Example of Computational Geosciences format
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Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format
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Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format Example of Computational Geosciences format
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open access Open Access

Computational Geosciences — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Computational Mathematics #25 of 152 down down by 10 ranks
Computational Theory and Mathematics #32 of 133 down down by 14 ranks
Computers in Earth Sciences #14 of 52 down down by 5 ranks
Computer Science Applications #207 of 693 down down by 78 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 358 Published Papers | 1533 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/07/2020
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Related Journals

open access Open Access
recommended Recommended

Oxford University Press

Quality:  
High
CiteRatio: 9.9
SJR: 3.599
SNIP: 2.056
open access Open Access

Springer

Quality:  
High
CiteRatio: 6.1
SJR: 1.461
SNIP: 1.491
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 14.9
SJR: 2.96
SNIP: 3.054
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 6.1
SJR: 1.882
SNIP: 1.743

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.807

14% from 2018

Impact factor for Computational Geosciences from 2016 - 2019
Year Value
2019 1.807
2018 2.108
2017 2.726
2016 1.602
graph view Graph view
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4.3

20% from 2019

CiteRatio for Computational Geosciences from 2016 - 2020
Year Value
2020 4.3
2019 5.4
2018 5.2
2017 4.5
2016 5.0
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 14% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 20% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.696

15% from 2019

SJR for Computational Geosciences from 2016 - 2020
Year Value
2020 0.696
2019 0.823
2018 0.746
2017 0.985
2016 0.894
graph view Graph view
table view Table view

1.325

7% from 2019

SNIP for Computational Geosciences from 2016 - 2020
Year Value
2020 1.325
2019 1.424
2018 1.342
2017 1.371
2016 1.451
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 15% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Computational Geosciences

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Springer

Computational Geosciences

Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and tr...... Read More

Mathematics

i
Last updated on
16 Jul 2020
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ISSN
1420-0597
i
Impact Factor
High - 1.423
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
SPBASIC
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S10596-010-9194-2
Recent progress on reservoir history matching: a review
Dean S. Oliver1, Yan Chen2

Abstract:

History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate... History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. In this review paper, we will summarize key developments in history matching and then review many of the accomplishments of the past decade, including developments in reparameterization of the model variables, methods for computation of the sensitivity coefficients, and methods for quantifying uncertainty. An attempt has been made to compare representative procedures and to identify possible limitations of each. read more read less

Topics:

Reservoir simulation (55%)55% related to the paper
726 Citations
open accessOpen access Journal Article DOI: 10.1007/S10596-012-9310-6
GEM-Selektor geochemical modeling package: revised algorithm and GEMS3K numerical kernel for coupled simulation codes

Abstract:

Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on... Reactive mass transport (RMT) simulation is a powerful numerical tool to advance our understanding of complex geochemical processes and their feedbacks in relevant subsurface systems. Thermodynamic equilibrium defines the baseline for solubility, chemical kinetics, and RMT in general. Efficient RMT simulations can be based on the operator-splitting approach, where the solver of chemical equilibria is called by the mass transport part for each control volume whose composition, temperature, or pressure has changed. Modeling of complex natural systems requires consideration of multiphase–multicomponent geochemical models that include nonideal solutions (aqueous electrolytes, fluids, gases, solid solutions, and melts). Direct Gibbs energy minimization (GEM) methods have numerous advantages for the realistic geochemical modeling of such fluid–rock systems. Substantial improvements and extensions to the revised GEM interior point method algorithm based on Karpov’s convex programming approach are described, as implemented in the GEMS3K C/C+ + code, which is also the numerical kernel of GEM-Selektor v.3 package (http://gems.web.psi.ch). GEMS3K is presented in the context of the essential criteria of chemical plausibility, robustness of results, mass balance accuracy, numerical stability, speed, and portability to high-performance computing systems. The stand-alone GEMS3K code can treat very complex chemical systems with many nonideal solution phases accurately. It is fast, delivering chemically plausible and accurate results with the same or better mass balance precision as that of conventional speciation codes. GEMS3K is already used in several coupled RMT codes (e.g., OpenGeoSys-GEMS) capable of high-performance computing. read more read less

Topics:

Numerical stability (51%)51% related to the paper
View PDF
609 Citations
open accessOpen access Journal Article DOI: 10.1007/S10596-014-9443-X
Reactive transport codes for subsurface environmental simulation

Abstract:

