3D seismic acquisition

Abstract

Disclosed are methods of marine 3D seismic data acquisition that do not require compensation for winds and currents.

Claims

1. A method of marine survey area imaging, the method comprising: developing a geology model of a marine survey area for a sample grid of a geophysically selected size; generating a compressed seismic imaging design (“CSI design”) by iteratively modifying the sample grid until the CSI design is stable; determining a set of sampling rules for the marine survey area and the CSI design, the set of rules providing coverage of the marine survey area such that minimum data is recorded for wavefield reconstruction using the CSI design; acquiring 3D seismic data traces as a series of cross-cutting passes of a vessel at one or more orientations over the marine survey area using the CSI design until the set of sampling rules is met; producing seismic data by reconstructing a wavefield and regularizing the 3D seismic data traces; and identifying where at least one of oil or gas may be trapped in sufficient quantities for exploration activities using the seismic data.

2. The method of claim 1, wherein the sample grid is determined by: constructing an optimization model given by min.sub.u∥Su∥.sub.1s.t.∥Ru−b∥.sub.2≤σ, wherein Su is a discrete transform matrix, b is seismic data on an observed grid, u is seismic data on a reconstruction grid, and matrix R is a sampling operator; defining a mutual coherence as: $\mu \le \sqrt{\frac{C}{S}\frac{m}{{\left(\log n\right)}^6}},$ wherein C is a constant, S is a cardinality of Su, m is proportional to a number of seismic traces on the observed grid, and n is proportional to a number of seismic traces on the reconstruction grid; deriving a mutual coherence proxy, wherein the mutual coherence proxy is a proxy for the mutual coherence when S is over-complete and wherein the mutual coherence proxy is exactly the mutual coherence when S is a Fourier transform; and determining the sample grid according to r*=arg minr μ(r).

3. The method of claim 2, wherein r*=arg minr μ(r) is non-convex.

4. The method of claim 2, wherein the mutual coherence proxy is derived using a fast Fourier transform.

5. The method of claim 1, wherein the sample grid is determined via a randomized greedy algorithm method.

6. The method of claim 5, wherein the randomized greedy algorithm method finds local minimum.

7. The method of claim 1, wherein the sample grid is determined via a stochastic global optimization method.

8. The method of claim 1, wherein the 3D seismic data traces are analyzed during acquisition to confirm coverage of the marine survey area using the set of sampling rules.

9. The method of claim 8, wherein an actual pathway travelled by the vessel is used to confirm the coverage.

10. The method of claim 1, wherein the set of sampling rules is determined by decimating one or more attributes of the CSI design until a final image degrades.

11. The method of claim 1, wherein a map of a subsurface formation of the marine survey area where the at least one of oil or gas may be trapped is mapped by processing the seismic data.

12. The method of claim 1, wherein a stability of the CSI design is determined based on a mutual coherence of the sample grid.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1A is a side view of a marine seismic survey vessel and seismic waves.

(2) FIG. 1B is a top view of a typical seismic vessel and streamers.

(3) FIG. 2A shows a Narrow Azimuth towed streamers.

(4) FIG. 2B shows a Multi-Azimuth towed streamers.

(5) FIG. 3 shows a Wide Azimuth Towed Streamer.

(6) FIG. 4 displays the traditional racetrack pattern of acquiring marine 3D seismic data.

(7) Comparing coverage obtained with various acquisition geometries: FIG. 5A Traditional Marine Survey; FIG. 5B Multi-Azimuth Survey acquired by one vessel; FIG. 5C Wide-Azimuth Survey acquired by several vessels; FIG. 5D, Rich-Azimuth Survey; and FIG. 5E, Coil Shooting single-vessel technique.

(8) FIG. 6 shows bin spacing for optimal 3-4 bin coverage of the smallest feature to be mapped. Geophones are thus set to achieve this bin spacing.

DETAILED DESCRIPTION

(9) The disclosure provides novel methods of acquiring seismic data, which eliminates oversampling, infill and the need to fight wind and current to provide a straight line data.

(10) Specifically, the methods herein described allow for the design and acquisition of marine seismic surveys using a rule-based mode without need for conventional designs that result in wasted coverage. This will decrease the cost of acquiring data because of the smaller sampling size and ease of determining when enough data has been collected and accelerate modeling times by decreasing the presence of unneeded data points.

