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S. Masaya
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1
Carbon dioxide capture, utilization, and storage (CCUS) are widely expected to play a significant role in decarbonization efforts. In discussing the commercialization of CCUS, it is essential to consider various factors, including the revenue and cost associated with CO2 utilization, carbon dioxide capture and storage (CCS), governmental subsidies, and carbon pricing. Among these, carbon pricing is particularly crucial for offsetting the costs of CCUS and enabling its commercialization. Carbon prices vary depending on factors such as the method of carbon credit issuance (e.g., forestry, solar, and wind energy), transaction mechanisms (e.g., cap-and-trade systems), carbon markets (e.g., voluntary markets), and country-specific regulations. Previous studies have investigated the impact of carbon pricing on carbon emissions; however, the pricing mechanisms are largely contingent on the method of carbon credit issuance. For instance, carbon credits generated through CCUS differ from those issued via forestry regarding associated costs, technologies, and subsidies. Therefore, carbon price models focusing on CCUS would be useful for the stakeholders, such as governments, firms, and investors. A significant challenge in developing an optimal carbon pricing model for CCUS lies in the complexity arising from uncertain parameters (e.g., carbon prices in voluntary markets, and revenue from CO2 utilization) across these stakeholders. This study presents a game theory-based model to provide an index of carbon credit revenue, i.e., the carbon price, to consider entry into the CCUS business. Our model aims to analytically simplify this complex problem with uncertain multi-parameters among several stakeholders and provide an index of the carbon price for CCUS in the voluntary carbon markets.
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Carbon dioxide capture, utilization, and storage (CCUS) are widely expected to play a significant role in decarbonization efforts. In discussing the commercialization of CCUS, it is essential to consider various factors, including the revenue and cost associated with CO2 utilization, carbon dioxide capture and storage (CCS), governmental subsidies, and carbon pricing. Among these, carbon pricing is particularly crucial for offsetting the costs of CCUS and enabling its commercialization. Carbon prices vary depending on factors such as the method of carbon credit issuance (e.g., forestry, solar, and wind energy), transaction mechanisms (e.g., cap-and-trade systems), carbon markets (e.g., voluntary markets), and country-specific regulations. Previous studies have investigated the impact of carbon pricing on carbon emissions; however, the pricing mechanisms are largely contingent on the method of carbon credit issuance. For instance, carbon credits generated through CCUS differ from those issued via forestry regarding associated costs, technologies, and subsidies. Therefore, carbon price models focusing on CCUS would be useful for the stakeholders, such as governments, firms, and investors. A significant challenge in developing an optimal carbon pricing model for CCUS lies in the complexity arising from uncertain parameters (e.g., carbon prices in voluntary markets, and revenue from CO2 utilization) across these stakeholders. This study presents a game theory-based model to provide an index of carbon credit revenue, i.e., the carbon price, to consider entry into the CCUS business. Our model aims to analytically simplify this complex problem with uncertain multi-parameters among several stakeholders and provide an index of the carbon price for CCUS in the voluntary carbon markets.
Energy exploration for low-carbon resources
A case study of deep geothermal and lithium extraction by passive seismic interferometry with curvelet denoising
Conference paper
(2021)
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Yohei Nishitsuji, Shogo Masaya
We have investigated technical feasibility of a first exploration tool for deep geothermal and lithium extraction that are seen to be promising future energy resources with low-carbon emission. The method we adapted is called seismic interferometry which can redatum source positions to their receiver ones and then estimate an image of the subsurface from seismic data. We applied seismic interferometry to a passive seismic dataset acquired in the Neuquen basin, Argentina where relatively vast amount of heat resources are expected to be present. This region is highly active in terms of active volcanoes that are being surrounded by a complex geological structure of Andes Mountains associated with Nazca slab subduction. Since there are a number of interferometric techniques available to these days, we compared crosscorrelation, crosscoherence and multi-dimensional deconvolution in order to evaluate individual performance. From the comparison, we found that the virtual reflection profiles given by curvelet denoising applied to multi-dimensional deconvolution, which is stabilized using a singular-value decomposition, showed a better fault image whose existence was previously interpreted by active seismic and exploration wells in this region. Although initial stages of these low-carbon businesses tend to cost a lot especially in frontier regions, our approach has a capability to provide at least a scale of major faults which are considered to be potentially crucial for reservoir performance with a relatively small footprint on the surface.
