Sparse coding for data-driven coherent and incoherent noise attenuation Sam T. Kaplan* and Mauricio D. Sacchi, University of Alberta, and Tadeusz J. Ulrych, University of British Columbia SUMMARY


We use sparse coding to construct a series expansion of data. Sparse coding gives a data-driven set of basis functions whose coefficients follow a sparse distribution (the coefficients are called the sparse code). We use sparse coding in two noise attenuation algorithms, one applicable to additive random noise, and another applicable to coherent noise (free-surface multiple removal). To illustrate, we extract and, then, filter a sparse code from noisy data. The filter is designed to remove the portion of the code that is more indicative of noise than signal. First, we attenuate additive random noise by applying a threshold to the sparse code. Next, we consider a normal-moveout corrected common midpoint gather corrupted by free-surface multiples, and construct a filter using the dominant wavenumbers in the sparse coding basis functions. This allows us to filter out flat events (the signal), leaving an estimate of the noise (multiples) that are, subsequently, subtracted from the original gather.

We consider data d(t, h) in time t and space h, and work to construct, from it, the series in equation (1) using sparse coding. The algorithm requires, as input, realizations from the random vector x, and produces as output the coefficients y and basis P. To construct the realizations of x, we apply a windowing algorithm to d where the ith window is Wi ∈ R m×n with m time samples and n traces. Presently, we let, Wi =

m X

yi j P j ,



be the expansion of Wi onto the basis patches P j , and coax equation (2) into the form of equation (1). In particular, • A window Wi , from equation (2), is mapped, via lexicographic reordering, to the ith realization of x, in equation (1). • The basis patches P j , from equation (2), are mapped, via lexicographic reordering, to the basis vectors p j , in equation (1).

INTRODUCTION We consider the data expansion, x=

m X

y j p j = Py,



ˆ ˜ where x ∈ R m and yT = y1 y2 · · · ym are random vectors (y j are random variables), and p j ∈ R m is the jth column of P ∈ R m×m . Here, p j are the basis vectors of the expansion, and y j are the corresponding coefficients. In other words, the vectors p j span a subspace containing x, and y is the projection of x onto the basis vectors p j . In this abstract, we use sparse coding (e.g. Hoyer, 1999) to find, simultaneously, the coefficients y j and basis p j of the expansion. That is, the basis are data-driven. The coefficients y j are called the sparse code. The bases of the sparse coding expansion are interesting, resembling, for example, the set of basis functions studied in the local Radon transform (Sacchi et al., 2004), and curvelet transform (e.g. Starck et al., 2002). We use the sparse coding representation of x to attenuate noise, both random and coherent. When the noise is random, we follow the work of, for example, Hyv¨arinen et al. (1998), and shrink the sparse code y (Kaplan and Ulrych, 2005). For a coherent noise example, we use the often studied application free-surface multiple attenuation (e.g. Hampson, 1986). In particular, we apply sparse coding to a common midpoint gather after normal moveout correction, filtering the sparse code based on the dominant wave-numbers in the corresponding basis p j . This allows us to filter out the primary events from the gather, leaving only multiples which are, subsequently, subtracted from the original data.

For example, consider windows such that Wi ∈ R 32×16 (i.e. 32 time samples by 16 traces). In this case, both x and y would have 512 dimensions. If several such windows are collected from d, then both x and y will have several realizations, one for each window. The output of the sparse coding algorithm are p j and y. First, the sparse code y is computed using a linear transformation of x, y j = bTj x

y = Bx,


where bTj is a row of B, and is chosen to satisfy some statistical criteria for y j . In particular, the entropy of y j is minimized providing a sparse representation of x. This is the same criteria used in, for example, the independent component analysis (ICA) community (Hyv¨arinen et al., 2001). We proceed to describe the linear transformation in equation (3) using ICA. ICA is commonly used for source separation (e.g. Common, 1994), and its description is most readily understood from this point of view. However, here we are only interested in finding the series expansion in equation (1). In the context of ICA, each component of the sparse code y j is called an independent component. It can be shown, via the central limit theorem, that if y j are found so that they are maximally non-Gaussian, then they will also be independent. Here, we consider a measure central to information theory, called differential entropy, Z ∞ h (pY ) = − pY (y j ) ln pY (y j )dy j , (4) −∞

where y j is described by the probability density function py .