(11) The invention includes one or more of the following embodiments, in any combination thereof: A method of acquiring marine 3D seismic data comprising acquiring 3D data of an area to be surveyed without compensating for wind and current, but instead allowing wind and current to direct travel, collecting data positioning data while acquiring 3D data, and continuing until said collected positioning data indicates that sufficient coverage of said area has been obtained. A method of marine 3D seismic data acquisition, comprising: obtaining a marine geology model of a marine survey area; determining a bin size and orientation based on a smallest feature to be mapped and a shape of said marine geology model; determine a stability of a compressed Seismic Imaging (CSI) design and if unstable modify said source & receiver station spacing and location, bin size or orientation and re-determine said stability of said CSI design iteratively to locate a stable solution for said CSI design; establish a set of rules for coverage of said marine survey area that meet the CSI design; and acquiring 3D seismic data over said marine survey area until said rules are met, wherein wind and current are not compensated for but allowed to direct vessel travel and or streamer shape at least a portion of the time. A method of imaging a marine 3D survey area, comprising: obtaining or developing a geology model of a marine survey area; determining a bin size and orientation based on a smallest feature to be mapped and a shape of said marine geology model; determining a stability of a compressed seismic imaging source and receiver station and sampling design (“CSI design”), and if unstable modify said bin size or orientation and re-determine said stability iteratively to produce a stable CSI design; establishing a set of sampling rules for coverage of said marine survey area to assure that appropriate data is recorded so said stable CSI design can be properly reconstructed; acquiring 3D seismic data traces over said marine survey area using said stable CSI design until said rules are met, wherein wind and current are not compensated for but allowed to direct vessel travel; reconstructing a wavefield and regularizing the traces using the appropriate reconstruction techniques to produce seismic data. In some method, the further steps of processing the seismic data and imaging the survey area are also included. A method of acquiring marine 3D seismic data comprising: acquiring 3D seismic data of an marine area to be surveyed without compensating for wind and current, but instead allowing wind and current to direct >25% of said travel; collecting data positioning data while acquiring 3D data; and continuing said acquiring step until said collected positioning data indicates that sufficient coverage of said marine area has been obtained such that a seismic map of said marine area can be constructed from said 3D seismic data. The wind and current may direct most of vessel travel, e.g., >50% or >75%, even when shifting. A method as herein described, wherein sufficient coverage is determined by the following steps: obtaining or developing a geology model of a marine survey area; determining a bin size and orientation based on a smallest feature to be mapped and a shape of said geology model; determining a stability of a CSI design and if unstable modify said bin size or orientation and re-determine said stability of said CSI design iteratively to locate a stable solution and produce a stable CSI design; establishing a set of sampling rules for coverage of said survey area to assure that appropriate data is recorded so the CSI design can be properly reconstructed; and acquiring 3D seismic data traces over said marine survey area using the stable CSI design until said rules are met, wherein wind and current are not compensated for but allowed to direct vessel travel; and reconstructing a wavefield and regularizing said traces using appropriate reconstruction techniques to produce seismic data. A method as herein described, including the further step of processing the seismic data using conventional techniques and imaging said survey area. A method as herein described, wherein said CSI design is determined by a method comprising: constructing an optimization model, via a computing processor, given by min.sub.u∥Su∥.sub.1s.t.∥Ru−b∥.sub.2≤σ wherein S is a discrete transform matrix, b is seismic data on an observed grid, u is seismic data on a reconstruction grid, and matrix R is a sampling operator; defining mutual coherence as:

(12) $\mu \le \sqrt{\frac{C}{S}\frac{m}{{\left(\log n\right)}^6}},$

(13) wherein C is a constant, S is a cardinality of Su, m is proportional to number of seismic traces on the observed grid, and n is proportional to number of seismic traces on the reconstruction grid; deriving a mutual coherence proxy, wherein the mutual coherence proxy is a proxy for mutual coherence when S is over-complete and wherein the mutual coherence proxy is exactly the mutual coherence when S is a Fourier transform; and determining a sample grid according to r*=arg minr μ(r). A method as herein described, wherein the sample grid is determined via randomized greedy algorithm method, and/or a randomized greedy algorithm method finds local minimum. A method as herein described, wherein the sample grid is determined via a stochastic global optimization method. A method as herein described, wherein r*=arg minr μ(r) is non-convex. A method as herein described, wherein the mutual coherence proxy is derived using fast Fourier transform. A method as herein described, wherein collected traces or data are analyzed in real time or near real time to confirm sufficient coverage and that said rules have been met. A method as herein described, wherein an actual pathway travelled is mapped and used to thereby confirm sufficient coverage and that said rules have been met. Any method described herein, including the further step of printing, displaying or saving the results of the method. A printout or 3D display of the results of the method. A non-transitory machine-readable storage medium containing or having saved thereto the seismic imaging results of the method. Any method described herein, further including the step of using said results in a seismic modeling program to predict e.g., reservoir performance characteristics, such as fracturing, production rates, total production levels, rock failures, faults, wellbore failure, and the like. Any method described herein, further including the step of using said results to design and implement a reservoir drilling, development, production or stimulation program. A non-transitory machine-readable storage medium, which when executed by at least one processor of a computer, performs the steps of the method(s) described herein.

