A. Rahimi Dalkhani
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1
Conference paper
(2025)
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A. Rahimi Dalkhani, F. Balestrini, A. Nath, V. Socco, G. Drijkoningen, E. Verschuur
This study investigates the feasibility of acquiring and analyzing Scholte waves within the water layer to estimate shallow shear-wave velocity profiles of marine sediments. Using a realistic subsurface model, 2D elastic wave simulations are performed with sources and receivers placed at varying heights above the seabed. Dispersion analysis confirmed that both fundamental and higher-mode Scholte waves remain detectable when the source and receivers are positioned just two meters above the seafloor. Even with added Gaussian noise, Scholte waves are clearly observable, demonstrating the practical potential of this approach. A Bayesian Markov Chain Monte Carlo inversion successfully recovers key features of the shear-wave velocity structure from the dispersion curves, though resolution diminishes with noise and reduced high-frequency content. These results suggest that effective Scholte wave acquisition and inversion are achievable without placing equipment directly on the seabed, offering an environmentally friendly alternative for shallow marine sediment characterization. However, implementing this setup in practice presents technical challenges that require further investigation.
...
This study investigates the feasibility of acquiring and analyzing Scholte waves within the water layer to estimate shallow shear-wave velocity profiles of marine sediments. Using a realistic subsurface model, 2D elastic wave simulations are performed with sources and receivers placed at varying heights above the seabed. Dispersion analysis confirmed that both fundamental and higher-mode Scholte waves remain detectable when the source and receivers are positioned just two meters above the seafloor. Even with added Gaussian noise, Scholte waves are clearly observable, demonstrating the practical potential of this approach. A Bayesian Markov Chain Monte Carlo inversion successfully recovers key features of the shear-wave velocity structure from the dispersion curves, though resolution diminishes with noise and reduced high-frequency content. These results suggest that effective Scholte wave acquisition and inversion are achievable without placing equipment directly on the seabed, offering an environmentally friendly alternative for shallow marine sediment characterization. However, implementing this setup in practice presents technical challenges that require further investigation.
Transdimensional surface wave inversion of the near surface
Application to distributed acoustic sensing data
Distributed acoustic sensing (DAS) is a novel technology, which allows the seismic wavefield to be sampled densely in space and time. This makes it an ideal tool for retrieving surface waves, which are predominantly sensitive to the S-wave velocity structure of the subsurface. In this study, we evaluate the potential of DAS to image the near surface (top 50 m) using active-source surface waves recorded with straight fibers on a field in the province of Groningen, the Netherlands. Importantly, DAS is used here in conjunction with a Bayesian transdimensional inversion approach, making this the first application of such an algorithm to DAS-acquired strain-rate wavefields. First, we extract laterally varying surface wave phase velocities (i.e., “local” dispersion curves [DCs]) from the fundamental mode surface waves. Then, instead of inverting each local DC separately, we use a novel 2D transdimensional algorithm to estimate the subsurface’s S-wave velocity structure. We develop a few modifications to improve the performance of the 2D transdimensional approach. Specifically, we develop a new birth-and-death scheme for perturbing the dimension of the model space to improve the acceptance probability. In addition, we use a Gibbs sampler to infer the noise hyperparameters more rapidly. Finally, we introduce local prior information (e.g., S-wave logs) as a constraint to the inversion, which helps the algorithm to converge faster. We first validate our approach by successfully recovering the S-wave velocity in a synthetic experiment. Then, we apply the algorithm to the field DAS data, resulting in a smooth laterally varying S-wave velocity model. The posterior mean and uncertainty profiles identify a distinct layer interface at approximately 20 m depth with a sharp increase in velocity and uncertainty at that depth, aligning with borehole log data that indicate a similar velocity increase at the same depth.
...
