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Relevant bibliographies by topics / Computational geochemistry

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Journal articles

Academic literature on the topic 'Computational geochemistry'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Computational geochemistry"

1

Buccianti, Antonella. "Advanced computational geochemistry." Computers & Geosciences 37, no. 5 (2011): 645. http://dx.doi.org/10.1016/j.cageo.2011.03.012.

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2

Gao, Wenlei, Gian Matharu, and Mauricio D. Sacchi. "Fast least-squares reverse time migration via a superposition of Kronecker products." GEOPHYSICS 85, no. 2 (2020): S115—S134. http://dx.doi.org/10.1190/geo2019-0254.1.

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Least-squares reverse time migration (LSRTM) has become increasingly popular for complex wavefield imaging due to its ability to equalize image amplitudes, attenuate migration artifacts, handle incomplete and noisy data, and improve spatial resolution. The major drawback of LSRTM is the considerable computational cost incurred by performing migration/demigration at each iteration of the optimization. To ameliorate the computational cost, we introduced a fast method to solve the LSRTM problem in the image domain. Our method is based on a new factorization that approximates the Hessian using a s

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3

Liu, Faqi, Guanquan Zhang, Scott A. Morton, and Jacques P. Leveille. "An optimized wave equation for seismic modeling and reverse time migration." GEOPHYSICS 74, no. 6 (2009): WCA153—WCA158. http://dx.doi.org/10.1190/1.3223678.

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The acoustic wave equation has been widely used for the modeling and reverse time migration of seismic data. Numerical implementation of this equation via finite-difference techniques has established itself as a valuable approach and has long been a favored choice in the industry. To ensure quality results, accurate approximations are required for spatial and time derivatives. Traditionally, they are achieved numerically by using either relatively very fine computation grids or very long finite-difference operators. Otherwise, the numerical error, known as numerical dispersion, is present in t

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4

Rustad, J. R., W. Dzwinel, and D. A. Yuen. "Computational Approaches to Nanomineralogy." Reviews in Mineralogy and Geochemistry 44, no. 1 (2001): 191–216. http://dx.doi.org/10.2138/rmg.2001.44.06.

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5

Yong, Peng, Romain Brossier, and Ludovic Métivier. "Parsimonious truncated Newton method for time-domain full-waveform inversion based on the Fourier-domain full-scattered-field approximation." GEOPHYSICS 87, no. 1 (2021): R123—R146. http://dx.doi.org/10.1190/geo2021-0164.1.

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To exploit Hessian information in full-waveform inversion (FWI), the matrix-free truncated Newton method can be used. In such a method, Hessian-vector product computation is one of the major concerns due to the huge memory requirements and demanding computational cost. Using the adjoint-state method, the Hessian-vector product can be estimated by zero-lag crosscorrelation of the first-/second-order incident wavefields and the second-/first-order adjoint wavefields. Different from the implementation in frequency-domain FWI, Hessian-vector product construction in the time domain becomes much mor

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6

Ignetik, Rainer. "Response by R. Ignetik to the author's reply." GEOPHYSICS 54, no. 11 (1989): 1502. http://dx.doi.org/10.1190/1.1486970.

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It would be interesting to compare the two computational algorithms proposed by Raiche with Boerner and West's approach, to see which of the three is computationally fastest under similar conditions. I also note that although the semi-analytic algorithm proposed by Raiche is more elegant and may require less storage, the paper does not make it entirely clear whether we get a real reduction in computing time; reduced computational time was the motivation for the procedure in the first place.

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7

Bensdorp, Silvian, Steen A. Petersen, Peter M. van den Berg, and Jacob T. Fokkema. "An approximate 3D computational method for real-time computation of induction logging responses." GEOPHYSICS 79, no. 3 (2014): E137—E148. http://dx.doi.org/10.1190/geo2013-0233.1.

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Over many years, induction logging systems have been used to create well formation logs. The major drawback for the utilization of these tools is the long simulation time for a single forward computation. We proposed an efficient computational method based on a contrast-type of integral-equation formulation, in which we applied an approximation for the 3D electromagnetic field. We assumed that the dominant contribution in the integral equation is obtained by the contribution around the singularity of Green’s kernel. It is expected that the approximation yields reliable results when the (homoge

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8

Carcione, José M., Christina Morency, and Juan E. Santos. "Computational poroelasticity — A review." GEOPHYSICS 75, no. 5 (2010): 75A229–75A243. http://dx.doi.org/10.1190/1.3474602.

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Computational physics has become an essential research and interpretation tool in many fields. Particularly in reservoir geophysics, ultrasonic and seismic modeling in porous media is used to study the properties of rocks and to characterize the seismic response of geologic formations. We provide a review of the most common numerical methods used to solve the partial differential equations describing wave propagation in fluid-saturated rocks, i.e., finite-difference, pseudospectral, and finite-element methods, including the spectral-element technique. The modeling is based on Biot-type theorie

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9

Goes, Saskia. "Computational methods for geodynamics." Geophysical Journal International 184, no. 2 (2010): 974. http://dx.doi.org/10.1111/j.1365-246x.2010.04898.x.

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10

Zhang, Yang, and M. Nafi Toksöz. "Impact of the cracks lost in the imaging process on computing linear elastic properties from 3D microtomographic images of Berea sandstone." GEOPHYSICS 77, no. 2 (2012): R95—R104. http://dx.doi.org/10.1190/geo2011-0126.1.

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With the current developments in imaging/computational techniques and resources, computational rock physics has been emerging as a new field of study. Properties of rocks are examined by carrying out extensive numerical simulations on rocks that have been digitized using high-resolution X-ray CT scans. The ultimate goal of computational rock physics is to supplement the traditional laboratory measurements, which are time consuming, with faster numerical simulations that allow the parameter space to be explored more thoroughly. We applied the finite-element method to compute the static effectiv

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