(14) The present disclosure also relates to a computing apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes of modeling, or it may comprise a general-purpose computer selectively activated or reconfigured by a spreadsheet program and reservoir simulation computer program stored in the computer. Such computer programs may be stored in a computer readable storage medium, preferably non-transitory, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

(15) In one embodiment, the computer system or apparatus may include graphical user interface (GUI) components such as a graphics display and a keyboard, which can include a pointing device (e.g., a mouse, trackball, or the like, not shown) to enable interactive operation. The GUI components may be used both to display data and processed data and to allow the user to select among options for implementing aspects of the method or for adding information about reservoir inputs or parameters to the computer programs. The computer system may store the results of the system and methods described above on disk storage, for later use and further interpretation and analysis. Additionally, the computer system may include on or more processors for running said spreadsheet and simulation programs.

(16) Hardware for implementing the inventive methods may preferably include massively parallel and distributed Linux clusters, which utilize both CPU and GPU architectures. Alternatively, the hardware may use a LINUX OS, XML universal interface run with supercomputing facilities provided by Linux Networx, including the next-generation Clusterworx Advanced cluster management system.

(17) Another system is the Microsoft Windows 7 Enterprise or Ultimate Edition (64-bit, SP1) with Dual quad-core or hex-core processor, 64 GB RAM memory with Fast rotational speed hard disk (10,000-15,000 rpm) or solid state drive (300 GB) with NVIDIA Quadro K5000 graphics card and multiple high resolution monitors.

(18) Slower systems could also be used, but are not preferred because the method is already compute intensive.

(19) The term “many-core” as used herein denotes a computer architectural design whose cores include CPUs and GPUs. Generally, the term “cores” has been applied to measure how many CPUs are on a giving computer chip. However, graphic cores are now being used to offset the work of CPUs. Essentially, many-core processors use both computer and graphic processing units as cores.

Marine Survey Equipment

(20) FIG. 1B shows an overhead view of a marine survey system 100 in accordance with at least some embodiments of the invention, wherein paravanes are used to control streamer positioning. In particular, FIG. 1B shows a survey vessel 102 having onboard equipment 104, such as navigation, energy source control, and data recording equipment. Survey vessel 102 is configured to tow one or more sensor streamers 106A-F through the water and one or more sources 130 (one shown here). While FIG. 1 illustratively shows six streamers 106, any number of streamers 106 may be equivalently used. In other surveys, ocean bottom cables (OBC) or ocean bottom nodes (OBN, cable free receivers) are used instead, thus obviating many towed streamer issues.

(21) The streamers 106 are coupled to towing equipment that maintains the streamers 106 at selected lateral positions with respect to each other and with respect to the survey vessel 102. The towing equipment may comprise two paravane tow lines 108A and 108B each coupled to the vessel 102 by way of winches 110A and 110B, respectively. The winches enable changing the deployed length of each paravane tow lines 108. The second end paravane tow line 108A is coupled to a paravane 112, and the second end of paravane tow line 108B is coupled to paravane 114. In each case, the tow lines 108A and 108B couple to their respective paravanes through respective sets of lines called a “bridle”.

(22) The paravanes 112 and 114 are each configured to provide a lateral force (transverse to the direction of motion) component to the various elements of the survey system when the paravanes are towed in the water, as will be explained below. The lateral force component of paravane 112 is opposed to that of paravane 114. For example, paravane 112 may create a force as illustrated by arrow 116, and the lateral component of force 116 is shown by arrow 117. Likewise, paravane 114 may create a force as illustrated by arrow 118, and the lateral component of force 118 is shown by arrow 119. The combined lateral forces of the paravanes 112 and 114 separate the paravanes from each other until they put one or more spreader lines 120, coupled between the paravanes 112 and 114, into tension. The paravanes 112 and 114 either couple directly to the spreader line 120, or as illustrated couple to the spreader line by way of spur lines 122A and 122B.

