Science Explorer Interactive view Map

Geochemistry and Geologic Mapping

Geochemistry and Geologic Mapping is a research topic within Artificial Intelligence. Science Explorer counts 80k research works in it since 1950. 16.3% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of machine learning, remote sensing, and compositional data analysis techniques for mineral prospectivity mapping. It explores the use of advanced technologies such as ASTER and hyperspectral imaging to identify geological features, geochemical anomalies, and hydrothermal alterations associated with mineralization. The cluster also delves into the challenges and opportunities in using support vector machines, fractal modeling, and statistical analysis for predicting undiscovered mineral deposits.

  • Machine Learning
  • Mineral Prospectivity
  • Remote Sensing
  • Compositional Data Analysis
  • Geological Mapping
  • Hyperspectral Imaging
  • Support Vector Machines
  • Fractal Modeling
  • Geochemical Anomalies
  • Lithological Mapping
Research works
80k
fractional, since 1950
In the world top 10%
13k
per year above
Top-10% rate
16.3%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+41%
the tick is no change

Which countries lead Geochemistry and Geologic Mapping research?

By volume, China and the United States publish the most (3.5k and 1.7k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 3.5k works
  2. 2 United States 1.7k works
  3. 3 Russia 1.1k works
  4. 4 India 772 works
  5. 5 Brazil 520 works
  6. 6 Nigeria 456 works
  7. 7 Australia 419 works
  8. 8 Canada 399 works
  9. 9 France 349 works
  10. 10 Germany 327 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 37.1%United States: 17.4%Russia: 11.4%India: 8.1%6 others listed: 26.0%37%largest
China3,527 · 37.1%United States1,656 · 17.4%Russia1,089 · 11.4%India772 · 8.1%6 others listed2,470 · 26.0%

Shares of the rows listed above, not of the whole node.

Which institutions lead Geochemistry and Geologic Mapping research?

By volume in 2022–2025, China University of Geosciences (Beijing) publishes the most Geochemistry and Geologic Mapping research, followed by China University of Geosciences and China Geological Survey.

Who are the leading researchers in Geochemistry and Geologic Mapping?

The most-cited researchers publishing on Geochemistry and Geologic Mapping include Brian F. Windley.

  1. 1 Brian F. Windley United Kingdom 3.9k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Geochemistry and Geologic Mapping research done?

The largest centres of Geochemistry and Geologic Mapping research in 2022–2025 are Beijing (China), Moscow (Russia), Wuhan (China) and Chengdu (China). Among places with at least 20 works in it, it is an unusually large share of all research in Reston.

Largest cities, 2022–2025

  1. 1 Beijing China 1.3k works
  2. 2 Moscow Russia 414 works
  3. 3 Wuhan China 242 works
  4. 4 Chengdu China 151 works
  5. 5 Cairo Egypt 140 works
  6. 6 Tehran Iran 127 works
  7. 7 Nanjing China 116 works
  8. 8 Novosibirsk Russia 115 works
  9. 9 Guangzhou China 114 works
  10. 10 Saint Petersburg Russia 112 works

Where it is the local speciality

  1. RestonUS · 107.9 works33×
← less than its size predictsmore →

Location quotient: how much more of its research is in Geochemistry and Geologic Mapping than the world average.

See Geochemistry and Geologic Mapping on the map

Where is the best place to study Geochemistry and Geologic Mapping?

Among universities, judged by research, Amirkabir University of Technology, China University of Geosciences and China University of Geosciences (Beijing) score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 29.81%fractional works in this node (log) →share in the world top 10% →Amirkabir University of Technology: 19, 53.6%China University of Geosciences: 181, 32.7%China University of Geosciences (Beijing): 226, 20.7%Curtin University: 35, 24.6%University of Peshawar: 14, 26.1%Laurentian University: 15, 25.7%East China University of Technology: 41, 18.7%Université du Québec à Chicoutimi: 8, 34.6%Universiti Malaysia Terengganu: 8, 40.1%Chengdu University of Technology: 71, 21.3%Amirkabir University…China University of …Curtin UniversityChina University of …
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Amirkabir University of TechnologyIran 65.453.6%5.5×19 +202.9%
2 China University of GeosciencesChina 65.232.7%28.5×181 -27.5%
3 China University of Geosciences (Beijing)China 59.220.7%55.7×226 -28.5%
4 Curtin UniversityAustralia 55.624.6%8.1×35 +90.6%
5 University of PeshawarPakistan 54.426.1%12.9×14 +70.1%
6 Laurentian UniversityCanada 53.425.7%32.2×15 +76.3%
7 East China University of TechnologyChina 52.218.7%32.5×41 +54.0%
8 Université du Québec à ChicoutimiCanada 51.934.6%10.7×8 +33.7%
9 Universiti Malaysia TerengganuMalaysia 51.240.1%4.2×8 +198.9%
10 Chengdu University of TechnologyChina 50.921.3%18.0×71 -60.4%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Geochemistry and Geologic Mapping research growing?

Output in 2018–2022 was 41% higher than in 2013–2017, peaking in 2024. The fastest-growing topics are Geochemistry and Geologic Mapping.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.