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S. Nakayama
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1
The quality and business aspects are both of particular importance in determining the type of seismic acquisition. Usually, a strong emphasis on cost reduction is inevitable. On the other hand, there is an increasing demand for the acquisition of high-quality seismic data that can contribute to the various stages in the field development profile. These conflicting desires eventually make conventional seismic surveys an inadequate option. The application of blended acquisition along with efficient detector and source geometries is capable of providing high-quality seismic data in a cost-effective and productive manner. This way of data acquisition also contributes to minimizing health, safety and environment exposure in the field. Blended acquisition allows multiple source-wavefields to be overlapped in time, space, and temporal and spatial frequency, causing blending interference. The acquisition of less data via sparse detector and source geometries likely violates the Nyquist sampling criterion. Therefore, to make the aforementioned approach technically justifiable, deficiencies in recorded data have to be dealt with through the course of subsequent processing steps. One way to encourage this technique is to minimize any imperfection in processing algorithms. In addition, one may derive survey parameters that enable a further improvement in these processes, which is the primary focus in this thesis.
...
The quality and business aspects are both of particular importance in determining the type of seismic acquisition. Usually, a strong emphasis on cost reduction is inevitable. On the other hand, there is an increasing demand for the acquisition of high-quality seismic data that can contribute to the various stages in the field development profile. These conflicting desires eventually make conventional seismic surveys an inadequate option. The application of blended acquisition along with efficient detector and source geometries is capable of providing high-quality seismic data in a cost-effective and productive manner. This way of data acquisition also contributes to minimizing health, safety and environment exposure in the field. Blended acquisition allows multiple source-wavefields to be overlapped in time, space, and temporal and spatial frequency, causing blending interference. The acquisition of less data via sparse detector and source geometries likely violates the Nyquist sampling criterion. Therefore, to make the aforementioned approach technically justifiable, deficiencies in recorded data have to be dealt with through the course of subsequent processing steps. One way to encourage this technique is to minimize any imperfection in processing algorithms. In addition, one may derive survey parameters that enable a further improvement in these processes, which is the primary focus in this thesis.
We introduce a blended-acquisition method: temporally signatured and/or modulated and spatially dispersed source array, namely S-/M-DSA. The former S-DSA has much less constraints in the encoding with operational flexibility, allowing non-uniform sampling and non-patterned shooting both in the space and time dimension. The latter M-DSA allows indeed straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We evaluated the deblending performance for several scenarios of blended acquisition. The results showed that: S-DSA attains the best acquisition productivity; M-DSA attains the best deblending performance, compared to other methods. Our S-/M-DSA method makes the blended-acquisition encoding and operations significantly simple and robust; the same is true for the deblending processing.
...
We introduce a blended-acquisition method: temporally signatured and/or modulated and spatially dispersed source array, namely S-/M-DSA. The former S-DSA has much less constraints in the encoding with operational flexibility, allowing non-uniform sampling and non-patterned shooting both in the space and time dimension. The latter M-DSA allows indeed straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We evaluated the deblending performance for several scenarios of blended acquisition. The results showed that: S-DSA attains the best acquisition productivity; M-DSA attains the best deblending performance, compared to other methods. Our S-/M-DSA method makes the blended-acquisition encoding and operations significantly simple and robust; the same is true for the deblending processing.
Blended-acquisition encoding with generalized blending operators
Signaturing with temporally amplitude-modulated and spatially dispersed source array
Recently, we established a generalized blending model, which can explain any methods of blended acquisition by including the encoding into the generalized operators. With this highly flexible and tolerant model, we come up with a challenging question: what it is to be, and how to find an optimal blended-acquisition design, which should be the most suitable for deblended-data reconstruction among plenty of concepts of blended acquisition. In this paper, we introduce a method of blended-acquisition encoding: temporally modulated and spatially dispersed source array, namely M-DSA, that jointly uses modulation sequencing in the time dimension and dispersed source array in the space dimension. This allows quite straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We run our blended-acquisition designing based on the deblending performance for several scenarios of blended acquisition. These examples show that: M-DSA attains the best deblending performance; this method has less constraints in the encoding with more operational flexibility, compared to other methods being developed in the industry today. Indeed, this method requires only simple signaturing in the encoding; merely frequency-banded and modulated signatures in the time dimension for each shot in the blended-source array. This could even render any other blending properties unnecessary. Those, such as distance separation among shot locations and time shifts among shot times, might not be required anymore. There might be no limitation on the number of sources, thus no limitation on the blending fold, in order to secure successful deblending. Furthermore, this method allows random sampling; randomly distributed sources in the space dimension in the blended-source array. Consequently, this method makes the blended-acquisition encoding and operations significantly simple and robust, as well as for the deblending processing. We believe that our M-DSA method should be one of the best methods of blended acquisition.
