three-parameter prestack seismic inversion based on l1-2
TRANSCRIPT
Three-parameter prestack seismic inversion based on L1-2 minimization
Lingqian Wang1, Hui Zhou1, Yufeng Wang1, Bo Yu1, Yuanpeng Zhang1, Wenling Liu2, andYangkang Chen3
ABSTRACT
Prestack inversion has become a common approach in reser-voir prediction. At present, the critical issue in the application ofseismic inversion is the estimation of elastic parameters in the thinlayers and weak reflectors. To improve the resolution and the ac-curacy of the inversion results, we introduced the difference of L1
and L2 norms as a nearly unbiased approximation of the sparsityof a vector, denoted as the L1−2 norm, to the prestack inversion.The nonconvex penalty function of the L1−2 norm can be decom-posed into two convex subproblems via the difference of convexalgorithm, and each subproblem can be solved efficiently by the
alternating direction method of multipliers. Compared with theL1 norm regularization, the L1−2 minimization can reconstructreflectivities more accurately. In addition, the f-x predictive filter-ing was introduced to guarantee the lateral continuity of the lo-cation and the amplitude of the reflectivity series. The generalizedlinear inversion and f-x predictive filtering are combined forstable elastic impedance inversion results, and three parametersof P-wave velocity, S-wave velocity, and density can be invertedwith the Bayesian linearized amplitude variation with offset in-version. The inversion results of synthetic and real seismic datademonstrate that the proposedmethod can effectively improve theresolution and accuracy of the inversion results.
INTRODUCTION
Reservoir characteristics, such as lithology, fluidity, and porosity,are important parameters in oil and gas exploration and develop-ment. They can be obtained from the P-wave velocity (VP), S-wavevelocity (VS), and density (ρ) with different petrophysical models(Karimi et al., 2010). Prestack seismic inversion can obtain moreelastic parameters than poststack seismic inversion because the am-plitude variation with offset (AVO) phenomenon is related to VP,VS, and ρ contrasts at an interface between two layers (Aki andRichards, 1980). Like other inverse problems, prestack inversionalso suffers difficulties stemming from noise contamination, bandlimitation, and nonuniqueness (Varela et al., 2006), so it is impor-tant to improve the quality of the prestack seismic inversion resultsby regularization. Some regularization approaches have been pro-posed to constrain the inverse problem with a priori information toeffectively solve the ill-posed problem. Tikhonov (1963) first intro-
duces the regularization method by assuming that the invertedparameters are smooth for a least-squares solution. Buland andOmre (2003) develop a Bayesian AVO inversion method whose ob-jective is to obtain the maximum posterior probability distributionsfor P-wave velocity, S-wave velocity, and density, and the solutionsof the AVO inversion are the posterior Gaussian probability distri-bution. Karimi et al. (2010) introduce Bayesian closed-skew Gaus-sian inversion defined as a generalization of traditional BayesianGaussian inversion.Recently, great effort has been made to improve the resolution
of the inversion results based on a sparsity assumption. In general,prestack inversion techniques for high resolution are roughly clas-sified into two categories: stochastic and deterministic inversionapproaches, each having its own merits and demerits. The formercategory of prestack inversion is being actively investigated. Thepriori knowledge is derived from multivariate geostatistical model-ing (González et al., 2008; Hansen et al., 2012; Connolly and
Manuscript received by the Editor 23 October 2018; revised manuscript received 21 April 2019; published ahead of production 5 June 2019; published online24 August 2019.
1China University of Petroleum, State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, Changping,Beijing 102249, China. E-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected].
2CNPC, Research Institute of Petroleum Exploration and Development, Xueyuan Road No. 20, Haidian, Beijing 10083, China. E-mail: [email protected].
3Zhejiang University, School of Earth Sciences, Hangzhou 310027, China. E-mail: [email protected].© 2019 Society of Exploration Geophysicists. All rights reserved.
R753
GEOPHYSICS, VOL. 84, NO. 5 (SEPTEMBER-OCTOBER 2019); P. R753–R766, 12 FIGS., 5 TABLES.10.1190/GEO2018-0730.1
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
Hughes, 2016; Siri and Deutsch, 2018). Stochastic inversions canproduce higher resolution results than conventional deterministicinversions and can help delineate thin beds. However, the majordisadvantage of stochastic methods is the computational expense.In contrast, the deterministic inversion approach can providehigh-resolution inversion results based on the deterministic sparsityconstraints in the transform domain (Yuan et al. 2015; She et al.2018) or the time domain (Alemie and Sacchi, 2011; Pérez et al.,2013; Chai et al., 2014). The L1 norm optimization framework wasintroduced into the prestack inversion for resolving thin layers andclearly delineating layer boundaries (Zhang and Castagna, 2011).Recovering sparse signals from linear measurements is also one ofthe central subjects of compressed sensing (CS) (Donoho, 2006).Here, we introduce the sparse reconstruction in CS into the prestackAVO inversion. The sparse reconstruction is conducted for thesparsest solution to an underdetermined linear system. BecauseL0 counts the number of nonzero elements, minimizing the L0 normis equivalent to finding the sparse solution. However, it has beenproven that it is NP-Hard to solve the optimization problem withthe L0 norm, which means that it would take too much time to ob-tain the solution.The convex relaxation techniques, as a kind of method to solve
the optimization problem with the L0 norm, will provide a biasedapproximate solution to the L0 minimization. Therefore, the convexrelaxation techniques may sometimes yield suboptimal perfor-mance. To address this issue, many nonconvex metrics, such as theLq quasi-norm with 0 < q < 1 (Chartrand and Yin, 2008), cappedL1 (Zhang, 2010), smoothly clipped absolute deviation (Fan and Li,2001), have been proposed to better approximate the L0 norm. Re-cently, the L1−2 norm was widely used in sparse reconstruction; theproperty to be more approximate to the L0 norm and the quicknessto find a solution have been demonstrated and developed in math-ematics (Esser et al., 2013; Lou et al., 2015). Yin et al. (2015) studythe analytical and numerical properties of L1−2 minimization forsparse representation. The superior performance of the L1−2 normwas verified in different applications including oversampled dis-crete cosine basis, differential absorption optical spectroscopy,and image denoising (Yin et al., 2015). To more appropriatelyapproximate the L0 norm, Ma et al. (2017a) propose a truncateddifference of the L1 and L2 norms discarding large magnitude en-tries in the regularization term and solve the objective function withthe difference of convex functions algorithm (DCA). This methodhas been validated on sparse vector recovery, matrix completion,and magnetic resonance imaging (Ma et al., 2017a). To improvethe computational efficiency of the L1−2 minimization, Lou andYan (2016) incorporate the proximal operator into forward-back-ward splitting and the alternating direction method of multipliers(ADMM). Recently, the L1−2 norm has been used in geophysics.Wang et al. (2018) perform seismic attenuation compensation viafirst solving the sparse reflectivity series and then convolving themwith the seismic wavelet to obtain the seismic record, rather thansolving directly for the compensated seismic record. They compen-sate the seismic record based on the stability and effective compu-tation of the sparse reconstruction.In this paper, we introduce the L1−2 minimization to the prestack
reflectivity reconstruction in terms of the sparsity of the reflectivityseries in the time domain. We study the ability to reconstruct therelative weak and close reflectivities with L1−2 minimization anddemonstrate the performance to estimate the thin beds and relative
weak reflection interfaces compared with L1 minimization. TheL1−2 minimization is divided into two stages. First, the original non-convex L1−2 norm objective function is decomposed into two con-vex subproblems via DCA (Tao and An, 1998; Liu and Pong, 2017).Second, each subproblem is efficiently solved by ADMM (Yin et al.,2015).In most cases, the inversion method does not consider the lateral
continuity of layers. When the results are combined to form an im-age profile, the trace-by-trace inversion results may be noisy andimportant geologic features may be masked (Hamid and Pidlisecky,2015; Hamid et al., 2018). To reduce and even eliminate these de-fects in the single-trace processing mode, a series of lateral con-straints has been used in seismic inversion and linearized AVOinversion. Many researchers have provided functionality for incor-porating different types of geologic information to impose spatialsmoothness constraints in their inversion algorithm frameworks(Delprat-Jannaud and Lailly, 1992; Zhang et al., 2013; Karimi,2015; Huang et al., 2017a; Huo et al., 2017; Ma et al., 2017b). Themajor disadvantage of the methodology of reforming the objectivefunction to improve the lateral continuity is that it is computation-ally expensive because of the expansion of the coefficient matrix.Transforming the reflectivities to impedance will magnify the lateraldiscontinuity associated with the different accuracies of the reflec-tivity reconstruction for different traces. The reconstruction accu-racy includes the location and the magnitude of reflectivities. Weintroduce the filtering method to the inversion to efficiently improvethe lateral continuity of the inversion results. It is known that the f-xpredictive filtering or its variation, i.e., f-x singular spectrum analy-sis, has been widely used in random noise attenuation (Canales,1984; Naghizadeh and Sacchi, 2012; Chen and Ma, 2014; Huanget al., 2016) and seismic trace interpolation (Spitz, 1991; Porsani,1999; Wang, 2002; Huang et al., 2017b) under the assumption thatthe events are nearly linear with respect to the offset dimension.Here, by assuming that the input seismic trace and the inverted re-flectivity exhibit similar spatial continuity, we apply f-x predictivefiltering to the reflectivity inversion and combine it with the gen-eralized linear inversion (GLI) to obtain the elastic impedance (EI)with well spatial continuity.In this paper, the prestack inversion is divided into three parts.
