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- Volume 70, Issue 6, 2022
Geophysical Prospecting - Volume 70, Issue 6, 2022
Volume 70, Issue 6, 2022
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How does anisotropy of focal region change structure of moment tensors?
By Çağrı DinerABSTRACTThe invariants of the moment tensor such as its norm, eigenvalues and trace are closely related to the physical properties of the seismic source and the focal region, for example, seismic moment, radiation pattern, non‐double‐couple components. In this study, we investigate the relationship between these invariants and the eigenvalues and eigenvectors of the transversely isotropic elasticity tensor of the focal region. More specifically, we study how these invariants change as the source orientations vary with respect to the symmetry axes of the transversely isotropic elasticity tensor, by plotting these invariants on the stereographic net. Fortunately, one can plot them since they are independent of the strike of the fault when the focal region is a vertical transversely isotropic medium . Eigenvalues of the elasticity tensor control the invariants of the moment tensor; for instance, the ratio of the maximum and minimum norms achieved for some orientations of source is equal to the ratio of the two specific eigenvalues of the elasticity tensor. Moreover, the separation of the eigenvectors of the moment tensor from the eigenvectors of the source tensor is related to the deviation of the eigenvalues of the transversely isotropic elasticity tensor from the eigenvalues of the closest isotropic elasticity tensor. It is also found that this deviation is responsible for the percentages of non‐double‐couple components of the resulting moment tensor. This linear algebra point of view makes it easier to understand why and how the structure of the moment tensor changes for different orientations of sources.
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High‐resolution and robust microseismic grouped imaging and grouping strategy analysis
ABSTRACTAs an advanced real‐time monitoring technique, microseismic source‐location imaging provides valuable information during hydraulic fracturing, for example, the development of fracture networks and the effective reservoir reconstruction volume. However, microseismic data always suffer from weak induced energy and susceptibility to noise interference. In the case of a low signal‐to‐noise ratio, it is extremely challenging to perform robust microseismic imaging. Here, we first introduce several state‐of‐the‐art imaging conditions and two hybrid imaging conditions, which are followed by a detailed analysis of the impact of different grouping strategies. Then, we briefly analyse the sensitivity of different imaging conditions to noise using a one‐dimensional signal. Next, several benchmark models, including two‐dimensional Marmousi‐II and three‐dimensional SEG Advanced Modeling, are used as numerical examples for testing the passive‐source imaging algorithms. Finally, three‐dimensional real microseismic data are used to further investigate the impact of the grouping strategy on the imaging. The numerical examples and field data demonstrate the effectiveness of the proposed grouping strategy for the grouped imaging conditions.
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Sparse seismic reflectivity inversion using an adaptive fast iterative shrinkage‐thresholding algorithm
Authors Chuanhui Li and Xuewei LiuABSTRACTSeismic reflectivity inversion using l1‐norm regularization produces sparse solutions by applying an l1‐norm constraint. The fast iterative shrinkage‐thresholding algorithm is one of the most effective methods to solve l1‐norm regularized inversion problems. A large number of iterations are commonly required in the fast iterative shrinkage‐thresholding algorithm because its solution converges slowly towards the sparse solution. To improve its convergence rate, we introduce a modifying strategy for the traditional fast iterative shrinkage‐thresholding algorithm. When implementing the soft‐thresholding operator, the thresholding value is adaptively adjusted by assigning the reciprocal of the solution in the previous iteration as a weight to the thresholding value of the current iteration. In this way, small variables in the solution produce large coefficients applied to the thresholding value, which causes small variables to quickly converge to zero. The adaptive fast iterative shrinkage‐thresholding algorithm shows significantly improved computational efficiency and accuracy compared to the traditional fast iterative shrinkage‐thresholding algorithm. It produces good results for both numerical and field examples of l1‐norm regularized seismic reflectivity inversion.
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Ratio‐Euler deconvolution and its applications
Authors Liang Huang, Henglei Zhang, Chun‐Feng Li and Jie FengABSTRACTEuler deconvolution of potential field data is widely applied to obtain the location of concealed sources automatically. A lot of improvements have been proposed to eliminate the dependence of Euler deconvolution on the structural index that are based on the use of high‐order derivatives of the potential field and, therefore, sensitive to data noise. We describe the elimination of the dependence on the Ratio‐Euler method, which is based on the original Euler deconvolution function. The proposed method does not involve high‐order derivatives. Testing on simulated and field data indicates that the proposed method has better noise resistance than the existing Tilt‐Euler method, which is based on high‐order derivatives. The proposed method is first applied to the ground magnetic data from Weigang iron deposit, Eastern China. It reveals that the ore body could be approximated by a horizontal prism with a considerable vertical extent with a top depth of 55 m, which are very close to the information obtained from drill holes. In addition, the proposed method works well in estimating the depth to a cavity centre from the ground gravity anomaly.