...
Recently, we established a generalized blending model, which can explain any methods of blended acquisition by including the encoding into the generalized operators. With this highly flexible and tolerant model, we come up with a challenging question: what it is to be, and how to find an optimal blended-acquisition design, which should be the most suitable for deblended-data reconstruction among plenty of concepts of blended acquisition. In this paper, we introduce a method of blended-acquisition encoding: temporally modulated and spatially dispersed source array, namely M-DSA, that jointly uses modulation sequencing in the time dimension and dispersed source array in the space dimension. This allows quite straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We run our blended-acquisition designing based on the deblending performance for several scenarios of blended acquisition. These examples show that: M-DSA attains the best deblending performance; this method has less constraints in the encoding with more operational flexibility, compared to other methods being developed in the industry today. Indeed, this method requires only simple signaturing in the encoding; merely frequency-banded and modulated signatures in the time dimension for each shot in the blended-source array. This could even render any other blending properties unnecessary. Those, such as distance separation among shot locations and time shifts among shot times, might not be required anymore. There might be no limitation on the number of sources, thus no limitation on the blending fold, in order to secure successful deblending. Furthermore, this method allows random sampling; randomly distributed sources in the space dimension in the blended-source array. Consequently, this method makes the blended-acquisition encoding and operations significantly simple and robust, as well as for the deblending processing. We believe that our M-DSA method should be one of the best methods of blended acquisition.
The application of blended acquisition along with irregular acquisition geometries contributes to the economic perspective of a seismic survey. The joint migration inversion scheme is capable of directly processing the data acquired in this way, i.e., without deblending or data reconstruction, and of subsequently estimating both reflectively and velocity models. The workflow proposed in this study aims to design the source blending operator as well as detector and source sampling operators. The approach iteratively computes these parameters in such a way that the quality of reflectivity and velocity models, which are directly estimated from blended and irregularly-sampled data, is adequate. The workflow integrates a genetic algorithm and a convolutional neural network to derive optimum parameters. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of a joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it passes optimal ones to the subsequent step, which successfully enhances the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimates attributed solely to the choice of survey parameters.
...
The application of blended acquisition along with irregular acquisition geometries contributes to the economic perspective of a seismic survey. The joint migration inversion scheme is capable of directly processing the data acquired in this way, i.e., without deblending or data reconstruction, and of subsequently estimating both reflectively and velocity models. The workflow proposed in this study aims to design the source blending operator as well as detector and source sampling operators. The approach iteratively computes these parameters in such a way that the quality of reflectivity and velocity models, which are directly estimated from blended and irregularly-sampled data, is adequate. The workflow integrates a genetic algorithm and a convolutional neural network to derive optimum parameters. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of a joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it passes optimal ones to the subsequent step, which successfully enhances the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimates attributed solely to the choice of survey parameters.
Research note
Deblended-data reconstruction using generalized blending and deblending models
Journal article
(2019)
-
Tomohide Ishiyama, Mohammed Y. Ali, Satoshi Ishikawa, Gerrit Blacquiere, Shotaro Nakayama
We introduce a concept of generalized blending and deblending, develop its models and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized blending and deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We applied our method to existing real data acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data. We discuss the complexity of blending properties on the deblending performance. In addition, we discuss the applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Conclusively, we should acquire blended data and reconstruct deblended data without serious problems but with the benefit of blended acquisition.
...
We introduce a concept of generalized blending and deblending, develop its models and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized blending and deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We applied our method to existing real data acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data. We discuss the complexity of blending properties on the deblending performance. In addition, we discuss the applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Conclusively, we should acquire blended data and reconstruct deblended data without serious problems but with the benefit of blended acquisition.
