John Queen

920 citations
55 papers · 672 indexed · h-index 14

John Queen

52 papers receiving 621 citations

Peers

John Queen
Comparison fields: 5 of 47
  • Geophysics 522
  • Ocean Engineering 317
  • Mechanical Engineering 224
  • Mechanics of Materials 92
  • Environmental Engineering 50
Replace B.P. Bonner with:
B.P. Bonner United States
W. Glaas Germany
Brian P. Bonner United States
Neelima Kandula France
Yibo Wang China
M. Markov Mexico
Emmanuel C. David United Kingdom
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John Queen relative to B.P. Bonner United States B.P. Bonner's profile →
Citations per field
00.5×2.8×
B.P. Bonner · 1×
Citations per year

Countries citing papers authored by John Queen

Since Specialization
Citations

This map shows the geographic impact of John Queen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Queen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Queen more than expected).

Fields of papers citing papers by John Queen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Queen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Queen. The network helps show where John Queen may publish in the future.

Co-authorship network

The 25 scholars most cited alongside John Queen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John Queen Line = papers co-authored together John Queen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada
20203
2
Preliminary report on applications of machine learning techniques to the Nevada geothermal play fairway analysis
20204
3
3D analysis of geothermal fluid flow favorability: Brady’s, Nevada, USA
20165
4 200748
5 200612
6 200625
7 200314
8 20034
9 200211
10 20024
11 20013
12 200085
13 200029
14 20002
15 199830
16 199724
17 19976
18 19969
19 19954
20 199213

About John Queen

John Queen is a scholar working on Geophysics, Ocean Engineering and Mechanical Engineering, having authored 55 papers that have together received 672 indexed citations. Recurring topics across this work include Seismic Imaging and Inversion Techniques (37 papers), Seismic Waves and Analysis (24 papers), Hydraulic Fracturing and Reservoir Analysis (23 papers), Drilling and Well Engineering (11 papers), Geophysical Methods and Applications (8 papers), Reservoir Engineering and Simulation Methods (6 papers), Rare-earth and actinide compounds (3 papers) and Seismology and Earthquake Studies (3 papers). The work is most often cited by research in Geophysics (522 citations), Ocean Engineering (317 citations) and Mechanical Engineering (224 citations). John Queen has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Enru Liu, О. В. Михайлов, M. Nafi Toksöz, Mark Chapman, Xiangyang Li, Stuart Crampin, Heloise B. Lynn, Thomas M. Daley, Zhongjie Zhang and N. Steinsberger. Their work appears in journals such as Geophysics, The Leading Edge, Geophysical Journal International, Geophysical Prospecting and Journal of Magnetism and Magnetic Materials.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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