Sparse coding for noise attenuation It is well known that if only the mean and variance of a continuous random variable y j are given, then y j has maximum differential entropy exactly when it has Gaussian statistics. Therefore, entropy gives a measure of Gaussianity in that minimizing entropy maximizes non-Gaussianity. Hence, y j is an independent component exactly when it has minimum entropy. Further, if we ensure that the non-Gaussianity is manifested as a positive kurtosis (i.e. super-Gaussian rather than sub-Gaussian), then y j will be sparse which is the desired feature for a sparse representation of x. Unfortunately, as is evident, from equation (4), computing entropy creates the rather difficult task of estimating integrals of probability density functions (pdfs). Hyv¨arinen (1998) introduces a method for approximating the integral by expanding the pdf onto a basis of non-polynomial functions rk (y j ) such that it satisfies the maximum entropy distribution under given moment constraints. The idea is to minimize the entropy of the maximum entropy distribution. Following a lengthy derivation (e.g. Kaplan, 2003), it can be shown that an estimate of a measure related to entropy, called negentropy J (pY (y)) is, l “ ” ` ` ´´ 1 X ` ` ´´ J pY y j = h pξ (ξ ) − h pY y j ≈ c2i , 2



` ` ´´ where ci = E ri y j and pξ (ξ ) is a Normal distribution with ` ´ the same mean and variance as pY y j . Hence, negentropy measures the distance from a Gaussian random variable, and minimizing the entropy of y j is equivalent to maximizing its negentropy. Therefore, independent components correspond to maxima of equation (5). To provide a constraint for the optimization of equation (5), x is whitened, z = Γx, where Γ is a whitening matrix that can be constructed by principal component analysis (e.g. Hyv¨arinen et al., 2001). The random variables zi , i = 1 . . . m are mutually uncorrelated. The utility of z is illustrated by understanding the relation between uncorrelated and independent. Namely that independent implies uncorrelated. Since the goal of ICA is to produce components that are independent, they are also uncorrelated and under orthogonal transformations they stay that way. Therefore, an appropriately chosen rotation transforms uncorrelated components into independent components. This immediately drops the degrees of freedom in the optimization problem by one. To utilize the whitening constraint, we define a matrix Q such that y = Qz, qTj is the jth row of Q and y j = qTj z is an independent component exactly when q j is chosen such that y j has maximum negentropy. Hence, an appropriate cost function (for minimization) is, “ “ ”” ` ´ ` ` ´´ φ q j = −J pY y j = −J pY qTj z . (6) As already mentioned, whitening the data further constrains the cost function. In `particular, with some loss of generality, ` ´ ´ we let var y j = 1, E y j = 0, and note ` the ´ independent components are uncorrelated such that E yi y j = 0, i 6= j, giving,  “ ” ` ´ 0 , i 6= j . E yi y j = qTi E zzT q j = 1 , i= j

Thus, the cost function need only be considered on the surface defined by qTj q j = 1, and multiple local minima may be found using orthogonality. Hyv¨arinen (1999) presents a method for optimizing equation (6) which employs Newton steps in an iterative scheme. In particular, the non-polynomial approximation of negentropy in equation (5) is considered using only one term in its series expansion which gives, for minimization, ` ´ 1 ˆ ` ` ´´˜2 φ q j = − E r1 y j , 2

(7) 2

where y j = qTj z, and for this work we choose, r1 (y j ) = ey j /2 . The gradient of φ is, ` ´ ` ` ´´ ` ` ´ ´ ∇φ q j = −E r1 y j E r10 y j z , ` ` ´´ and ignoring the scalar value −E r1 y j allows for computation of an approximate Hessian H such that, “ ` ´ ” ` ` ´´ “ ” ` ` ´´ H ≈ E r100 y j zzT ≈ E r100 y j E zzT = E r100 y j I. This approximation gives a Hessian which is easily inverted, leading to the approximate Newton step (from iteration k to k + 1) given by ` ` ´ ´ E r0 y j z (k+1) (k) (8) qj = q j − ` 100 ` ´´ . E r1 y j Multiplying equation (8) through by the denominator in its third term yields, ` ` ´´ (k+1) ` ` ´´ (k) ` ` ´ ´ E r100 y j q j = E r100 y j q j − E r10 y j z . Hence, an appropriate update rule for the jth row of Q is, ` ` ´´ (k) ` ` ´ ´ (k+1) qj = E r100 y j q j − E r10 y j z (k+1) . (9) qj (k+1) qj ← (k+1) ||q j


The projection back onto the unit circle accounts for the constraint qTj q j = 1. For the algorithm used in this abstract, all rows of Q are updated simultaneously. That is, for each iteration of the optimization routine, (i) Each row qTj of Q is updated according to equation (9), and (ii) the rows of Q are made orthogonal using symmetric orthogonalization such that, “ ”−1/2 Q ← Q QT Q . Once an optimal Q is found, P, the basis in sparse coding, is readily computed. In particular, noting that Q is orthogonal, y = Qz and z = Γx, we find x = Γ−1 QT y. Hence, it follows from equation (1) that P = Γ−1 QT .