(23) The streamers 106 are each coupled, at the ends nearest the vessel 102 to a respective lead-in cable termination 124A-F. The lead-in cable terminations 124 are coupled to or are associated with the spreader lines 120 so as to control the lateral positions of the streamers 106 with respect to each other and with respect to the vessel 102. It should be noted that the spacings between 106 can be uniform or non-uniform depending on the CSI implementation chosen. Electrical and/or optical connections between the appropriate components in the recording system 104 and the sensors (e.g., 109A, 109B) in the streamers 106 may be made using inner lead-in cables 126A-F. Much like the tow lines 108 associated with respective winches 110, each of the lead-in cables 126 may be deployed by a respective winch or similar spooling device such that the deployed length of each lead-in cable 126 can be changed.

(24) During periods of time when the survey vessel 102 is traveling in an approximately straight line, the speed of the paravanes 112 and 114 through the water is approximately the same, and thus the lateral force created by similarly configured paravane 112 and 114 may be approximately the same. However, when the survey vessel 102 executes a turn (e.g., a 180 degree turn to align the vessel for the next pass over the survey area), the paravane on the outside of the turn tends to move faster through the water than the paravane on the inside of the turn, the providing greater lateral force than paravane 112. The paravanes also compensate somewhat for water currents.

(25) The paravanes 112 and 114 can have adjustable lateral force, such that the tension on the spreader lines 120 can be controlled. The paravanes 112 and 114 according at least some embodiments comprise systems to controllably redirect the flow of water past the paravane, and/or adjust the angle of attack to control the amount of lateral force developed. Angle of attack for purposes of this disclosure and claims shall be a relationship between the direction of motion of the tow vessel 102 and a long dimension of one or more frames (described below) of the paravane.

(26) FIG. 1B shows the angle of attack (AoA) for the illustrative situation of FIG. 1. Such control may be helpful in a variety of situations, such as during turns.

Compressive Sensing

(27) A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task seems impossible because there is no way to reconstruct a signal during the times that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized.

(28) An early breakthrough in signal processing was the Nyquist-Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal's frequencies, fewer samples are needed to reconstruct the signal.

(29) Around 2004, Emmanuel Candés, Terence Tao, and David Donoho proved that given knowledge about a signal's sparsity, the signal may be reconstructed with even fewer samples than the sampling theorem requires. This idea is the basis of compressed sensing. Compressive sensing is described in further detail in 61/898,960 filed Nov. 1, 2013, and US20150124560, each incorporated by reference herein in its entirety for all purposes. See also U.S. Pat. No. 8,681,581 and US20130250720. A short summary is presented herein, and the reader is referred to the above cases for additional detail.

(30) Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity, which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals.

(31) Two classes of optimization models, synthesis- and analysis-based optimization models, are considered. For the analysis-based optimization model, a novel optimization algorithm (SeisADM) is presented. SeisADM adapts the alternating direction method with a variable-splitting technique, taking advantage of the structure intrinsic to the seismic data reconstruction problem to help give an efficient and robust algorithm. SeisADM is demonstrated to solve a seismic data reconstruction problem for both synthetic and real data examples. In both cases, the SeisADM results are compared to those obtained from using a synthesis based optimization model. Spectral Projected Gradient L1 solver (SPGL1) method can be used to compute the synthesis-based results.

(32) Through both examples, it is observed that data reconstruction results based on the analysis-based optimization model are generally more accurate than the results based on the synthesis-based optimization model. In addition, for seismic data reconstruction, the SeisADM method requires less computation time than the SPGL1 method.

(33) Compressive sensing can be successfully applied to seismic data reconstruction to provide a powerful tool that reduces the acquisition cost, and allows for the exploration of new seismic acquisition designs, such as that described herein. Most seismic data reconstruction methods require a predefined nominal grid for reconstruction, and the seismic survey must contain observations that fall on the corresponding nominal grid points. However, the optimal nominal grid depends on many factors, such as bandwidth of the seismic data, geology of the survey area, and noise level of the acquired data. It is understandably difficult to design an optimal nominal grid when insufficient information is available. In addition, it may be that the acquired data contain positioning errors with respect to the planned nominal grid. An interpolated compressive sensing method is thus presented, which is capable of reconstructing the observed data on an irregular grid to any specified nominal grid, provided that the principles of compressive sensing are satisfied. The interpolated compressive sensing method provides an improved data reconstruction compared to results obtained from some conventional compressive sensing methods.

