The ultimate goal of survey design is to find the acquisition parameters that enable acquiring high-quality data suitable for optimal imaging, while fulfilling budget, health, safety, and environmental constraints. We develop a target-oriented acquisition design algorithm based on full-wavefield migration. The algorithm optimizes a receiver density function that indicates the number of receivers per unit area required for obtaining the best possible image quality. The method makes use of available seismic data to create a reference model that is included in our objective function. To make the design target oriented, the objective function is multiplied with a mask that gives more weight to the target areas of interest. The results of the 2D and 3D implementations indicate an optimized receiver density function with higher values at the zones where more data are needed for improving image quality. The corresponding receiver geometries have more receivers placed in these areas. We validate the results by computing the images of the target zone using uniform and optimized geometries. The use of the latter indicates an improvement in the image quality at the target zone. In addition, we compute the number of receivers required for achieving a certain signal-to-noise ratio after imaging based on the optimized receiver density function.

The ultimate goal in survey design is to obtain the acquisition parameters that enable acquiring the most affordable data that fulfills certain image quality requirements. We propose a method that allows optimization of the receiver geometry for a fixed source distribution. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiverdensity function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging.

The ultimate goal in survey design is to obtain the acquisition parameters that allow to acquire the most affordable data that fulfills certain image quality requirements. We propose a method that allows to optimize the receiver geometry for a fixed source distribution, e.g., think of ocean bottom nodes. The former is parameterized with a receiver density function that determines the number of receivers per unit area. We optimize this receiver density function through an iterative gradient descent scheme that minimizes the difference between the image obtained with the current acquisition geometry and a reference image. The reference image is obtained from prior subsurface information that is assumed to be available. We tested the method with different subsurface models. The results show that the acquisition geometry is optimized according to the complexity of each subsurface model. The receivers are moved towards the areas where more data is needed for obtaining better imaging. Currently, we work on extending our method for the source distribution.

Seismic data is traditionally acquired based on spatial sampling requirements, noise properties, and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps, may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfills a pre-defined quality criterion. With our method we estimate a set of point-spread functions that correspond to the chosen combination and we analyse their resolution and illumination-detection properties in the image and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve resolution and illumination-detection compared to the use of primaries only.

In seismic exploration methods, imperfect spatial sampling at the surface causes a lack of illumination at the target in the subsurface. The hampered image quality at the target area of interest causes uncertainties in reservoir monitoring and production, which can have a substantial economic impact. Especially in the case of a complex overburden, the impact of surface sampling on target illumination can be significant. The target-oriented acquisition analysis based on wavefield propagation and a known velocity model has been used to provide guidance for optimizing the acquisition parameters. Seismic acquisition design is usually a manual optimization process, with consideration of many aspects. In this study, we develop a methodology that automatically optimizes an irregular receiver geometry when the source geometry is fixed or vice versa. The methodology includes objective functions defined by two criteria: optimizing the image resolution and optimizing the angle-dependent illumination information. We use a two-step parameterization in order to make the problem more linear and, thereby, solve the acquisition design problem by using a gradient descent algorithm. With simple and complex velocity models, we demonstrate that the proposed method is effective, while the involved computational cost is acceptable. Interestingly, the optimization results in our examples show that the conventional uniform geometry already satisfies the resolution requirement, while optimizing for angle coverage can provide a large uplift and is strongly dependent on the velocity model.

Seismic data are traditionally acquired based on spatial sampling requirements, noise properties and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfils a pre-defined quality criterion. With our method, we estimate a set of point-spread functions that correspond to the chosen combination, and we analyse their resolution and illumination-detection properties in the spatial and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve amplitude and resolution compared with the use of primaries only. The proposed analysis method is also used to evaluate the effect of blending noise when blended acquisition is carried out.

It is well known that source deghosting can best be applied to common-receiver gathers, whereas receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in the common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, that is, the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we have trained a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver-ghost wavefield to obtain the training data. Due to reciprocity, these training data are a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine-learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach can remove the source-ghost wavefield from the coarsely sampled common-receiver gathers.

Acquisition of incomplete data, i.e., blended, sparsely sampled, and narrowband data, allows for cost-effective and efficient field seismic operations. This strategy becomes technically acceptable, provided that a satisfactory recovery of the complete data, i.e., deblended, well-sampled, and broadband data, is attainable. Hence, we explore a machine-learning approach that simultaneously performs suppression of blending noise, reconstruction of missing traces, and extrapolation of low frequencies. We have applied a deep convolutional neural network in the framework of supervised learning in which we train a network using pairs of incomplete-complete data sets. Incomplete data, which are never used for training and use different subsurface properties and acquisition scenarios, are subsequently fed into the trained network to predict complete data. We develop matrix representations indicating the contributions of different acquisition strategies to reducing the field operational effort. We also determine that the simultaneous implementation of source blending, sparse geometry, and band limitation leads to a significant data compression where the size of the incomplete data in the frequency-space domain is much smaller than the size of the complete data. This reduction is indicative of survey cost and duration that our acquisition strategy can save. Synthetic and field data examples demonstrate the applicability of the proposed approach. Despite the reduced amount of information available in the incomplete data, the results obtained from the numerical and field data cases clearly show that the machine-learning scheme effectively performs deblending, trace reconstruction, and low-frequency extrapolation in a simultaneous fashion. It is noteworthy that no discernible difference in prediction errors between extrapolated frequencies and preexisting frequencies is observed. The approach potentially allows seismic data to be acquired in a significantly compressed manner while subsequently recovering data of satisfactory quality.

