Warick Brown

678 total citations
19 papers, 549 citations indexed

About

Warick Brown is a scholar working on Artificial Intelligence, Mechanical Engineering and Geophysics. According to data from OpenAlex, Warick Brown has authored 19 papers receiving a total of 549 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 9 papers in Mechanical Engineering and 6 papers in Geophysics. Recurrent topics in Warick Brown's work include Geochemistry and Geologic Mapping (18 papers), Mineral Processing and Grinding (9 papers) and Remote-Sensing Image Classification (6 papers). Warick Brown is often cited by papers focused on Geochemistry and Geologic Mapping (18 papers), Mineral Processing and Grinding (9 papers) and Remote-Sensing Image Classification (6 papers). Warick Brown collaborates with scholars based in Australia and Japan. Warick Brown's co-authors include David I. Groves, Robert G. Barnes, Tom Gedeon, T.D. Gedeon, David Groves, T. A. P. Kwak, F. P. Bierlein, Stephen Fraser, Chun Che Fung and Richard C. Price and has published in prestigious journals such as International Journal of Remote Sensing, Economic Geology and Computers & Geosciences.

In The Last Decade

Warick Brown

18 papers receiving 524 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Warick Brown Australia	8	443	198	171	154	139	19	549
Ryoichi Kouda Japan	9	411 0.9×	178 0.9×	201 1.2×	137 0.9×	94 0.7×	27	482
Guocheng Pan United States	13	489 1.1×	227 1.1×	252 1.5×	165 1.1×	81 0.6×	36	593
Fan Xiao China	15	473 1.1×	130 0.7×	152 0.9×	240 1.6×	132 0.9×	47	603
Zhao Peng-da China	11	228 0.5×	71 0.4×	79 0.5×	67 0.4×	113 0.8×	42	365
Xiaohui Li China	16	577 1.3×	199 1.0×	174 1.0×	178 1.2×	200 1.4×	54	772
Ehsan Farahbakhsh Australia	9	257 0.6×	90 0.5×	100 0.6×	167 1.1×	59 0.4×	26	352
C. M. Knox‐Robinson Australia	7	529 1.2×	112 0.6×	176 1.0×	92 0.6×	323 2.3×	8	595
Daniel Wedge Australia	11	243 0.5×	101 0.5×	50 0.3×	46 0.3×	131 0.9×	38	395
Stephen Kuhn United States	9	256 0.6×	100 0.5×	49 0.3×	44 0.3×	140 1.0×	25	418

Countries citing papers authored by Warick Brown

Since Specialization

Citations

This map shows the geographic impact of Warick Brown'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 Warick Brown with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Warick Brown more than expected).

Fields of papers citing papers by Warick Brown

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Warick Brown. 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 Warick Brown. The network helps show where Warick Brown may publish in the future.

Co-authorship network of co-authors of Warick Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Warick Brown. A scholar is included among the top collaborators of Warick Brown based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Warick Brown. Warick Brown is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

1.

Baddeley, Adrian, et al.. (2025). Mineral prospectivity analysis is unstable to changes in pixel size. Computers & Geosciences. 204. 105965–105965. 1 indexed citations

2.

Baddeley, Adrian, Warick Brown, Robin K. Milne, et al.. (2020). Optimal Thresholding of Predictors in Mineral Prospectivity Analysis. Natural Resources Research. 30(2). 923–969. 6 indexed citations

3.

Bierlein, F. P., et al.. (2008). Advanced methodologies for the analysis of databases of mineral deposits and major faults. Australian Journal of Earth Sciences. 55(1). 79–99. 28 indexed citations

4.

Fung, Chun Che, et al.. (2006). Mineral prospectivity prediction using interval neutrosophic sets. Murdoch Research Repository (Murdoch University). 235–239. 4 indexed citations

5.

Wong, Kok Wai, et al.. (2006). Quantification Of Uncertainty In Mineral Prospectivity Prediction Using Neural Network Ensembles And Interval Neutrosophic Sets. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

6.

Fung, Chun Che, et al.. (2006). Quantification of Uncertainty in Mineral Prospectivity Prediction Using Neural Network Ensembles and Interval Neutrosophic Sets. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 12. 3034–3039. 4 indexed citations

7.

Fung, Chun Che, et al.. (2006). Quantification of Uncertainty in Mineral Prospectivity Prediction Using Neural Network Ensembles and Interval Neutrosophic Sets. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 3034–3039. 1 indexed citations

8.

Fung, Chun Che, et al.. (2006). Neural Network Ensembles using Interval Neutrosophic Sets and Bagging for Mineral Prospectivity Prediction and Quantification of Uncertainty. 1–6. 2 indexed citations

9.

Fung, Chun Che, et al.. (2005). Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction. 16. 1–5. 2 indexed citations

10.

Fung, Chun Che, et al.. (2004). Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment. 5. 411–414 Vol. 2. 3 indexed citations

11.

Brown, Warick, David Groves, & T.D. Gedeon. (2003). Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping. Natural Resources Research. 12(3). 183–200. 59 indexed citations

12.

Brown, Warick, T.D. Gedeon, & David I. Groves. (2003). Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples. Natural Resources Research. 12(2). 141–152. 62 indexed citations

13.

Brown, Warick, T.D. Gedeon, & Robert G. Barnes. (2003). The use of a multilayer feedforward neural network for mineral prospectivity mapping. 1. 160–165. 1 indexed citations

14.

Brown, Warick, T.D. Gedeon, Adrian Baddeley, & Declan Groves. (2002). Bivariate J-function and other graphical statistical methods help select the best predictor variables as inputs for a neural network method of mineral prospectivity mapping. UWA Profiles and Research Repository (University of Western Australia). 257–268. 5 indexed citations

15.

Brown, Warick, Tom Gedeon, David I. Groves, & Robert G. Barnes. (2000). Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences. 47(4). 757–770. 273 indexed citations

16.

Brown, Warick, et al.. (1997). Textural neural network and version space classifiers for remote sensing. International Journal of Remote Sensing. 18(4). 741–762. 19 indexed citations

17.

Kwak, T. A. P., et al.. (1986). Fe solubilities in very saline hydrothermal fluids; their relation to zoning in some ore deposits. Economic Geology. 81(2). 447–465. 55 indexed citations

18.

Brown, Warick, et al.. (1984). Geology and geochemistry of a F-Sn-W skarn system—The Hole 16 deposit, Mt Garnet, North Queensland, Australia. Australian Journal of Earth Sciences. 31(3). 317–340. 15 indexed citations

19.

Price, Richard C., et al.. (1983). The geology, geochemistry and origin of late‐Silurian high‐Si igneous rocks of the Upper Murray Valley, NE Victoria. Journal of the Geological Society of Australia. 30(3-4). 443–459. 8 indexed citations

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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