Acquisition geometry design aims at finding the most affordable acquisition geometry that satisfies the objectives of the seismic survey. The parameters of an acquisition geometry can be specified in terms of the number of sources and detectors, their location, the blending parameters and the DSA (dispersed source array) parameters. In our acquisition geometry design, we include the effects of the (expected) subsurface, i.e., we assume the subsurface to be known. Consequently, the ideal data set – carpet shooting and carpet detection – can be modeled. A practical data set can be considered to be a subset of this ideal one. Acquisition design comes down to determining the optimum subset. Following compressive sensing, this subset is sparse and irregular. As a quality measure, we apply decompression (deblending and interpolation) to the subset, which leads to an estimate of the ideal data set, and then compare this estimate with the known ideal data set. The difference is the residue that should satisfy a predefined quality criterion. This procedure is the inner loop of a genetic algorithm. A CNN (convolutional neural network) is trained to improve the efficiency of the genetic algorithm by enhancing the effectiveness of each next generation. Furthermore, the solution space is limited to reduce the amount of computations needed. Finally, in this application it is acceptable to end up in a local minimum. The latter corresponds to an acquisition geometry that fully satisfies the quality and economic criteria (although some acquisition geometry may exist that provides even better results). Our design method leads to results that are better than those obtained with randomized acquisition geometries.
...
Acquisition geometry design aims at finding the most affordable acquisition geometry that satisfies the objectives of the seismic survey. The parameters of an acquisition geometry can be specified in terms of the number of sources and detectors, their location, the blending parameters and the DSA (dispersed source array) parameters. In our acquisition geometry design, we include the effects of the (expected) subsurface, i.e., we assume the subsurface to be known. Consequently, the ideal data set – carpet shooting and carpet detection – can be modeled. A practical data set can be considered to be a subset of this ideal one. Acquisition design comes down to determining the optimum subset. Following compressive sensing, this subset is sparse and irregular. As a quality measure, we apply decompression (deblending and interpolation) to the subset, which leads to an estimate of the ideal data set, and then compare this estimate with the known ideal data set. The difference is the residue that should satisfy a predefined quality criterion. This procedure is the inner loop of a genetic algorithm. A CNN (convolutional neural network) is trained to improve the efficiency of the genetic algorithm by enhancing the effectiveness of each next generation. Furthermore, the solution space is limited to reduce the amount of computations needed. Finally, in this application it is acceptable to end up in a local minimum. The latter corresponds to an acquisition geometry that fully satisfies the quality and economic criteria (although some acquisition geometry may exist that provides even better results). Our design method leads to results that are better than those obtained with randomized acquisition geometries.
The application of blended acquisition has drawn considerable attention owing to its ability to improve the operational efficiency as well as the data quality and health, safety and environment performance. Furthermore, the acquisition of less data contributes to the business aspect, while the desired data density is still realizable via subsequent data reconstruction. The use of fewer detectors and sources also minimizes operational risks in the field. Therefore, a combined implementation of these technologies potentially enhances the value of a seismic survey further. One way to encourage this is to minimize any imperfection in deblending and data reconstruction during processing. In addition, one may derive survey parameters that enable a further improvement in these processes as introduced in this study. The proposed survey design workflow iteratively performs the following steps to derive the survey parameters responsible for source blending as well as the spatial sampling of detectors and sources. The first step is the application of blending and sampling operators to unblended and well-sampled data. We then apply closed-loop deblending and data reconstruction. The residue for a given design from this step is evaluated and subsequently used by genetic algorithms to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into the next iteration until they satisfy the given termination criteria. We also propose a repeated encoding sequence to form a parameter sequence in genetic algorithms, making the size of problem space manageable. The results of the proposed workflow are outlined using blended dispersed source array data incorporating different scenarios that represent acquisition in marine, transition zone and land environments. Clear differences attributed solely to the parameter design are easily recognizable. Additionally, a comparison among different optimization schemes illustrates the ability of genetic algorithms along with a repeated encoding sequence to find better solutions within a computationally affordable time. The optimized parameters yield a notable enhancement in the deblending and data reconstruction quality and consequently provide optimal acquisition scenarios.
...