Focal beam analysis has built a bridge between the acquisition parameters on the surface and the image quality of underground targets. However, as a practical matter, it is still difficult to answer how to choose a proper acquisition geometry according to the complexity of medium, especially considering the contradictory effects of multiple reflections on spatial resolution as they can be considered to be either potential signal or additional noise, depending on the envisioned imaging technology. We introduce an order-controlled, closed-loop focal beam method in which the migration operator and the resolution function can be analysed in the process of the closed-loop migration with full control over the order of the surface and internal multiples considered. This method highlights the effects of primary and different-order multiple wavefields on the imaging resolution for different acquisition geometries and various overburden strata. We apply the method to analyse the predicted resolution of seismic acquisition geometries considering multiples as either noise or signal. Results show, in the acquisition geometry design, that when the primaries cannot provide a complete spatial illumination for the subsurface target, e.g. because of the limited-aperture acquisition geometries or the complicated overburden, we should use the closed-loop focal beam analysis to assess the contradictory effects of multiples as both signal and noise, in which the maximum order of multiples ought to be chosen according to the core aim of the acquisition analysis. We can apply the second-order closed-loop focal beam analysis to quantify the effects of acquisition geometries on multiple-wave suppression and can also perform the high-order closed-loop focal beam analysis to quantify the effects of acquisition geometries on high-resolution imaging (migration). This method can also be used to choose the optimal order of multiples in the closed-loop migration.

Imperfect spatial sampling causes lack of illumination at the target in the subsurface. The hampered image quality at the target area of interest can cause high uncertainties in reservoir monitoring and production, which can have a high economic impact. Previously we have presented a method that optimizes the receiver geometry while the source side is carpet shooting. In this work, we develop a method that optimizes both the source and receiver geometries in order to obtain a good illumination of a chosen target point, such that they can compensate the missing illumination for each other. With numerical examples, we demonstrate that the proposed method is effective.

Acquisition of complete data, i.e., unblended, well-sampled and broadband data, is technically desirable. Obviously, such a scenario is prohibitively expensive to realize. To deal with economic considerations in a seismic survey without seriously compromising data quality, we propose a machine-learning approach that offers an opportunity to acquire incomplete data, i.e., blended, sparsely-sampled and narrowband data, while still benefitting from being able to process complete data. In this study, we utilize a deep convolutional neural network. The incomplete data are fed into the applied network that simultaneously performs suppression of blending noise, reconstruction of missing traces and extrapolation of low frequencies such that prediction of the complete data is attainable. We validate the performance of the proposed method using both synthetic and field datasets. Acquisition scenarios implemented to generate incomplete datasets impose a significant reduction of data size in the frequency-space domain. Despite the limited information available in the input data, the prediction results obtained from both numerical and field data examples clearly confirm that the proposed machine-learning approach is capable of dealing with deficiencies in the incomplete data and subsequently deriving the complete data of sufficient quality. In addition to suppression of blending noise and reconstruction of missing traces, no discernible difference in prediction errors between preexisting and extrapolated frequencies is observed, which is hardly realizable with existing geophysics-based approaches. As a consequence, the proposed scheme allows for optimal data enhancement even when seismic acquisition is performed in a blended, sparsely-sampled and narrowband fashion.

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 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.

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.

The sea surface acts as a very strong reflector because of the large impedance contrast between water and air. The reflection coefficient is -1 in a very good approximation. Apart from the surface multiples, the sea surface is also responsible for generating the source and receiver ghost wavefields. These cause the well-known ghost notches in the spectrum: areas where the signal-to-noise ratio is very low. To model the ghost wavefields, ghost operators can be computed and applied to ghost-free data. Modeling experiments indicate that in the case of a flat sea surface, the character of the notches in various gather types, e.g., receiver gather, common-offset gather, shot record, is largely determined by the complexity of the earth. In a simple earth, e.g., horizontally layered, the notches are always well-defined and deep, but in a complex earth, they become blurry in some of the gather types. Therefore, in the case of a complex subsurface, source deghosting is best carried out in the common-receiver domain and receiver deghosting is best carried out in the common-shot domain. In the case of a simple subsurface, deghosting can be carried out in all domains. An additional factor is that the sea surface may be rough and dynamic. This causes blurry ghost notches in all gather types, even in the case of a simple earth. To model the source ghost for this situation, an effective static rough sea surface suffices. This keeps the computations simple. The condition is that the source has an impulsive character. However, to model the receiver ghost (and the source ghost for a nonimpulsive source), the dynamics of the sea surface must be included. This can be done by composing the final result from the results computed for several "frozen" snapshots of the dynamic sea surface.

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.

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 calculation of air gun source signatures gives insight into applications such as air gun array design, deghosting and the impact of sound on marine life. Single air gun source signatures were calculated from the numerical solution of a set of differential equations based on different branches of physics. Some characteristic parameters of air guns were obtained from the Svein Vaage broadband air gun data set (SVBAD) measurements to calibrate the model. The comparison between measured and modeled air gun signals helps to investigate the accuracy of air gun source models. The modeled air gun signatures compared well with measurements from the SVBAD for the case of a calm sea surface of sea state 2 or less. The source ghost signal modeled for a rough sea surface showed amplitude and phase changes, affecting the ghost notches, which may explain discrepancies between the SVBAD measurements and modeled air gun signals at frequencies above 150 Hz.

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.