A general description of the mathematical and numerical formulations used in modern numerical reactive transport codes relevant for subsurface environmental simulations is presented. The formulations are followed by short descriptions of commonly used and available subsurface simulators that consider continuum representations... A general description of the mathematical and numerical formulations used in modern numerical reactive transport codes relevant for subsurface environmental simulations is presented. The formulations are followed by short descriptions of commonly used and available subsurface simulators that consider continuum representations of flow, transport, and reactions in porous media. These formulations are applicable to most of the subsurface environmental benchmark problems included in this special issue. The list of codes described briefly here includes PHREEQC, HPx, PHT3D, OpenGeoSys (OGS), HYTEC, ORCHESTRA, TOUGHREACT, eSTOMP, HYDROGEOCHEM, CrunchFlow, MIN3P, and PFLOTRAN. The descriptions include a high-level list of capabilities for each of the codes, along with a selective list of applications that highlight their capabilities and historical development. read more read less
View PDF
600 Citations
Journal Article DOI: 10.1023/A:1021291114475
An Introduction to Multipoint Flux Approximations for Quadrilateral Grids
Ivar Aavatsmark1

Abstract:

Control-volume discretizations using multipoint flux approximations (MPFA) were developed in the last decade. This paper gives an introduction to these methods for quadrilateral grids in two and three dimensions. The introduction is kept on a basic level, and a brief summary to more advanced results is given. Only the O-metho... Control-volume discretizations using multipoint flux approximations (MPFA) were developed in the last decade. This paper gives an introduction to these methods for quadrilateral grids in two and three dimensions. The introduction is kept on a basic level, and a brief summary to more advanced results is given. Only the O-method with surface midpoints as continuity points is discussed. Flux expressions are derived both in physical and in curvilinear space. Equations for calculation of the transmissibility coefficients are given, and an explicit solution is shown for constant coefficients. K-orthogonality, stability and monotonicity are discussed, and an iterative solution technique is presented. Two numerical examples close the paper. read more read less

Topics:

Quadrilateral (56%)56% related to the paper, Constant coefficients (51%)51% related to the paper, Curvilinear coordinates (51%)51% related to the paper
557 Citations
Journal Article DOI: 10.1023/A:1011591328604
Improved energy estimates for interior penalty, constrained and discontinuous Galerkin methods for elliptic problems. Part I
Béatrice Rivière1, Mary F. Wheeler1, Vivette Girault2

Abstract:

Three Galerkin methods using discontinuous approximation spaces are introduced to solve elliptic problems. The underlying bilinear form for all three methods is the same and is nonsymmetric. In one case, a penalty is added to the form and in another, a constraint on jumps on each face of the triangulation. All three methods a... Three Galerkin methods using discontinuous approximation spaces are introduced to solve elliptic problems. The underlying bilinear form for all three methods is the same and is nonsymmetric. In one case, a penalty is added to the form and in another, a constraint on jumps on each face of the triangulation. All three methods are locally conservative and the third one is not restricted. Optimal a priori hp error estimates are derived for all three procedures. read more read less

Topics:

Discontinuous Galerkin method (66%)66% related to the paper, Galerkin method (57%)57% related to the paper, Triangulation (social science) (54%)54% related to the paper, Bilinear form (52%)52% related to the paper, Quarter period (51%)51% related to the paper
421 Citations
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With SciSpace, you do not need a word template for Computational Geosciences.

It automatically formats your research paper to Springer formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

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Time taken to format a paper and Compliance with guidelines

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Computational Geosciences format uses SPBASIC citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Computational Geosciences in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Computational Geosciences guidelines and auto format it.

2. Do you follow the Computational Geosciences guidelines?

Yes, the template is compliant with the Computational Geosciences guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Computational Geosciences?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Computational Geosciences citation style.

4. Can I use the Computational Geosciences templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Computational Geosciences.

5. Can I use a manuscript in Computational Geosciences that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Computational Geosciences that you can download at the end.

6. How long does it usually take you to format my papers in Computational Geosciences?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Computational Geosciences.

7. Where can I find the template for the Computational Geosciences?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Geosciences's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Computational Geosciences's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Computational Geosciences an online tool or is there a desktop version?

SciSpace's Computational Geosciences is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Computational Geosciences?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Computational Geosciences?”

11. What is the output that I would get after using Computational Geosciences?

After writing your paper autoformatting in Computational Geosciences, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational Geosciences's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Computational Geosciences?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Computational Geosciences. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Computational Geosciences?

The 5 most common citation types in order of usage for Computational Geosciences are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Computational Geosciences?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Geosciences's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Computational Geosciences in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Computational Geosciences Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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