ADDITIVE RANDOM NOISE EXAMPLE For our first example, we revisit sparse coding applied to random noise attenuation (Kaplan and Ulrych, 2005) which we apply to a simple synthetic data example. The aim of the example is two-fold. First, it illustrates the nature of the sparse

Sparse coding for noise attenuation



Figure 1: (a) The basis patches found from sparse coding. (b) (left) Data from which the basis patches are found, (centre) filtered data, and (right) the residual panel.

Sparse coding for noise attenuation (a)

coding basis patches P j . Second, it illustrates their usefulness in noise attenuation. The left panel in Figure 1b is the data d(t, h) with t being its vertical axis and h its horizontal axis. The data consists of three apex shifted hyperbolic events and three linear events. The data are aliased, and corrupted with band-limited random noise. We extract Wi ∈ R 32×16 from d(t, h) using a moving window. The set of windows {Wi } sample the entirety of d(t, h), and have redundancy so that the lower left corner of window Wi is shifting by either 5 traces or 5 time samples to give Wi+1 . The windows give the realizations of x whose dimension is reduced from 512 to 200 using principal component analysis. From the rank reduced x, we compute the sparse code y and the basis P. The basis patches are mapped, lexicographically, from the columns of P, and are shown in Figure 1a. Interestingly, these data-driven basis functions have captured, locally, the behaviour of the signal.






We shrink the sparse code y j using a soft threshold, yj ←

“ ” ` ´ 1 sign y j max 0, |y j | − σ 2 b , 2 1+σ a


with a = b = 0.5. Our estimate of the variance of the noise in the data is set to σ . The threshold is constructed using a sparse pdf for y, satisfying the sparse assumption used in its construction. We plot the filtered data in the centre panel of Figure 1b. It is built using equation (1) with the basis in Figure 1a and the shrunken sparse code (equation (10)). Finally, the right panel in Figure 1b shows the difference between between the noisy data and the filtered data. There is some leakage of signal visible in the residual plot.

COHERENT NOISE EXAMPLE For our second example, we introduce a new application for sparse coding, applying it to free-surface multiple attenuation. In particular, we consider a single common midpoint (CMP) data gather which has undergone normal moveout (NMO) correction. Due to NMO, the primary events are flat, but the multiples are under-corrected, and remain hyperbolic. The idea, then, is that the hyperbolic and linear events are captured by disjoint subsets of the sparse coding basis functions, and so the multiples can be isolated, and subsequently subtracted from the data. To test this idea we consider the real-data example in Figure 2. In Figure 2b, we plot d(t, h), the NMO corrected CMP gather. The realizations of x are extracted from d(t, h) using the same windowing procedure described in the previous section. Then, we compute the sparse code y and basis P, plotting the lexicographic reordered columns of P (the basis patches) in Figure 2a. The ordering of the basis patches depends on the initialization of the optimization procedure. We associate a measure of flatness with each basis patch, found by its dominant wave-number in the f k domain. We assume that events with a dominant wave-number close to zero encode the flat primary events in the signal, and events with non-zero dominant wave-numbers encode the hyperbolic multiple events. Using

Figure 2: (a) Basis patches found by sparse coding. (b) The CMP gather from which the basis are found. (c) Multiples estimate found using sparse coding. (d) Multiple suppression using sparse coding. (e) Multiples estimate using the parabolic Radon transform. (f) Multiple suppression using the parabolic Radon transform.

the latter of these two disjoint sets, we reconstruct the multiple events in Figure 2c, and subtract them from the data to give Figure 2d, giving the multiple suppression result. For comparison, we plot estimated multiples using the Radon transform (e.g. Hampson, 1986) in Figure 2e, and the difference between Figures 2b and 2e is shown in Figure 2f.

CONCLUSIONS We presented a review of sparse coding and its application to random noise attenuation. Additionally, we introduced the use of sparse coding for multiple attenuation of NMO corrected gathers. Sparse coding is utilized to estimate from the data themselves the basis functions required to represent signal and noise. In the random noise scenario, filtering can be implemented via thresholding the coefficients of the data expansion. This is similar to de-noising via the wavelet transform where small coefficients represent noise. The coherent noise scenario requires the selection of a subset of basis functions. The latter calls for an algorithm to label the basis function according to a physical attribute that differentiates signal from noise. For the multiple suppression exercise we have used the dominant wave number of the basis function to decide which components need to be retained in order to model the noise.

