Patent Application
20170235003
Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems for subsurface exploration, and, in particular, to methods and systems for marine seismic data acquisition and processing that enable efficient interpolation and regularization in compressive sensing to either improve image quality or reduce acquisition time while achieving the same image quality.
Discussion of the Background
Probing underground formations in search of hydrocarbon resources is an ongoing process driven by continually increasing worldwide demand. Seismic surveys are used for exploration, hydrocarbon reservoir field development, and production monitoring (time lapse).
Vessel 110 (or another vessel) may also tow seismic source 120 configured to generate acoustic waves 122a. Note that, in this document, the terms “acoustic” and “seismic” are interchangeably used to indicate the same type of mechanical energy propagation (i.e., waves). Acoustic waves 122a propagate downward and penetrate the seafloor 124. For simplicity,
A seismic source 120 is typically made up of a number of individual source elements that each emits acoustic energy (i.e., seismic waves). The individual source elements may be air guns, sparkers, vibrators, etc., or a combination of these types of source elements. Subsets of the source elements are normally attached one after another to the same towing cable to form source strings (also known as source sub-arrays, the seismic source being then called source array). A typical marine seismic source includes two or more source strings. The combination of different source strings that are simultaneously activated defines the center of the seismic source. By combining the source strings differently, the center of the seismic source is virtually changed. The source strings and/or the entire source may be configured to be steered cross-line. Some examples of steerable sources are described in U.S. Pat. Nos. 8,462,581; 8,891,331; and 8,889,332, the entire contents of which are incorporated herein by reference.
Thus, each time a seismic source is activated, it emits a seismic signal that travels downward through the explored underground formation, is reflected, and, upon its return, is received by the detectors along the streamer(s). The detected signals related to multiple seismic source and seismic sensor position combinations are recorded as seismic data, which is then processed to generate a profile (structural image) of the underground formation.
Data acquisition geometry is determined by the equipment employed and by the manner the equipment is operated. The basic arrangement in
A seismic trace is a term associated with acoustic energy that is detected by one or more seismic sensors (i.e., detectors). Typically, a trace is determined by combining a group of seismic sensors over a certain length, sometimes referred to as a “receiver length” or “group length,” A group of seismic sensors may also be referred to as a “receiver,” In marine seismic, this group length is typically between 3.125 to 12.5 m but, in some examples, a seismic trace can also be a recording of a received seismic signal from one single seismic sensor. In other words, a “seismic sensor” refers to a single seismic sensor or a group of seismic sensors.
A location on a horizontal plane, which is halfway between the center of the seismic source and the center of the seismic sensor, is referred to as a common mid-point (CMP). Plural pairs of seismic source centers and seismic sensors may have the same CMP. The CMP of every trace in a seismic survey is carefully tracked (e.g., it may be included in the trace header information) to allow the information contained within the traces to be correlated with specific underground locations. The locations of seismic sources when seismic waves are generated, and of seismic sensors detecting reflections of the seismic waves, respectively, are used for a variety of acquisition and/or processing purposes.
During processing, the area (known as the “survey area”) in a horizontal plane explored during a seismic survey is customarily divided into a regular grid of small, adjacent cells (called “bins”) characterized by widths Δx (along the towing direction) and Δy (perpendicular to the towing direction). Each CMP pertains to a bin and it is associated with the bin's midpoint. Presence of at least measurement (CMP-trace) inside each bin of the survey area simplifies processing and ensures uniform survey area coverage (with no big holes in the acquisition). The bin size is typically in the range of 3.125 to 50.0 m both inline and cross-line.
Bin sizes are determined by the imaging objective: the smaller the bins, the better the resolution, revealing smaller structural features of the underground formation. However, high resolution also requires that sufficient reflected acoustic energy be received from the locations of interest (“targets”). This last requirement may be difficult to fulfill for deep targets or for targets beneath complex geological features.
During the planning stage of a seismic survey, so-called pre-plots are generated. The pre-plots represent the intended positions of sources and receivers for each shot (i.e., source activation) during a survey. The pre-plots are used to design a plan for source and streamer trajectories and a source activation schedule so that each bin will be well-sampled.
