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- Volume 71, Issue 6, 2023
Geophysical Prospecting - Volume 71, Issue 6, 2023
Volume 71, Issue 6, 2023
- ISSUE INFORMATION
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- ORIGINAL ARTICLES
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Generating complete synthetic datasets for high‐resolution amplitude‐versus‐offset attributes deep learning inversion
Authors Shuai Sun, Junguang Nie, Benfeng Wang, Luanxiao Zhao, Zhiliang He, Hong Zhang, Dong Chen and Jianhua GengAbstractDeep learning has been used in seismic exploration to solve seismic inversion problems, however it requires sufficient and diverse training samples and labels to obtain satisfactory results. Insufficient training labels are a common problem since labels usually come from well‐logging data, which are limited and sparsely distributed. This can result in a trained network with poor generalizability. A novel complete synthetic dataset‐driven method utilizing convolutional neural network is presented for seismic amplitude‐versus‐offset attributes P and G inversion. Gaussian simulation sampling with physical constraints is used to generate a complete elastic parameter dataset by traversing the entire elastic parameter model space. By randomly combining elastic parameters from the full elastic parameters model space, sufficient synthetic pre‐stack amplitude‐versus‐offset gathers and attribute datasets are generated for training the convolutional neural network. Compared with the limited real data‐driven convolutional neural network, the complete synthetic dataset‐driven convolutional neural network has better generalizability. Broadband training labels improve the accuracy and resolution of the complete synthetic dataset‐driven convolutional neural network's inversion results beyond that of the conventional least‐square data‐fitting method. The complete synthetic dataset‐driven convolutional neural network is robust for processing noise‐contaminated seismic data, but if the frequency band of the labels for training the network is too wide and the signal‐to‐noise ratio of pre‐stack amplitude‐versus‐offset gathers is too low, the quality of the inversion results will reduce. The Marmousi II model and field data examples show that the novel complete synthetic dataset‐driven convolutional neural network can extract higher‐resolution amplitude‐versus‐offset attributes P and G.
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An attention‐guided convolution neural network for denoising of distributed acoustic sensing–vertical seismic profile data
Authors Yue Li, Yipan Zhang, Xintong Dong and Hongzhou WangAbstractDistributed acoustic sensing technology is a new type of signal acquisition technology, and this technology has been widely used in obtaining vertical seismic profile data in recent years. Distributed acoustic sensing technology has the advantages of high sampling density and strong tolerance to a harsh environment. However, in the real distributed acoustic sensing–vertical seismic profile data, the effective signal will be annihilated by various noises, which significantly complicates data analysis and interpretation. Deep learning approaches have developed rapidly in the noise suppression field in recent years. In order to eliminate the noise in distributed acoustic sensing–vertical seismic profile data, based on traditional convolution neural network, we add channel attention and spatial attention modules to the network to enhance the feature extraction ability of the network and use extended convolution to increase the receptive field to build a more efficient denoising model. In addition, we use different indicators to evaluate the quality of denoising, including signal‐to‐noise ratio, mean absolute error, kurtosis and skewness. The experimental results show that our method can recover the uplink wave field and downlink wave field, remove horizontal noise, optical system noise, random noise and other noises and improve the overall signal‐to‐noise ratio before and after denoising by 22 dB, reflecting a good ability of denoising and recovering effective signals.
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Upscaling method based on sedimentary cycle prior and its application to well‐to‐seismic tie
Authors Yajun Tian, Alexey Stovas and Jinghuai GaoAbstractA well‐to‐seismic tie is a typical upscaling problem. The traditional well‐to‐seismic tie methods often use the velocity log directly to tie well logs to seismic data without considering the upscaling problem led by the velocity dispersion due to thin beds. We propose an upscaling method based on the Backus average method to upscale the well‐log velocity to seismic velocity and apply it to a well‐to‐seismic tie problem. For Backus average method, choosing an appropriate average window is a crucial issue. We introduce a synchrosqueezing optimal basic wavelet transform to calculate an average window. The synchrosqueezing optimal basic wavelet transform is a time–frequency representation method, which can extract the sedimentary cycle information from seismic data. Considering the relationship between the sedimentary cycle and the thin‐bed thickness, we propose a workflow to estimate the instantaneous thin‐bed thickness based on synchrosqueezing optimal basic wavelet transform and determine the window length of the Backus average at each depth point. Then, the Backus average is used to estimate the effective velocity, which is further used to calculate the initial time–depth function and transfer the reflection coefficients from the depth domain to the time domain. Choosing an appropriate seismic wavelet is another fundamental problem for the well‐to‐seismic tie process. This paper introduces an alternating iterative deep neural network‐based method to estimate the seismic wavelet. Thus realistic synthetic seismograms can be computed by convolving the estimated seismic wavelet and the reflection coefficient, which can be stretched or squeezed to match the near‐well seismic trace. We show that the proposed workflow effectively reduces the errors in a well‐to‐seismic tie procedure caused by velocity dispersion of thin beds.
