Mineral Deposit Exploration—Discovery Trends: 1900–2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Over the past 124 years (1900–2023), global mineral exploration has undergone dramatic changes in the number and type of discoveries made, the methods used, who made the discoveries, and where they were made. Presently, more than 15,000 significant mineral deposits are estimated to exist, and the number is increasing annually by 70 to 90 deposits. At the start of the 20th century, nearly half of all discoveries were made in just three countries—the United States, Canada, and Australia. By the 21st century these countries contributed only one quarter of discoveries, supplanted by Africa, Latin America, and China. Between 1900 and 2023, gold (37%) and base metals (25%) dominated discoveries. However, iron ore discoveries were periodically strong (1938–1970 and 2000–2013), as were those of uranium (1946–1985) and lithium (2010–2023). In the early 1900s, >90% of all discovered deposits cropped out and were located by prospectors. Subsequently, other players and discovery techniques came to the fore, especially post-World War II using airborne geophysics and after the 1960s by applying high-sensitivity atomic absorption spectroscopy to large-scale geochemical surveys. Over the past eight decades, 80 significant innovations lowered costs and improved discovery, and artificial intelligence/machine learning is expected to continue the trend. Major mining companies made 30 to 40% of all discoveries from the 1930s to the early 2000s. Governments discovered an additional 15 to 30% of all deposits. Major companies and governments have now been overtaken by junior companies, accounting for 77% of Western world discoveries in 2023. Most recently, the economic boom in China in the early 2000s increased exploration expenditure tenfold globally (2005–2012) because of the growth of metal demand, reaching US$43.8 billion in 2012 and lifting labor and drilling costs. Expanded exploration company numbers (mostly inexperienced juniors) and few high-quality exploration targets inflated the cost-per-discovery fivefold. While discovery costs have since improved, they are still more than twice pre-2005 levels. To address this, the focus of investors, exploration managers, and geologists will need to be on high-quality targets that are generally perceived to have the greatest chance of delivering high-value deposits, including in covered regions and/or adjacent to known high-grade deposits and mines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it