Time-lapse 4D geophysical imagining combines at least two surveys acquired for the same area to determine changes that have occurred inside the underground formation in the time interval between the surveys (which may be a few months or years). It is desirable that the two or more surveys reproduce both source and receiver positions as closely as possible. The post-plots of a first survey (known as the “base”) may be used as pre-plots for planning the later survey(s) (known as “monitor(s)”).
Image resolution with traditional processing and acquisition techniques is limited according to the Shannon-Nyquist sampling theorem. In view of this limitation, sample density and the number of samples per bin are determined to successfully image the underground formation. Compressed sensing (also known as compressive sensing, compressive sampling, sparse inversion or sparse sampling) is a more recently developed signal-processing technique for efficiently acquiring and/or reconstructing a sparsely sampled signal, by finding solutions to underdetermined linear systems. This signal processing technique exploits the sparsity of the signal samples to optimally recover information about the underground formation's structure from far fewer samples than those required according to the Shannon-Nyquist sampling theorem. Two conditions have to be met to make this recovery possible.
The first condition is sparsity, which requires that a sparse representation of the signal be present. Any transform domain in which the signal is decomposed in an orthonormal basis so that the signal is represented by only a few non-zero coefficients is applicable. Such a transformation may be, for example, the Fourier transform, the wavelet transform, the curvelet transform, the f-k transform or the Gabor transform.
The second condition is incoherence in the sparsifying domain. Irregular sampling in the acquisition domain breaks under-sampling artifacts by turning them into harmless noise. The signal of interest is then reconstructed by using sparsity-promoting inversion methods such as, for instance, the spectral projected gradient for L1 norm (SPGL1).
To break the coherence and make marine seismic data more favorable to compressed sensing reconstruction, it has been suggested (see, e.g., U.S. Pat. No. 8,897,094 the entire content of which is incorporated herein by reference) to vary cross-line intervals between parallel streamers and/or towing the streamers in such a way that a cross-line distance there-between increases along the streamer's lengths. Such cross-line distance variations create static irregularities in the cross-line direction but do not generate inline irregularities.
Li et al.'s article, “Marine towed streamer data reconstruction based on compressive sensing,” published in SEG Technical Program Expanded Abstracts 2013, pp. 3597-3602, and Mosher et al.'s article, “Increasing the efficiency of seismic data acquisition via compressive sensing,” published in The leading edge, v. 33 no. 4, pp. 386-391, suggest using irregular shot spacing to create inline irregularities. U.S. Pat. No. 9,188,693 also describes a method for acquiring marine seismic data according to which the seismic source is actuated such that distances between actuation positions vary randomly, thereby creating inline irregularities.
U.S. Pat. No. 8,780,669 proposes improving seismic data azimuth coverage by steering the vessel in a sinusoidal pattern. Such source steering yields irregularities both inline and cross-line. However, a vessel is only able to slowly vary its course. The shot-to-shot irregularities are therefore small, and the resulting irregularities are not optimal for compressed sensing reconstruction.
U.S. Pat. Nos. 8,681,581 and 8,711,654 describe randomizing the distribution of receivers and sources during a coil shoot acquisition by varying the turn radius and coil circle centers of vessel(s). Although this is an effective approach in a deep-water environment, for more shallow targets, the shot-to-shot irregularities would be small, since a vessel turns slowly compared to the typical bin size. Additionally, such methods are not relevant for conventionally acquired data, along relatively straight lines.
U.S. Patent Application Publication No. 2011/0317517 describes a data acquisition system allowing individual seismic sensors of a streamer be turned on or off. However, such on-off toggling of the receivers apparently yields static inline irregularities and actually artificially forfeits information.
Thus, there is a need to propose methods that better ensure irregularities in data acquisition, rendering the data more favorable to compressive sensing or sparse inversion-based reconstruction and regularization.