(34) Compressive sensing is utilized for seismic data reconstruction and acquisition design. Compressive sensing theory provides conditions for when seismic data reconstruction can be expected to be successful. Namely, that the cardinality of reconstructed data is small under some, possibly over-complete, dictionary; that the number of observed traces are sufficient; and that the locations of the observed traces relative to that of the reconstructed traces (i.e. the sampling grid) are suitably chosen. If the number of observed traces and the choice of dictionary are fixed, then choosing an optimal sampling grid increases the chance of a successful data reconstruction.

(35) To that end, a mutual coherence proxy is considered which is used to measure how optimal a sampling grid is. In general, the computation of mutual coherence is prohibitively expensive, but one can take advantage of the characteristics of the seismic data reconstruction problem so that it is computed efficiently. The derived result is exact when the dictionary is the discrete Fourier transform matrix, but otherwise the result is a proxy for mutual coherence. The mutual coherence proxy in a randomized greedy optimization algorithm is used to find an optimal sampling grid, and show results that validate the use of the proxy using both synthetic and real data examples.

(36) One example of a computer-implemented method for determining optimal sampling grid during seismic data reconstruction includes: a) constructing an optimization model, via a computing processor, given by:
min.sub.u∥Su∥.sub.1s.t.∥Ru−b∥.sub.2≤σ
wherein S is a discrete transform matrix, b is seismic data on an observed grid, u is seismic data on a reconstruction grid, and matrix R is a sampling operator; b) defining mutual coherence:

(37) $\mu \le \sqrt{\frac{C}{S}\frac{m}{{\left(\log n\right)}^6}}$
wherein C is a constant, S is a cardinality of Su, m is proportional to number of seismic traces on the observed grid, and n is proportional to number of seismic traces on the reconstruction grid; c) deriving a mutual coherence proxy, wherein the mutual coherence proxy is a proxy for mutual coherence when S is over-complete and wherein the mutual coherence proxy is exactly the mutual coherence when S is a Fourier transform; and d) determining a sample grid:
r*=arg min.sub.rμ(r)

(38) In some embodiments, the sample grid is determined via randomized greedy algorithm method, and the randomized greedy algorithm method finds local minimum. In others, the sample grid is determined via stochastic global optimization method. In still other embodiments, r*=arg min.sub.rμ(r) is non-convex. In yet others, the mutual coherence proxy is derived using fast Fourier transform.

Data Acquisition Method

(39) In rule-based modeling, a set of rules is used to indirectly specify a mathematical model. The rule-set can either be translated into a model such as Markov chains or differential equations, or be treated using tools that directly work on the rule-set in place of a translated model, as the latter is typically much bigger. Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns.

(40) The present method establishes an independent bin grid of some geophysically selected size that is used to determine the needed locations of the source and receivers to properly populate the area to obtain an accurate image of the data. Once the locations are determined, specific project rules for the seismic acquisitions can be developed and applied in the field to determine if additional data needs to be acquired. Thus, only the needed data is acquired, and expense is saved in avoiding over-acquiring excess data.

(41) The first step to implementing the method is to select a trial bin grid of a geophysically determined size. Bin grids are created during seismic trace processing by calculating the theoretical common mid point (usually called CMP) locations for each shot—receiver pair and then summing the traces together based on a mathematical gridding algorithm. Thus, the size of the bin grid is based upon the spacing of the sensors in the cables and the spacing of the streamers in the water. When the bin grid size matches the area of interest, then an appropriate amount of sampling is obtained. However, for larger or smaller areas, oversamples occurs resulting in the accumulation of unnecessary data points the slow down processing and analysis of the seismic data.

(42) The geologic model for the selected trial bin grid is used to estimate the stability of a Compressed Seismic Imaging (CSI) design. Using prior knowledge of the likely geology in the targeted region an overall geologic model is constructed. Using the sampling spacings or station layouts is then tested against the proposed CSI design to test the stability of the solution. This process is effectively repeated for all possible CSI approaches for the particular design and then the best sampling is selected that results in the maximum mutual incoherence.