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We have investigated technical feasibility of a first exploration tool for deep geothermal and lithium extraction that are seen to be promising future energy resources with low-carbon emission. The method we adapted is called seismic interferometry which can redatum source positions to their receiver ones and then estimate an image of the subsurface from seismic data. We applied seismic interferometry to a passive seismic dataset acquired in the Neuquen basin, Argentina where relatively vast amount of heat resources are expected to be present. This region is highly active in terms of active volcanoes that are being surrounded by a complex geological structure of Andes Mountains associated with Nazca slab subduction. Since there are a number of interferometric techniques available to these days, we compared crosscorrelation, crosscoherence and multi-dimensional deconvolution in order to evaluate individual performance. From the comparison, we found that the virtual reflection profiles given by curvelet denoising applied to multi-dimensional deconvolution, which is stabilized using a singular-value decomposition, showed a better fault image whose existence was previously interpreted by active seismic and exploration wells in this region. Although initial stages of these low-carbon businesses tend to cost a lot especially in frontier regions, our approach has a capability to provide at least a scale of major faults which are considered to be potentially crucial for reservoir performance with a relatively small footprint on the surface.
Oil and gas companies evaluate the possibility of finding oil and gas fields carefully more than ever because it has been difficult to find gigantic discoveries which directly leads to their capital. Since a conventional evaluation contains human interpretation, luck and uncertainties, a variety of ranges of the reserves are often inferred from different interpreters given even identical dataset and conditions. As a consequence, there are differences between actual reserves and evaluated reserves. In this paper, using certain cases of how much actual reserves are deviated from interpreted reserves, deep learning is applied to mitigate such differences for unknown data which do not have actual reserves information. We find that our approach stably predicts the actual model by decreasing the misfit between the human and actual in comparison with the validation data on our workflow. The approach could be used to de-risk reserves estimation without changing traditional way of interpretations.
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Oil and gas companies evaluate the possibility of finding oil and gas fields carefully more than ever because it has been difficult to find gigantic discoveries which directly leads to their capital. Since a conventional evaluation contains human interpretation, luck and uncertainties, a variety of ranges of the reserves are often inferred from different interpreters given even identical dataset and conditions. As a consequence, there are differences between actual reserves and evaluated reserves. In this paper, using certain cases of how much actual reserves are deviated from interpreted reserves, deep learning is applied to mitigate such differences for unknown data which do not have actual reserves information. We find that our approach stably predicts the actual model by decreasing the misfit between the human and actual in comparison with the validation data on our workflow. The approach could be used to de-risk reserves estimation without changing traditional way of interpretations.
Conference paper
(2018)
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Shogo Masaya, D.J. Verschuur
Nowadays waveform modeling-based seismic imaging schemes, such as RTM and FWI, are widely used for the subsurface imaging. The use of waveform modeling-based imaging schemes for land seismic data is not always a
straightforward process, and their pre-processing plays key role in the final result. One of the pre-processing steps is surface-consistent amplitude correction to deal with source/receiver response sensitivity and adjust the amplitude of field data to make it suitable for the used imaging scheme. In this paper, we present a novel surface amplitude correction framework based on utilizing the amplitude spectrum of the modeled data produced from some synthetic models in order to tackle these problem. A synthetic and a field data example are shown to discuss the effectiveness of the proposed approach. ...
straightforward process, and their pre-processing plays key role in the final result. One of the pre-processing steps is surface-consistent amplitude correction to deal with source/receiver response sensitivity and adjust the amplitude of field data to make it suitable for the used imaging scheme. In this paper, we present a novel surface amplitude correction framework based on utilizing the amplitude spectrum of the modeled data produced from some synthetic models in order to tackle these problem. A synthetic and a field data example are shown to discuss the effectiveness of the proposed approach. ...