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Joint inversion of muon tomography and gravity gradiometry for improved monitoring of steam‐assisted gravity drainage reservoirs
Authors Sara Pieczonka, Doug Schouten and Alexander BraunABSTRACTSteam‐assisted gravity drainage reservoirs require an immense amount of energy and water resources, and proper monitoring of steam evolution and depletion patterns is integral to the economic and environmental efficiency of the operation. Muon tomography is a passive sensing technique, which has proven to successfully model density anomalies in a variety of applications but has not yet been applied to the oil and gas field. A previous study simulated muon intensity data to model density changes in a realistic steam‐assisted gravity drainage reservoir at 1.25 and 5 years after initial production. The results showed that muon tomography is a promising technique for monitoring steam‐assisted gravity drainage reservoirs with high spatial resolution and over short time intervals of weeks to months. Here we demonstrate the advantage of using vertical gravity gradient data and muon tomography data in a joint inversion to improve the muon‐only inverse models. Forward models for simulated muon and gravity gradient data are jointly inverted for a realistic steam‐assisted gravity drainage reservoir at 230 and 130 m total vertical depth at 1.25 years after initial production. Results show that the addition of gravity gradient data helps to constrain the density change models mainly in depth and to a smaller extent laterally. For a sparse muon sensor array of 48 sensors over a reservoir at depth, the joint inversion using gravity gradient data reduces the difference between the inverse and true model by 12% compared to a muon‐only inversion. The improvement is smaller at depth with 6%. The improvement in resolvability metrics is summarized, and limitations are discussed. The addition of multiple data types in a joint inversion improves the resulting models leading to an overall decrease in model uncertainty which can be used for improved operational efficiency in steam‐assisted gravity drainage operations.
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Fine grid model for the dielectric characteristics of ground‐penetrating radar in mixed media
Authors Tonghua Ling, Wenchao He, Xianjun Liu, Sheng Zhang, Fu Huang and Fei HuaABSTRACTThe Fisher–Yates random shuffling algorithm combined with the finite‐difference time‐domain method is proposed to construct a fine grid model for the forward simulation of ground‐penetrating radar in mixed media. First, the finite‐difference time‐domain method was used to divide the coarse grid model into several fine grid models by conforming to the boundary conditions of different media, and the corresponding dielectric parameters were assigned to Yee cells in each fine grid model. Then, the Fisher–Yates random shuffling algorithm was used to randomly scramble all Yee cells with equal probability, and the array of scrambled Yee cells was recombined into a coarse grid model. Finally, the geoelectric model of mixed media was generated with the finite‐difference time‐domain method, and a ground‐penetrating radar image excited by electromagnetic wave pulses was obtained. To explore the characteristic signals and dielectric properties of the ground‐penetrating radar electromagnetic response in mixed media, image entropy theory was used to describe the ground‐penetrating radar image, and waveform analysis and wavelet transform mode maximum methods were used to analyse the single‐channel ground‐penetrating radar signal of the mixed media. The results showed that the Fisher–Yates random shuffling–finite‐difference time‐domain method can be used to construct a valid and stable fine grid model for simulating ground‐penetrating radar in mixed media. The model effectively inhibits electromagnetic attenuation and energy dissipation, and the wavelet transform mode maximum method explains the relative dielectric permittivity distribution of the mixed media. The findings of this study can be used as a theoretical basis for correcting radar parameters and interpreting images when ground‐penetrating radar is applied to mixed media.
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Regularized 2D Savitzky–Golay derivative filter in estimating the second vertical derivative of potential field data
More LessABSTRACTA robust method of estimating the second vertical derivative of two‐dimensional (2D) potential field data is proposed. The proposed method uses a 2D variant of Savitzky–Golay derivative filtering. The design of the 2D Savitzky–Golay derivative filter, unlike its one‐dimensional (1D) counterpart, is a non‐trivial exercise. This is due to the inherent complexity associated with 2D polynomial regression, which increases with the increase in the degree of the polynomial and the dimension of the filter window. The measure of complexity increases manifold, as the polynomial order increases from cubic to quintic. A larger polynomial order demands a larger dimension of the filter window patch as a minimum requirement. The large window patch which, in turn, poses computational challenges, becomes an ill‐posed problem and is computationally inefficient. To alleviate such a problem, an appropriate set of filter parameters is proposed, which ensures computational efficiency while maintaining sufficient robustness. The computational issue arising from the ill‐posed condition of the system matrix is addressed via shrinkage‐based regularization. A numerical experiment was conducted on a synthetically generated 2D dataset without and with a moderate amount of Gaussian random noise in order to check the applicability of the proposed method. The performance in terms of robustness was also compared with the other, usually considered as a benchmark, method. The proposed method is then successfully applied to determine the second vertical derivative of the high‐resolution Bouguer gravity anomaly data over an impact crater in Lake Wanapitaei, Canada. A qualitative interpretation of the second vertical derivative map over Lake Wanapitei is given.
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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 18 (1970 - 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 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)