Distributed acoustic sensing (DAS) is a novel technology, which allows the seismic wavefield to be sampled densely in space and time. This makes it an ideal tool for retrieving surface waves, which are predominantly sensitive to the S-wave velocity structure of the subsurface. In this study, we evaluate the potential of DAS to image the near surface (top 50 m) using active-source surface waves recorded with straight fibers on a field in the province of Groningen, the Netherlands. Importantly, DAS is used here in conjunction with a Bayesian transdimensional inversion approach, making this the first application of such an algorithm to DAS-acquired strain-rate wavefields. First, we extract laterally varying surface wave phase velocities (i.e., “local” dispersion curves [DCs]) from the fundamental mode surface waves. Then, instead of inverting each local DC separately, we use a novel 2D transdimensional algorithm to estimate the subsurface’s S-wave velocity structure. We develop a few modifications to improve the performance of the 2D transdimensional approach. Specifically, we develop a new birth-and-death scheme for perturbing the dimension of the model space to improve the acceptance probability. In addition, we use a Gibbs sampler to infer the noise hyperparameters more rapidly. Finally, we introduce local prior information (e.g., S-wave logs) as a constraint to the inversion, which helps the algorithm to converge faster. We first validate our approach by successfully recovering the S-wave velocity in a synthetic experiment. Then, we apply the algorithm to the field DAS data, resulting in a smooth laterally varying S-wave velocity model. The posterior mean and uncertainty profiles identify a distinct layer interface at approximately 20 m depth with a sharp increase in velocity and uncertainty at that depth, aligning with borehole log data that indicate a similar velocity increase at the same depth.
Transdimensional surface wave inversion
1D, 2D and 3D applications
Surface wave inversion is a powerful tool for subsurface imaging at various scales, spanning from near-surface characterization to crustal imaging. This study focuses on the transdimensional Markov chain Monte Carlo (McMC) algorithm and its effectiveness for surface wave inversion. We explore its application at different dimensions (1D, 2D, 3D) and scales, encompassing both near-surface and crustal subsurface characterization.
We first demonstrate the fundamentals of surface wave inversion using a least-squares algorithm, a conventional McMC method, and the transdimensional McMC algorithm applied to the (non-linear) 1D inversion problem. We compare these algorithms by applying them to both synthetic and field data. In contrast to the least-squares method, the transdimensional algorithm successfully recovers rapid variations in velocity, particularly at a depth of around 3 km. This observation emphasizes the automatic and localized smoothing applied in the transdimensional McMC algorithm. Furthermore, the transdimensional McMC algorithm yields, inherently, the posterior probability density of the shear wave velocity as a function of depth, offering valuable insights.
In Chapter 3, we extend the study to two dimensions using surface waves retrieved by means of Distributed Acoustic Sensing (DAS), validating the transdimensional algorithm's potential for near-surface applications. We first determine the dispersive behavior of the Rayleigh wave considering lateral variation in the subsurface. This leads to a local dispersion curve at the location of each receiver. To recover a 2D shear wave velocity section of the subsurface, we develop a 2D transdimensional approach to invert all the dispersion curves simultaneously. This approach retains lateral correlations of the recovered shear wave velocities. This two-dimensional application demonstrates the ability of the transdimensional McMC algorithm to reconstruct the two-dimensional shear wave velocity structure of the near-surface.
Finally, we extend the study to three dimensions by recovering the crustal shear wave velocity structure of the Reykjanes Peninsula. As input, we use travel times extracted from Rayleigh waves that were retrieved through the application of seismic interferometry to recordings of ambient seismic noise. We then modify and use the one-step 3D transdimensional surface wave tomography algorithm. This implies that we depart from conventional two-step approaches. Synthetic tests showcase adaptability to ray density, yielding higher resolution in densely sampled areas. Reduced computational costs by modifying the algorithm enhance the applicability for 3D crustal imaging. Our application of the one-step 3D transdimensional algorithm to ambient-noise data provides the first comprehensive shear wave velocity model of the Reykjanes Peninsula. The detailed shear wave velocity model offers valuable geological and geothermal insights into the subsurface structure, confirming the algorithm's potential as a routine tool for surface wave tomography.
