Field of the invention

[0001] The present invention relates to methods for separating contributions from two or more different sources in a common set of measured signals representing a wavefield, particularly of seismic sources and of sets of recorded and/or processed seismic signals.

Description of related art

[0002] A common and long-standing problem in physical

wavefield experimentation is how to separate recorded signals from two or more simultaneously emitting sources. In

particular, for more than a decade, the simultaneous source problem has (arguably) been the most pertinent problem to solve to efficiently acquire data for 3D reflection seismic imaging of complex Earth subsurface structures.

[0003] Modern digital data processing of wavefields (or signals) uses a discretized version of the original wavefield, say g , that is obtained by sampling g on a discrete set. The Nyquist-Shannon sampling theorem shows how g can be recovered from its samples; for an infinite number of equidistant samples and given sample rate k _s , perfect reconstruction is guaranteed provided that the underlying signal was bandlimited to \k\≤k _N = k _s /2 (Shannon, 1949; Nyquist, 1928), where k _N is the so-called Nyquist wavenumber. The Nyquist-Shannon sampling theorem is equally relevant both to signals generated from a single source being recorded on multiple receivers (receiver- side sampling) as well as signals generated from multiple sources and recorded at a single receiver (source-side

sampling) . [0004] Assume that the wavefield g is measured at a specific recording location for a source that is excited at different source positions along an essentially straight line. The sampling theorem then dictates how the source locations must be sampled for a given frequency of the source and phase velocity of the wavefield. One aspect of the sampling problem addressed in the current invention is as follows. Instead of using one source, we want to use two (or more) sources to for instance increase the rate at which data can be acquired. The second source is triggered simultaneously or close in time with the first source while moving along another arbitrarily oriented line to excite the wavefield h. At the recording location the wavefields interfere and the sum of the two wavefields, f = g + h, is now measured. There is no known published exact solution to perfectly separate the wavefields g and h that were produced from each source from the combined measurement / (e.g., see Ikelle, 2010; Abma et al . , 2015; Kumar et al, 2015; Mueller et al . , 2015) .

[0005] It may therefore be seen as an object of the invention, to present new and/or improved methods for generating source- separated data. It may also be seen as an object of the present invention to extend such methods into fields, where they have not been applied before, such as estimation and removal of seismic interference.

Brief summary of the invention

[0006] A method for separating wavefields generated by two or more source contributing to a common set of measured or recorded signals are provided suited for seismic applications and other purposes, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

[0007] Advantages, aspects and novel features of the present invention, as well as details of an illustrated embodiment thereof, may be more fully understood from the following description and drawings .

Brief Description of the Drawings

[0008] In the following description reference is made to the attached figures, in which:

Figs. 1A, B illustrate how in a conventional marine seismic survey all signal energy of sources typically sits inside a "signal cone" bounded by the propagation velocity of the recording medium and how this energy can be split in a transform domain by applying a modulation to a second source;

Fig. 2 shows jointly recorded wavefield data from two sources measured at a stationary receiver;

Fig. 3 shows time delays as applied to the second source in the data of Fig. 2;

Figs. 4 A - 4 C show the original and the reconstructed wavefield of the first source in the data of Fig. 2 and the reconstruction error when reconstructing the wavefield of the source after applying the separation method;

Fig. 5 shows the relative time delays between two sources as used in another example;

Figs. 6A, B illustrate the construction and separate reconstruction of the wavefields of two sources in the example of Fig. 5; Detailed Description

[0009] The following examples may be better understood using a theoretical overview as presented below.

[0010] The slowest observable (apparent) velocity of a signal along a line of recordings in any kind of wave experimentation is identical to the slowest physical propagation velocity in the medium where the recordings are made. As a result, after a spatial and temporal Fourier transform, large parts of the frequency-wavenumber (a)k) spectrum inside the Nyquist

frequency and wavenumber tend to be empty. In particular, for marine reflection seismic data (Robertsson et al . , 2015), the slowest observable velocity of arrivals corresponds to the propagation velocity in water (around 1500m/s) .

[0011] Fig. 1(A) illustrates how all signal energy when represented in or transformed into the frequency-wavenumber (a)k) domain sits inside a "signal cone" centered at k = 0 and bounded by the propagation velocity of the recording medium.