First, the reflectivity reconstruction is formulated with the L1−2minimization in the time domain. The solver for the L1−2 minimi-zation is provided for efficiently implementing reflectivity inversionin the framework of the unconstrained least-squares inversion, inwhich the DCA and ADMM algorithms are, respectively, usedfor decomposing the nonconvex problem into two convex subpro-blems. Then, GLI and the f-x predictive filtering are combined totransform the reflectivity series to the EI of different angles for thepurpose of improving the lateral continuity. After that, three param-eters of VP, VS, and ρ can be inverted with the Bayesian linearizedAVO inversion. To demonstrate the effectiveness of the proposedinversion scheme, we apply it to synthetic and field data and showthe inversion results.
METHOD
Reflectivity inversion
As usual, we rely on the convolutional model of a seismic traceunder the assumption that the earth structure can be represented by aseries of horizontal layers of constant material properties separated
R754 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
by planar interfaces (Yilmaz, 2001). Hence, the angle gather can beexpressed as
bðθiaÞ ¼ AðθiaÞ � xðθiaÞ þ nðθiaÞ; ia ¼ 1; 2; : : : ; na; (1)
where θia is the incident angle, bðθiaÞ is a seismic trace, AðθiaÞ rep-resents the source wavelet corresponding to angle θia, xðθiaÞ is thereflectivity, nðθiaÞ stands for random noise, � denotes convolution,and na is the number of angle gathers. We assume that the wavelet isknown and band limited, it is obtained via wavelet extraction basedon well log information or perhaps alternatively via the statisticalmethodology. Equation 1 can be represented in a matrix form ofAx ¼ b for a specific incident angle that is omitted for brevity, re-gardless of the noise:
2666664
b1b2...
bN−1bN
3777775¼
266666666664
w1 0 · · · 0
..
.w1
. .. ..
.
wk... . .
.0
0 wk...
w1
..
. . .. . .
. ...
0 · · · 0 wk
377777777775
2666664
x1x2...
xN−1xN
3777775; (2)
where biði ¼ 1; 2; · · · ; NÞ represents the ith sample of the seismictrace, and N is the number of sampling points, wiði ¼ 1; 2; · · · ; kÞrepresents the ith sampling point of the wavelet with a length of k.Due to the noise contamination, band limitation, and nonunique-
ness, the numerical inversion for finding x using equation 2 is an ill-posed problem. To obtain a solution of equation 2, regularization isgenerally required. The general regularization is equivalent to solvea constrained optimization problem:
min JðxÞ;subject to Ax ¼ b;
Δ1⩽cðxÞ⩽Δ2; (3)
where JðxÞ denotes an objective function that is defined to describethe characteristics of x, cðxÞ is the constraint to the solution, Δ1 andΔ2 are the solution intervals that are given based on the prior infor-mation. Here, we assume that the reflectivity series is sparse in thetime domain. The constrained optimization problem in equation 3can be expressed as
min kxk1; subject to Ax ¼ b: (4)
The constrained optimization problem in equation 4 can be con-verted to the unconstrained minimization problem:
min fðxÞ ≔ kAx − bk22 þ λkxk2; (5)
where λ > 0 is a scalar regularization parameter. The optimizationproblem represented by equation 5 is also known as a Lasso prob-lem. There are many state-of-the-art algorithms available for Lasso,such as ADMM (Boyd et al., 2011), fixed-point continuation (Haleet al., 2008), the fast iterative shrinkage-thresholding algorithm(FISTA) (Beck and Teboulle, 2009), and split Bregman (Goldsteinand Osher, 2009). However, L1 minimization may sometimes yield
a suboptimal solution due to its biased approximation to L0 norm inthe sense that L1 is dominated by entries with large magnitudes;differently, all nonzero entries have equal contributions to the L0
norm (Elad, 2010). In general, L1 minimization has two effects:first, elimination of the insignificant nonzero elements leading toincreased sparsity and, second, reduction in the magnitude of thenonzero elements (Jafarpour et al., 2009). These characteristics leadto the miss of weak reflectivities and the mask of the thin beds. Toaddress this issue, many nonconvex metrics have been proposed tobetter approximate L0. Here, we replace the L1 penalty by a novelL1−2 norm in equation 5. The objective function for L1−2 regular-ized reflectivity inversion can be written as
minx
1
2kAx − bk22 þ λðkxk1 − αkxk2Þ; (6)
where the weight parameter α in the range of [0,1] is provided todeal with ill-conditioned matrices when the L1−2 minimization failsto obtain a good solution (Lou et al., 2015). The value of λ > 0 is ascalar regularization parameter. A large value of λ would generatesparse solutions, whereas a small value of λ could make the forwardmodeling results more consistent with the seismic record. Althoughmany researchers have studied the optimization of the regularizationparameters, it remains a complex problem to determine them appro-priately (Tikhonov and Glasko, 1965; Clapp et al., 2004; Wang andSacchi, 2007; Brezinski et al., 2008). In this paper, we do not focuson how to select optimal parameters. However, we optimize themby trial and error. The tuning parameter λ is calibrated in terms ofthe correlation coefficient between the inversion results and the truemodels for synthetic tests or the well log data for the field data tests.In equation 6, the L1−2 minimization is equivalent to the L1 mini-mization when α is assigned to zero. Therefore, the L1 minimizationproblem can be seen as a particular case in the L1−2 minimizationoptimization problem.To intuitively understand the L1−2 metric and the meaning that
L1−2 approximates better to the L0 to find sparse solution, we plotthe images in Figure 1 corresponding to L1 and L1−2 norms of a 2Dvector with two elements of x and y. The value of the L0 norm(Figure 1a) is 0 at the origin, 1 at the axes, and 2 elsewhere. Thedistribution pattern of the L1−2 metric image (Figure 1c) is closer to
–1 0 1x
–1
0
1
y
0
0.5
1
1.5
2Magnitude
–1 0 1x
–1
0
1
y
0
0.5
1
1.5
2Magnitude
–1 0 1x
–1
0
1
y
0
0.2
0.4
Magnitude
a)
c)
b)
Figure 1. The images of metrics of (a) L0, (b) L1, and (c) L1−2.
