We Two Seismic Interference Attenuation Methods Based on Automatic Detection of Seismic Interference Moveout

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1 We Two Sesmc Interference Attenuaton Methods Based on Automatc Detecton of Sesmc Interference Moveout S. Jansen* (Unversty of Oslo), T. Elboth (CGG) & C. Sanchs (CGG) SUMMARY The need for effcent sesmc nterference () attenuaton solutons s essental to reduce tme-sharng that s very costly. We present two attenuaton methods, both based on the automatc vector feld estmaton of the local moveout. The frst method proposes an automatc determnaton of tau-p mute parameters, whle the second one uses lne ntegral convoluton and the estmated vector feld to separate from reflecton hyperbolas. Because of the automatc detecton of attrbutes, they requre lttle user nteracton and the processng tme s short. Both methods are tested on real shot gathers and successfully attenuate the whle preservng the reflectons. 75 th EAGE Conference & Exhbton ncorporatng SPE EUROPEC 213

2 Introducton A frequently encountered problem n sesmc data s the presence of varous types of coherent nose and n partcular, marne sesmc nterference (). s encountered when several sesmc vessels operate smultaneously n close proxmty. If the ampltude and/or moveout of the exceed certan lmts, then the operatng vessels normally have to commence tme-sharng. Unfortunately, ths s costly and can also lead to sgnfcant delays n survey completon. Operatng vessels have been known to commence tme-sharng at dstances up to 1 km. However, as a rough gudelne, are often seen as problematc when vessels are closer than 4 km, whch s often the case n busy summer seasons offshore Northern Europe and n the Gulf of Mexco. Generally, and coherent nose removal algorthms can be classfed nto two groups. The frst group s based on the realzaton that coherent energy n the shot doman often appears as random nose n other domans (Larner et al. 1983). Random nose attenuaton tools lke f-x predcton flterng (Canales 1984) or thresholdng methods (Elboth et al. 21) are then appled to the data, before t s sorted back to the shot doman. Ths attenuaton approach has been used by Akbulut et al. (1984) and more recently by Gulunay (28). However, n the case where shot-pont ntervals of nterferng vessels are synchronzed, appears at the same arrval tme n consecutve shot gathers. Therefore, cannot be randomzed when sorted to other domans and the whole approach breaks down. Another lmtaton concerns methods that nvolve ampltude thresholdng. They requre the ampltudes to be larger than the reflectons ampltudes, whch s not always the case. The second group of removal tools s based on nose modellng and subtracton. An early example s Krln and Done (199) that uses sngular value decomposton to dentfy coherent events n the data and then subtract them. Fnally, more recent approaches estmate the source poston and/or frng tmes of the. The are then modelled and subtracted lke n Brttan et al. (28). The success of these methods strongly depends on ther ablty to buld up an accurate model of the. Ths artcle presents a method for an automatc estmaton of moveout, whch s then used as an ntal step n two technques. The frst technque conssts of the automatc generaton of a tau-p mute, whle the second technque uses the lne ntegral convoluton method and an estmate of vector felds to separate from reflecton hyperbolas. moveout estmaton In a commercal settng where a processor has to go through ggabytes of data, the user nteracton needs to be mnmzed. We present here a method that works on ndvdual shot gathers and estmates the moveout wthout user nteracton. Ths step wll be used further by both technques presented later. We start by dvdng the shot gather nto J space wndows where each wndow contans all the samples and the same number of traces. We scan each space wndow by usng a small sldng wndow n tme and space. In each sldng wndow, two neghbor traces are cross-correlated to obtan a vector v = (v x v t ) T that represents the local moveout n the data. The trace spacng v x s always equal to 1 sample whle v t s the tme delay n ms estmated by cross-correlaton. For most wndows, v gves the local moveout of the reflecton hyperbolas. However, f s present, v may also represent the moveout of the. A total of K vectors v are then estmated for each space wndow, as Fgure 1 Estmaton of moveout shown n Fg.1. The challenge now s to dentfy from the K vectors v vectors v n each sldng wndow. two dfferent vector felds. One should ndcate the moveout of the whle the other should ndcate the 75 th EAGE Conference & Exhbton ncorporatng SPE EUROPEC 213