[0012] In a wavefield experiment it may be that a source is excited sequentially for multiple source locations along a line while recording the reflected wavefield on at least one receiver. The source may be characterized by its temporal signature. In the conventional way of acquiring signals representing a wavefield the source may be excited using the same signature from source location to source location, denoted by integer n. Next, consider the alternative way of acquiring such a line of data using a periodic sequence of source signatures: every second source may have a constant signature and every other second source may have a signature which can for example be a scaled or filtered function of the first source signature. Let this scaling or convolution filter be denoted by (t), with frequency-domain transform Α(ω) .

Analyzed in the frequency domain, using for example a receiver gather (one receiver station measuring the response from a sequence of sources) recorded in this way, can be constructed from the following modulating function m(n) applied to a conventionally sampled and recorded set of wavefield signals:

which can also be written as

[0013] By applying the function m in Eq. 0.1 as a modulating function to data f(n) before taking a discrete Fourier

transform in space (over the following result

can be obtained:

which follows from a standard Fourier transform result

(wavenumber shift) (Bracewell, 1999) .

[0014] Eq. 0.2 shows that the recorded data / will be mapped into two places in the spectral domain as illustrated in Fig. 1(B) and as quantified in Tab. I for different choices of

TAB. I. Mapping of signal to cone centered at k = 0 (H+) and cone centered at k = k _N (H_) for different choices of Α(ω) for signal separation or signal apparition in Eq. (0.2) .

[0015] Part of the data will remain at the signal cone

centered around k = 0 (denoted by H ₊ in Fig. 1(b)) and part of the data will be mapped to a signal cone centered around k _N (denoted by H_) . It can be observed that by only knowing one of these parts of the data it is possible to predict the other .

[0016] A particular application of interest that can be solved by using the result in Eq. (0.2) is that of simultaneous source separation. Assume that a first source with constant signature is moved along an essentially straight line with uniform sampling of the source locations where it generates the wavefield g . Along another essentially straight line a second source is also moved with uniform sampling. Its signature is varied for every second source location according to the simple deterministic modulating sequence m(n),

generating the wavefield h. The summed, interfering data = g + h are recorded at a receiver location.

[0017] In the frequency-wavenumber domain, where the recorded data are denoted by F = G + H, the H-part is partitioned into two components H ₊ and H_ with H = H ₊ + H_ where the //.-component is nearly "ghostly apparent" and isolated around the Nyquist- wavenumber [Fig. 1(B)], whereas G and H ₊ are overlapping wavefields around k = 0. Furthermore, H_ is a known, scaled function of H. The scaling depends on the chosen Α(ω) function (Tab. I), and can be deterministically removed, thereby producing the full appearance of the transformed wavefield H. When H is found, then G = F— H yielding the separate wavefields g and h in the time-space domain.

[0018] The concept may be extended to the simultaneous

acquisition of more than two source lines by choosing

different modulation functions for each source. [0019] Acquiring a source line where the first two source locations have the same signature, followed by another two with the same signature but modified from the previous two by the function Α(ω) and then repeating the pattern again until the full source line has been acquired, will generate

additional signal cones centered around +k _N /2 .

[0020] This process may be referred to as "wavefield

apparition" or "signal apparition" in the meaning of "the act of becoming visible". In the spectral domain, the wavefield caused by the periodic source sequence is nearly "ghostly apparent" and isolated.

[0021] Fig. 1(B) also illustrates a possible limitation of signal apparition. The H ₊ and H_ parts are separated within the respective lozenge-shaped regions in Fig. 1(B) . In the triangle-shaped parts they interfere and may no longer be separately predicted without further assumptions. In the example shown in Fig. 1(B), it can therefore be noted that the maximum unaliased frequency for a certain spatial sampling is reduced by a factor of two after applying signal apparition. Assuming that data are adequately sampled, the method

nevertheless enables full separation of data recorded in wavefield experimentation where two source lines are acquired simultaneously .