The application of blended acquisition has drawn considerable attention owing to its ability to improve the operational efficiency as well as the data quality and health, safety and environment performance. Furthermore, the acquisition of less data contributes to the business aspect, while the desired data density is still realizable via subsequent data reconstruction. The use of fewer detectors and sources also minimizes operational risks in the field. Therefore, a combined implementation of these technologies potentially enhances the value of a seismic survey further. One way to encourage this is to minimize any imperfection in deblending and data reconstruction during processing. In addition, one may derive survey parameters that enable a further improvement in these processes as introduced in this study. The proposed survey design workflow iteratively performs the following steps to derive the survey parameters responsible for source blending as well as the spatial sampling of detectors and sources. The first step is the application of blending and sampling operators to unblended and well-sampled data. We then apply closed-loop deblending and data reconstruction. The residue for a given design from this step is evaluated and subsequently used by genetic algorithms to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into the next iteration until they satisfy the given termination criteria. We also propose a repeated encoding sequence to form a parameter sequence in genetic algorithms, making the size of problem space manageable. The results of the proposed workflow are outlined using blended dispersed source array data incorporating different scenarios that represent acquisition in marine, transition zone and land environments. Clear differences attributed solely to the parameter design are easily recognizable. Additionally, a comparison among different optimization schemes illustrates the ability of genetic algorithms along with a repeated encoding sequence to find better solutions within a computationally affordable time. The optimized parameters yield a notable enhancement in the deblending and data reconstruction quality and consequently provide optimal acquisition scenarios.
Blended acquisition along with efficient spatial sampling is capable of providing high-quality seismic data in a cost-effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.
...
Blended acquisition along with efficient spatial sampling is capable of providing high-quality seismic data in a cost-effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.
Conference paper
(2018)
-
Tomohide Ishiyama, Mohammed Y. Ali, Satoshi Ishikawa, Shotaro Nakayama, Gerrit Blacquière
We introduce a generalized concept of blending and deblending, establish its models, and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real-life situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We established and applied this method to existing real datasets acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data
...
We introduce a generalized concept of blending and deblending, establish its models, and accordingly establish a method of deblended-data reconstruction using these models. The generalized models can handle real-life situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We established and applied this method to existing real datasets acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data
We introduce a workflow to derive survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The proposed workflow iteratively performs the following three steps. The first step is
application of blending and sub-sampling to an unblended and well-sampled data. We then apply a closed-loop deblending and data reconstruction enabling a robust estimate of a deblended and reconstructed data. The residue
for a given design from this step is evaluated, and subsequently used by genetic algorithms (GAs) to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into a next iteration till they satisfy given stopping criteria. We also propose repeated encoding sequence (RES) used to form a parameter sequence in GAs, making the proposed designing workflow computationally affordable. We demonstrate the results of the workflow using numerically simulated examples that represent blended dispersed source array data. Difference attributable only to a way to design parameters is easily recognizable. The optimized parameters yield clear improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition scenarios. Additionally, comparison among different optimization schemes illustrates ability of GAs along with RES to efficiently find better solutions. ...
application of blending and sub-sampling to an unblended and well-sampled data. We then apply a closed-loop deblending and data reconstruction enabling a robust estimate of a deblended and reconstructed data. The residue
for a given design from this step is evaluated, and subsequently used by genetic algorithms (GAs) to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into a next iteration till they satisfy given stopping criteria. We also propose repeated encoding sequence (RES) used to form a parameter sequence in GAs, making the proposed designing workflow computationally affordable. We demonstrate the results of the workflow using numerically simulated examples that represent blended dispersed source array data. Difference attributable only to a way to design parameters is easily recognizable. The optimized parameters yield clear improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition scenarios. Additionally, comparison among different optimization schemes illustrates ability of GAs along with RES to efficiently find better solutions. ...
We introduce a workflow to derive survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The proposed workflow iteratively performs the following three steps. The first step is
application of blending and sub-sampling to an unblended and well-sampled data. We then apply a closed-loop deblending and data reconstruction enabling a robust estimate of a deblended and reconstructed data. The residue
for a given design from this step is evaluated, and subsequently used by genetic algorithms (GAs) to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into a next iteration till they satisfy given stopping criteria. We also propose repeated encoding sequence (RES) used to form a parameter sequence in GAs, making the proposed designing workflow computationally affordable. We demonstrate the results of the workflow using numerically simulated examples that represent blended dispersed source array data. Difference attributable only to a way to design parameters is easily recognizable. The optimized parameters yield clear improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition scenarios. Additionally, comparison among different optimization schemes illustrates ability of GAs along with RES to efficiently find better solutions.
application of blending and sub-sampling to an unblended and well-sampled data. We then apply a closed-loop deblending and data reconstruction enabling a robust estimate of a deblended and reconstructed data. The residue
for a given design from this step is evaluated, and subsequently used by genetic algorithms (GAs) to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into a next iteration till they satisfy given stopping criteria. We also propose repeated encoding sequence (RES) used to form a parameter sequence in GAs, making the proposed designing workflow computationally affordable. We demonstrate the results of the workflow using numerically simulated examples that represent blended dispersed source array data. Difference attributable only to a way to design parameters is easily recognizable. The optimized parameters yield clear improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition scenarios. Additionally, comparison among different optimization schemes illustrates ability of GAs along with RES to efficiently find better solutions.
