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.

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.

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.

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.

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 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.

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.

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.