GF with ambient traffic noise. Additionally, exploring alternative sampling methods like continuous recording could address one of the limitations of this research. Finally, employing decomposition methods may help in increasing the SNR.
Two datasets are examined: a validation dataset from a laboratory experiment simulating ambient noise on a pre-stressed concrete girder, and real-world traffic noise data from the Maastunnel in Rotterdam. For each dataset, the following aspects are analyzed: (1) signal characteristics, including amplitudes and frequency distributions; (2) the optimal pre-processing scheme, incorporating temporal and spectral normalization, along with frequency filtering; and (3) the coherence of the resulting GF estimation from interferometry, particularly time of wave arrivals.
The results from the validation dataset demonstrate that ambient noise interferometry can reliably reconstruct the GF for concrete medium, indicating its effectiveness for monitoring changes such as crack formation and strain changes. However, the analysis of actual traffic noise data did not provide sufficient evidence to support its use for SHM with the current setup. Although a coherent and usable frequency range for traffic noise was identified, the limited amount of data led to a low signal-to-noise ratio (SNR), which made it challenging to highlight relevant features.
Moving forward, future researchers are encouraged to collect sufficient amount of data for analysis to better determine the feasibility of reconstructing the GF with ambient traffic noise. Additionally, exploring alternative sampling methods like continuous recording could address one of the limitations of this research. Finally, employing decomposition methods may help in increasing the SNR.