Methods and devices according to various embodiments design and/or enhance inline and cross-line seismic data irregularities so that acquired seismic data to be suitable for compressive sensing reconstruction. The irregularities may be obtained dynamically using source, vessel(s) and/or streamer steering.
According to an embodiment, there is a method for designing a survey plan that achieves inline and cross-line seismic data irregularities suitable for compressive sensing reconstruction. The method includes determining shot positions and corresponding detector positions of a survey plan with inline and cross-line irregularities caused by at least one data acquisition geometry-varying action. The method further includes evaluating a cost function value for the determined shot and detector positions, and determining, using the cost function value, whether seismic data acquired according to the survey plan is suitable compressive sensing reconstruction.
According to an embodiment, there is a survey control method for monitoring and adjusting data acquisition geometry to achieve inline and cross-line seismic data irregularities suitable for compressive sensing reconstruction. The method repeatedly performs evaluating a cost function for shot positions and corresponding detector positions acquired to include inline and cross-line irregularities caused by at least one data acquisition geometry-varying action. A corrective action is sought if the evaluated cost differs from a planned cost by more than a predetermined threshold value. The corrective action has to cause irregularities that alter the shot positions and corresponding detector positions such as to bring an updated cost value based on altered shot and detector positions within the predetermined threshold value from the planned cost.
According to yet another embodiment, there is a data acquisition control apparatus configured to design and/or adjust source, vessel and/or streamer trajectories during a survey to achieve inline and cross-line seismic data irregularities optimized for compressive sensing reconstruction. The apparatus includes an interface configured to receive data acquisition related information and to output a survey plan and/or a correction thereof, and a data processing unit connected to the interface. The data processing unit is configured to evaluate a cost function value for a set of shot and detector positions with inline in cross-line irregularities caused by at least one data acquisition geometry-varying action, and to determine, using the cost function value, whether seismic data acquired according to the set of shot and detector positions is suitable compressive sensing reconstruction.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate one or more embodiments and, together with the description, explain these embodiments. In the drawings:
The following description of the exemplary embodiments refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. The following embodiments are discussed, for simplicity, with regard to the terminology of marine seismic surveys. However, the inventive concepts to be discussed next are not limited to marine seismic surveys, but may also be applied for land surveys and surveys with electromagnetic waves.
Reference throughout the specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” in various places throughout the specification is not necessarily referring to the same embodiment. Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
Recently developed signal processing methods (referred to as “compressive sensing”) enable under certain conditions (as discussed in the Background section) more accurate recovery of a sparsely sampled signal than the regularly sampled data. The embodiments described in this section are related to acquiring/sampling marine seismic data irregularly for compressive sensing reconstruction (i.e., to optimally recover the structural information related to the probed substructure, which information is embedded in the signals). These embodiments simultaneously achieve both inline and cross-line irregularities, at least one of which is dynamic. The vessel, source and/or streamer steering are used optimize cross-line and inline irregularities for compressive sensing reconstruction, enabling improved quality and/or improved efficiency. For example, the quality of seismic imaging may be improved for the same source-receiver sampling density as conventionally acquired, or the image quality may be maintained in spite of reduced source-receiver sampling density (and, thus, reduced acquisition time). The techniques are described for towed streamers and sources but may also be applied for seismic acquisition with autonomous underwater vehicles (AUVs) and for ocean bottom nodes or cable (OBN/OBC) seismic acquisition, with any type of source and receiver.
In order to successfully recover a signal through compressive sensing methodology, the signal needs to be sparse (at least in some suitable transform domain), and the sub-sampling artifacts must be incoherent in the sparse domain. The seismic data acquisition for compressive sensing aims to create Gaussian noise-like sub-sampling artifacts, which can be effectively attenuated by a sparse recovery algorithm that separates the noise from the desired signal.
Mathematically, if acquired data is the n×1 vector y and the interpolation grid is the N×1 vector f where n<<N, then the restriction equation is:
y=Rf (1)
where R is a n×N restriction matrix. The acquired data does not necessarily fit into the interpolation grid and, therefore, the restriction matrix may include interpolation coefficients derived from a p-order interpolation of acquired data fitting into the interpolation grid, p being an integer greater or equal to 1.