3 moveout of the reflecton hyperbolas. The reflectons moveout vector feld can be estmated by usng knowledge of survey geometry and pcked subsurface veloctes. The moveout of the vector feld, however, has to be dentfed. usually arrves farly lnear through a shot gather compared to the reflecton hyperbolas. Usng ths observaton, we look at the dstrbuton of the K moveout components v t of vectors v for each space wndow. Our assumpton s that the vectors ndcatve of, comng from a sgnfcant dstance, have a rather constant moveout n space and tme whle vectors ndcatve of reflecton hyperbolas have more varatons. In partcular, v t s smaller at near offset and ncreases wth offset. Therefore, we expect the moveout to be the one wth mnmum relatve standard devaton. For each moveout value of the dstrbuton, v t,, the standard devaton v, of the number of occurrences n,j for j=1,,j, s calculated over the J representatons avalable. The relatve standard devaton s then the σ normalzed standard devaton gven by v, σ = 1. Let us consder a real data example max ( n, j ) j shown n Fg.3a wth a sngle nterferng vessel. Ths shot gather has been dvded nto J=8 space wndows, contanng 81 traces each. The dstrbuton of v t components for each space wndow s shown n Fg.2 (left), whle Fg.2 (rght) shows the relatve standard devaton for each moveout value. The mnmum s acheved at moveout pˆ arg mn σ 18 ms. To valdate ths estmate, we transform ths dataset to the tau-p doman and locate the moveout at approxmately 185 ms (Fg. 3c). Ths value s close to our moveout estmate pˆ whch s consdered to be suffcently accurate. Fnally, we obtan an vector feld n the shot gather V by keepng only the vectors whose estmated moveout s wthn pˆ Δp nterval. In Fg.3c, Δp = 3 ms. The 2 nd method requres a dense representaton of vector feld. Mssng vectors are then nterpolated and averaged over the exstng ones to obtan one vector per sample locaton. We denote ths dense vector feld by V. v t, Fgure 2 (Left) Dstrbuton of the moveout component v t for J=8 equally szed wndows. (Rght) Relatve standard devaton for each moveout value. Method 1: Automatc tau-p mute Our frst removal method conssts of the generaton of a tau-p mute usng the moveout estmate pˆ. However, to refne the tau-p mute, we also estmate the central arrval tme of the n the shot domanτˆ, thereby provdng us wthτˆ n tau-p doman. To do so, for each sample of the frst trace (t 1 ) T, we calculate the total dstance (n samples) to all the vectors (t x ) T of vector feld V : d = tot t' ) d ( where d 2 2 = ( x 1) + ( t t'). Thereafter, an estmate of τˆ s obtaned at 75 th EAGE Conference & Exhbton ncorporatng SPE EUROPEC 213

4 mnmum dstance ˆ τ arg mn d. The determnaton of τˆ for our real dataset s shown n Fg. 3b. t ' tot Thus, the estmaton of τˆ and pˆ defnes a farly accurate tau-p mute shown n Fg. 3c (green wndow). Snce a forward-nverse tau-p transform s not consdered as sgnal preservng, we choose to mute out everythng but the n tau-p doman and then perform an nverse tau-p transform. Fnally, we adaptvely subtract the from the orgnal shot gather, provdng an attenuated shot gather (Fg. 3d). The dfference plot n Fg. 3e shows a good preservaton of reflecton data. Fgure 3 a) A shot gather contamnated by, (b) the correspondng dstance-plot, (c) tau-p representaton and the localzed mute, (d) shot gather after attenuaton and (e) dfference plot. Method 2: Lne ntegral convoluton The second method uses a method referred to as lne ntegral convoluton (Cabral and Leedom 1993). The lne ntegral convoluton (LIC) s an magng technque that uses texture advecton to densely vsualze vector felds and render mages wth a large amount of detals. Compared wth smpler ntegraton-lke technques, where one follows the flow vector at each pont to produce a lne, t has the advantage of producng a whole mage at every step. We have adapted the LIC technque for removal, takng advantage of the fact that can be expected to locally be rather coherent. For every sample of the nput gather (t, x), a local streamlne that starts at ths center sample s calculated n the forward and backward drectons for 2L+1 samples, followng the nput vector feld V. The output value n (t, x) s then the medan value of all the ampltudes along ths streamlne. Snce s assumed to be coherent along the lne ntegral, t adds up constructvely. Conversely, the reflectons hyperbolas are not coherent over the same lne ntegral and n most cases just stack out. Here, LIC flters the nput shot gather along local streamlnes defned by V to generate an estmate of the wth large amount of detals. Ths estmate s then subtracted from the nput shot gather to produce a attenuated gather. Fgure 4 shows two real shot gathers contamnated by from a sngle nterferng vessel: one wth from ahead and one wth from astern (top and bottom, left). Both are processed wth method 2 for 75 th EAGE Conference & Exhbton ncorporatng SPE EUROPEC 213

5 L=3 samples. Output gathers (mddle) are farly clean wth successfully attenuated and the dfference plots (rght) show that reflecton hyperbolas are well preserved. Dscusson and concluson We presented two effcent removal methods based on automatc moveout estmaton. Both are fully data drven and only need one shot gather to be appled. The processng tme s short and thereby, the methods have the potental to be appled n real-tme whle a sesmc survey s conducted. As arrval tme and drecton of may change from shot to shot gathers, the automatc detecton of attrbutes automatcally generates accurate mutes wth lttle nteracton from the processor. References Akbulut, K., Saeland, O-K., Farmer, P., and Curts, T. [1984] Suppresson of sesmc nterference nose on Gulf of Mexco data. SEG, Expanded Abstracts, Brttan, J., Pdsley, L., Cavaln, D., Ryder, A. and Turner, G. [28] Optmzng the removal of sesmc nterference nose. The Leadng Edge, 27, Cabral, B. and Leedom, L. C. [1993] Imagng Vector Felds Usng Lne Integral Convoluton. Proceedngs of ACM GGRAPH 1993, Canales, L. [1984] Random nose reducton. SEG, Expanded Abstracts, Elboth, T., Presterud, I. and Hermansen, D. [21] Tme-frequency sesmc data de-nosng. Geophyscal Prospectng, 58, No 3, Gulunay, N. [28] Two dfferent algorthms for sesmc nterference nose attenuaton. The Leadng Edge, 27, Krln, R.L. and Done, W.J. [199] Suppresson of coherent nose n sesmc data. US patent Larner, K., Chambers, R., Yang, M., Lynn, W., and Wa, W. [1983] Coherent nose n marne sesmc data. Geophyscs, 48, Fgure 4 Two shot gathers wth from ahead (top left) and from astern (bottom left). Shot gathers after processng (mddle) and dfference plots (rght). A streamlne example s shown n top left. 75 th EAGE Conference & Exhbton ncorporatng SPE EUROPEC 213

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