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Time‐lapse PS‐wave time‐shifts and quantitative pressure‐saturation discrimination applications
Authors Ali Tura, James Simmons, Andrea Damasceno and Marihelen HeldAbstractWe show that time‐lapse (4D) time‐shifts from PS‐wave data are valuable as they are very sensitive to pressure changes, whereas 4D time‐shifts from PP‐wave data are sensitive to both pressure and saturation changes. This gives another opportunity from using 4D amplitudes alone to understand and discriminate areas of pressure changes from areas of saturation changes in field data. 4D PS time‐shifts sense transmission related changes taking into consideration propagation through the altered medium, whereas amplitude changes are reflection or backscattered signals from the boundary. Therefore, these two attributes will be complimentary in 4D interpretation. Rock‐physics‐based time‐shift cross‐plots are generated to analyse how pressure and saturation changes in a reservoir impact PP‐ and PS‐wave time‐shifts. The developments of 4D time‐shifts are shown fixing the reservoir properties and varying reservoir layer thickness. Synthetic seismic data from a wedge is modelled to understand differences between 4D amplitude and time‐shift changes and the value of PS time‐shifts. Cases with pressure changes only and saturation changes only are also analysed with the wedge model. The method is next applied to two 4D field data sets. One is in the North Sea and the other offshore Brazil. In these field data sets, with different levels of noise in the 4D data, we see interpretable results from PS‐wave time‐shifts that can help in 4D signal understanding and improve reservoir characterization and monitoring practices. We believe that with modern acquisition and processing, this technology is realizable and provides quantitative and accurate 4D interpretations. It is shown that the use of PS‐wave data can lead to a larger chance of success in placing development wells.
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Deep decomposition learning for reflectivity inversion
Authors Kristian Torres and Mauricio D SacchiAbstractWe report a combination of classical regularization theory with a null space neural network approach based on deep decomposition learning, paying particular attention to the solution of one ubiquitous problem in seismic exploration: the recovery of full‐band reflectivity from band‐limited seismic traces. The method extends the popular post‐processing approach by learning how to improve an initial reconstruction with estimated missing components from the null space of the forward operator, which in our case, are the missing frequency components of the reflectivity. We integrate the null space element prediction to act in conjunction with convolutional neural network based denoising and a data‐consistent algorithm. The proposed framework honours the input measurements while enforcing generalization. Numerical experiments on synthetic and real datasets show that the proposed method naturally enforces a high‐resolution prediction consistent with the low‐resolution input seismic traces. We compare its performance with state‐of‐the‐art thin‐bed reflectivity estimation methods.
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Simultaneous random plus outlier attenuation and deblending based on L1‐norm misfit function
Authors Yaoguang Sun, Siyuan Cao, Siyuan Chen, Liuqing Yang and Yuxin SuAbstractDeblending technology aims at separating simultaneous source seismic data between adjacent shots by allowing multiple sources to be shot simultaneously. Conventional deblending methods based on sparse inversion assume that the primary source is coherent, and the secondary source is randomized. The L2‐norm minimization constraint can effectively minimize the Gaussian random noise while deblending in the transform domain. Nonetheless, the L2‐norm misfit function is highly sensitive to outliers, negatively influencing the deblending performance. An effective optimization strategy is developed with deblending in pre‐stack seismic data to eliminate outliers and enhance deblending accuracy. For this reason, we introduce the deblending algorithm in the morphological component analysis framework, modify the L2‐norm misfit function to outlier‐robust L1‐norm and provide the corresponding derivation in detail via the alternating direction method of multipliers. Applications to synthetic and field data sets prove the improved robustness and efficiency of our deblending method.