L1-2 minimization for prestack inversion R755
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
the L0 image (Figure 1a) than the L1 norm (Figure 1b), which in-dicates that the L1−2 metric is better than the L1 norm in terms ofsparsity. Because the distribution pattern of the L0 norm means thatthe solution exhibits in the axis for the least nonzero elements. Thesimilar distribution pattern indicates similar sparsity. Therefore, thesolution of the L1−2 minimization is more approximate to the truesparse solution. Geometrically, minimizing a sparse measure isequivalent to finding an interception of the straight line correspond-ing to the linear constraints Ax ¼ b with a level set of the sparsenorm shown in Figure 1. The solution is sparser when the intersec-tion is closer to a coordinate axis. For L1, it is possible that thestraight line coincides with a segment of a level set. In this case, L1
minimization fails to find a sparse solution. For L1−2, due to itscurved level set, the interception is more likely to occur at the co-ordinate axis. Figure 1 demonstrates that the advantage of the L1−2norm over L1 is its unbiased characterization of sparse vectors. It isbetter for the nonconvex constraints to promote sparsity comparedwith the L1 norm. However, there is a challenge of computation in anonconvex optimization problem. Iterative reweighted least-squaresas one of the conventional methods to solve the nonconvex mini-mization may get stuck at a local minimum (Sacchi and Ulrych,1995). Meanwhile, the conventional nonconvex constraints alwayshave a priori unknown parameters and are non-Lipschitz. Differentfrom them, the L1−2 norm is Lipschitz continuous and free ofparameters. Therefore, it can be minimized by the DCA withoutadditional smoothing or regularization (Tao and An, 1997). Theglobal minimum can be obtained with DCA empirically. Therefore,the replacement of the L1 norm with L1−2 as the sparsity constraintcan improve the accuracy of the solution. Meanwhile, the computa-tional performance can also be guaranteed with DCA. The detailedmethod to solve the L1−2 minimization is shown as follows.To solve the L1−2 minimization, we first decompose the noncon-
vex L1−2 into two convex subproblems via DCA, and then we solvethe subproblems of the L1 norm by ADMM. The DCA is a robustand efficient descent method introduced by Tao and An (1998),which deals with the objective function composed of the differenceof two convex functions,
FðxÞ ¼ GðxÞ −HðxÞ; (7)
where GðxÞ ¼ 1∕2kAx − bk22 þ λkxk1; HðxÞ ¼ αλkxk2. This algo-rithm converts the original problem 7 to the calculation of twosequences fxng and fyng iteratively:
�yn ∈ ∂HðxnÞ;xnþ1 ¼ min
xGðxÞ − ðHðxnÞ þ hyn; x − xniÞ; (8)
where yn is a subgradient of HðxÞ at xn and is expressed as
yn ¼�0; if xn ¼ 0;αλ xn
kxnk2 ; otherwise: (9)
In each DCA iteration, fyng can be obtained directly. Meanwhile,an L1 regularized convex subproblem
xnþ1 ¼ argminx
1
2kAx − bk22 þ λkxk1 þ hx; yni (10)
is solved to update fxng.
To transform the original problem into a form that can be solvedwith ADMM, we introduce an auxiliary variable z and we redefinethe minimization problem as
xnþ1 ¼ argminx
1
2kAx − bk22 þ hyn; xi þ λkzk1;
subject to x − z ¼ 0: (11)
After applying the augmented Lagrangian multiplier, the minimi-zation problem can be expressed as
Lβ¼1
2kAx−bk22þhyn;xiþλkzk1þωTðx−zÞþβ
2kx−zk22;
(12)
where β > 0 is a penalty parameter, ω is the Lagrange multiplier.The augmented term is the square penalty term of ðβ∕2Þkx − zk22.The unconstrained problem in equation 12 can be solved effi-
ciently by ADMM, which consists of the following steps of thelþ 1th inner iteration of ADMM:
8<:
xlþ1 ¼ ðATAþ βIÞ−1ðATb − yn þ βzl − ωlÞ;zlþ1 ¼ Sðxlþ1 þ ωl∕β; λ∕βÞ;ωlþ1 ¼ ωl þ βðxlþ1 − zlþ1Þ:
(13)
In the proposed DCA + ADMM algorithm to solve L1−2 mini-mization, there are two layers of iterations: the outer DCA loop andthe inner ADMM loop. Their stopping condition of DCA andADMM is the same:
� kxkþ1 − xkkmaxfkxkþ1k2; kxkk2g
⩽ε
�∪ ðk > MÞ; (14)
where ε > 0 is a given tolerance to control the circulation and M isthe maximum number of iterations.
Elastic impedance inversion
The reflectivity series can describe the layer boundaries, but it hasno information of the elastic parameters. It is necessary to transformthe reflectivity series to impedance and then to other parameters toevaluate reservoirs. Much effort has been made to solve the decon-volution problem effectively and accurately. The inverted reflectiv-ity can be substituted into one of the standard recursion formulas toobtain the impedance. However, the results of these recursionschemes are sensitive to the error of the inverted reflectivity seriesand the accuracy of the EI of the first sample determined from theinitial model. The relationship between the reflection coefficientsand the EI is linearized by assuming that the absolute values of re-flection coefficients are small. For the purpose of improving thelateral continuity of the inversion results, we combine f-x predictivefiltering and the GLI method.The most popular seismic inversion technique being used is re-
cursive inversion. The reflectivity at the ith sampling point can beexpressed as the following function of EIiðθÞ and EIiþ1ðθÞ:
Riðθ;EIi;EIiþ1Þ ¼EIiþ1ðθÞ − EIiðθÞEIiþ1ðθÞ þ EIiðθÞ
; (15)
R756 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
by which EIiþ1ðθÞ at an incident angle of θ is obtained by a givenreflection coefficient Riðθ;EIi;EIiþ1Þ and its adjacent elastic imped-ance EIiðθÞ. Equation 15 can be rewritten to express EIiþ1ðθÞ interms of Riðθ;EIi;EIiþ1Þ and EIiðθÞ:
EIiþ1ðθÞ ¼EIiðθÞ½1þ Riðθ;EIi;EIiþ1Þ�
1 − Riðθ;EIi;EIiþ1Þ: (16)
The extended recursive inversion to all samples is expressed as
EINðθÞ ¼ EI1ðθÞYNi¼2
1þ Ri−1ðθ;EIi−1;EIiÞ1 − Ri−1ðθ;EIi−1;EIiÞ
: (17)
Expanding equation 17 using the Taylor series and truncating thehigh-order terms, we obtain the reflectivity at the ith samplingpoint:
Riðθ;EIi;EIiþ1Þ ¼ R0i ðθ;EI0i ;EI0iþ1Þ
þ ∂Riðθ;EI0i ;EI0iþ1Þ∂EIi
δEIiðθÞþ∂Riðθ;EI0i ;EI0iþ1Þ
∂EIiþ1
δEIiþ1ðθÞ;
(18)
where R0i ðθ;EI0i ;EI0iþ1Þ can be obtained with the initial low-
frequency EI based on well log information, δEIiðθÞ ¼ EIiðθÞ−EI0i ðθÞ is the correction of the initial EI model. Here,Riðθ;EIi;EIiþ1Þ denotes the reflectivity series obtained with L1−2minimization. We can correct the initial EI model iteratively. The ma-trix form of equation 18 can be expressed as
26664δR1ðθ;EI1;EI2ÞδR2ðθ;EI2;EI3Þ...
δRN−1ðθ;EIN−1;EINÞ
37775 ¼ AE
2666664
δEI1ðθÞδEI2ðθÞ...
δEIN−1ðθÞδEINðθÞ
3777775; (19)
where
AE¼
2666664
∂R1ðθ;EI1 ;EI2Þ∂EI1
∂R1ðθ;EI1 ;EI2Þ∂EI2
0 ··· 0
0∂R2ðθ;EI2 ;EI3Þ
∂EI2∂R2ðθ;EI2 ;EI3Þ
∂EI3··· 0
··· ··· ··· ··· ···0 ··· 0
∂RN−1ðθ;EIN−1 ;EIN Þ∂EIN−1
∂RN−1ðθ;EIN−1 ;EIN Þ∂EIN
3777775:
(20)
The stopping criterion for GLI is the same as for DCA andADMM shown in equation 14. By introducing the low-frequencymodel obtained from well log data, seismic velocity analysis, or akriging estimation may improve the lateral continuity of the inver-sion result of the EI and reduce the local anomaly in the invertedresult. However, there may be no obvious improvement for the thinlayers because the GLI method does not take into account the lateralcontinuity of the relatively high-frequency term of the EI.The characteristic, which a seismic event is predictable in the fre-
quency domain under the linear assumption that seismic events arecontinuous in space, has been applied widely to random noise attenu-ation and seismic trace interpolation (Canales, 1984; Spitz, 1991).
