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Jiang G.,Key Laboratory of Geo detection China University of Geosciences Beijing | Jiang G.,Beijing University of Technology | Zhao D.,Tohoku University | Zhang G.,Beijing University of Technology
Physics of the Earth and Planetary Interiors

Seismic tomography and numerical simulations show that the western Pacific slab bends horizontally when it reaches the boundary between the upper mantle and lower mantle beneath northeast Asia. It is expected that a metastable olivine wedge (MOW) exists in the cold core of the slab because of a delayed phase transition from olivine to its high-pressure polymorphs. However, it is still debated whether the MOW actually exists or not, and even if it exists, its physical properties, such as seismic velocity and density, are still unclear. In this work we use high-quality arrival-time data of 17 deep earthquakes occurring within the Pacific slab under northeast Asia to study the detailed structure of the slab. The deep earthquakes are relocated precisely by applying a modified double-difference location method to arrival-time data recorded at both Chinese and Japanese stations. Based on the precise hypocentral locations, a forward modeling method and differential travel-time residuals data are used to estimate seismic velocity within the deep source zone, which can decrease or remove the influence of ambient velocity heterogeneities. Our results show that the MOW does exist within the Pacific slab under northeast Asia, and the MOW has a mean velocity anomaly of 7-9% lower than the iasp91 Earth model. The existence of MOW in the slab has important geodynamic implications. It can reduce the speed of slab subduction and affect the generation of deep earthquakes. © 2014 Elsevier B.V. Source

Tan M.,Key Laboratory of Geo detection China University of Geosciences Beijing | Tan M.,Beijing University of Technology | Wang P.,Beijing University of Technology | Mao K.,Sinopec
Journal of Applied Geophysics

Three-dimensional nuclear magnetic resonance (3D NMR) logging can simultaneously measure transverse relaxation time (T2), longitudinal relaxation time (T1), and diffusion coefficient (D). These parameters can be used to distinguish fluids in the porous reservoirs. For 3D NMR logging, the relaxation mechanism and mathematical model, Fredholm equation, are introduced, and the inversion methods including Singular Value Decomposition (SVD), Butler-Reeds-Dawson (BRD), and Global Inversion (GI) methods are studied in detail, respectively. During one simulation test, multi-echo CPMG sequence activation is designed firstly, echo trains of the ideal fluid models are synthesized, then an inversion algorithm is carried on these synthetic echo trains, and finally T2-T1-D map is built. Futhermore, SVD, BRD, and GI methods are respectively applied into a same fluid model, and the computing speed and inversion accuracy are compared and analyzed. When the optimal inversion method and matrix dimention are applied, the inversion results are in good aggreement with the supposed fluid model, which indicates that the inversion method of 3D NMR is applieable for fluid typing of oil and gas reservoirs. Additionally, the forward modeling and inversion tests are made in oil-water and gas-water models, respectively, the sensitivity to the fluids in different magnetic field gradients is also examined in detail. The effect of magnetic gradient on fluid typing in 3D NMR logging is stuied and the optimal manetic gradient is choosen. © 2014. Source

Zhang F.-Q.,Key Laboratory of Geo detection China University of Geosciences Beijing | Wei F.-J.,Sinopec | Wang Y.-C.,Key Laboratory of Geo detection China University of Geosciences Beijing | Wang W.-J.,Petrochina | Li Y.,Petrochina
Chinese Journal of Geophysics (Acta Geophysica Sinica)

AVO forward modeling is always constructed by the approximation of Zoeppritz equation in traditional three-term AVO inversion. But the approximation is limited in the case of critical angle and elastic parameters varying severely. Given this problem, we can use the exact Zoeppritz equation to construct the inversion objective function. Because the relationship between P wave reflection coefficient and elastic parameters is nonlinear, the common approach is to use nonlinear optimization algorithm which hasn't been widespread because of the large computation. The alternative is to use generalized linear inversion which uses the linear equation to express the nonlinear relation through the expansion of P wave reflection coefficient into a truncated Taylor series. The GLI can get high accuracy through several iterations in theory. But GLI is unstable sometimes because of the large conditional number of Jacobian matrix. Bayesian inversion combines the prior distribution of model parameters with the likelihood function of the noise to form the posterior distribution of model parameters, which transforms the minimization of objective function into the maximization of the posterior probability distribution. Because of the introduction of the prior information of model parameters, the ill-posed problem can be reduced dramatically. This article combines the ideas of the two methodologies, which uses the idea of GLI to construct AVO forward modeling for improving the accuracy of inverting the large incident angle seismic data and uses Bayesian theory to introduce the model parameters prior information to construct the regularization of inversion objective function for reducing the ill-posed problem of inversion. This algorithm assumes that the prior distribution of the model parameters honors trivariate Cauchy distribution. Source