Sparse coding for noise attenuation @Article{common94, author = {P.~Common}, title = {Independent component analysis, a new concept?}, journal = {Signal Processing}, year = {1994}, volume = {36}, pages = {287-314}, } @Article{hampson86, author = {D.~Hampson}, title = {Inverse velocity stacking for multiple estimation}, journal = {56th Annual International Meeting, SEG, Expanded Abstracts}, year = {1986}, pages = {422-424} } @MastersThesis{hoyer99, school = {Helsinki University of Technology}, author = {P.~Hoyer}, year = {1999}, title = {Independent component analysis in image denoising} } @InProceedings{hoyer01, author = {A.~Hyv\"{a}rinen and P.~Hoyer and E.~Oja}, title = {Image denoising by sparse code shrinkage}, booktitle = {Neural Networks Proceedings}, year = {1998}, pages = {859-864}, volume = {2}, publisher = {IEEE Press} } @InBook{hyvarinen98, author = {A.~Hyv\"{a}rinen}, title = {New Approximation of Differential Entropy for Independent Component Analysis and Projection Pursuit in {M}.~{I}.~{J}ordan, {M}.~{J}.~{K}earns and {S}.~{A}.~{S}olla, eds., {A}dvances in {N}eural {I}nfomration {P}rocessing {S}ystems 10}, booktitle = {Advances in Neural Information Procesing Systems 10}, pages = {273-279}, year = {1998}, editor = {{M}.~{I}.~{J}ordan and {M}.~{J}.~{K}earns and {S}.~{A}.~{S}olla}, OPTpublisher = {MIT Press}, } @Article{hyvarinen99, author = {Aapo Hyv\"{a}rinen}, title = {Fast and Robust Fixed-Point Algorithms for Independent Component Analysis}, journal = {IEEE Transactions on Neural Networks}, year = {1999}, volume = {10}, number = {3}, pages = {626-634}, month = {May}, }

Sparse coding for noise attenuation @Book{hyvarinen01b, author = {A.~Hyv\"{a}rinen and J.~Karhunen and E.~Oja}, editor = {Simon Haykin}, title = {Independent Component Analysis}, publisher = {John Wiley \& Sons, Inc.}, year = {2001}, OPTseries = {Adaptive and Learning Systems for Signal Processing, Communications, and Control}, } @MastersThesis{kaplan03, author = {S.~T.~Kaplan}, title = {Principal and Independent Component Analysis for Seismic Data}, school = {University of British Columbia}, year = {2003}, } @Article{kaplan05, author = {S.~T.~Kaplan and T.~J.~Ulrych}, title = {Noise suppression in seismic data with sparse coding: 9th {I}nternational {C}ongress, {SBG}f, {E}xpanded {A}bstracts}, year = {2005}, } @Article{sacchi04, author = {M.~D.~Sacchi and D.~J.~Verchuur and P.~M.~Zwartjes}, title = {Data reconstruction by generalized deconvolution: 74th {A}nnual {I}nternational {M}eeting, {SEG}, {E}xpanded {A}bstracts}, year = {2004}, pages = {1989-1992} } @Article{stark02, author = {J.~S.~Starck and E.~J.~Cand\‘{e}s and D.~L.~Donoho}, title = {The curvelet transform for image denoising}, journal = {IEEE Transactions on Image Processing}, volume = {2}, year = {2002}, pages = {670-684} }

Sparse coding for noise attenuation REFERENCES Common, P., 1994, Independent component analysis, a new concept?: Signal Processing, 36, 287–314. Hampson, D., 1986, Inverse velocity stacking for multiple estimation: 56th Annual International Meeting, SEG, Expanded Abstracts, 422–424. Hoyer, P., 1999, Independent component analysis in image denoising: Master’s thesis, Helsinki University of Technology. Hyv¨arinen, A., 1998, New approximation of differential entropy for independent component analysis and projection pursuit in M. I. Jordan, M. J. Kearns and S. A. Solla, eds., Advances in Neural Infomration Processing Systems 10, 273–279. ——–, 1999, Fast and robust fixed-point algorithms for independent component analysis: IEEE Transactions on Neural Networks, 10, 626–634. Hyv¨arinen, A., P. Hoyer, and E. Oja, 1998, Image denoising by sparse code shrinkage: Neural Networks Proceedings, 859–864, IEEE Press. Hyv¨arinen, A., J. Karhunen, and E. Oja, 2001, Independent component analysis: John Wiley & Sons, Inc. Kaplan, S. T., 2003, Principal and independent component analysis for seismic data: Master’s thesis, University of British Columbia. Kaplan, S. T. and T. J. Ulrych, 2005, Noise suppression in seismic data with sparse coding: 9th International Congress, SBGf, Expanded Abstracts. Sacchi, M. D., D. J. Verchuur, and P. M. Zwartjes, 2004, Data reconstruction by generalized deconvolution: 74th Annual International Meeting, SEG, Expanded Abstracts: 1989– 1992. Starck, J. S., E. J. Cand`es, and D. L. Donoho, 2002, The curvelet transform for image denoising: IEEE Transactions on Image Processing, 2, 670–684.

Sparse coding for data-driven coherent and incoherent ...

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