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Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach
Authors Jincheng Xu and Jianfeng ZhangAbstractAn accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.
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Monitoring fluid saturation in reservoirs using time‐lapse full‐waveform inversion
Authors Amir Mardan, Bernard Giroux, Gabriel Fabien‐Ouellet and Mohammad Reza SaberiAbstractMonitoring the rock physics properties of the subsurface is of great importance for reservoir management. For either oil and gas applications or CO2 storage, seismic data are a valuable source of information for tracking changes in elastic properties which can be related to fluids saturation and pressure changes within the reservoir. Changes in elastic properties can be estimated with time‐lapse full‐waveform inversion. Monitoring rock physics properties, such as saturation, with time‐lapse full‐waveform inversion is usually a two‐step process: first, elastic properties are estimated with full‐waveform inversion, then the rock physics properties are estimated with rock physics inversion. However, multiparameter time‐lapse full‐waveform inversion is prone to crosstalk between parameter classes across different vintages. This leads to leakage from one parameter class to another, which, in turn, can introduce large errors in the estimated rock physics parameters. To avoid inaccuracies caused by crosstalk and the two‐step inversion strategy, we reformulate time‐lapse full‐waveform inversion to estimate directly the changes in the rock physics properties. Using Gassmann's model, we adopt a new parameterization containing porosity, clay content and water saturation. In the context of reservoir monitoring, changes are assumed to be induced by fluid substitution only. The porosity and clay content can thus be kept constant during time‐lapse inversion. We compare this parameterization with the usual density–velocity parameterization for different benchmark models. Results indicate that the proposed parameterization eliminates crosstalk between parameters of different vintages, leading to a more accurate estimation of saturation changes. We also show that using the parameterization based on porosity, clay content and water saturation, the elastic changes can be monitored more accurately.
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Tight sandstone gas reservoir characterization via amplitude‐versus‐offset attributes
Authors Yajun Tian, Alexey Stovas, Jinghuai Gao, Wenhao Xu and Jiaxin YuAbstractThe low porosity and low permeability of tight sandstone pose considerable challenges on characterizing the reservoir fluid. Moreover, the geophysical characteristics of the tight sandstone are usually similar to the surrounding rocks, leading to small impedance contrast, compounding the difficulty of reservoir parameters inversion. Amplitude‐versus‐offset technology is an important tool to invert the reservoir parameters from seismic data. The existing amplitude‐versus‐offset inversion methods are challenged to predict the tight sandstone reservoir parameters for the reasons above. In this paper, we propose a new workflow based on amplitude‐versus‐offset attributes to characterize the tight sandstone gas reservoirs in the Xihu Depression, East China Sea. The proposed workflow utilizes the Backus average method and Gassmann's equation to construct the rock physics model. Based on the analysis of the anisotropic model, we introduce Rüger's anisotropic equation for amplitude‐versus‐offset forward modelling. We compare the amplitude‐versus‐offset gathers with the real data and adjust the reservoir parameters. Through the sensitivity analysis, we select the gas saturation and porosity as the inversion parameters. The relationship between the reservoir parameters and amplitude‐versus‐offset attributes is established by fitting the fifth‐order polynomials. We use Shuey's equation and the least squares inversion method to calculate the amplitude‐versus‐offset attributes (intercept and gradient). For real data, we introduce the Toeplitz‐sparse matrix factorization method to calculate the reflectivity of the amplitude‐versus‐offset gathers. Finally, we proposed a multi‐well joint inversion approach to invert the gas saturation and porosity. The application results demonstrate the effectiveness of the proposed workflow for the characterization of the gas reservoir in tight sandstone for the describedcase.
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Ground penetrating radar investigation and georeferencing without global satellite navigation systems: The case history of the amphitheatre of Lecce, Italy
Authors Raffaele Persico and Giuseppe MuciAbstractIn this work, we present a case history relative to ground penetrating radar measurements performed close to the Roman amphitheatre of Lecce, Italy. We have performed a classical data elaboration with focusing of the data and slicing putting into evidence hidden details of the structure of the monument. It will be shown that the interpretation of the ground penetrating radar data is meaningfully helped by the consultation of ancient documents, which makes the final result multidisciplinary. Finally, we have georeferenced the data matching the shape of the depth slices with the shape of the investigated roads. In fact, we did not have at disposal any differential global satellite navigation systems. Indeed, this can be a method exploitable in cases when satellite data are not available, either because the area is shadowed or because the user does not have a differential global satellite navigation system. The proposed geographical matching is achieved by means of the matching between the shape of the slices and those of the physical obstacles present in the field. Therefore, it is essentially based on a continuous of points, and so it is probably more precise than a method based on the only vertexes. In particular, the proposed procedure does not require any deformation of the shape of the slice.