(43) Once the proper design and their offsets are determined, rules can be developed. The set of rules will be unique to the chosen region and to the CSI algorithm that is applied, but in effect are a measure of how badly can the data be sampled and still properly reconstruct the correct wavefield and image. These rules mainly cover the rules for acquisition of data, such as no region greater than 3 bins can be missing two unique offset planes in a row. Other rules relating to gaps in coverage or orientation of the shot/receivers and/or distribution of the trace data can also be developed. These rules are necessary to make sure that the minimum required data to properly reconstruct the wavefield using CSI techniques are collected in the field. The normal approach for creation of the rules is to take the perfectly acquired CSI design and then start decimating various attributes like sampling until the final image degrades. In the chosen example of 3 bins in a row missing 2 unique offset planes, depending on the CSI design, if 3 unique offset planes in a row are missing data then the solution is degraded and an artifact in the final process image is created. This creates a rule that when acquiring the data must be met or acquisitions continue until it is met and the survey is completed.

(44) Unlike a conventional marine survey, the rules-based method does not require a uniform grid of offsets and azimuths. Thus, the survey can be collected using whichever pattern is quickest, cheapest, and most reliable. Series or cross-cutting passes of the vessel at different orientations due to winds and tides are expected to be the best pattern for collecting data.

(45) If winds change, the surveying ship can simply move with the wind instead of trying to maintain a set pattern such as the racetrack approach. Thus, time and money can be saved by utilizing nature to collect data instead of fighting currents and tides to adhere to a rigid collection pattern. In effect, although the ship may attempt to collect the data is some predetermined pattern, it need not stick to that pattern as wind and current change are not problematic, thus, a degree of meandering can be tolerated, and even a high degree of what appears to be meandering. However, from the view-point of the data acquisition, the operator will not be meandering but simply driving where needed to cover the field, and not fighting wind and currents to do so.

(46) By continuing to shoot until the rules are met, the resulting survey will have no gaps and no infill, or at least un-needed infill will be substantially reduced. This will aid in reducing modeling time and cost. On the other hand, there may be areas where there is a surplus of data acquired because of the way the tides and winds worked out. These oversampled areas are a byproduct of not standing by for weather and winds and follow the goal of excess data is better than no data and paying to wait on weather.

(47) In order to know that sufficient coverage has been obtained to meet the rules, the data will either be analyzed in real time or near real time, or one can track the actual pathway travelled, and thereby confirm sufficient coverage.

(48) A variety of commercially available acquisition and tracking programs can be used herein. See e.g., the Omni 3D Design package, NORSAR-3D modeling package, the Nucleus software package, Globe claritas, Delph, Seismix, and the like.

(49) While the invention is described above in detail, it should be understood that various changes, substitutions, and alterations can be made without departing from the spirit and scope of the invention as defined by the following claims. Those skilled in the art may be able to study the preferred embodiments and identify other ways to practice the invention that are not exactly as described herein. It is the intent of the inventors that variations and equivalents of the invention are within the scope of the claims while the description, abstract and drawings are not to be used to limit the scope of the invention. The invention is specifically intended to be as broad as the claims below and their equivalents.

(50) The following references are incorporated by reference in their entirety. Buia M. et al., Shooting Seismic Surveys in Circles, Oilfield Review, Autumn 2008:18-31. Wang Y., Recovery of Seismic Wavefields Based on Compressive Sensing by an 11-Norm Constrained Trust Region Method and the Piecewise Random Sub-sampling Geophys. J. Int. (2010) 000, 1-19. 61/898,960, filed Nov. 1, 2013, and US20150124560 Compressive sensing US20100265799 Compressive sensing system and method for bearing estimation of sparse sources in the angle domain U.S. Pat. No. 8,681,581 Randomization of data acquisition in marine seismic and electromagnetic acquisition US20130250720 Method for acquiring marine seismic data U.S. Pat. No. 8,711,654 Random sampling for geophysical acquisitions U.S. Pat. No. 8,897,094 L Marine seismic data acquisition using designed non-uniform streamer spacing US201108011354277 WO2012166737 Two-way wave equation targeted data selection for seismic acquisition of complex geologic structures US2012300585 Reciprocal method two way wave equation targeted data selection for seismic acquisition of complex geologic structures US20120014212 Continuous composite relatively adjusted pulse U.S. Pat. No. 5,079,703 3-dimensional migration of irregular grids of 2-dimensional seismic data