[0022] As is evident from Tab. I, the special case A = l corresponds to regular acquisition and thus produces no signal apparition. Obviously, it is advantageous to choose A

significantly different from unity so that signal apparition becomes significant and above noise levels. The case where A = — 1 (acquisition of data where the source signature flips polarity between source locations) may appear to be the optimal choice as it fully shifts all energy from k = 0 to k _N (Bracewell, 1999) . Although this is a valid choice for

modeling, it is not practical for many applications (e.g., for marine air gun sources, see Robertsson et al . , 2015) as it requires the ability to flip polarity of the source signal. The case where A = 0 (source excited every second time only) may be a straightforward way to acquire simultaneous source data but has the limitation of reduced sub-surface

illumination. A particularly attractive choice of Α(ω) for wave experimentation seems to let every second source be excited a time shift T later compared to neighbouring

recordings, that is, select

[0023] The above description assumes a simple modulating sequence m(n), and thus generating the wavefield h .

Applications in which such a simple modulating sequence can be applied may however be limited in practice. In practice it is difficult to obtain perfectly periodic time shifts from a measurement setup. It is for example common practice for seismic vessels to shoot or trigger their sources at

predetermined (essentially equidistant) positions, and due to practical variations (vessel velocity etc.) it will be

difficult to realize shots at both predetermined locations and times .

[0024] In the following, there are therefore described further methods accommodating applications in which the modulation sequence includes variations or deviations, signal dither, or, in extreme circumstances, gives rise to aperiodic source signals. We refer herein to modulations sequences including any such variations as non-periodic.

[0025] First are presented methods for treating simultaneous source data using quasi-periodic time delays in the shots as well as position variations. Quasi-periodic time delays can be understood as delays with periodic carrying signal overlayed with a non-periodic (for instance random) pattern. The

resulting combined variation can therefore be considered to be non-periodic. In the case where the recorded data is band- limited, and sufficiently densely sampled, this may provide a way to conduct separation or deblending of two or more source contributions for moderately sized non-periodic variations in the time shift. [0026] Let us start by recapitulating some one-dimensional properties of the Fourier transform. We will use the notation

for the Fourier transform in one variable, and consequently for the Fourier transform of two dimensional function with a time (t) and spatial (x) dependence.

[0027] The Poisson sum formula

can be modified (by using the Fourier modulation rule) as

[0028] By the standard properties of the Fourier transform it is straightforward to show that

hold for the spatial sampling parameter Δ _x and some fixed spatial shift x ₀ . Suppose that are two

spatially bandlimited functions. By rescaling of units we can thus assume that they satisfy

[0029] The recorded data is now modelled as a sum of the two functions, but where one of them has been subjected to a periodic time shift. Denote the recorded data by d = d(t, x) , this blending can thus be described by

where we assume that the data is spatially sampled at equally spaced points x = k .

In a more general version more general filtering operations than time shifts can be applied. Let a _k be filter operators (acting on the time variable) where the k dependence is such it only depends on if k is odd or even, i.e., that

[0030] Applying the result described above, it then follows that a modulated sum of d over all even points (2k) in

combination with a one-dimensional Fourier transform in the time variable yield the relation

similar treatment of the odd terms (2k + 1) gives

[0032] We now define as the sum that includes both odd

and even terms. It thus holds that

[0033] Because of the support assumption (1.1), we see that then most of the terms above vanish, and it is therefore possible to obtain the values of from

[0034] The values of i can be obtained in a similar fashion by considering instead

[0035] The role of the filtering operations are thus

merely multiplicative, and for the sake of simplifying the presentation we therefore satisfy with considering the case where for which it holds that

and

[0036] Like in the method above this procedure uses recovering the respective function at the cones that shifted away from the origin. While the values of can be obtained in a

very direct manner, the procedure is heavily relying on the periodicity of the alternating shifts in sampling, and may therefore be sensitive to perturbations of these time shifts.

[0037] It should be noted that a similar result can be

obtained by modulating the amplitude instead of the time. In the most general case the modulation can be a filter with frequency dependent amplitude and phase.

[0038] Another way to recover is to consider the case

where for which (1.2) simplifies to

and similarly for the shifted version

[0039] This implies that for each pair of values (with can be obtained by

solving the linear system

[0040] In this way the function can thus be recovered by considering the data in the central cone (e.g. as shown in Fig 1(B)) alone. The different formulations could have different advantages, for instance in the presence of noise.

[0041] Let us now consider a more general sampling setup. Let t = (t _k ) and s = (s _k ) be two sequences and assume that data is modelled by

[0042]

[0043] In the case where

the setup of the previous section is obtained. Let us

introduce the operators by

and

respectively. Note that (1.4) can now be written as

[0044] Note also that for instance since the

two time shifts cancel out.