Here, we want to use this property to suppress the discontinuityof inverted reflectivity series by applying an f-x predictive filter.Assuming that the inverted reflectivity is a linear function of x,
thus, the reflectivity in the t-x domain can be expressed as
Rðt; hþ 1Þ ¼ Rðt − hψΔx; 1Þ; (21)
where ψ stands for the slope of the linear reflectivity series, Δx de-notes the trace interval, and h represents the trace number changingfrom 1 to Ntr. After Fourier transformation, equation 21 can be ex-pressed in the frequency domain as
~Rðf; hþ 1Þ ¼ ~Rðf; 1Þ expð−i2πfhψΔxÞ ¼ aðf; 1Þ ~Rðf; hÞ;(22)
where aðf; 1Þ ¼ expð−i2πfhψΔxÞ represents one-step predictorand ~Rðf; hÞ stands for the Fourier transform of the reflectivity seriesof the hth trace.This recursion is an autoregressive (AR) model of first order be-
cause the predictive value is known in advance. The first order meansthat the reflectivity series of a trace is predicted with only one knowntrace. The extension of the first-order AR model to the pth-order ARmodel, which predicts the reflectivity series of one trace with pknowns, is represented as
~Rðf; hþ 1Þ ¼Xpi¼1
aðf; iÞ ~Rðf; hþ 1 − iÞ; (23)
where aðf; iÞði ¼ 1; 2; · · · ; pÞ is the predictive filter. There are punknowns to be solved. By predicting pmore traces, overdeterminedequations are constructed to obtain the filtering operator aðf; iÞ. Interms of the assumption on the linear continuity of the events, thetuning parameter p should be determined based on the actual seismicrecords. The tuning parameter p is large when the seismic record issimple and the events are obviously linear. And it is small when theevents in the seismic record are complicated. In the following tests,the turning parameter p for f-x filtering is relatively small to avoideliminating the effective reflectivity at the cost of a relative largeamount of computation. Here, we take forward and backward ARmodeling into account to predict the reflection profile. With the f-xprediction filter of length L, the prediction equation is
~RhðfÞ ¼XLj¼1
ajðfÞ ~R 0h−jðfÞ; h ¼ Lþ 1; Lþ 2; · · · ; Ntr;
(24)
~R�hðfÞ¼
XLj¼1
a0jðfÞ ~R0
hþjðfÞ; h¼1;2; ···;Ntr−L; (25)
where ~RhðfÞ and ~R�hðfÞ represent the frequency-domain reflectivity
after forward and backward f-x predictive filtering from the originalreflectivity series ~RhðfÞ, aðfÞ is the forward predictive filter, anda 0ðfÞ is the backward predictive filter. After the forward and back-ward filtering, we take the average of the forward and backward pre-dictive results as the final predictive result.Our method to transform the reflectivity series to EI consists of
three steps:
L1-2 minimization for prestack inversion R757
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
Step 1: Apply the forward and backward f-x predictive filter to thereconstructed reflectivity series using equations 24 and 25, andtake the filtered reflectivity series as the input of GLI.
Step 2: Calculate the reflectivity series of the initial EI model, andcalculate the correction of the initial model with equation 19and update the initial model.
Step 3: Repeat step 2 and take the updated model as the initial modelin step 2 until the updated amount meets the requirement.
Due to the predictable property of the reflectivity series, which isthe same as that of the seismic trace, the lateral coherency of thefinal reflectivity profile can be improved by the f-x predictive filter.As a result, the inverted EI obtained by combining GLI and f-xpredictive filtering can be improved.
AVO inversion for three parameters
The EI is expressed by (Connolly 1998, 1999)
EIðθÞ ¼ VaPV
bSρ
c; (26)
where
a¼1þtan2θ; b¼−8Ksin2θ; c¼1−4Ksin2θ; K¼ðVS∕VPÞ2:(27)
If we have EI corresponding to na ðna⩾3Þ angle gathers, threeparameters of VP, VS, and ρ can be inverted, and their relationshipcan be expressed in a matrix form as
0BBB@lnEIðθ1ÞlnEIðθ2Þ...
lnEIðθna Þ
1CCCA
¼
0BBB@
1þ tan2θ1 −8Ksin2θ1 1−4Ksin2θ11þ tan2θ2 −8Ksin2θ2 1−4Ksin2θ2
..
. ... ..
.
1þ tan2θna −8Ksin2θna 1−4Ksin2θna
1CCCA0@lnVP
lnVS
lnρ
1A:
(28)
Equation 28 is a general matrix form to relatethree unknowns of ln VP; ln VS; ln ρ to na mea-surements of lnEIðθ1Þ;lnEIðθ2Þ; ···;lnEIðθnaÞ.In general, K can be calculated through the low-frequency model. The practical problem is thatthe coefficient matrix of equation 28 is poorly con-ditioned unless the angle spacing is sufficientlylarge. However, the angle range is small in practicalapplications. There are two reasons why this anglerange is small. First, the convolutional model doesnot work for the seismic data of large offsets; it isonly valid for the plane-wave propagation in iso-tropic layered media with no geometry spread, noscattering and transmission loss, no mode con-verted wave, and no multiples (Mallick, 2001).Second, the difference between the true reflectivityof the Zoeppritz equations and AVO/EI equationswill be greater with the increasing incident angle.To obtain a unique and stable solution of the ill-posed problem, we invert the three parametersbased on the Bayesian rule. We compute the sol-ution efficiently with a statistical model based onthe maximum a posteriori estimation.First, we expand the solution of a single point
to a single trace based on the EI of different angles:
26664ln EI1ln EI2...
ln EIna
37775 ¼
26664
A1 B1 C1
A2 B2 C2
..
. ... ..
.
Ana Bna Cna
3777524 ln VP
ln VS
ln ρ
35; (29)
where ln EIi is the logarithm of the EI of the ith angle, ln VP,ln VS, and ln ρ are the logarithms of VP, VS, and ρ of a single trace,
Table 1. The correlation coefficient between the true andreconstructed reflectivity by L1 and L1−2 minimization fromthe noise-free seismic records.
MinimizationReflectivity
of 0°Reflectivity
of 15°Reflectivity
of 30°
L1 0.9530 0.9685 0.8806
L1−2 0.9964 0.9995 0.9959
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.3 0 0.3Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.5 0 0.5Amplitude
50
100
150
Tim
e (m
s)
–0.5 0 0.5Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.3 0 0.3Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.3 0 0.3Amplitude
50
100
150
Tim
e (m
s)
a) c)
b) d)
Figure 2. (a) True reflectivity series, (b) noise-free record, (c) reconstructed reflectivitywith L1−2 minimization, (d) reconstructed reflectivity with L1 minimization. The left,middle, and right plots in each panel show the information at an angle of 0°, 15°, and 30°,respectively.
R758 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
Ai, Bi, and Ci represent the diagonal matrixes of N × N. Thecoefficient matrix on the right-hand side of equation 29 is denotedas G, the vector on left-hand side is denoted as d, and the vector onthe right-hand side is denoted as m. Using the Bayesian rule, theposterior distribution of m is given by
PðmjdÞ ¼ PðdjmÞPðmÞPðdÞ ∝ PðdjmÞPðmÞ; (30)
where PðdjmÞ is the likelihood function of the logarithm of the EI,PðmÞ is the prior distribution, and PðdÞ is the marginal probabilitydistribution. Assuming the errors of d are Gaussian with the averagevalue of zero, we express the likelihood function as
PðdjmÞ ¼ P0 exp½−ð2σ2nÞ−1ðd −GmÞTðd −GmÞ�; (31)
where P0 ¼ 1∕ð2πÞNtrN∕2σNtrNn , σn is the standard deviation of
the errors, which is difficult to determine. In thefollowing tests, the solution is relatively stabledue to the sparsity constraints in the reflectivityreconstruction. Therefore, we assign a relativelysmall value to σn to stabilize the solution andavoid smoothing the solution. The Gaussianprobability distribution for the prior probabilitydistribution PðmÞ is given by
PðmÞ ¼ 1
ð2πÞ3N∕2jP jN∕2
exp
�−1
2
XNi¼1
ðmi − μmÞTΣ−1ðmi − μmÞ�;
(32)
where Σ is the covariance matrix of 3 × 3, mi isthe three parameters at the ith sample point, andμm is the mean of the three parameters from thewell log or the low-frequency model. Substitutionof equations 31 and 32 into equation 30, after somealgebraic operation, the objective function for themaximum posterior probability can be expressedas
minm
ðd −GmÞTðd −GmÞ
þ σ2nXNi¼1
ðmi − μmÞTΣ−1ðmi − μmÞ:
(33)
By solving equation 33, we obtain m, which is the logarithm ofVP, VS, and ρ, and we finally obtain the inverted VP, VS, and ρ byexponentiating the solution m.
