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- Volume 69, Issue 5, 2021
Geophysical Prospecting - Volume 69, Issue 5, 2021
Volume 69, Issue 5, 2021
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Prestack seismic data interpolation and enhancement with common‐reflection‐surface–based migration and demigration
More LessABSTRACTThe standard common‐reflection‐surface stacking method simulates high‐quality zero‐offset stacked data from multi‐coverage prestack data and, as by‐products, provides three kinematic wavefield attributes in two dimensions that can be applied to solve reflection seismic problems. One of the most significant applications of those attributes is for interpolation and enhancement of prestack data through the partial common‐reflection‐surface stack approach. Because this interpolation method is based on the stacking process, it usually introduces spurious noises that can create artefacts in the migration results. In order to overcome these limitations, a new prestack data interpolation and enhancement method is presented by applying the attributes and the common‐reflection‐surface operator based on two fundamental seismic imaging operations: the Kirchhoff‐type time migration and demigration. The diffraction curves required to apply these two fundamental operations are particular cases of the common‐reflection‐surface stack operator, which better fits the diffraction events and, consequently, is the best approximation for the Kirchhoff migration operator. From synthetic and real data, impulse responses of the migration and demigration operations are shown to demonstrate how this new approach regularizes and interpolates prestack data. A simple synthetic example shows good accuracy for reconstructed reflection events and well‐resolved conflicting dip events. The application example in real data reveals that the proposed approach provides cleaner regularized sections with better reconstructed events than the results of the well‐known partial common‐reflection‐surface stack approach. It has been successfully shown that the proposed approach is a well‐founded alternative for interpolation and denoising of seismic data and produces better preconditioned data for prestack migration.
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A gradient‐based Markov chain Monte Carlo algorithm for elastic pre‐stack inversion with data and model space reduction
More LessABSTRACTThe main challenge of Markov chain Monte Carlo sampling is to define a proposal distribution that simultaneously is a good approximation of the posterior probability while being inexpensive to manipulate. We present a gradient‐based Markov chain Monte Carlo inversion of pre‐stack seismic data in which the posterior sampling is accelerated by defining a proposal that is a local, Gaussian approximation of the posterior model, while a non‐parametric prior is assumed for the distribution of the elastic properties. The proposal is constructed from the local Hessian and gradient information of the log posterior, whereas the non‐linear, exact Zoeppritz equations constitute the forward modelling engine for the inversion procedure. Hessian and gradient information is made computationally tractable by a reduction of data and model spaces through a discrete cosine transform reparameterization. This reparameterization acts as a regularization operator in the model space, while also preserving the spatial and temporal continuity of the elastic properties in the sampled models. We test the implemented algorithm on synthetic pre‐stack inversions under different signal‐to‐noise ratios in the observed data. We also compare the results provided by the presented method when a computationally expensive (but accurate) finite‐difference scheme is used for the Jacobian computation, with those obtained when the Jacobian is derived from the linearization of the exact Zoeppritz equations. The outcomes of the proposed approach are also compared against those yielded by a gradient‐free Monte Carlo sampling and by a deterministic least‐squares inversion. Our tests demonstrate that the gradient‐based sampling reaches accurate uncertainty estimations with a much lower computational effort than the gradient‐free approach.
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Pre‐migration diffraction separation using generative adversarial networks
Authors Brydon Lowney, Ivan Lokmer, Gareth S. O'Brien and Christopher J. BeanABSTRACTDiffraction imaging is the process of separating diffraction events from the seismic wavefield and imaging them independently, highlighting subsurface discontinuities. While there are many analytic‐based methods for diffraction imaging which use kinematic, dynamic or both, properties of the diffracted wavefield, they can be slow and require parameterization. Here, we propose an image‐to‐image generative adversarial network to automatically separate diffraction events on pre‐migrated seismic data in a fraction of the time of conventional methods. To train the generative adversarial network, plane‐wave destruction was applied to a range of synthetic and real images from field data to create training data. These training data were screened and any areas where the plane‐wave destruction did not perform well, such as synclines and areas of complex dip, were removed to prevent bias in the neural network. A total of 14,132 screened images were used to train the final generative adversarial network. The trained network has been applied across several geologically distinct field datasets, including a 3D example. Here, generative adversarial network separation is shown to be comparable to a benchmark separation created with plane‐wave destruction, and up to 12 times faster. This demonstrates the clear potential in generative adversarial networks for fast and accurate diffraction separation.