[0045] Given a support constraint similar to (1.1), we follow the reconstruction procedure in a similar fashion as in the previous section. The analogous system of equations becomes

[0046] Using a standard discretization in the frequency domain (and time) , the operators may be realized using standard FFT in combination with shift operators in the case of equally spaced sampling in the spatial variable, or by using

algorithms for unequally spaced FFT in the general case. The linear system (1.6) can for instance be solved by using an iterative solver. If (1.5) is approximately satisfied, the solution (1.3) may be used as a preconditioner . This means that a solution should be obtained in one iteration if (1.5) is satisfied and in case it is almost satisfied, only a few iterations should be required. The formulation above can be used in the case of irregular sampling in time; in space; or for both of the at the same time. Perturbations that are completely irregular (not following the tensor structure indicated here) can also be dealt with using the same

framework .

[0047] For computational reasons it is important to be able to use fast solvers when applicable. By a fast solver we mean a method by which we can solve the problem of computing either forward or inverse operators in a method with low time

complexity, for instance that of FFT.

[0048] The method set out above can be applied to various cases in which a separation of sources may be considered as advantageous. In the following the application of the problem of seismic interference cancellation is demonstrated.

[0049] Seismic interference (SI) occurs when two or more different seismic crews are acquiring seismic data in vicinity of each other. SI can be a major source of noise that can be difficult to remove particularly if it is arriving in the cross-line direction as the moveout of the signal is very similar to that of the interfering signal which can also be strong compared to deeper reflections that it may overlie. If the dominant azimuth of seismic interference is known, it is possible to shift the acquired data so that it appears as far as possible from the seismic interference noise in (e.g.,

noise centered at k = 0 and signal at The application requires real-time knowledge of exact firing times of the interfering vessel.

[0050] Knowledge of the exact firing time is of course a major limitation as a competing seismic crew likely will not want to share this information. It may also be somewhat irregular if the crew shoots on position and not time. However, using for instance the quasi-periodic apparition method presented above we can not only solve the simultaneous source problem (as described above) but also that of seismic interference since we do not need to know the exact firing times of the

interfering crew beforehand. As long as we utilize a periodic or quasi-periodic acquisition sequence ourselves we will record a data set where the interfering signal can be

apparated and removed from the acquired data. Once the data have been recorded we can detect the exact arrival time of the seismic interference in the data (this can be as simple as a windowed autocorrelation/crosscorrelation process) .

[0051] The interfering data will likely be shot on position and therefore have a slight variation in arrival time from shot to shot. However, all we have to do is to include these estimated perturbations in arrival time of the seismic

interference to apparate (i.e., shift in wavenumber) the seismic interference away from the acquired data. As a side note we note that the interfering survey is likely also seeing interference from the first crew using the apparition firing sequence. The method applied gives the opportunity to generate a separate set of data for the interfering survey.

[0052] Synthetic data were computed using a finite-difference solution of the wave equation for a typical two-dimensional (2D) North Sea subsurface velocity and density model

consisting of sub horizontal sediment layers overlying rotated fault blocks. The data consist of the waveforms recorded at a single stationary receiver (located on the seafloor) for shots along a line. The data are shown in Fig. 2. The time shifts t that have been applied to the second source are shown in Fig. 3, and the spatial sampling s is equidistant. An iterative scheme (preconditioned conjugate gradient) has been applied to the formulation using a preconditioner to obtain separated reconstructions of the data originating from each of the two sources. The data and the corresponding reconstruction and its error for the first source are shown in Fig. 4, while the counterparts for the second source (not shown) can be either derived directly from the above method or by subtracting the wavefield of the first source from the blended data. In this case 20 iterations of the iterative scheme were used. In this case the data were assumed to satisfy the bandlimit condition (1.1), and it is clear from the Fig. 4 that the reconstruction errors in this case can be made very small.

[0053] In the following variant of the method as described above is focusing on the cases of quasi-periodic and or pseudo-random timeshifts. Note, however, that the method can also be applied to the case of quasi-periodic or pseudo-random amplitude perturbations or to such perturbations of shot positions or of frequency-dependent amplitude and phase perturbations or arbitrary combinations thereof. This variant of the method, in its most general form, applies to any known (set of) modulation function (s) .