EXAMPLES
Synthetic data example
We test the inversion method in three terms in this subsection.First, we compare the performance of our proposed L1−2 regular-ized deconvolution with the L1 regularized deconvolution for a
single trace with different angles. Second, we conduct the L1−2minimization test on a synthetic angle gather with incident anglesranging from 0° to 45°. Third, we use the SEG/EAGE overthrustmodel to demonstrate the effectiveness of our algorithm in prestackthree-parameter inversion.The first test is conducted on noise-free seismograms. The reflec-
tivity series of 0°, 15°, and 30° are shown in Figure 2a. We use aRicker wavelet with a dominant frequency of 50 Hz as the source.The seismograms (Figure 2b) are generated by convolving the reflec-tivity series with the given wavelet. The reflectivity reconstructionresults obtained by the L1−2 and L1 regularized deconvolution meth-ods are shown in Figure 2c and 2d, respectively. In this example, wechoose a relatively small balancing parameter λ ¼ 5e − 4, α ¼ 1 tostabilize the sparse solution, and the weight parameter α is fixed in allof the following tests. Meanwhile, the balancing parameter λ is thesame in the L1 minimization and the L1−2 minimization. The twomethods can reconstruct the reflectivity series accurately and efficiently
Table 2. The correlation coefficient between the true andreconstructed reflectivity by L1 and L1−2 minimization fromthe noisy seismic records with S∕N � 6.
MinimizationReflectivity
of 0°Reflectivity
of 15°Reflectivity
of 30°
L1 0.7988 0.8121 0.7175
L1−2 0.9326 0.9281 0.9375
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)–0.3 0 0.3
Amplitude
50
100
150
Tim
e (m
s)
–0.5 0 0.5Amplitude
50
100
150
Tim
e (m
s)
–0.5 0 0.5Amplitude
50
100
150
Tim
e (m
s)
–0.5 0 0.5Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.3 0 0.3Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.2 0 0.2Amplitude
50
100
150
Tim
e (m
s)
–0.3 0 0.3Amplitude
50
100
150
Tim
e (m
s)
a) c)
b) d)
Figure 3. (a) True reflectivity series, (b) noisy record, (c) reconstructed reflectivity withL1−2 minimization, and (d) reconstructed reflectivity with L1 minimization. The left,middle, and right plots in each panel show the information at an angle of 0°, 15°, and 30°,respectively.
L1-2 minimization for prestack inversion R759
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
in the absence of noise. To illustrate the improvement of the re-flectivity reconstruction precision with the L1−2 minimization, weuse the correlation coefficient between the true and reconstructed
reflectivity as the quantitative evaluation. Table 1 shows the correla-tion coefficient for the noise-free seismic records. The reconstructedreflectivity by the L1 minimization and the L1−2 minimization is
acceptable in the absence of noise.Further, we test the method on a noisy seismic
record. Figure 3a shows the exact reflectivityseries as the reference, and Figure 3b shows anoisy seismogram with a signal-to-noise ratioðS∕NÞ ¼ 6. The S/N is the ratio of the root-mean-square amplitude of the signal to that ofthe noise with normal distribution. The invertedresults with the L1 and L1−2 norms are shown inFigure 3c and 3d, respectively. In this test, weselect a relatively large balancing parameter λ ¼8e − 3 in the L1 and L1−2 minimizations to sup-press the effect of random noise. The excellentmagnitude recovery property of our proposedmethod is verified when the reflectivity is rela-tively weak, and the record is embedded in thenoise (e.g., the reflectivity at 13 and 160 ms),especially in the 15° reflectivity series with a rel-atively small magnitude. Meanwhile, the magni-tude of the reconstructed reflectivity with the L1
norm is smaller compared to the true reflectivitysuch as the reflectivity of 30° at 77 and 129 ms.Table 2 shows the correlation coefficient betweenthe true and reconstructed reflectivity from thenoisy seismic record. The correlation coefficientdecreases compared with the reconstructedresults from the noise-free seismic record. Thecorrelation coefficient between the true and re-constructed reflectivity from the L1−2 minimiza-tion is larger compared with the reflectivitycorrelation coefficient from the L1 minimization.Therefore, the reflectivity can be reconstructedmore precisely with the L1−2 norm constraint.We next test the prestack seismic inversion
scheme based on the L1−2 minimization on a synthetic angle gatherwith incident angles ranging from 0° to 45° with a gap of 5°. Theentire seismic records with different angles are applied to invert thevelocities and density. This synthetic angle gather (Figure 4a) isgenerated by convoluting a wavelet with a 50 Hz dominant fre-quency extracted from the seismic record with a section of welllog data. Figure 4b shows the noisy angle gather with S∕N ¼ 2.In this synthetic data inversion test, σn is assigned to 10−15, andthe tuning parameter λ ¼ 10−4 is relatively small in the absence ofnoise, and it is λ ¼ 5 × 10−2 in the case of a noisy seismic recordwith S∕N ¼ 2. Figure 4c and 4d displays the inverted VP, VS, and ρ(the red lines) from the noise-free synthetic data (Figure 4a) andnoisy data (Figure 4b), and the true VP, VS, and ρ (black lines),respectively. The initial models are also plotted in Figure 4c and 4dwith blue lines, which are the low-pass-filtered well log data. Wefind that the inversion results match well with the true velocities anddensity when there is no noise in the data. The inversion resultsshown in Figure 4d also indicate the stability of the inversionmethod when we add random Gaussian noise to the synthetic traces.Table 3 lists the correlation coefficient between the true and in-verted elastic parameters from the seismic records without and withnoise of S∕N ¼ 2. We find that the inverted VS is easier to be
Table 3. The correlation coefficient between the true and theinverted elastic parameters by L1−2 minimization from thenoise-free and noisy seismic records.
Seismic data P-wave velocity S-wave velocity Density
Noise-free 0.9638 0.9017 0.9476
Noisy 0.8772 0.8281 0.8533
0 5 10 15 20 25 30 35 40 45
Incident angle (°)
660
710
760
810
860
910
960
Tim
e (m
s)
0 5 10 15 20 25 30 35 40 45
Incident angle (°)
660
710
760
810
860
910
960
Tim
e (m
s)
2000 3000
660
710
760
810
860
910
960
Tim
e (m
s)
VP (m/s)
1200 1600
660
710
760
810
860
910
960
VS (m/s)
2300 2400
660
710
760
810
860
910
960
(kg/m3)
2400 3000
660
710
760
810
860
910
960
Tim
e (m
s)
VP (m/s)
1200 1600
660
710
760
810
860
910
960
VS (m/s)
2300 2400
660
710
760
810
860
910
960
(kg/m3)
a) c)
b) d)
Figure 4. One-dimensional tests applied to a piece of real well log data. (a) The syn-thetic traces without noise, (b) the noisy synthetic traces with S∕N ¼ 2, (c) the inversionresults of VP, VS, and density (red lines) from noise-free synthetic data, and (d) theinversion results from noisy data of S∕N ¼ 2. The low-frequency initial models (bluelines) and the true models (black lines) are also shown in (c and d).
Table 4. The MSE of the estimated elastic parameters fromthe seismic records with L1 and L1−2 minimization combinedwith and without f -x filtering.
