C.P.A. Wapenaar
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When reflection images are studied, often only the zero-offset reflectivity is considered, however, taking into account the angle-dependent reflectivity can add additional information about the Earth's subsurface. This additional information can be used to extract the properties of the subsurface using the amplitude variation with offset (AVO) analysis techniques. However, the presence of a complex overburden can significantly deteriorate the AVO response, especially for deep targets. To overcome this problem, the overburden effects can be removed by redatuming the reflection response at a depth level below the overburden. The Marchenko method has the potential to correctly retrieve the angle-dependent reflectivity in acoustic media without distortions due to multiple scattering caused by the overburden. The method estimates the downgoing and upgoing Green's functions of a virtual source located in the subsurface from surface reflection data and an estimate of the direct arrival from the location of the virtual source. The estimated Green's functions represent accurate upgoing and downgoing wavefields as they contain all orders of internal multiple reflections of the subsurface. These internal multiple reflections contribute to retrieving the reflectivity accurately in the redatumed reflection response. By deconvolving the retrieved upgoing Green’s function with the downgoing Green’s function, a new reflection response is obtained, with virtual sources and virtual receivers in the subsurface. The resultant reflection response is free of spurious events related to internal multiples in the overburden and contains the correct amplitudes. The angle-dependent reflectivity of the redatumed response can be obtained by summing the reflection coefficients along lines of constant ray parameter or angle. Potentially, the retrieved angle-dependent reflection coefficients obtained by this method can be used as input in a subsequent inversion process to obtain the velocity and density of the subsurface.
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When reflection images are studied, often only the zero-offset reflectivity is considered, however, taking into account the angle-dependent reflectivity can add additional information about the Earth's subsurface. This additional information can be used to extract the properties of the subsurface using the amplitude variation with offset (AVO) analysis techniques. However, the presence of a complex overburden can significantly deteriorate the AVO response, especially for deep targets. To overcome this problem, the overburden effects can be removed by redatuming the reflection response at a depth level below the overburden. The Marchenko method has the potential to correctly retrieve the angle-dependent reflectivity in acoustic media without distortions due to multiple scattering caused by the overburden. The method estimates the downgoing and upgoing Green's functions of a virtual source located in the subsurface from surface reflection data and an estimate of the direct arrival from the location of the virtual source. The estimated Green's functions represent accurate upgoing and downgoing wavefields as they contain all orders of internal multiple reflections of the subsurface. These internal multiple reflections contribute to retrieving the reflectivity accurately in the redatumed reflection response. By deconvolving the retrieved upgoing Green’s function with the downgoing Green’s function, a new reflection response is obtained, with virtual sources and virtual receivers in the subsurface. The resultant reflection response is free of spurious events related to internal multiples in the overburden and contains the correct amplitudes. The angle-dependent reflectivity of the redatumed response can be obtained by summing the reflection coefficients along lines of constant ray parameter or angle. Potentially, the retrieved angle-dependent reflection coefficients obtained by this method can be used as input in a subsequent inversion process to obtain the velocity and density of the subsurface.
Injection and production of fluids into/from the subsurface has been known to trigger earthquakes, referred to as induced seismicity. This seismicity may occur when anthropogenically caused changes in the in-situ stress conditions result in reactivation of pre-existing faults in the subsurface causing slip accompanied by sudden release of energy. Several studies have numerically modelled the induced stresses due to production/injection in reservoirs of various geometries. In this report we present a simplified three-dimensional reservoir with a displaced fault and derive analytical expressions for induced stresses in and outside the reservoir due to production and injection of fluids. We use the calculated stresses for the three-dimensional model and analyse onset of slip across the fault. The research builds upon the analytical two-dimensional plane-strain analysis for induced stresses and slip initiation in \citet{Jansen2019} to which our work contributed. We reaffirm the findings from the plane-strain analysis in \citet{Jansen2019} and conclude that the effects of incorporating third dimension on induced stresses and slip behaviour are limited. We find infinite peaks in resultant shear stresses at the reservoir boundaries and observe a distinctly different pattern in induced stresses and slip behaviour between production and injection scenarios. In case of production, the slip patches are predicted to grow inwards into the reservoir initially until they merge, while for injection the slip patches grow separately into the overburden and underburden. The findings in this report are in agreement with the previous analytical and numerical studies on induced seismicity. In this research we also introduce geometrical complexity in the reservoir in the form of laterally varying height of the reservoir and we observe that the effects of variation in reservoir thickness are also minimal, however the induced stress patterns and slip initiation is significantly impacted by fault throw, initial stress conditions and fault frictional characteristics.
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Injection and production of fluids into/from the subsurface has been known to trigger earthquakes, referred to as induced seismicity. This seismicity may occur when anthropogenically caused changes in the in-situ stress conditions result in reactivation of pre-existing faults in the subsurface causing slip accompanied by sudden release of energy. Several studies have numerically modelled the induced stresses due to production/injection in reservoirs of various geometries. In this report we present a simplified three-dimensional reservoir with a displaced fault and derive analytical expressions for induced stresses in and outside the reservoir due to production and injection of fluids. We use the calculated stresses for the three-dimensional model and analyse onset of slip across the fault. The research builds upon the analytical two-dimensional plane-strain analysis for induced stresses and slip initiation in \citet{Jansen2019} to which our work contributed. We reaffirm the findings from the plane-strain analysis in \citet{Jansen2019} and conclude that the effects of incorporating third dimension on induced stresses and slip behaviour are limited. We find infinite peaks in resultant shear stresses at the reservoir boundaries and observe a distinctly different pattern in induced stresses and slip behaviour between production and injection scenarios. In case of production, the slip patches are predicted to grow inwards into the reservoir initially until they merge, while for injection the slip patches grow separately into the overburden and underburden. The findings in this report are in agreement with the previous analytical and numerical studies on induced seismicity. In this research we also introduce geometrical complexity in the reservoir in the form of laterally varying height of the reservoir and we observe that the effects of variation in reservoir thickness are also minimal, however the induced stress patterns and slip initiation is significantly impacted by fault throw, initial stress conditions and fault frictional characteristics.