The application of 3D symmetric and dense sampling is technically desirable when designing a seismic survey with wide-azimuth sampling. However, budgetary and operational constraints often hinder us from pursuing this ideal particularly under the current economic circumstances in the industry. The combined implementation of blending and efficient acquisition geometries enables us to acquire seismic data in a cost-effective manner. Subsequent deblending and data reconstruction allow for the retrieval of deblended and well-sampled data from blended and irregularly sampled data such that the desired data quality is also attainable. We propose a survey-design workflow that involves an iterative scheme to update survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The optimized parameters subsequently yield satisfactory deblending and data reconstruction results. Numerical examples demonstrate the results of the proposed survey-design workflow for wide azimuth 3D geometries. Differences attributable solely to the parameter design are clearly recognizable. The optimized parameters lead to the improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition designs.
...
The application of 3D symmetric and dense sampling is technically desirable when designing a seismic survey with wide-azimuth sampling. However, budgetary and operational constraints often hinder us from pursuing this ideal particularly under the current economic circumstances in the industry. The combined implementation of blending and efficient acquisition geometries enables us to acquire seismic data in a cost-effective manner. Subsequent deblending and data reconstruction allow for the retrieval of deblended and well-sampled data from blended and irregularly sampled data such that the desired data quality is also attainable. We propose a survey-design workflow that involves an iterative scheme to update survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The optimized parameters subsequently yield satisfactory deblending and data reconstruction results. Numerical examples demonstrate the results of the proposed survey-design workflow for wide azimuth 3D geometries. Differences attributable solely to the parameter design are clearly recognizable. The optimized parameters lead to the improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition designs.
We introduce a generalized concept of blending and deblending, and establish the generalized-blending and - deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. ...
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. ...
We introduce a generalized concept of blending and deblending, and establish the generalized-blending and - deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data.
these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent and inhomogeneous shooting, signature stamping, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works all for shot-generated-wavefields separation, spectrum recovery and balancing, designature, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this, we consider an iterative optimization scheme using a so-called closed-loop approach with the generalized-blending and -deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed
loop. We established and applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data.
We introduce a generalized concept of the so-called blending and deblending, and establish the generalizedblending and -deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent shooting, inhomogeneous shooting, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works for all shot-generated-wavefields separation, spectrum recovery and balancing, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this challenge, we consider an iterative optimization scheme using a so-called closed-loop approach. We use the properties of blended signal specified by the blending code: the coherency of blended signal versus the incoherency of blending noise in the pseudo-deblended domain. This can be posed as an inverse problem with quantifying the coherency and its solutions by selecting optimal metrics of the coherency. We applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. We discuss its applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Our methodology should reduce the repeatability problem because reconstructing deblended data in monitor surveys is much more realistic and reliable than positioning sources and receivers exactly as the baseline survey.
...
We introduce a generalized concept of the so-called blending and deblending, and establish the generalizedblending and -deblending models. Accordingly, we establish a method of deblending, or deblended-data reconstruction, using these models. The generalized blending can handle real-life situations; this includes random encoding both in the space and time domain, both at the source and receiver side, thus all incoherent shooting, inhomogeneous shooting, non-uniform and under sampling. Similarly, the generalized deblending includes data reconstruction that works for all shot-generated-wavefields separation, spectrum recovery and balancing, regularization and interpolation, again both at the source and receiver side. However, we do face a challenging question: how to fully reconstruct deblended data from the fully generalized blended data. To address this challenge, we consider an iterative optimization scheme using a so-called closed-loop approach. We use the properties of blended signal specified by the blending code: the coherency of blended signal versus the incoherency of blending noise in the pseudo-deblended domain. This can be posed as an inverse problem with quantifying the coherency and its solutions by selecting optimal metrics of the coherency. We applied this method to synthetic datasets. The results show that our method succeeded to fully reconstruct deblended data from the fully generalized blended data. We discuss its applicability to time-lapse seismic monitoring as it ensures high repeatability of the surveys. Our methodology should reduce the repeatability problem because reconstructing deblended data in monitor surveys is much more realistic and reliable than positioning sources and receivers exactly as the baseline survey.