If S is the N×N orthogonal sparsifying matrix and the N×1 vector x is the sparse representation of the interpolation grid, then the sparse transform is:
x=Sf. (2)
Combining the sparse transform and restriction equation leads to:
y=RS
H
x=Ax, (3)
where A=RSH is the dictionary matrix.
Compressive sensing reconstruction consists of the sparse inversion of equation (3), i.e., minimizing the norm of x subject to ∥y−Ax∥2≦σ where σ represents the noise variance.
One way of assessing incoherence/irregularity is to analyze a kx-ky spectrum (two-dimensional, 2D, spatial Fast Fourier Transform, FFT) of the sample points in a 2D spatial grid. In a seismic data acquisition, the sample points are typically the CMP positions within a given offset range. Examples of 2D grids are inline-cross-line, offset-inline, offset-cross-line or any other combinations familiar to anyone skilled in the art of seismic data processing. If the spatial sampling were uniformly random, no coherent events would be present in this spectrum, and a zero-centered Gaussian noise would spread over the entire frequency spectrum. The more regular sample points yield coherent events in the kx-ky spectrum that are observed as vertical or horizontal stripes.
The suitability of a proposed sampling/acquisition plan may be assessed using a cost function that evaluates incoherence, for example, in the kx-ky domain. The cost function may be, for example, mutual coherence, entropy or discrepancy. The mutual coherence of matrix A is defined as the maximum absolute value of the cross-correlations between the columns of the matrix. The entropy of matrix A measures the amount of “disorder” by summing a probability mass function. These cost functions however do not ensure that no large gaps occur between sample (acquisition) points. Discrepancy is a measure of deviation from uniformity of a sequence of points in the half-open interval D=[0,1). Consider the s-dimensional half-open unit cube Is=[0,1)s, s≧1. For N points x1, x2, . . . , xNεIs in this space and for any sub-interval JεIs, if A(J) is the number of points xi in J and V(J) is the volume of J, then discrepancy D(J,N) is defined as:
According to one definition, the discrepancy is the difference between the proportion of points in J compared to the full unit cube Is and the volume of the “box” J compared to Is.
D(1,6)=| 3/6−½=0. (5)
Similarly, the discrepancy of boxes B and C are ≈0.1267 and 0.8 respectively. The above presented formula is the conventional definition of discrepancy. However, in a more practical setting we can also define discrepancy as:
With this definition, a value D=0 for all sub-intervals J outlines a perfectly uniform distribution of points.
To illustrate the difference between conventional sampling and irregular sampling, consider the pairs of graphs 3A-3B, 4A-4B and 5A-5B.
In another example, the cost function is computed on the dictionary matrix A. Minimizing the cost function between the restriction matrix R and the sparse matrix S, makes x as sparse as possible (see, e.g., the article by Candès et al., published in Communications on Pure and Applied Mathematics 59 (8), pp. 1207-1223, the content of which is incorporated herein by reference).
In yet another example, the sub-sampling artifacts can be evaluated through a convolution matrix that characterizes the off-diagonal elements of the Gram matrix AH A (see, e.g., Hennenfent et al.'s 2008 article, “Simply denoise: wavefield reconstruction via jittered undersampling,” published in Geophysics, 73, no. 3, pp. V19-V28, the content of which is incorporated herein by reference):
L=A
H
A−αI, (7)
where α is defined such that diag(L)=0. Thereby, the sub-sampling artifacts in the sparse domain for a given interpolation grid f0 are then defined as:
z≡Lx
0, (8)
where x0=Sf0. In this case, the cost function is applied to z to ensure as few coherent events as possible.
A comparison of
The cost function may be used to design a survey plan or may be evaluated during the survey based on the actual acquired data. Ultimately, the cost function may be built based on the underground structure's image quality (e.g., data quality after migration). Such an approach may seem costly, but it is achievable using ever-increasing computing power. For example, the bandwidth of the recovered data may be evaluated, or the frequency at which spatial aliasing starts to appear may be determined. The image quality may be assessed based on the fk-plots of regularized and interpolated gathers at various processing stages.