Collectively, these findings advocate the use of one-step transdimensional inversion algorithms for seismic tomography. Their adaptability, efficiency gains, and interpretability contribute to advancing our understanding of subsurface velocity structures. As seismic data volumes grow, embracing innovative inversion approaches becomes imperative, and the transdimensional algorithms showcased in this thesis emerge as a promising tool for pushing the boundaries of seismic tomography.
...
We first demonstrate the fundamentals of surface wave inversion using a least-squares algorithm, a conventional McMC method, and the transdimensional McMC algorithm applied to the (non-linear) 1D inversion problem. We compare these algorithms by applying them to both synthetic and field data. In contrast to the least-squares method, the transdimensional algorithm successfully recovers rapid variations in velocity, particularly at a depth of around 3 km. This observation emphasizes the automatic and localized smoothing applied in the transdimensional McMC algorithm. Furthermore, the transdimensional McMC algorithm yields, inherently, the posterior probability density of the shear wave velocity as a function of depth, offering valuable insights.
In Chapter 3, we extend the study to two dimensions using surface waves retrieved by means of Distributed Acoustic Sensing (DAS), validating the transdimensional algorithm's potential for near-surface applications. We first determine the dispersive behavior of the Rayleigh wave considering lateral variation in the subsurface. This leads to a local dispersion curve at the location of each receiver. To recover a 2D shear wave velocity section of the subsurface, we develop a 2D transdimensional approach to invert all the dispersion curves simultaneously. This approach retains lateral correlations of the recovered shear wave velocities. This two-dimensional application demonstrates the ability of the transdimensional McMC algorithm to reconstruct the two-dimensional shear wave velocity structure of the near-surface.
Finally, we extend the study to three dimensions by recovering the crustal shear wave velocity structure of the Reykjanes Peninsula. As input, we use travel times extracted from Rayleigh waves that were retrieved through the application of seismic interferometry to recordings of ambient seismic noise. We then modify and use the one-step 3D transdimensional surface wave tomography algorithm. This implies that we depart from conventional two-step approaches. Synthetic tests showcase adaptability to ray density, yielding higher resolution in densely sampled areas. Reduced computational costs by modifying the algorithm enhance the applicability for 3D crustal imaging. Our application of the one-step 3D transdimensional algorithm to ambient-noise data provides the first comprehensive shear wave velocity model of the Reykjanes Peninsula. The detailed shear wave velocity model offers valuable geological and geothermal insights into the subsurface structure, confirming the algorithm's potential as a routine tool for surface wave tomography.
Collectively, these findings advocate the use of one-step transdimensional inversion algorithms for seismic tomography. Their adaptability, efficiency gains, and interpretability contribute to advancing our understanding of subsurface velocity structures. As seismic data volumes grow, embracing innovative inversion approaches becomes imperative, and the transdimensional algorithms showcased in this thesis emerge as a promising tool for pushing the boundaries of seismic tomography.
...
Surface wave inversion is a powerful tool for subsurface imaging at various scales, spanning from near-surface characterization to crustal imaging. This study focuses on the transdimensional Markov chain Monte Carlo (McMC) algorithm and its effectiveness for surface wave inversion. We explore its application at different dimensions (1D, 2D, 3D) and scales, encompassing both near-surface and crustal subsurface characterization.
We first demonstrate the fundamentals of surface wave inversion using a least-squares algorithm, a conventional McMC method, and the transdimensional McMC algorithm applied to the (non-linear) 1D inversion problem. We compare these algorithms by applying them to both synthetic and field data. In contrast to the least-squares method, the transdimensional algorithm successfully recovers rapid variations in velocity, particularly at a depth of around 3 km. This observation emphasizes the automatic and localized smoothing applied in the transdimensional McMC algorithm. Furthermore, the transdimensional McMC algorithm yields, inherently, the posterior probability density of the shear wave velocity as a function of depth, offering valuable insights.