[0054] Let d(n) represent some data acquired as a function of a spatial coordinate x, i.e., at discrete locations x _n . The well-known convolution theorem states that the convolution in space of the data d(n) with some spatial filter f(n) can be computed by multiplication of the (discrete) Fourier

transforms of the data and of the spatial filter, followed by inverse (discrete) Fourier transform,

i.e., Here we exploit the lesser-known

dual of the convolution theorem, which states that

multiplication in the space domain of d(n) with a so-called modulation function/operator m(n), corresponds to cyclic convolution of the (discrete) Fourier transform of the data, D(k), with the (discrete) Fourier transform of the modulation operator M(k) = T (m(n)), followed by inverse (discrete) Fourier transform. Furthermore, we exploit the fact that cyclic convolution can be expressed conveniently as a matrix

multiplication with a Toeplitz matrix.

[0055] Moreover, such a matrix multiplication can be rapidly evaluated by means of fast solvers such as the FFT, since the discrete Fourier matrices diagonalize cyclic matrices. This property is not true for the inverse of Toeplitz matrices.

However, both Toeplitz matrices and their inverses have displacement rank 2, which allows for rapid evaluation of the application of these operators. The time complexity for these operations are bounded by the complexity for FFT.

[0056] Without loss of generality, let's assume that the data have been acquired in the following manner: a first source, sailing with a constant ground speed, fires with regular time intervals, and a second source, sailing also with a

constant ground speed (and along the same line) , fires

alternately at the same time as s ₁ and at after where is drawn, e.g., from a normal distribution, ² with mean, μ, and variance, σ ² . Since s ₁ and s ₂ are fired at the same time or shortly after each other, a recording of subsurface

reflected waves will comprise a superposition of waves due to these two sources.

[0057] As shown below, we can formulate the separation of such data as the (least-squares) solution of a system of equations in the frequency-wavenumber domain. Let the N shots be

enumerated by n, and let T _n denote the relative timing of the n-th shots fired by s ₁ and s ₂ . Thus, according to the paragraph above, T _n = 0 for odd shots for even shots

. In the temporal frequency domain,

the effect of the timeshift is multiplication with

where the sign depends on the choice of the definition of the temporal Fourier transform. Thus, for a particular

frequency ω' and shot n' the time delay acts as a complex modulation As before, let denote the spatial (discrete) Fourier transform of the modulation function m(n) . A cyclic convolution matrix C can be formed by taking as the columns of C, circularly shifted versions of M, with the circular shift increasing by one each column, and having as many columns as number of points in M.

[0058] Since the timeshifts are formulated as relative to the s ₁ times, the effective modulation function for the part of the simultaneous data due to s ₁ is constant equal to 1 for each s ₁ shot and each frequency. Thus, the transform of the implied modulation function for s ₁ is a (discrete) delta function in wavenumber (at zero wavenumber) and the corresponding cyclic convolution matrix the identity matrix, /.

[0059] Consider without loss of generality that the number of simultaneous source recorded traces N is even. Let Δχ denote the spatial (shot) sampling interval. Then the spatial Nyquist wavenumber, K _N , is K _N = 1/(2Δχ), and the wavenumber resolution, Ak, after a discrete Fourier transform over space is

The discrete wavenumbers (after "FFT-shifting" the zero- wavenumber component to the center of the spectrum by swapping halves of the obtained decrete Fourier transform values) are K(k) = -K _N + (k - l)Ak (k = 1,2, ... , N) . In this case, the index corresponding to the zero wavenumber, k _Q , is

[0060] Let the unknowns be the data in the frequency

wavenumber domain due to sources s ₁ and s ₂ , i.e. D^k) and D ₂ (k) , respectively, without relative timeshifts. Let k _min and k _max denote the indices of the discrete wavenumbers closest to the minimum and maximum wavenumbers K _m i _n and K _max to be inverted, but smaller and larger, respectively. The data between —K _N and K _min and K _max and K _N are assumed to be zero (i.e., the support in the wavenumber domain of D ₁ (k) and D ₂ (k) is confined to K _m i _n and K _max ) .