Nowadays waveform modeling-based seismic imaging schemes, such as RTM and FWI, are widely used for the subsurface imaging. The use of waveform modeling-based imaging schemes for land seismic data is not always a
straightforward process, and their pre-processing plays key role in the final result. One of the pre-processing steps is surface-consistent amplitude correction to deal with source/receiver response sensitivity and adjust the amplitude of field data to make it suitable for the used imaging scheme. In this paper, we present a novel surface amplitude correction framework based on utilizing the amplitude spectrum of the modeled data produced from some synthetic models in order to tackle these problem. A synthetic and a field data example are shown to discuss the effectiveness of the proposed approach.
straightforward process, and their pre-processing plays key role in the final result. One of the pre-processing steps is surface-consistent amplitude correction to deal with source/receiver response sensitivity and adjust the amplitude of field data to make it suitable for the used imaging scheme. In this paper, we present a novel surface amplitude correction framework based on utilizing the amplitude spectrum of the modeled data produced from some synthetic models in order to tackle these problem. A synthetic and a field data example are shown to discuss the effectiveness of the proposed approach.
Audio signal separation through complex tensor factorization
Utilizing modulation frequency and phase information
Journal article
(2018)
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Shogo Masaya
I propose a complex-valued tensor factorization algorithm for audio-source separation to exploit not only amplitude but phase information of audio signals in the modulation frequency (MF) domain. The proposed algorithm is extended from complex non-negative matrix factorization, which is capable of decomposing an arbitrary complex matrix such as the complex spectrum in the acoustic frequency domain. The proposed method enables us to factorize an arbitrary complex tensor of order 3. The detailed performance of the proposed algorithm for single-channel source separation is investigated through numerical experiments. I examine the quantitative contributions of the MF domain and phase information examined by additionally presenting three tensor factorization algorithms and using five objective indices for source separation.
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I propose a complex-valued tensor factorization algorithm for audio-source separation to exploit not only amplitude but phase information of audio signals in the modulation frequency (MF) domain. The proposed algorithm is extended from complex non-negative matrix factorization, which is capable of decomposing an arbitrary complex matrix such as the complex spectrum in the acoustic frequency domain. The proposed method enables us to factorize an arbitrary complex tensor of order 3. The detailed performance of the proposed algorithm for single-channel source separation is investigated through numerical experiments. I examine the quantitative contributions of the MF domain and phase information examined by additionally presenting three tensor factorization algorithms and using five objective indices for source separation.
Journal article
(2018)
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Shogo Masaya, D. J. Eric Verschuur
This paper proposes a reflectivity constraint for velocity estimation to optimally solve the inverse problem for active seismic imaging. This constraint is based on the velocity model derived from the definition of reflectivity and acoustic impedance. The constraint does not require any prior information of the subsurface and large extra computational costs, like the calculation of so-called Hessian matrices. We incorporate this constraint into the joint migration inversion algorithm, which simultaneously estimates both the reflectivity and velocity model of the subsurface in an iterative process. Using so-called full wavefield modelling, the misfit between forward modelled and measured data is minimized. Numerical and field data examples are given to demonstrate the validity of our proposed algorithm in case accurate initial models and the low-frequency components of observed seismic data are absent.
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This paper proposes a reflectivity constraint for velocity estimation to optimally solve the inverse problem for active seismic imaging. This constraint is based on the velocity model derived from the definition of reflectivity and acoustic impedance. The constraint does not require any prior information of the subsurface and large extra computational costs, like the calculation of so-called Hessian matrices. We incorporate this constraint into the joint migration inversion algorithm, which simultaneously estimates both the reflectivity and velocity model of the subsurface in an iterative process. Using so-called full wavefield modelling, the misfit between forward modelled and measured data is minimized. Numerical and field data examples are given to demonstrate the validity of our proposed algorithm in case accurate initial models and the low-frequency components of observed seismic data are absent.