In Chapter 3, we extend the study to two dimensions using surface waves retrieved by means of Distributed Acoustic Sensing (DAS), validating the transdimensional algorithm's potential for near-surface applications. We first determine the dispersive behavior of the Rayleigh wave considering lateral variation in the subsurface. This leads to a local dispersion curve at the location of each receiver. To recover a 2D shear wave velocity section of the subsurface, we develop a 2D transdimensional approach to invert all the dispersion curves simultaneously. This approach retains lateral correlations of the recovered shear wave velocities. This two-dimensional application demonstrates the ability of the transdimensional McMC algorithm to reconstruct the two-dimensional shear wave velocity structure of the near-surface.
Finally, we extend the study to three dimensions by recovering the crustal shear wave velocity structure of the Reykjanes Peninsula. As input, we use travel times extracted from Rayleigh waves that were retrieved through the application of seismic interferometry to recordings of ambient seismic noise. We then modify and use the one-step 3D transdimensional surface wave tomography algorithm. This implies that we depart from conventional two-step approaches. Synthetic tests showcase adaptability to ray density, yielding higher resolution in densely sampled areas. Reduced computational costs by modifying the algorithm enhance the applicability for 3D crustal imaging. Our application of the one-step 3D transdimensional algorithm to ambient-noise data provides the first comprehensive shear wave velocity model of the Reykjanes Peninsula. The detailed shear wave velocity model offers valuable geological and geothermal insights into the subsurface structure, confirming the algorithm's potential as a routine tool for surface wave tomography.
Collectively, these findings advocate the use of one-step transdimensional inversion algorithms for seismic tomography. Their adaptability, efficiency gains, and interpretability contribute to advancing our understanding of subsurface velocity structures. As seismic data volumes grow, embracing innovative inversion approaches becomes imperative, and the transdimensional algorithms showcased in this thesis emerge as a promising tool for pushing the boundaries of seismic tomography.
We first demonstrate the fundamentals of surface wave inversion using a least-squares algorithm, a conventional McMC method, and the transdimensional McMC algorithm applied to the (non-linear) 1D inversion problem. We compare these algorithms by applying them to both synthetic and field data. In contrast to the least-squares method, the transdimensional algorithm successfully recovers rapid variations in velocity, particularly at a depth of around 3 km. This observation emphasizes the automatic and localized smoothing applied in the transdimensional McMC algorithm. Furthermore, the transdimensional McMC algorithm yields, inherently, the posterior probability density of the shear wave velocity as a function of depth, offering valuable insights.
In Chapter 3, we extend the study to two dimensions using surface waves retrieved by means of Distributed Acoustic Sensing (DAS), validating the transdimensional algorithm's potential for near-surface applications. We first determine the dispersive behavior of the Rayleigh wave considering lateral variation in the subsurface. This leads to a local dispersion curve at the location of each receiver. To recover a 2D shear wave velocity section of the subsurface, we develop a 2D transdimensional approach to invert all the dispersion curves simultaneously. This approach retains lateral correlations of the recovered shear wave velocities. This two-dimensional application demonstrates the ability of the transdimensional McMC algorithm to reconstruct the two-dimensional shear wave velocity structure of the near-surface.
Finally, we extend the study to three dimensions by recovering the crustal shear wave velocity structure of the Reykjanes Peninsula. As input, we use travel times extracted from Rayleigh waves that were retrieved through the application of seismic interferometry to recordings of ambient seismic noise. We then modify and use the one-step 3D transdimensional surface wave tomography algorithm. This implies that we depart from conventional two-step approaches. Synthetic tests showcase adaptability to ray density, yielding higher resolution in densely sampled areas. Reduced computational costs by modifying the algorithm enhance the applicability for 3D crustal imaging. Our application of the one-step 3D transdimensional algorithm to ambient-noise data provides the first comprehensive shear wave velocity model of the Reykjanes Peninsula. The detailed shear wave velocity model offers valuable geological and geothermal insights into the subsurface structure, confirming the algorithm's potential as a routine tool for surface wave tomography.