MinimizationP-wavevelocity
S-wavevelocity Density
L1 2.7050 × 104 1.5696 × 104 784.1338
L1 with f-x filtering 2.4668 × 104 1.2932 × 104 711.6990
L1−2 2.1970 × 104 8.9134 × 103 492.6038
L1−2 with f-x filtering 2.0089 × 104 7.1800 × 103 450.1180
R760 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
contaminated by random noise and the inverted VP is more stableand accurate compared with the other two parameters.As a benchmark model, the SEG/EAGE overthrust model (Fig-
ure 5a–5c) is used as an inversion example. The correspondingreflectivity model is computed from the VP, VS, and ρmodels usingthe Aki-Richards equation (Aki and Richards, 1980) for the PP re-flection coefficient, and then it is convolved with a Ricker waveletof a dominant frequency 40 Hz, which is discretized with a sampleinterval of 1 ms to generate a discrete wavelet with 81 samples.Three angle gathers of 0°, 15°, and 30° are applied to invert threeparameters. White Gaussian noise is added to the angle gathersto generate the sections with an S∕N ¼ 6 (Figure 5d–5f). Thesesections are used to estimate the VP, VS, and ρ models by removingthe effect of the wavelet via deconvolution and obtain the three
parameters using the Aki-Richards equation. The initial models(Figure 6a–6c) are created by smoothing the true models. In thissynthetic test, we set the scalar parameter λ to 5 × 10−3 and theparameter p for f-x filter to 10. The other tuning parameters are thesame as the values of the second synthetic data tests. In this syn-thetic test, we use the mean-square-error (MSE) of the P-wavevelocity, S-wave velocity, and density to evaluate the accuracy ofthe inversion results with L1 and L1−2 minimizations. To illustratethe efficiency of the GLI combined with f-x filtering, we alsocompare the inversion results with and without f-x filtering, respec-tively. The inversion results with L1 minimization combined with-out and with f-x filtering are shown in Figure 7, and the inversionresults with L1−2 minimization combined without and with f-xfiltering are shown in Figure 8. From the inversion results, we
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0° angle gather
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
15° angle gather
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
–0.2
–0.1
0
0.1
0.2
30° angle gather
a) b) c)
d) e) f)
Figure 5. The true SEG/EAGE overthrust model of (a) VP, (b) VS, and (c) density. The angle gathers with S∕N ¼ 6 of (d) 0°, (e) 15°, and(f) 30°.
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)a) b) c)
Figure 6. The initial model of (a) VP, (b) VS, and (c) density.
L1-2 minimization for prestack inversion R761
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)
a) b) c)
d) e) f)
Figure 7. The inversion results of (a) VP, (b) VS, and (c) density with L1 minimization without f-x filtering and (d) VP, (e) VS, and (f) densitywith L1 minimization combined with f-x filtering.
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2500
3000
3500
4000
4500
5000
5500
6000VP (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
1000
1500
2000
2500
3000
VS (m/s)
1 200 400 600 800CDP number
0
50
100
150
Tim
e (m
s)
2200
2300
2400
2500
2600
2700
(kg/m3)
a) b) c)
d) e) f)
Figure 8. The inversion results of (a) VP, (b) VS, and (c) density with L1−2 minimization without f-x filtering and (d) VP, (e) VS, and (f) densitywith L1−2 minimization combined with f-x filtering.
R762 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
can find some small-scale features, such as thinbeds and pinch-outs, in the overthrust model. It isshown that the inversion results with f-x filteringcan improve the lateral stability effectively. In ad-dition, through the MSE for the four algorithmslisted in Table 4, we find that L1−2 minimizationcan obtain more accurate inversion results com-pared with L1 minimization and the f-x filteringmay not improve the inversion results obviously.However, the f-x filtering can eliminate the trailsand improve the lateral continuity obviously in2D inversion results shown in Figures 7 and 8.The magnitudes of MSE are different in P-wavevelocity, S-wave velocity, and density associatedwith their different ranges of values. To summa-rize, the L1−2 minimization can improve the ac-curacy of the inversion results compared withL1 minimization. Meanwhile, f-x filtering canimprove the lateral continuity of the inversionresults without losing the accuracy.
Field data example
Before prestack inversion is conducted, theoriginal prestack seismic data should be processedmainly with three steps. At first, the noise is sup-pressed for getting more stable and accurate inver-sion results. Then, an amplitude compensation isapplied for compensating the loss of energy due to
3 6 9 12 15 18 21 24
Incident angle (°)
650
700
750
800
850
900
950
1000
Tim
e (m
s)
3 6 9 12 15 18 21 24
Incident angle (°)
650
700
750
800
850
900
950
1000
Tim
e (m
s)
3 6 9 12 15 18 21 24
Incident angle (°)
650
700
750
800
850
900
950
1000
Tim
e (m
s)
3 6 9 12 15 18 21 24
Incident angle (°)
650
700
750
800
850
900
950
1000
Tim
e (m
s)
a) c)
b) d)
Figure 9. The angle gathers ranging from 3° to 24° at (a) CDP 120, (b) CDP 150,(c) CDP 210, and (d) CDP 260.
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
–8000
–6000
–4000
–2000
0
2000
4000
6000
8000
Seismic
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
–6000
–4000
–2000
0
2000
4000
Seismic
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
–6000
–4000
–2000
0
2000
4000
6000Seismic
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
2500
2600
2700
2800
2900
3000
3100
3200
3300VP (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
1000
1100
1200
1300
1400
1500
1600
VS (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
2320
2340
2360
2380
2400
2420 (kg/m3)
a) b) c)
d) e) f)
Figure 10. Partially stacked angle profiles of (a) small-angle range, (b) middle-angle range, and (c) large-angle range. The initial model of(d) VP, (e) VS, and (f) density.
L1-2 minimization for prestack inversion R763
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
spherical diffusion and formation absorption in the propagation ofthe seismic wavelet, and for making sure that the final prestack am-plitudes represent the reflection strength of subsurface interfaces asaccurately as possible. Finally, the preserved-amplitude prestack mi-gration improves the spatial resolution and the S/N. A small 2D fielddata set is used for the inversion test. We choose common depth point(CDP) gathers in the time-angle domain of a survey line, on whichthere are three known wells at CDP 136, CDP 263, and CDP 287.The maximum incident angle is at approximately 24°. Figure 9 showsthe angle gathers at different CDPs of 120, 150, 210, and 260. Thethree wells are used to establish a low frequency (high-cut frequencyof 10–15 Hz), initial velocities, and density models as shown in Fig-ure 10d–10f. Figure 10a–10c illustrates the partially stacked anglegathers with small, middle, and large incident angles ranging from6° to 12°, 12° to 18°, and 18° to 24°, respectively. In the field datatest, the optimal λ is 5 × 10−2 and the turning parameter p ¼ 10 interms of the lateral continuity of the seismic events. Figure 11d–11fdepicts the inverted P-wave velocity, S-wave velocity, and densitywith L1−2 minimization. To illustrate the advanced performance ofour method, we also show the inversion results in Figure 11a–11cbased on the model constraint that has been used widely in industry.From the inversion results (Figure 11) and the initial models(Figure 10d–10f), we find that there are rapid varying values of VP,VS, and density profiles at 700 and 950 ms caused by the change ofthe strata. Although the inversion results shown in Figure 11a–11cmay reveal the change of the strata, the thin beds cannot be clearlyobserved. It is obvious that the resolution of the inversion results withour method is improved.
Then, we compare the inversion results (Figure 11d and 11e)with the well logs to evaluate its accuracy. The black lines inFigure 11d–11f represent the location of the well at CDP 136 usedfor the comparison with well logs. Because the original well logdata contain very high-frequency components, they cannot be useddirectly to make comparison with the inversion results. The well logdata are processed by a low-pass filter with high-cut frequency of70–80 Hz before the comparison is conducted. We should seewhether the trends of the inversion results agree with the filteredwell log data. Figure 12 shows the original well log data by redlines; low-pass-filtered well log data by black lines; and invertedVP, VS, and ρ by blue lines. From Figure 12, we see that the invertedVP, VS, and ρ obtained with the L1−2 minimization show a goodagreement with the known logs after low-pass filtering. Thoughthere are also some mismatches in numerical values between theinversion results and the well log data, they follow the same trend.Table 5 records the correlation coefficient between the filtered welllog data and the inverted elastic parameters with L1−2 minimizationand the inversion results with the model constraint. It is found thatthe correlation coefficients of three elastic parameters are larger than0.7, which means that the accuracy of the inversion results isacceptable.
DISCUSSION
The challenge of the inversion of thin layers and a weak reflectioninterface is associated with the band limitation and noise contami-nation. The sparsity norm plays an important role in high-resolution
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
1500
2000
2500
3000
3500
4000VP (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
500
1000
1500
2000
2500VS (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
2150
2200
2250
2300
2350
2400
2450
2500 (kg/m3)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
1500
2000
2500
3000
3500
4000VP (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
500
1000
1500
2000
2500VS (m/s)
100 200 300 400CDP number
650
700
750
800
850
900
950
1000
Tim
e (m
s)
2150
2200
2250
2300
2350
2400
2450
2500 (kg/m3)
a) b) c)
d) e) f)
Figure 11. Estimated parameters obtained by the inversion method based on the model constrained. (a) VP, (b) VS, and (c) density. Theinversion results of (d) VP, (e) VS, and (f) density with the proposed inversion approach. The black lines represent the location of the wellat CDP 136.