[0061] Thus, the (column) vector of unknowns can be denoted D = [D ₁ (k _min :k _max ) ^T D ₂ (k _min : k _max ) ^T ] ^T where ^T denotes transpose and the colon denotes all or part of a range. Note, however, that because of the modulation function applied to s ₁ data, the range of observed wavenumbers is not restricted to from to

K _max . However, since the unknowns are restricted to that range, the forward modelling operator should be similarly restricted along the columns (i.e., on the model side) but not along the rows (i.e., on the measurement side) . Thus, the forward modelling operator matrix G can be formed:

Thus, if we denote D _tot (k) the FFT-shifted (discrete) Fourier transform of the observed aperiodic, (near- ) simultaneous source data d _tot (rri) we have the following system of equations:

And we can compute, e.g., the least-squares solution D ^LS

Where the stabilisation λ ² is usually chosen to be a percentage (e.g. 0.1%) of the maximum of G ^H G and ^H denotes complex- conjugate (Hermitian) transpose.

[0062] As in the example above, this variant can be applied successfully to the separation of a dataset to which two sources have been contributing to with one of the sources being modulated in such a manner.

[0063] In Fig. 5 and Fig. 6, the methodology described above is illustrated. In Fig. 5, the relative time delays between source 1 and source 2 are shown. Note that every other trace, the relative time delay is zero. In Fig. 6A, the reference data for source 1 and source 2 are shown (first and second panel from the left) . In the third and fourth panel from the left, the modulated s ₂ data and the simultaneous source data, i.e., s ₁ reference + s ₂ modulated are shown. The latter represents the input data d _tot for the method described above. The resulting least-squares reconstruction of the data for s ₁ and s ₂ are shown in Fig. 6B . As can be seen, the two sources have been separated correctly.

[0064] Note that the exactly the same methods can be used to separate or approximately separate sources for which the relative time delay varies non-periodically for every trace (i.e., every shot) . Such variations, if intentionally, can be termed pseudo-random variations.

[0065] Note that the description above has focussed on the separation of at least two simultaneous or near-simultaneous sources. But the methods described herein can also be applied to wavefields where one source is physical and a second source is virtual as induced by the presence of a free surface which is the subjected of a separate patent application. In such a case the current invention allows for the separation of recorded wavefields into ghost and ghost-free wavefields.

Further details to such an application can be found in the patent application by the same applicant entitled "Method for deghosting and redatuming operator estimation" and filed in the United Kingdom at the same priority date as this

application .

[0066] Throughout the description of the present invention we have made use of Fourier transforms to transform the data separating and isolating wavefields. It will be appreciated by those skilled in the art that other transforms such as Radon transforms, tau-p transforms, etc., can also be used for the same purpose. Furthermore, those skilled in the art may also prefer to implement the methods presented directly in the space and/or time domain. In such cases the transforms

presented herein are replaced by their respective

representations or mathematical equivalents in such domains, which can take either explicit or approximated/truncated forms and applied to the wavefield data representation in the respective domain.

[0067] A bounded support in a domain is the well-known mathematical generalisation of the better-known concept of being bandlimited (such as in equation (1.1)) . Examples of limited support are presented above.

[0068] It is well known, for example, that due to the

"uncertainty principle", a function and its Fourier transform cannot both have bounded support. As (seismic) data are necessarily acquired over a finite spatial (and temporal) extent, the terms "bounded support" and "limited support" herein are used not in the strict mathematical sense, but rather to describe an "effective numerical support", that can be characterised, e.g., by the (amplitude) spectrum being larger than a certain value. For instance, larger than a certain noise threshold, or larger than the quantization error of the analog-to-digital converters used in the measurement equipment. Further, it is understood that by explicitly windowing space and/or space-time domain data, the support of a function may be spread over a larger region of, e.g., the wavenumber-frequency domain and in such cases the term

"bounded support" and "limited support" will also be

understood as "effective numerical support" as it will still be possible to apply the methods described herein.

[0069] Furthermore, the terms "cone" and "cone-shaped" used herein are used to indicate the shape of the "bounded" or "effective numerical" support of the data of interest (e.g., the data that would be recorded firing the sources

individually [i.e. non-simultaneously] ) in the frequency- wavenumber domain. In many cases, it will still be possible to apply the methods described herein if the actual support is approximately conic or approximately cone-shaped. For example, at certain frequencies or across certain frequency ranges the support could be locally wider or less wide than strictly defined by a cone. Such variations are contemplated and within the scope of the appended claims. That is, the terms "cone" and "cone-shaped" should be understood to include

approximately conic and approximately cone-shaped. In

addition, in some cases we use the terms "bounded support" or "limited support" and "effective numerical support" to refer to data with "conic support" or "cone-shaped support" even though in the strict mathematical sense a "cone" is not bounded (as it extends to infinite temporal frequency) . In such cases, the "boundedness" should be understood to refer to the support of the data along the wavenumber axis/axes, whereas "conic" refers to the overall shape of the support in the frequency-wavenumber domain.