Master thesis
(2017)
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Floris van den Broek, JOA Robertsson, DJ van Manen, Björn Melinder, Kees Wapenaar
In this work, a novel subspace-based algorithm is presented for automated random noise reduction in online recorded music. Musical signal enhancement is a separate issue from the well-studied speech enhancement problem due to the particularly wide range of signal characteristics encountered, and thus requires a very general approach. Because similar issues drive denoising advances in seismic signal processing, it is argued that an algorithm can be developed through
a cross-disciplinary approach. Inspired by an enhancement method for seismic sections, noise reduction is achieved by applying a singular value decomposition-based image enhancement technique, known as eigenimage filtering, to the time-frequency representation of the musical signal. Classic eigenimage filtering approximates a full-rank matrix by its closest rank-deficient
approximation; the preserved and discarded parts of the matrix correspond to the signal and noise subspaces, respectively. Under the assumption of a quasi-stationary signal, this technique is applied to the short-time Fourier transform of the signal. However, because the standard eigenimage filtering approach results in unwanted residual noise characteristics when applied in this domain, an adapted version of the technique is used. In this adaptation, all singular values are altered but none are set to zero, and the alteration is dependent on the singular values encountered.
Therefore, the method is data-adaptive. Subjective and objective performance measures indicate that the method is capable of improving the quality of noisy recordings, and that its quality is competitive compared with an open-source noise reduction algorithm whilst having the advantages of automation and fewer user-defined parameters. ...
a cross-disciplinary approach. Inspired by an enhancement method for seismic sections, noise reduction is achieved by applying a singular value decomposition-based image enhancement technique, known as eigenimage filtering, to the time-frequency representation of the musical signal. Classic eigenimage filtering approximates a full-rank matrix by its closest rank-deficient
approximation; the preserved and discarded parts of the matrix correspond to the signal and noise subspaces, respectively. Under the assumption of a quasi-stationary signal, this technique is applied to the short-time Fourier transform of the signal. However, because the standard eigenimage filtering approach results in unwanted residual noise characteristics when applied in this domain, an adapted version of the technique is used. In this adaptation, all singular values are altered but none are set to zero, and the alteration is dependent on the singular values encountered.
Therefore, the method is data-adaptive. Subjective and objective performance measures indicate that the method is capable of improving the quality of noisy recordings, and that its quality is competitive compared with an open-source noise reduction algorithm whilst having the advantages of automation and fewer user-defined parameters. ...
In this work, a novel subspace-based algorithm is presented for automated random noise reduction in online recorded music. Musical signal enhancement is a separate issue from the well-studied speech enhancement problem due to the particularly wide range of signal characteristics encountered, and thus requires a very general approach. Because similar issues drive denoising advances in seismic signal processing, it is argued that an algorithm can be developed through
a cross-disciplinary approach. Inspired by an enhancement method for seismic sections, noise reduction is achieved by applying a singular value decomposition-based image enhancement technique, known as eigenimage filtering, to the time-frequency representation of the musical signal. Classic eigenimage filtering approximates a full-rank matrix by its closest rank-deficient
approximation; the preserved and discarded parts of the matrix correspond to the signal and noise subspaces, respectively. Under the assumption of a quasi-stationary signal, this technique is applied to the short-time Fourier transform of the signal. However, because the standard eigenimage filtering approach results in unwanted residual noise characteristics when applied in this domain, an adapted version of the technique is used. In this adaptation, all singular values are altered but none are set to zero, and the alteration is dependent on the singular values encountered.
Therefore, the method is data-adaptive. Subjective and objective performance measures indicate that the method is capable of improving the quality of noisy recordings, and that its quality is competitive compared with an open-source noise reduction algorithm whilst having the advantages of automation and fewer user-defined parameters.
a cross-disciplinary approach. Inspired by an enhancement method for seismic sections, noise reduction is achieved by applying a singular value decomposition-based image enhancement technique, known as eigenimage filtering, to the time-frequency representation of the musical signal. Classic eigenimage filtering approximates a full-rank matrix by its closest rank-deficient
approximation; the preserved and discarded parts of the matrix correspond to the signal and noise subspaces, respectively. Under the assumption of a quasi-stationary signal, this technique is applied to the short-time Fourier transform of the signal. However, because the standard eigenimage filtering approach results in unwanted residual noise characteristics when applied in this domain, an adapted version of the technique is used. In this adaptation, all singular values are altered but none are set to zero, and the alteration is dependent on the singular values encountered.
Therefore, the method is data-adaptive. Subjective and objective performance measures indicate that the method is capable of improving the quality of noisy recordings, and that its quality is competitive compared with an open-source noise reduction algorithm whilst having the advantages of automation and fewer user-defined parameters.