R764 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
inversion. Zhang et al. (2013) propose a basis pursuit inversion al-gorithm based on the L1 norm to resolve sharp boundaries of strata.Because the L1 norm is dominated with the large absolute value, therelatively weak reflectivities cannot be reconstructed accurately.We should stress that the main point of this paper is to use theL1−2 norm in prestack reflectivity reconstruction taking advantageof the unbiased approximation to the L0 norm. Different from theequal contribution to the L0 norm of each nonzero element, the L1−2norm as an unbiased approximation to the L0 norm can providemore accurate and sparser solution including the relative weak re-flectivity. Wang et al. (2018) perform seismic attenuation compen-sation via initially solving sparse reflectivity series and thenconverting it into the seismic record, rather than directly compen-sating the seismic record. They study the computational efficiencyand convergence of the L1−2 minimization. The stability and thewavelet independence of the proposed method have been demon-strated through synthetic tests. In this paper, we mainly study theability to estimate the thin beds and the weak reflection interfaceswith L1−2 minimization. It can be considered an extension ofthe L1−2 minimization in Wang et al. (2018) and the sparsityreconstruction of Zhang et al. (2013). As shown in Figure 3, theoverall accuracy of L1−2 and L1 minimizations is slightly degradedin the presence of noise. The amplitude of the results with L1 min-imization can hardly reflect the true situations, and the relativelyweak reflectivities may be cut away. However, L1−2 minimizationcan accurately reconstruct the location of all reflectivities. In addi-tion, we also introduce the f-x predictive filtering, GLI algorithm,and Bayesian method for a more stable inversion results. The f-x
predictive filtering is introduced to guarantee the lateral continuityof the location and the amplitude of the reflectivity series.
CONCLUSION
The three-parameter prestack AVO inversion scheme discussed isformulated with the L1−2 minimization, the GLI combined with thef-x predictive filtering and the AVO inversion based on the Baye-sian inference framework. First, we invert the reflectivity series withthe L1−2 norm, which is solved with the DCA and the ADMM toobtain a more accurate solution. Second, we combine the GLI andf-x predictive filtering to improve the lateral continuity of the EIcorresponding to different angles. We calculate the three parametersof P-wave velocity, S-wave velocity, and density based on the Baye-sian inference framework. The advantage of our method is that wetake the following factors into account: the sparsity of the reflec-tivity, the lateral continuity of the subsurface parameters, and theprobability distribution of the parameters to be inverted. The inver-sion tests with synthetic data demonstrate that the inverted resultsare in good agreement with the true models. The inversion test withreal seismic data also shows that the reconstructed velocities anddensity fit well with the well log measurements and the layer delin-eation is improved, which provides additional information for sub-sequent interpretation.
ACKNOWLEDGMENTS
This work was supported in part by the National Key Science andTechnology Program (grant no. 2016ZX05010-001), the NationalNatural Science Foundation of China (grant no. 41630314), andthe New Geophysical Technology and Method Project of Deep andUnconventional Seismic of CNPC (grant no. 2016A-3302), and Sci-entific Research and Development of CNPC (grant no. 2017D-5006-16).
DATA AND MATERIALS AVAILABILITY
Data associated with this research are confidential and cannot bereleased.
REFERENCES
Aki, K., and P. G. Richards, 1980, Quantitative seismology: Theory andmethods: W. H. Freeman.
Alemie, W., and M. D. Sacchi, 2011, High-resolution three-term AVOinversion by means of a Trivariate Cauchy probability distribution: Geo-physics, 76, no. 3, R43–R55, doi: 10.1190/1.3554627.
Beck, A., and M. Teboulle, 2009, A fast iterative shrinkage-thresholdingalgorithm for linear inverse problems: SIAM Journal on Imaging Scien-ces, 2, 183–202, doi: 10.1137/080716542.
Boyd, S., N. Parikh, E. Chu, B. Peleato, and J. Eckstein, 2011, Distributedoptimization and statistical learning via the alternating direction methodof multipliers: Foundations and Trends in Machine Learning, 3, 1–122,doi: 10.1561/2200000016.
Brezinski, C., G. Rodriguez, and S. Seatzu, 2008, Error estimates for linearsystems with applications to regularization: Numerical Algorithms, 49,85–104, doi: 10.1007/s11075-008-9163-1.
Buland, A., and H. Omre, 2003, Bayesian linearized AVO inversion: Geo-physics, 68, 185–198, doi: 10.1190/1.1543206.
Canales, L., 1984, Random noise reduction: 54th Annual InternationalMeeting, SEG, Expanded Abstracts, 525–527, doi: 10.1190/1.1894168.
Chai, X., S. Wang, S. Yuan, J. Zhao, L. Sun, and X. Wei, 2014, Sparse re-flectivity inversion for nonstationary seismic data: Geophysics, 79, no. 3,V93–V105, doi: 10.1190/geo2013-0313.1.
Chartrand, R., and W. Yin, 2008, Variable selection via nonconcave penal-ized likelihood and its oracle properties: IEEE International Conferenceon Acoustics, 3869–3872.
Table 5. The correlation coefficient between the well log andthe inverted elastic parameters with L1−2 minimization andthe model constraint from the field data.
Constraint P-wave velocity S-wave velocity Density
Model constrained 0.8237 0.8184 0.7923
L1−2 minimization 0.8269 0.8257 0.8094
2200 2400 2600
600
650
700
750
800
850
900
950
1000
Tim
e (m
s)
(kg/m3)
2000 4000
600
650
700
750
800
850
900
950
1000
VP (m/s)
1000 2000
600
650
700
750
800
850
900
950
1000
VS (m/s)
Figure 12. Inverted density, VP and VS (blue lines), well log den-sity, VP and VS (red lines), and low-pass-filtered results of the welllogs (black lines).
L1-2 minimization for prestack inversion R765
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/
Chen, Y., and J. Ma, 2014, Random noise attenuation by f-x empirical-modedecomposition predictive filtering: Geophysics, 79, no. 3, V81–V91, doi:10.1190/geo2013-0080.1.
Clapp, R. G., B. Biondi, and J. F. Claerbout, 2004, Incorporating geologicinformation into reflection tomography: Geophysics, 69, 533–546, doi:10.1190/1.1707073.
Connolly, P., 1998, Calibration and inversion of non-zero offset seismic: 68thAnnual International Meeting, SEG, Expanded Abstracts, 182–184, doi: 10.1190/1.1820288.
Connolly, P., 1999, Elastic impedance: The Leading Edge, 18, 438–452, doi:10.1190/1.1438307.
Connolly, P., and M. Hughes, 2016, Stochastic inversion by matching tolarge numbers of pseudo-wells: Geophysics, 81, no. 2, M7–M22, doi:10.1190/geo2015-0348.1.
Delprat-Jannaud, F., and P. Lailly, 1992, What information on the earth modeldo reflection travel time provide?: Journal of Geophysical Research, 97,19827–19844, doi: 10.1029/92JB01739.
Donoho, D. L., 2006, Compressed sensing: IEEE Transactions on Informa-tion Theory, 52, 1289–1306, doi: 10.1109/TIT.2006.871582.
Elad, M., 2010, Sparse and redundant representations: Springer.Esser, E., Y. Lou, and J. Xin, 2013, A method for finding structured sparse
solutions to nonnegative least squares problems with applications: SIAMJournal on Imaging Sciences, 6, 2010–2046, doi: 10.1137/13090540X.
Fan, J., and R. Li, 2001, Variable selection via nonconcave penalized like-lihood and its oracle properties: Journal of the American Statistical Asso-ciation, 96, 1348–1360, doi: 10.1198/016214501753382273.
Goldstein, T., and S. Osher, 2009, The split Bregman method for L1-regu-larized problems: SIAM Journal on Imaging Science, 2, 323–343, doi: 10.1137/080725891.
González, E., T. Mukerji, and G. Mavko, 2008, Seismic inversion combiningrock physics and multiple-point geostaticstics: Geophysics, 73, no. 1,R11–R21, doi: 10.1190/1.2803748.