Seismic imaging based on Joint Migration Inversion
Handling missing low frequencies and horizontally propagating waves
Doctoral thesis
(2018)
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Shogo Masaya
Seismic imaging is a significant technology to provide the image of the subsurface in several fields such as hydrocarbon exploration/production and civil engineering. A fundamental problem in seismic imaging is that both the depth reflectivity and velocity distribution of the subsurface have to be predicted by only seismic events observed at the surface, and it still remains a challenging research topic. Joint migration inversion (JMI) is one of the seismic waveform imaging algorithms that were recently proposed. JMI is capable of simultaneously estimating velocity and reflectivity models of the subsurface by exploiting reflected waves including internal multiples. The seismic modeling algorithm in the JMI process is a method termed full wavefield modeling (FWMod), which is a one-way propagator-based reflection modeling algorithm, including higher-order scattering and transmission effects. In this thesis, two directions to improve the accuracy of seismic imaging based on JMI are discussed. On one hand, an extension of FWMod is proposed to correctly deal with not only reflected waves but refracted/diving waves via one-way propagators in the horizontal direction, and this method is extended to a new JMI algorithm. On the other hand, we assume that only reflected waves including internal multiples are utilized in the imaging based on JMI and present two novel methods for the inversion and pre-processing: 1) iterative reflectivity-constrained velocity estimation, 2) surface amplitude correction via learning from synthetic models for land seismic data. The reflectivity-constrained velocity estimation is employed to improve the accuracy of the estimated velocity by exploiting the estimated reflectivity in the JMI process. The surface amplitude correction process is introduced to mitigate the influence of the amplitude variations caused by source/receiver response sensitivities and the difference of the features between observed land seismic data and the simulated data by the used imaging scheme. The numerical and field data examples for both land and marine cases demonstrate that the proposed approach is capable of effectively estimating reflectivity and velocity model, even though the low frequency components of the observed data are absent.
...
Seismic imaging is a significant technology to provide the image of the subsurface in several fields such as hydrocarbon exploration/production and civil engineering. A fundamental problem in seismic imaging is that both the depth reflectivity and velocity distribution of the subsurface have to be predicted by only seismic events observed at the surface, and it still remains a challenging research topic. Joint migration inversion (JMI) is one of the seismic waveform imaging algorithms that were recently proposed. JMI is capable of simultaneously estimating velocity and reflectivity models of the subsurface by exploiting reflected waves including internal multiples. The seismic modeling algorithm in the JMI process is a method termed full wavefield modeling (FWMod), which is a one-way propagator-based reflection modeling algorithm, including higher-order scattering and transmission effects. In this thesis, two directions to improve the accuracy of seismic imaging based on JMI are discussed. On one hand, an extension of FWMod is proposed to correctly deal with not only reflected waves but refracted/diving waves via one-way propagators in the horizontal direction, and this method is extended to a new JMI algorithm. On the other hand, we assume that only reflected waves including internal multiples are utilized in the imaging based on JMI and present two novel methods for the inversion and pre-processing: 1) iterative reflectivity-constrained velocity estimation, 2) surface amplitude correction via learning from synthetic models for land seismic data. The reflectivity-constrained velocity estimation is employed to improve the accuracy of the estimated velocity by exploiting the estimated reflectivity in the JMI process. The surface amplitude correction process is introduced to mitigate the influence of the amplitude variations caused by source/receiver response sensitivities and the difference of the features between observed land seismic data and the simulated data by the used imaging scheme. The numerical and field data examples for both land and marine cases demonstrate that the proposed approach is capable of effectively estimating reflectivity and velocity model, even though the low frequency components of the observed data are absent.