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An efficient 2.5‐D forward algorithm based on the spectral element method for airborne transient electromagnetics data
Authors Zhen Ke, Lihua Liu, Lin Huang, Ziyang Zhao, Yicai Ji, Xiaojun Liu and Guangyou FangAbstractThe airborne transient electromagnetics method has been widely used in geophysical exploration in recent years, but it still faces challenges in balancing the accuracy and efficiency of electromagnetic data interpretation. As far as forward modelling is concerned, the higher the dimension of the geophysical model, the higher the accuracy of data interpretation, but, correspondingly, the more computing resources need to be consumed, which will greatly reduce investigation efficiency and practicability. In this paper, the spectral element method is first introduced for solving the 2.5‐dimensional forward modelling of the airborne transient electromagnetic system, which has a smaller computing scale than three‐dimensional modelling and is closer to the actual geological structure than either one‐ or two‐dimensional modelling. In the forward algorithm, a non‐uniform quadrilateral structured mesh is adopted to simplify the computing scale, and the Talbot algorithm rather than Gaver–Stefest algorithm is applied to the inverse Laplace transform to improve the numerical precision of this conversion. Moreover, we use parallel computing technology to improve the algorithm efficiency while keeping satisfactory accuracy. The study shows that, whether a low‐resistivity or high‐resistivity layered geophysical model, the numerical solutions of the proposed spectral element method forward algorithm agree well with the analytical solutions of the corresponding models; furthermore, the key factors affecting the accuracy of the numerical solution are analysed by experiments. Finally, we successfully applied it to the 2.5‐dimensional geoelectric model simulation.
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Volumes & issues
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Volume 72 (2023 - 2024)
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Volume 71 (2022 - 2023)
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Volume 70 (2021 - 2022)
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Volume 69 (2021)
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Volume 68 (2020)
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Volume 67 (2019)
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Volume 66 (2018)
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Volume 65 (2017)
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Volume 64 (2015 - 2016)
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Volume 63 (2015)
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Volume 62 (2014)
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Volume 61 (2013)
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Volume 60 (2012)
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Volume 59 (2011)
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Volume 58 (2010)
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Volume 57 (2009)
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Volume 56 (2008)
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Volume 55 (2007)
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Volume 54 (2006)
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Volume 53 (2005)
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Volume 52 (2004)
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Volume 51 (2003)
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Volume 50 (2002)
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Volume 49 (2001)
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Volume 48 (2000)
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Volume 47 (1999)
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Volume 46 (1998)
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Volume 45 (1997)
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Volume 44 (1996)
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Volume 43 (1995)
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Volume 42 (1994)
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Volume 41 (1993)
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Volume 40 (1992)
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Volume 39 (1991)
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Volume 38 (1990)
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Volume 37 (1989)
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Volume 36 (1988)
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Volume 35 (1987)
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Volume 34 (1986)
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Volume 33 (1985)
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Volume 32 (1984)
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Volume 31 (1983)
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Volume 30 (1982)
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Volume 29 (1981)
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Volume 28 (1980)
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Volume 27 (1979)
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Volume 26 (1978)
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Volume 25 (1977)
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Volume 24 (1976)
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Volume 23 (1975)
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Volume 22 (1974)
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Volume 21 (1973)
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Volume 20 (1972)
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Volume 19 (1971)
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Volume 18 (1970)
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Volume 17 (1969)
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Volume 16 (1968)
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Volume 15 (1967)
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Volume 14 (1966)
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Volume 13 (1965)
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Volume 12 (1964)
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Volume 11 (1963)
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Volume 10 (1962)
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Volume 9 (1961)
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Volume 8 (1960)
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Volume 7 (1959)
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Volume 6 (1958)
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Volume 5 (1957)
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Volume 4 (1956)
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Volume 3 (1955)
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Volume 2 (1954)
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Volume 1 (1953)