Hale, E. T., W. Yin, and Y. Zhang, 2008, Fixed-point continuation forl1-minimization: Methodology and convergence: SIAM Journal onOptimization, 19, 1107–1130.
Hamid, H., and A. Pidlisecky, 2015, Multitrace impedance inversion withlateral constraints: Geophysics, 80, no. 6, M101–M111, doi: 10.1190/geo2014-0546.1.
Hamid, H., A. Pidlisecky, and L. Lines, 2018, Prestack structurallyconstrained impedance inversion: Geophysics, 83, no. 2, R89–R103,doi: 10.1190/geo2016-0703.1.
Hansen, T., K. Cordua, and K. Mosegaard, 2012, Inverse problems with non-trivial priors: Efficient solution through sequential Gibbs sampling: Com-putational Geoscience, 16, 593–611, doi: 10.1007/s10596-011-9271-1.
Huang, W., R. Wang, Y. Chen, H. Li, and S. Gan, 2016, Damped multichan-nel singular spectrum analysis for 3D random noise attenuation: Geophys-ics, 81, no. 4, V261–V270, doi: 10.1190/geo2015-0264.1.
Huang, W., R. Wang, X. Gong, and Y. Chen, 2017a, Iterative deblendingof simultaneous-source seismic data with structuring median constraint:IEEE Geoscience and Remote Sensing Letters, 15, 58–62, doi: 10.1109/LGRS.2017.2772857.
Huang, W., R. Wang, H. Li, and Y. Chen, 2017b, Unveiling the signals fromextremely noisy microseismic data for high-resolution hydraulic fracturingmonitoring: Scientific Reports, 7, 1–16, doi: 10.1038/s41598-017-09711-2.
Huo, G., Q. Du, and X. Wang, 2017, AVO inversion constrained simulta-neously in vertical and lateral directions: Chinese Journal of Geophysics,60, 271–282, doi: 10.6038/cjg20170122.
Jafarpour, B., V. Goyal, D. B. McLaughlin, and W. Freeman, 2009, Trans-form-domain sparsity regularization for inverse problems in geosciences:Geophysics, 74, no. 5, R69–R83, doi: 10.1190/1.3157250.
Karimi, O., H. Omre, and M. Mohammadzadeh, 2010, Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material proper-ties: Geophysics, 75, no. 1, R1–R11, doi: 10.1190/1.3299291.
Karimi, P., 2015, Structure-constrained relative acoustic impedance usingstratigraphic coordinates: Geophysics, 80, no. 3, A63–A67, doi: 10.1190/geo2014-0439.1.
Liu, T., and T. K. Pong, 2017, Further properties of the forward-backwardenvelope with applications to difference-of-convex programming: Compu-tational Optimization and Applications, 67, 489–520, doi: 10.1007/s10589-017-9900-2.
Lou, Y., S. Osher, and J. Xin, 2015, Computational aspects of constrainedL1-L2 minimization for compressive sensing: Journal of Infectious Dis-eases, 198, 1327–1333, doi: 10.1007/978-3-319-18161-5_15.
Lou, Y., and M. Yan, 2016, Fast l1-l2 minimization via a proximal operator:Journal of Scientific Computing, 74, 767–785, doi: 10.1007/s10915-017-0463-2.
Ma, M., S. Wang, and S. Yuan, 2017b, Multichannel spatially correlatedreflectivity inversion using block sparse Bayesian learning: Geophysics,82, no. 4, V191–V199, doi: 10.1190/GEO2016-0366.1.
Ma, T.-H., Y. Lou, and T.-Z. Huang, 2017a, Truncated l1-2 models for sparserecovery and rank minimization: SIAM Journal on Imaging Sciences, 10,1346–1380, doi: 10.1137/16M1098929.
Mallick, S., 2001, AVO and elastic impedance: The Leading Edge, 20,1094–1104, doi: 10.1190/1.1487239.
Naghizadeh, M., and M. Sacchi, 2012, Multicomponent f-x seismic randomnoise attenuation via vector autoregressive operators: Geophysics, 77,no. 2, V91–V99, doi: 10.1190/geo2011-0198.1.
Pérez, D. O., D. R. Velis, and M. D. Sacchi, 2013, High-resolution prestackseismic inversion using a hybrid FISTA least-squares strategy: Geophys-ics, 78, no. 5, R185–195.
Porsani, M. J., 1999, Seismic trace interpolation using half-step predictionfilters: Geophysics, 64, 1461–1467.
Sacchi, M. D., and T. J. Ulrych, 1995, High-resolution velocity gathers andoffset space reconstruction: Geophysics, 60, 1169–1177.
She, B., Y. Wang, J. Liang, Z. Liu, C. Song, and G. Hu, 2018, A data-drivenamplitude variation with offset inversion method via learned dictionariesand sparse representation: Geophysics, 83, no. 6, R725–R748.
Siri, M. H., and C. V. Deutsch, 2018, Multivariate stochastic seismic inver-sion with adaptive sampling: Geophysics, 83, no. 5, R429–R448.
Spitz, S., 1991, Seismic trace interpolation in the F-X domain: Geophysics,56, 785–794. doi: 10.1190/1.1443096.
Tao, P. D., and L. T. H. An, 1997, Convex analysis approach to D. C.programming: Theory, algorithms and applications: Acta MathematicaVietnamica, 22, 289–355.
Tao, P. D., and L. T. H. An, 1998, A DC optimization algorithm for solvingthe trust-region subproblem: SIAM Journal on Optimization, 8, 476–505,doi: 10.1137/S1052623494274313.
Tikhonov, A. N., 1963, Solution of incorrectly formulated problems and theregularization method: Soviet Mathematical Doklady, 4, 1035–1038.
Tikhonov, A. N., and V. Glasko, 1965, Use of the regularization method innonlinear problems: USSR Computational Mathematics and Mathemati-cal Physics, 5, 93–107, doi: 10.1016/0041-5553(65)90150-3.
Varela, O. J., C. Torres-Verdín, and M. K. Sen, 2006, Enforcing smoothnessand assessing uncertainty in non-linear one-dimensional prestack seismicinversion: Geophysical Prospecting, 54, 239–259, doi: 10.1111/j.1365-2478.2006.00531.x.
Wang, J., and M. D. Sacchi, 2007, High-resolution wave-equation ampli-tude-variation-with-ray-parameter (AVP) imaging with sparseness con-straints: Geophysics, 72, no. 1, S11–S18, doi: 10.1190/1.2387139.
Wang, Y., 2002, Seismic trace interpolation in the f-x-y domain: Geophysics,67, 1232–1239.
Wang, Y., H. Zhou, X. Ma, and Y. Chen, 2018, L1-2 minimization for exactand stable seismic attenuation compensation: Geophysical JournalInternational, 213, 1629–1646.
Yilmaz, O., 2001, Seismic data analysis: Processing, inversion, and interpre-tation of seismic data: SEG.
Yin, P., Y. Lou, Q. He, and J. Xin, 2015, Minimization of l1-2 for compressedsensing: SIAM Journal on Scientific Computing, 37, A536–A563, doi: 10.1137/140952363.
Yuan, S., S. Wang, C. Luo, and Y. He, 2015, Simultaneous multitrace in-version with transform-domain sparsity promotion: Geophysics, 80, no. 2,R71–R80.
Zhang, R., and J. Castagna, 2011, Seismic sparse-layer reflectivity inversionusing basis pursuit decomposition: Geophysics, 76, no. 6, R147–R158,doi: 10.1190/geo2011-0103.1.
Zhang, R., M. K. Sen, and S. Srinivasan, 2013, A prestack basis pursuit seismicinversion: Geophysics, 78, no. 1, R1–R11, doi: 10.1190/geo2011-0502.1.
Zhang, T., 2010, Analysis of multi-stage convex relaxation for sparse regu-larization: Journal of Machine Learning Research, 11, 1081–1107, doi: 10.1145/1756006.1756041.
R766 Wang et al.
Dow
nloa
ded
12/0
4/19
to 6
1.50
.138
.153
. Red
istr
ibut
ion
subj
ect t
o SE
G li
cens
e or
cop
yrig
ht; s
ee T
erm
s of
Use
at h
ttp://
libra
ry.s
eg.o
rg/