Book chapter
(2017)
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S. Masaya, D.J. Verschuur
Book chapter
(2017)
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S. Masaya, D.J. Verschuur
Conference paper
(2017)
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S. Masaya, D. J. Verschuur
A complex near surface is a large challenge in land seismic imaging due to its strong lateral heterogeneity. Furthermore, since the reflectors within the near-surface area are not always well measured, it is difficult to accurately estimate its velocity distribution of the subsurface. In this paper, we present a method to effectively exploit internal multiples in Joint Migration Inversion (JMI) for near-surface imaging. JMI is an inversion algorithm to automatically provide both velocity and reflectivity of the subsurface by utilizing primaries and all higher-order scattering. Our proposed method aims to improve the inverted velocity and reflectivity models through JMI by partially enhancing the residual between observed data and forward modeled data and suppressing the influence of diving waves and the insufficiently measured reflectors directly originating from the near-surface region. We give two numerical examples for synthetic models including a complex near-surface model to show the effectiveness of the proposed method.
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A complex near surface is a large challenge in land seismic imaging due to its strong lateral heterogeneity. Furthermore, since the reflectors within the near-surface area are not always well measured, it is difficult to accurately estimate its velocity distribution of the subsurface. In this paper, we present a method to effectively exploit internal multiples in Joint Migration Inversion (JMI) for near-surface imaging. JMI is an inversion algorithm to automatically provide both velocity and reflectivity of the subsurface by utilizing primaries and all higher-order scattering. Our proposed method aims to improve the inverted velocity and reflectivity models through JMI by partially enhancing the residual between observed data and forward modeled data and suppressing the influence of diving waves and the insufficiently measured reflectors directly originating from the near-surface region. We give two numerical examples for synthetic models including a complex near-surface model to show the effectiveness of the proposed method.
Conference paper
(2017)
-
Shogo Masaya, D.J. Verschuur
We present a seismic modeling process based on one-way propagators to handle both reflection and refraction. In this process, vertical reflectivity, horizontal reflectivity, and velocity models are independently defined by their own coordinate positions in a so-called staggered grid. We introduce the concept of intermediate propagation, such as down-rightgoing and up-leftgoing wavefields, which are propagation modes between the horizontal and vertical reflectivity grid. In addition, internal multiples can also be generated by this process. This modeling method can serve as the engine for a controlled full wavefield inversion process. A numerical example is shown to demonstrate the validity of the proposed method.
...
...
We present a seismic modeling process based on one-way propagators to handle both reflection and refraction. In this process, vertical reflectivity, horizontal reflectivity, and velocity models are independently defined by their own coordinate positions in a so-called staggered grid. We introduce the concept of intermediate propagation, such as down-rightgoing and up-leftgoing wavefields, which are propagation modes between the horizontal and vertical reflectivity grid. In addition, internal multiples can also be generated by this process. This modeling method can serve as the engine for a controlled full wavefield inversion process. A numerical example is shown to demonstrate the validity of the proposed method.
Conference paper
(2017)
-
Shogo Masaya, D.J. Verschuur
We present a seismic modeling process based on one-way propagators to handle both reflection and refraction. In this process, vertical reflectivity, horizontal reflectivity, and velocity models are independently defined by their own coordinate positions in a so-called staggered grid. We introduce the concept of intermediate propagation, such as down-rightgoing and up-leftgoing wavefields, which are propagation modes between the horizontal and vertical reflectivity grid. In addition, internal multiples can also be generated by this process. This modeling method can serve as the engine for a controlled full wavefield inversion process. A numerical example is shown to demonstrate the validity of the proposed method.
...
We present a seismic modeling process based on one-way propagators to handle both reflection and refraction. In this process, vertical reflectivity, horizontal reflectivity, and velocity models are independently defined by their own coordinate positions in a so-called staggered grid. We introduce the concept of intermediate propagation, such as down-rightgoing and up-leftgoing wavefields, which are propagation modes between the horizontal and vertical reflectivity grid. In addition, internal multiples can also be generated by this process. This modeling method can serve as the engine for a controlled full wavefield inversion process. A numerical example is shown to demonstrate the validity of the proposed method.