Table 1 below sets forth actions or data acquisition geometry features yielding cross-line and/or inline irregularities. Irregularities may also be achieved in the z-direction (i.e., depth), by steering steamers up and down (i.e., dynamic irregularities) or by having various streamers and sources at varying depths (i.e., static irregularities). Such irregularities may be beneficial, especially for deghosting.
The actions or data acquisition geometry features in Table 1 may be used to achieve simultaneously inline and cross-line irregularities, that is, irregularities in an inline-cross-line seismic restriction matrix. The term “static” indicates irregularities that do not change data acquisition geometry during the survey, while the term “dynamic” refers to irregularities that change the data acquisition geometry during the survey. The “Other” column of Table 1 indicates actions or data acquisition geometry features that yield irregularities both inline and cross-line (as opposed to only inline or only cross-line in the other columns of Table 1). In other words, if each table item is a “dimension,” these dimensions are not always orthogonal.
The irregularity-inducing data acquisition geometry features are limited by practical considerations. For example, the detectors are typically placed at regular intervals along the seismic streamers. The detector placement is not easily changed on existing conventional streamers, but it may be more easily achieved for OBN/OBC equipment. Further, seismic sources, especially air-gun sources, have a minimum cycle time depending on the source size, the number of source elements and compressor capacity, etc. This minimum cycle time may be 2-10 s, which together with efficiency concerns (i.e., one does not want to tow equipment without acquiring data) limits the shot point positions/time irregularities. Active steering (of a vessel, streamer or source) is limited by a maximum speed, phase, and maximum amplitude within the data acquisition geometry in order to functionally maintain the towing arrangement. For example, it is common practice to have a minimum cross-line distance, typically 20-50 m, in order to reduce the risk of streamer and/or source tangling. On the other hand, large separations of the source and receivers causes a decrease in the signal-to-noise ratio, with the level of the noise-like artifacts in the sparsifying domain becoming too large for the sparsity-promoting inversion methods to successfully estimate the signal. Referring to Table 1, there are at least 10 different “dimensions” where one can introduce irregularities in the seismic sampling. In addition, the environment causes irregularities through swells, currents, etc. With ˜10 degrees of freedom (the number of dimensions), the search space for an optimal acquisition strategy is huge.
Further, method 900 includes evaluating a cost function value for the determined shot and detector positions at 920. Method 900 also includes determining, using the cost function value, whether seismic data acquired according to the survey plan is suitable compressive sensing reconstruction at 930.
The data acquisition geometry-varying action may be actively steering cross-line a source using source steering means, actively steering cross-line one or more streamers using streamer steering means, the one or more streamers carrying detectors, actively steering cross-line a source vessel, or shooting a source at irregular intervals. The data acquisition geometry-varying action may also be actively and incoherently modifying a distance between a source and a vessel towing the source, and/or actively and incoherently modifying one or more distances between one or more streamer front ends and a vessel towing the streamers.
Compressive sensing cannot reconstruct signal for regularly sub-sampled (beyond Nyquist) data. However, for irregularly sampled data, experience has shown that many good solutions exist that are close to the best solution. The solution space is fairly flat, i.e., many good solutions exist that are close to the theoretical optima. A practical approach is, therefore, to determine a near-optimal solution by iterating over a large number of dithering approaches, and then to choose among those the one that minimize a cost function. In other words, the steps 810-830 of claim 1 may be repeated to identify an optimum survey plan.
In an embodiment, the search for an optimal plan may be codified by the following pseudocode algorithm 1:
for i=1:number of iterations
Introduce irregularities in one dimension spacing with given constraints
Compute a cost function value to assess these irregularities
end
Select the dimension spacing resulting in the minimum cost function
The term “dimension spacing” used in the pseudocode means any single or combination of irregularity-inducing strategies listed in Table 1.