[0070] Note that the term "cone-shaped support" or similar refers to the shape of the support of e.g. the data of interest (in the frequency-wavenumber domain), if it were regularly sampled along a linear trajectory in 2D or Cartesian grid in 3D. That is, it refers only to the existence of such a support and not to the actual observed support of the data of interest in the simultaneous source input data or of the separated data of interest sampled as desired. The support of both of these depends on the chosen regularly or irregularly sampled straight or curved input (activation) and output

(separation) lines or grids. Such variations are within the scope of the appended claims.

[0071] For example consider a case where the input data are acquired using simultaneous curved shot lines. In this case, the methods described herein can either be applied directly to the input data, provided the curvature has not widened the support of the data interest such that it significantly overlaps with itself. In this case, the support used in the methods described herein can be different from cone-shaped. Alternatively, the methods described herein are used to reconstruct the data of interest in a transform domain which corresponds to, e.g., best-fitting regularly sampled and/or straight activation lines or Cartesian grids, followed by computing the separated data of interest in the non- transformed domain at desired regular or irregularly sampled locations .

[0072] As should be clear to one possessing ordinary skill in the art, the methods described herein apply to different types of wavefield signals recorded (simultaneously or non- simultaneously) using different types of sensors, including but not limited to; pressure and/or one or more components of the particle motion vector (where the motion can be:

displacement, velocity, or acceleration) associated with compressional waves propagating in acoustic media and/or shear waves in elastic media. When multiple types of wavefield signals are recorded simultaneously and are or can be assumed (or processed) to be substantially co-located, we speak of so- called "multi-component" measurements and we may refer to the measurements corresponding to each of the different types as a "component". Examples of multi-component measurements are the pressure and vertical component of particle velocity recorded by an ocean bottom cable or node-based seabed seismic sensor, the crossline and vertical component of particle acceleration recorded in a multi-sensor towed-marine seismic streamer, or the three component acceleration recorded by a

microelectromechanical system (MEMS) sensor deployed e.g. in a land seismic survey.

[0073] The methods described herein can be applied to each of the measured components independently, or to two or more of the measured components jointly. Joint processing may involve processing vectorial or tensorial quantities representing or derived from the multi-component data and may be advantageous as additional features of the signals can be used in the separation. For example, it is well known in the art that particular combinations of types of measurements enable, by exploiting the physics of wave propagation, processing steps whereby e.g. the multi-component signal is separated into contributions propagating in different directions (e.g., wavefield separation) , certain spurious reflected waves are eliminated (e.g., deghosting) , or waves with a particular (non-linear) polarization are suppressed (e.g., polarization filtering) . Thus, the methods described herein may be applied in conjunction with, simultaneously with, or after such processing of two or more of the multiple components.

[0074] Furthermore, in case the obtained wavefield signals consist of / comprise one or more components, then it is possible to derive local directional information from one or more of the components and to use this directional information in the reduction of aliasing effects in the separation.

[0075] While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not of limitation. For example it should be noted that where filtering steps are carried out in the frequency-wavenumber space, filters with the equivalent effect can also be implemented in many other domains such as tau-p or space-time.

[0076] Further, it should be understood that the various features, aspects and functionality described in one or more of the individual embodiments are not limited in their

applicability to the particular embodiment with which they are described, but instead can be applied, alone or in various combinations, to one or more of the other embodiments of the invention .

[0077] For example, it is understood that the techniques, methods and systems that are disclosed herein may be applied to all marine, seabed, borehole, land and transition zone seismic surveys, that includes planning, acquisition and processing. This includes for instance time-lapse seismic, permanent reservoir monitoring, VSP and reverse VSP, and instrumented borehole surveys (e.g. distributed acoustic sensing) . Moreover, the techniques, methods and systems disclosed herein may also apply to non-seismic surveys that are based on wavefield data to obtain an image of the

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