Collectively, these findings advocate the use of one-step transdimensional inversion algorithms for seismic tomography. Their adaptability, efficiency gains, and interpretability contribute to advancing our understanding of subsurface velocity structures. As seismic data volumes grow, embracing innovative inversion approaches becomes imperative, and the transdimensional algorithms showcased in this thesis emerge as a promising tool for pushing the boundaries of seismic tomography.
A geomechanics program for wellbore stability analysis has been developed consisting of two modules: an analytical-based solution and a numerical-based solution. In the first part, input data are imported, including petrophysical well logs, pressure data, formation well tops, and a well path. Lithology intervals are set with proper prediction equations to calculate rock mechanical properties based on laboratory tests. In-situ stress and pore pressure are determined using different methods, including the poroelastic plane strain model and stress polygon. From the theory of plane strain, new equations are solved to determine horizontal tectonic strains (ϵ h, ϵ H) from drilling events such as total mud loss and breakout during drilling. Safe mud weight bounds are calculated through depth and in different azimuths and inclinations applying the Mohr-Coulomb and the Mogi-Coulomb failure criteria. The latter underestimated the minimum mud weight to prevent wellbore breakout. The transversely vertical isotropy of shale formation is programmed with multiple stress transformations via the weak-plane method. In the second module, a 3D model around the wellbore is discretized with hexahedral eight-point elements and programmed using the finite-element (FE) method. Rock mechanical property and displacement boundary conditions are applied to solve FE equations. Stress from the numerical model matched to the Kirsch model and results show that maximum stress concentration around the wellbore corresponds to the wellbore breakout, which has analytically been established. A new well plan across the 3D model was examined to obtain the safe mud weight bounds and results were in agreement with the analytical calculations.
...
A geomechanics program for wellbore stability analysis has been developed consisting of two modules: an analytical-based solution and a numerical-based solution. In the first part, input data are imported, including petrophysical well logs, pressure data, formation well tops, and a well path. Lithology intervals are set with proper prediction equations to calculate rock mechanical properties based on laboratory tests. In-situ stress and pore pressure are determined using different methods, including the poroelastic plane strain model and stress polygon. From the theory of plane strain, new equations are solved to determine horizontal tectonic strains (ϵ h, ϵ H) from drilling events such as total mud loss and breakout during drilling. Safe mud weight bounds are calculated through depth and in different azimuths and inclinations applying the Mohr-Coulomb and the Mogi-Coulomb failure criteria. The latter underestimated the minimum mud weight to prevent wellbore breakout. The transversely vertical isotropy of shale formation is programmed with multiple stress transformations via the weak-plane method. In the second module, a 3D model around the wellbore is discretized with hexahedral eight-point elements and programmed using the finite-element (FE) method. Rock mechanical property and displacement boundary conditions are applied to solve FE equations. Stress from the numerical model matched to the Kirsch model and results show that maximum stress concentration around the wellbore corresponds to the wellbore breakout, which has analytically been established. A new well plan across the 3D model was examined to obtain the safe mud weight bounds and results were in agreement with the analytical calculations.
Abstract
(2023)
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Cornelis Weemstra, Amin Rahimi Dalkhani, Þorbjörg Ágústsdóttir, Egill Árni Guðnason, Gylfi Páll Hersir, Xin Zhang
We report on a Bayesian (i.e., probabilistic) inversion for the shear-wave velocity structure of the Reykjanes peninsula, SW Iceland. Travel times of Rayleigh waves traversing the peninsula served as input to the probabilistic algorithm. These Rayleigh waves were retrieved through the application of seismic interferometry to yearlong recordings of ambient seismic noise. The Reykjanes peninsula is well placed for this technique because it is surrounded by ocean, which implies a relatively uniform seismic noise illumination; the latter being a condition for accurate interferometric surface wave retrieval. The Bayesian algorithm uses a variable model parametrization by employing Voronoi cells in conjunction with a reversible jump Markov chain Monte Carlo sampler. The algorithm is entirely data-driven, meaning that, contrary to conventional deterministic tomographic inversions, the user does not need to define any regularization or parameterization parameters to solve the inverse problem.