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A Green's function approach to the study of effective anisotropic properties of the Barnett Shale
Authors Avradip Ghosh and Sharif MorshedABSTRACTThe purpose of this paper is to derive the Green's function of an anisotropic elastic medium and validate it with the effective stiffness tensor of Barnett Shale. We have derived the frequency‐dependent Green's function by using the spectral theorem for matrices, thus simplifying the process of computing Green's function and obtaining analytical solutions. Evaluating the inverse of the Green–Christoffel tensor is an essential part of computing the Green's function. Based on the degeneracy of the eigenvalues of the Green–Christoffel tensor, the inverse of the Green–Christoffel tensor is expressed in the form of partial fractions. Consequently, the stiffness tensors from the measurement of core samples of Barnett Shale are used to validate the Green's function. We use the generalized singular approximation method of effective medium theory to model the effective stiffness of the core samples from microstructural properties. The generalized singular approximation method also allows us to compute the theoretical stiffness tensor of the Barnett Shale for porosity variations. The behaviour of the Green's function, which reflects the behaviour of the media, is studied in the static, low‐ and high‐frequency domains and under different physical parameters. It is observed that the variations of crack‐induced porosity produce different trends in Green's function for vertically transverse isotropic and horizontally transverse isotropic media. Thus, the variation of porosity is observed to be influential in differentiating between transverse isotropic media that have inclusions. The Green's function results presented in this paper have direct applications in the construction of synthetic seismograms for unbounded transversely isotropic media.
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Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach
Authors Yang Gao, Pingqi Zhao, Guofa Li and Hao LiABSTRACTRandom noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the following steps: Firstly, we randomly mute a part of the input data of the neural network according to a certain percentage, and then the network outputs the reconstructed data influenced by this randomly mute. The objective function measures the distance between the input data and the reconstructed data. Secondly, we use the adaptive moment estimation algorithm to minimize the distance, and the network adjusts its internal parameters so that sparse representations can be captured by the multiple processing layers of the neural network. Finally, we take the same proportion of random mute on the raw seismic data which are fed to the trained neural network. Through this network, reconstruction of seismic data and attenuation of random noise are completed simultaneously. We use both synthetic and field data to testify the feasibility and applicability of the proposed method. Synthetic data experiment indicates that the proposed method achieves better denoised results than the conventional methods. Field data applications further demonstrate its superiority and practicality.
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Anisotropic diffusion filtering based on fault confidence measure and stratigraphic coherence coefficients
Authors Jing Wang, Junhua Zhang, Yong Yang and Yushan DuABSTRACTWe develop an algorithm for seismic data filtering to preserve the boundary information of faults and other geological bodies while suppressing the noise. Based on the theory of anisotropic diffusion filtering, this method explores the relationship between eigenvalues of the structure tensor and local structural features of three‐dimensional seismic images and innovatively introduces the definition of stratigraphic coherence coefficients. Then we propose a new system to design the eigenvalues of the diffusion tensor by using the fault confidence measure and stratigraphic coherence coefficients, which can control the filtering intensity of seismic data in different orientations. The value of fault confidence measure is close to 0 where there are flat continuous reflectors and the intensity of diffusion is strong. On the contrary, the diffusion is very weak within presumptive fault zones. Results of both synthetic model and real data prove that the proposed method can effectively suppress noise, preserve faults well and enhance the continuity of the reflectors, which can provide basic data with high signal‐to‐noise ratio for subsequent seismic interpretation.
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Microseismic waveform and shear‐wave splitting analysis with model data
Authors Isabel White, Matthew Bray and James SimmonsABSTRACTAccurate and precise estimation of microseismic event location in anisotropic reservoirs is challenging with borehole acquisition, due to limited azimuthal and inclination coverage. Inclusion of shear‐wave splitting information from microseismic data can improve estimates of event directivity, source location and the degree of reservoir anisotropy compared with using P‐waves alone. Additionally, S‐waves typically have a higher signal‐to‐noise ratio than P‐waves, due to higher amplitudes, decreasing the overall azimuthal and depth uncertainty in comparison with P‐wave only location methodologies. We present a joint P‐ and S‐wave hodogram workflow to reduce azimuthal uncertainty and estimate shear‐wave splitting. In this paper, finite‐difference waveform synthetics verify the workflow and illustrate interference effects associated with reflections and head‐waves. Multiple shear‐wave splitting techniques are applied to the synthetic data set in order to understand the limitations between splitting methodologies. Combining the P‐wave hodogram calculation of azimuth and inclination with the shear‐wave splitting analysis lowers the azimuthal uncertainty and improves waveform rotation results. From the comparison of three splitting methods, the minimum second eigenvalue is the most optimum for the data set. Post‐splitting analysis was key as the results are affected by the complexities in the waveforms and arrivals. Using receiver‐by‐receiver splitting quality analysis with shot and receiver clustering improves the identification of quality splitting results.
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Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination
More LessABSTRACTThe application of distributed acoustic sensing in borehole measurements allows for the use of fibre optic cables to measure strain. This is more efficient in terms of time and costs compared with the deploying of conventional borehole seismometers. Nevertheless, one known drawback for temporary deployment is represented by the freely hanging wireline cable slapping and ringing inside the casing, which introduces additional coherent coupling noise to the data. The present study proposes an explanation for the mechanism of noise generation and draws an analogy with similar wave propagation processes and phenomena, such as ghost waves in marine seismics. This observation allows to derive a ringing noise filter function, to study its behaviour and to consider known effects of the gauge length filter. After examining existing methods aimed at eliminating ringing noise and results of their application, we propose a two‐step approach: (1) developing a denoising method based on a matching pursuit decomposition with Gabor atoms and (2) subtracting the noise model for imaging improvement. The matching pursuit method focuses on decomposing the original input signal into a weighted sum of Gabor functions. Analysing Gabor atoms properties for frequency, amplitude and position in time provides the opportunity to distinguish parts of the original signal denoting noise caused by the vibrating cable. The matching pursuit decomposition applied to the distributed acoustic sensing‐vertical seismic profiling data at the geothermal test site Groß Schönebeck provides a versatile processing instrument for noise suppression.