Book chapter
(2016)
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Shogo Masaya, Eric Verschuur
Book chapter
(2016)
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D.J. Verschuur, Shogo Masaya
Joint Migration Inversion (JMI) automatically provides a structual imaging and velocity model of the subsurface using primaries and all higher order scattering. In the recent implementation and numerical examples, it has been shown that JMI is a robust algorithm to estimate reflectivity and velocity models. However, the estimated velocity models are relatively smooth and lack detail. In this chapter we present a velocity estimation procedure based on a reflectivity constraint in JMI in order to improve the resolution of the velocity model. The residual between the estimated velocity by JMI and the approximate velocity that is derived from the estimated reflectivity is minimized through an additional constraint in the objective function. An initial synthetic example is shown to demonstrate the validity of the proposed method.
...
Joint Migration Inversion (JMI) automatically provides a structual imaging and velocity model of the subsurface using primaries and all higher order scattering. In the recent implementation and numerical examples, it has been shown that JMI is a robust algorithm to estimate reflectivity and velocity models. However, the estimated velocity models are relatively smooth and lack detail. In this chapter we present a velocity estimation procedure based on a reflectivity constraint in JMI in order to improve the resolution of the velocity model. The residual between the estimated velocity by JMI and the approximate velocity that is derived from the estimated reflectivity is minimized through an additional constraint in the objective function. An initial synthetic example is shown to demonstrate the validity of the proposed method.
Conference paper
(2016)
-
Shogo Masaya, D. J. Verschuur
Joint Migration Inversion (JMI) automatically provides a structual image and velocity model of the subsurface by exploiting primaries and all higher order scattering. It has been shown that JMI is a robust algorithm to estimate reflectivity and velocity models, avoiding local minima. However, the estimated velocity models are relatively smooth and lack detail, as they only describe the propagation of waves. More- over, some improvement in JMI seems to be needed to generate rapid velocity variations like salt structures. In this paper we present a velocity estimation procedure including a reflectivity constraint in JMI in order to improve the accuracy of the velocity model. The residual between the estimated velocity by JMI and the approximate velocity that is derived from the estimated reflectivity is minimized through an additional constraint in the objective function. Synthetic examples are shown to demonstrate the validity of the proposed method.
...
Joint Migration Inversion (JMI) automatically provides a structual image and velocity model of the subsurface by exploiting primaries and all higher order scattering. It has been shown that JMI is a robust algorithm to estimate reflectivity and velocity models, avoiding local minima. However, the estimated velocity models are relatively smooth and lack detail, as they only describe the propagation of waves. More- over, some improvement in JMI seems to be needed to generate rapid velocity variations like salt structures. In this paper we present a velocity estimation procedure including a reflectivity constraint in JMI in order to improve the accuracy of the velocity model. The residual between the estimated velocity by JMI and the approximate velocity that is derived from the estimated reflectivity is minimized through an additional constraint in the objective function. Synthetic examples are shown to demonstrate the validity of the proposed method.
Conference paper
(2016)
-
S. Masaya, D. J. Verschuur
Complex near-surface areas pose major problems in the imaging process for land seismic data, especially because the near-surface area can be very heterogeneous. In addition, the responses from shallow reflectors are not properly measured in order to estimate an accurate velocity model. In this paper we present an approach based on Joint Migration Inversion (JMI) for the velocity estimation and imaging. JMI automatically derives a physically consistent near-surface propagation model using the full wavefield, where the near-surface effects are encoded in the higher-order scattering effects. This approach uses all measured reflection events simultaneously and includes transmission effects and all multiples generated in both the near surface and below. Synthetic examples under realistic near-surface conditions are given to show the effectiveness of the approach.
...
Complex near-surface areas pose major problems in the imaging process for land seismic data, especially because the near-surface area can be very heterogeneous. In addition, the responses from shallow reflectors are not properly measured in order to estimate an accurate velocity model. In this paper we present an approach based on Joint Migration Inversion (JMI) for the velocity estimation and imaging. JMI automatically derives a physically consistent near-surface propagation model using the full wavefield, where the near-surface effects are encoded in the higher-order scattering effects. This approach uses all measured reflection events simultaneously and includes transmission effects and all multiples generated in both the near surface and below. Synthetic examples under realistic near-surface conditions are given to show the effectiveness of the approach.