The geology in the area of interest is characterized by four NE-SW trending volcanic systems, orientated oblique to the divergent plate boundary cutting across the Reykjanes Peninsula. These are from west to east; Reykjanes, Svartsengi, Fagradalsfjall and Krýsuvík, of which all except Fagradalsfjall host a known high-temperature geothermal field. We observe relatively high shear wave velocity patches close to the Earth’s surface (top two kilometers) at the location of these known high-temperature fields. These high velocity anomalies invert to relatively low shear wave velocities (in comparison to shear wave velocities in the same horizontal plane) at depths greater than 3 km. The latter low-velocity anomalies are relatively small below Reykjanes and Svartsengi. At depths of 5 to 8 km, a low-velocity anomaly extends horizontally below Reykjanes and Svartsengi, correlating relatively well with the inferred brittle-ductile transition below the high-temperature fields at 4-5 km depth. The low-velocity anomaly below Krýsuvík is much larger and coincides with a deep-seated low electrical resistivity anomaly. Horizontally, it coincides with the center of an inflation source at 4–5 km depth. For example, in 2010 this resulted in an uplift exceeding 50 mm/year, but several periods of alternating uplift and subsidence associated with increased seismicity have been observed in Krýsuvík since 2009. Our results both confirm and add details to previous models obtained in the area. Our study demonstrates the potential of Bayesian surface wave inversion as a complementary geophysical tool for geothermal exploration.
...
We report on a Bayesian (i.e., probabilistic) inversion for the shear-wave velocity structure of the Reykjanes peninsula, SW Iceland. Travel times of Rayleigh waves traversing the peninsula served as input to the probabilistic algorithm. These Rayleigh waves were retrieved through the application of seismic interferometry to yearlong recordings of ambient seismic noise. The Reykjanes peninsula is well placed for this technique because it is surrounded by ocean, which implies a relatively uniform seismic noise illumination; the latter being a condition for accurate interferometric surface wave retrieval. The Bayesian algorithm uses a variable model parametrization by employing Voronoi cells in conjunction with a reversible jump Markov chain Monte Carlo sampler. The algorithm is entirely data-driven, meaning that, contrary to conventional deterministic tomographic inversions, the user does not need to define any regularization or parameterization parameters to solve the inverse problem.
The geology in the area of interest is characterized by four NE-SW trending volcanic systems, orientated oblique to the divergent plate boundary cutting across the Reykjanes Peninsula. These are from west to east; Reykjanes, Svartsengi, Fagradalsfjall and Krýsuvík, of which all except Fagradalsfjall host a known high-temperature geothermal field. We observe relatively high shear wave velocity patches close to the Earth’s surface (top two kilometers) at the location of these known high-temperature fields. These high velocity anomalies invert to relatively low shear wave velocities (in comparison to shear wave velocities in the same horizontal plane) at depths greater than 3 km. The latter low-velocity anomalies are relatively small below Reykjanes and Svartsengi. At depths of 5 to 8 km, a low-velocity anomaly extends horizontally below Reykjanes and Svartsengi, correlating relatively well with the inferred brittle-ductile transition below the high-temperature fields at 4-5 km depth. The low-velocity anomaly below Krýsuvík is much larger and coincides with a deep-seated low electrical resistivity anomaly. Horizontally, it coincides with the center of an inflation source at 4–5 km depth. For example, in 2010 this resulted in an uplift exceeding 50 mm/year, but several periods of alternating uplift and subsidence associated with increased seismicity have been observed in Krýsuvík since 2009. Our results both confirm and add details to previous models obtained in the area. Our study demonstrates the potential of Bayesian surface wave inversion as a complementary geophysical tool for geothermal exploration.