The optimal dimension spacing can then be used to create a pre-plot for the acquisition. Implicitly, various practical constraints such as, for example, the minimum and maximum allowed streamer separation can also be built into the cost function (e.g., using Langrage multipliers). Computationally, this approach is fast and efficient. In one dimension, the search space is normally limited, and a brute force approach is practical. The shot positions inaccuracies/uncertainties in source and receiver positioning of about 1 m taken into consideration render meaningless a dither step-length of less than 1 m.
In another embodiment, a near-optimal solution is sought by systematically running algorithm 1 with varying parameters for items (i.e., dimension) in Table 1. For example, an optimal static cross-line streamer separation may be determined first. Then, with this given streamer separation, an optimal dynamic irregular shot point interval (i.e., yielding inline irregularities) is determined. Then an optimal source steering may be determined (yielding dynamic cross-line irregularities) while maintaining the already-determined optimal static cross-line streamer separation and optimal source steering. One or more additional irregularity-inducing measures may be considered, before finally outputting a plan for the acquisition. The dimension spacings are both static (like streamer spacing which, at least near the front end of a spread, is set by a separation rope), and dynamic such as, for example, irregularities introduced by steering the source from side to side.
The above-described one-dimensional optimization may not lead to a global minimum and may also result in a non-optimal sampling with large un-sampled areas. In another embodiment these problems are alleviated, the search for an optimal plan being codified by the following pseudocode algorithm 2:
for i=1:number of iterations
Introduce dither in two or more dimensions
Compute a cost function value to assess a current plan
end
Select the dithers associated with the minimum cost function
In other words, a multi-dimensional search is performed to identify the optimal solution. The search may be shortened by using mathematical methods such as (but not limited to) “steepest ascent.”
In another embodiment, the irregularities are generated by introducing so called low-discrepancy sequences. A low-discrepancy sequence is a sequence of numbers with the property that for all values of N, its subsequence x1, . . . , xN are close to uniformly distributed. Low-discrepancy sequences are also sometimes called quasi-random or sub-random sequences, due to their common use as a replacement of uniformly distributed random numbers. Examples of low-discrepancy sequences are Halton sequences and Hammersley sets. Numerical experiments have shown that low-discrepancy sequences result in restriction matrices or dictionary matrices with reasonably low mutual coherence or high entropy. They have, in addition, desirable feature such as an almost uniform distribution that ensures no large gaps between the sample acquisition points.
In yet another embodiment, the optimal dither for some dimensions is determined by using Algorithm 2, while the dither in other dimensions is set with the help of a low-discrepancy sequence. For example, with static dimensions like streamer spacing (set by the front-rope separation), the inline start position of each streamer of the receiver inline position is set using low-discrepancy sequences, and the dynamic dimensions, such as an irregular shot point interval or steering the receivers, source or vessels, are determined using Algorithm 2 (which may also use low-discrepancy sequences).
In another embodiment, dynamic 4D steering is used to achieve the desired irregularities in the acquisition. In this approach, vessel steering (amplitude and phase of turning) may be used to introduce long-period dithering, while streamer and source steering are used to introduce shorter-period dithering. The steering ability defines the maximum irregularities achievable for a survey. Steering can also be combined with dithering of the shot-point interval (inline) and using winches for the sources and the streamers. The restriction matrix or the dictionary matrix may be evaluated for several adjacent sail lines to ensure that no big holes are left in the acquisition.
The maximum size of gaps between acquisition points may be set as a function of geology. For a “flat” and uniform geology, larger holes might be acceptable compared to when the geology is rapidly changing in the subsurface.
In a survey system with multiple vessels, the source vessels normally have much more flexibility than the streamer vessels. The source vessels are therefore better suited to introduce both dynamic inline and cross-line irregularities in the seismic restriction or dictionary matrix. Furthermore, more static irregularities may be achieved by changing/dithering vessel separations both inline and cross-line.
Deghosting also introduces irregularities in the sampling matrix.