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A joint imaging method of vertical seismic profiling data by generalized radon transform migration
Authors Wuqun Li, Weijian Mao, Jianguo Li, Quan Liang and Jian FangABSTRACTThe images produced by conventional vertical seismic profile imaging methods often suffer from the problems of illumination limitation and uncompleted wavefield separation. In this paper, we give a deep insight into the illumination behaviours of the upgoing primary and downgoing multiple signals and illustrate their merits and demerits in illuminating the subsurface structure around the well trajectory. Based on this, we develop an efficient joint imaging method of acoustic vertical seismic profile data based on the generalized Radon transform migration inversion theory. By analysing the interference characteristics of the primary and multiple reflections, we verify the capability of this method that can handle the joint imaging directly. The proposed method combines the advantages of primary and multiple contributions to the subsurface image without additional wavefield separation. The numerical results of synthetic data and field data demonstrate high‐quality construction of the structure in the vicinity of the borehole, also away from the well trajectory.
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Diffraction separation by variational mode decomposition
Authors Peng Lin, Jingtao Zhao, Suping Peng and Xiaoqin CuiABSTRACTDiffracted wavefields with superior illumination encode key geologic information about small‐scale geologic discontinuities or inhomogeneities in the subsurface and thus possess great potential for high‐resolution imaging. However, the weak diffracted wavefield is easily masked by the dominant reflected data. Diffraction separation from specular reflected data still plays an important role and plays a major role in diffraction imaging implementation. To solve this problem, a new diffraction‐separation method is proposed that uses variational mode decomposition to suppress reflected data and separate diffracted wavefields in the common‐offset or poststack domains. The variational mode decomposition algorithm targets reflected wavefield by decomposing seismic data into an ensemble of band‐limited intrinsic mode functions representing linear and strong reflected data. This method is based on the principle of energy sparsity and can utilize the kinematic and dynamic differences between reflected and diffracted wavefields to effectively predict linear reflected data. Synthetic and field data examples using complex body geometries demonstrate the effectiveness and performance of the proposed method in enhancing diffracted wavefield and attenuating reflected data as well as increasing the signal‐to‐noise ratio, which helps to clearly image small‐scale subwavelength geologic structures.
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Detection of hidden reservoirs under strong shielding based on bi‐dimensional empirical mode decomposition and the Teager–Kaiser operator
Authors Xudong Jiang, Junxing Cao, Shaohuan Zu, Hanqing Xu and Jun WangABSTRACTIn this paper, we propose a method for revealing hidden reservoirs that are shielded by strong amplitudes. The bi‐dimensional empirical mode decomposition algorithm is used to decompose pre‐stack seismic data into several localized components, which generated from different discontinuities in the subsurface elastic properties. The two‐dimensional Teager–Kaiser energy operator process is applied to the first component, which includes a strong signal, to further locate the strong event. According to the located results, an energy weight matrix is established. By weighted summation of all the components, the strong event is suppressed, and the hidden reservoir becomes more prominent. Tests on a synthetic data and field data from Daniudi confirm that this method can separate strong signals from weaker responses and efficiently reveal shielded reservoirs.
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Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey
Authors Kaiguang Zhu, Cong Peng, Chunyang Jing, Tianjiao Fan and Yang YangABSTRACTThe movement of the receiver coil in a magnetic field produces motion noise in the airborne electromagnetic field. With an amplitude that is several orders of magnitude higher than that of other noise sources, this motion noise is one of the main low‐frequency (<1 kHz) noise sources. Especially in the late decay period, the residual motion noise produces false anomalous information, which is a key problem of low‐base‐frequency airborne electromagnetic surveys. In this paper, a method for calculating the motion noise is proposed. This method takes into account both the earth's magnetic field and the active secondary magnetic field and introduces additive and multiplicative motion noise. Then, a linear‐homomorphic filtering algorithm is proposed to handle the additive and multiplicative motion noises. In the linear filtering part of our algorithm, the minimum noise fraction transform is applied to suppress additive motion noise. It provides sets of components ordered by the signal‐to‐noise ratio by deriving the eigenvalues and eigenvectors for the signal and uncorrelated noise covariance matrices. In the homomorphic filtering part, the multiplicative motion noise is removed. To do this, the adaptive‐width smoothing algorithm is applied to the logarithm of the filtered with the linear filtering process profile data. The results of synthetic and field data demonstrate that the linear filtering process based on the minimum noise fraction transform is effective for very low signal‐to‐noise ratios of the late‐time data, and the homomorphic filtering process can help distinguish small anomalies of these channels.
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Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran
Authors Shokouh Riahi, Abbas Bahroudi, Maysam Abedi, Soheila Aslani and Gholam‐Reza ElyasiABSTRACTThis study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.
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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)