Journal article
(2023)
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Amin Rahimi Dalkhani, Thorbjörg Áústsdóttir, Egill Árni Gudnason, Gylfi Páll Hersir, Xin Zhang, Cornelis Weemstra
Ambient noise seismic tomography has proven to be an effective tool for subsurface imaging, particularly in volcanic regions such as the Reykjanes Peninsula (RP), SW Iceland, where ambient seismic noise is ideal with isotropic illumination. The primary purpose of this study is to obtain a reliable shear wave velocity model of the RP, to get a better understanding of the subsurface structure of the RP and how it relates to other geoscientific results. This is the first tomographic model of the RP which is based on both on- and off-shore seismic stations. We use the ambient seismic noise data and apply a novel algorithm called one-step 3-D transdimensional tomography. The main geological structures in the study area (i.e. covered by seismic stations) are the four NE-SW trending volcanic systems, orientated highly oblique to the plate spreading on the RP. These are from west to east; Reykjanes, Eldvörp-Svartsengi, Fagradalsfjall and Krýsuvík, of which all except Fagradalsfjall host a known high-temperature geothermal field. Using surface waves retrieved from ambient noise recordings, we recovered a 3-D model of shear wave velocity. We observe low-velocity anomalies below these known high-temperature fields. The observed low-velocity anomalies below Reykjanes and Eldvörp-Svartsengi are significant but relatively small. The low-velocity anomaly observed below Krýsuvík is both larger and stronger, oriented near-perpendicular to the volcanic system, and coinciding well with a previously found low-resistivity anomaly. A low-velocity anomaly in the depth range of 5-8 km extends horizontally along the whole RP, but below the high-temperature fields, the onset of the velocity decrease is shallower, at around 3 km depth. This is in good agreement with the brittle-ductile transition zone on the RP. In considerably greater detail, our results confirm previous tomographic models obtained in the area. This study demonstrates the potential of the entirely data-driven, one-step 3-D transdimensional ambient noise tomography as a routine tomography tool and a complementary seismological tool for geothermal exploration, providing an enhanced understanding of the upper crustal structure of the RP.
...
Ambient noise seismic tomography has proven to be an effective tool for subsurface imaging, particularly in volcanic regions such as the Reykjanes Peninsula (RP), SW Iceland, where ambient seismic noise is ideal with isotropic illumination. The primary purpose of this study is to obtain a reliable shear wave velocity model of the RP, to get a better understanding of the subsurface structure of the RP and how it relates to other geoscientific results. This is the first tomographic model of the RP which is based on both on- and off-shore seismic stations. We use the ambient seismic noise data and apply a novel algorithm called one-step 3-D transdimensional tomography. The main geological structures in the study area (i.e. covered by seismic stations) are the four NE-SW trending volcanic systems, orientated highly oblique to the plate spreading on the RP. These are from west to east; Reykjanes, Eldvörp-Svartsengi, Fagradalsfjall and Krýsuvík, of which all except Fagradalsfjall host a known high-temperature geothermal field. Using surface waves retrieved from ambient noise recordings, we recovered a 3-D model of shear wave velocity. We observe low-velocity anomalies below these known high-temperature fields. The observed low-velocity anomalies below Reykjanes and Eldvörp-Svartsengi are significant but relatively small. The low-velocity anomaly observed below Krýsuvík is both larger and stronger, oriented near-perpendicular to the volcanic system, and coinciding well with a previously found low-resistivity anomaly. A low-velocity anomaly in the depth range of 5-8 km extends horizontally along the whole RP, but below the high-temperature fields, the onset of the velocity decrease is shallower, at around 3 km depth. This is in good agreement with the brittle-ductile transition zone on the RP. In considerably greater detail, our results confirm previous tomographic models obtained in the area. This study demonstrates the potential of the entirely data-driven, one-step 3-D transdimensional ambient noise tomography as a routine tomography tool and a complementary seismological tool for geothermal exploration, providing an enhanced understanding of the upper crustal structure of the RP.
Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network.
...
Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network.