Liu Z.,Beijing University of Technology | Liu Z.,Key Laboratory of Geo detection China University of Geosciences Beijing | Huang J.,Beijing University of Technology | Huang J.,Key Laboratory of Geo detection China University of Geosciences Beijing | Yao H.,Hefei University of Technology
Physics of the Earth and Planetary Interiors

The ambient noise data recorded by 249 seismic stations in the permanent and temporary networks in Northeast China are used to invert for the isotropic phase velocity and azimuthal anisotropy of Rayleigh waves in the period band 5-50 s. The inversion results reflect the structure from the shallow crust to upper mantle up to approximately 120 km depth. Beneath the Songliao basin, both the fast direction in shallow crust and strike of a low-velocity anomaly in the middle crust are NNE-SSW, which is coincident with the main tectonic trend of the (Paleo) Pacific tectonic domain. This indicates that the rifting of the Songliao basin is influenced by the subduction of (Paleo) Pacific plate. The upper mantle of Songliao block (except the central area of Songliao basin) to the west of Mudanjiang fault, and the east of the North-South Gravity Lineament, is characterized by high-velocity and weak anisotropy up to approximately 120 km depth. We infer that there is delamination of lithospheric mantle beneath the Songliao block. Obvious N-S, NE-SW, and E-W trending fast directions are found in the lithospheric mantles of the east, west, and south sides of Songliao block, respectively, which coincide with the strikes of the Paleozoic tectonic in these areas. This suggests that the frozen-in anisotropic fabric in the lithospheric mantle can be used to indicate the historical deformation of the lithosphere. In the northern margin of the North China Craton, the spatial variations of phase velocity and azimuthal anisotropy are more dramatic than those in Northeast China blocks, which indicates that the lithosphere of the North China Craton has experienced more complicated tectonic evolution than that of the Northeast China blocks. © 2016 Elsevier B.V. Source

Tan M.,Key Laboratory of Geo detection China University of Geosciences Beijing | Tan M.,China University of Geosciences | Song X.,China University of Geosciences | Yang X.,Sinopec | Wu Q.,Sinopec
Journal of Natural Gas Science and Engineering

Organic shale is one of the most important unconventional oil and gas resources. Hydrocarbon potential prediction of organic shale such as total organic carbon (TOC) is an important evaluation tool, which primarily uses empirical equations. A support-vector machine is a set of supervised tools used for classification and regression problems. In this study, a support-vector machine for regression (SVR) is investigated to estimate the TOC content in gas-bearing shale. First, SVR technology is introduced including its basic concepts, associated regression algorithms and kernel functions, and a TOC prediction sketch that uses wireline logs. Then, one example is considered to compare three different regression algorithms and four different kernel functions in a packet dataset validation process and a leave-one-out cross-validation process. Error analysis indicates that the SVR method with the Epsilon-SVR regression algorithm and the Gaussian kernel produces the best results. The method of choosing the optimum Gamma value in the Gaussian kernel function is also introduced. Next, for comparison, the SVR-derived TOC with the optimal model and parameters is compared with the empirical formula and the δlogR methods. Finally, in a real continuous TOC prediction using wireline logs, TOC prediction tests are performed using SVR to choose the optimal logs as inputs, and the optimal input is finally chosen. Additionally, the radial basis network (RBF) is also applied to perform tests with different inputs; the results of these tests are compared with those of the SVR method. This study shows that SVR technology is a powerful tool for TOC prediction and is more effective and applicable than a single empirical model, δlogR and some network methods. © 2015 Elsevier B.V. Source

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