Besides the planned irregularities are combined with unplanned irregularities due to the environment. Currents, wind and swell frequently cause feathering both for both sources and receivers. These environment-caused irregularities are unpredictable and, therefore, cannot be evaluated in a planning stage before the survey. Streamer and source feathering may in practice make it impossible to steer a source or a streamer exactly as planned. This problem can be solved by running a near-real-time optimization, where a cost function continuously monitored and the data acquisition plan is updated to optimally counterbalance or take advantage of the environmental effects.
The planned survey is then adjusted to integrate the corrective action (e.g., steering the vessel(s), the source, and/or the streamer) determined at 1120 in the survey plan. Such adjustments are tuned to achieve a level of irregularities controlled by a quality control of a cost function. The survey control method ensures the inline and cross-line irregularities while reducing the constraints on the positions themselves. It allows preserving the properties of the irregularities even in challenging environmental conditions. There are also benefits in terms of mechanical fatigue and maintenance needs to control the pattern of use of the steering systems. The steering systems may not need to counter the environmental effects, but can (partly) act independently of them.
For data acquisition systems where the receivers are not towed or not deployed on the sea floor, but released inside the water column, such as AUVs, and move with currents, the natural irregularities of the distribution of AUVs is complemented by the steering of towed sources (vessels steering, source steering and dithered shooting patterns) in order to optimize the required irregularities for compressive sensing. Initial cost function evaluation based on current prediction may be constantly updated during the acquisition in order to maintain the predefined compressive sensing requirements (e.g., a mutual coherence threshold). The same method is applied if sources are deployed through a flotilla of AUVs.
The above-discussed methods may be implemented in a computing device 1400 as illustrated in
Exemplary computing device 1400 suitable for performing the activities described in the exemplary embodiments may include a server 1401. Server 1401 may include a central processor (CPU) 1402 coupled to a random access memory (RAM) 1404 and to a read-only memory (ROM) 1406. ROM 1406 may also be other types of storage media to store programs, such as programmable ROM (PROM), erasable PROM (EPROM), etc. Processor 1402 may communicate with other internal and external components through input/output (I/O) circuitry 1408 and bussing 1410 to provide control signals and the like. Processor 1402 carries out a variety of functions as are known in the art, as dictated by software and/or firmware instructions.
Server 1401 may also include one or more data storage devices, including hard drives 1412, CD-ROM drives 1414 and other hardware capable of reading and/or storing information, such as DVD, etc. In one embodiment, software for carrying out the above-discussed steps may be stored and distributed on a CD-ROM or DVD 1416, a USB storage device 1418 or other form of media capable of portably storing information. These storage media may be inserted into, and read by, devices such as CD-ROM drive 1414, disk drive 1412, etc. Server 1401 may be coupled to a display 1420, which may be any type of known display or presentation screen, such as LCD, plasma display, cathode ray tube (CRT), etc. A user input interface 1422 is provided, including one or more user interface mechanisms such as a mouse, keyboard, microphone, touchpad, touch screen, voice-recognition system, etc.
Server 1401 may be coupled to other devices, such as sources, detectors, etc. The server may be part of a larger network configuration as in a global area network (GAN) such as the Internet 1428, which allows ultimate connection to various computing devices.
The disclosed exemplary embodiments provide methods for designing or adjusting a survey plan that achieves inline and cross-line seismic data irregularities suitable for compressive sensing reconstruction. It should be understood that this description is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to cover alternatives, modifications and equivalents, which are included in the spirit and scope of the invention as defined by the appended claims. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the claimed invention. However, one skilled in the art would understand that various embodiments may be practiced without such specific details.
Although the features and elements of the present exemplary embodiments are described in the embodiments in particular combinations, each feature or element can be used alone without the other features and elements of the embodiments or in various combinations with or without other features and elements disclosed herein.
This written description uses examples of the subject matter disclosed to enable any person skilled in the art to practice the same, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.
The present application is related to and claims the benefit of priority of U.S. Provisional Application 62/294,344 filed Feb. 12, 2016, and U.S. Provisional Application 62/306,178 filed Mar. 10, 2016, the entire contents of which are incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 62306178 | Mar 2016 | US | |
| 62294344 | Feb 2016 | US |
