Geologists and geoscientists
Official title Geoscientists, Except Hydrologists and Geographers (SOC 19-2042)
Also called Geoscientist, Geologist, Geophysicist, Exploration geologist, Petroleum geologist
In brief
In May 2025, BLS counted 23,470 US geologists and geoscientists, with median pay of $101,920 a year. Of the BLS counts since 2021, the highest was 25,230, in May 2022. The earliest figure here is an estimate of 1,100 from 1870, not measured the same way as today's count. The last full technology era for geologists and geoscientists: Leapfrog implicit geological modeling, 2006 to 2022.
What the sources say
Official outlook
BLS projects employment to grow 5.1% from 2025 to 2035.
AI exposure studies
- 47.8% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.
Last updated .
Cite this page
Future History. (2026, October 11). Geologists and geoscientists: jobs and pay since 1870 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-2042
Future History. "Geologists and geoscientists: jobs and pay since 1870." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-2042.
@misc{futurehistory_19_2042, author = {{Future History}}, title = {Geologists and geoscientists: jobs and pay since 1870}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-2042}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }
The access date is today's date on your device.
≈ $99,307 in 2024 prices (derived by us, CPI-adjusted)
William Maclure begins his systematic traverse of the United States to create the first geological map. He will cross the Allegheny Mountains more than fifty times before submitting his findings to the American Philosophical Society in 1809. His map, which classifies the country's rock formations into Werner's neptunian categories, is primitive by later standards -- but it is the founding act of professional American geology. Maclure is later called the "father of American geology" by historians of science.
March 3, 1879: Congress creates the United States Geological Survey under the Department of the Interior. The first director is Clarence King, who had led the rigorous King Survey of the 40th Parallel (1867-1878). The USGS consolidates the Hayden, Powell, Wheeler, and King surveys, creating the first permanent federal institution for geological research in the United States and establishing the career path of the government geoscientist that continues today.
The Association of American State Geologists is founded to advance the science and practical application of geology across the United States and its territories. By 1908 most American states have established geological surveys, creating a nationwide network of public-sector geoscientists conducting geological mapping, mineral resource assessment, and hazard evaluation. The AASG remains the institutional home of state geological surveys today.
The first commercial seismic reflection survey for oil exploration is conducted at the Orchard Salt Dome in Texas. This single technical event launches the geophysics specialization within geoscience and transforms how petroleum companies explore for oil. Within a decade, seismic crews are working across the Gulf Coast, Permian Basin, and Mid-Continent, creating the first large-scale industrial demand for geophysicists.
The Arab oil embargo of 1973-1974 and the resulting tripling of oil prices triggers the largest sustained growth in geoscience employment in the profession's history. Major oil companies (Exxon, Mobil, Chevron, Amoco, Conoco, Shell, Gulf) expand exploration departments dramatically; university geoscience enrollment surges; starting salaries for petroleum geologists with BS degrees reach levels that rival engineering. The boom lasts through the early 1980s.
The collapse of global oil prices in 1986 -- triggered by Saudi Arabia's decision to flood the market to protect market share -- causes the most severe employment contraction in geoscience history. Major oil companies lay off tens of thousands of geoscientists and geophysicists. University geoscience enrollment falls sharply through the late 1980s and early 1990s. Many geoscientists leave the petroleum sector permanently, redirecting to environmental consulting, government surveys, or other careers. The bust reshapes the demographic and employment structure of the profession for a generation.
The shale revolution, driven by the combination of horizontal drilling and hydraulic fracturing refined from the mid-2000s onward, generates a second wave of petroleum geoscience hiring. Unconventional plays in the Bakken, Eagle Ford, Marcellus, and Permian Basin require high-volume well planning and geological correlation across thousands of horizontal wells. Geoscientist employment grows through the early 2010s shale boom, though the workforce remains below its 1982 peak.
KoBold Metals announces the Mingomba copper-cobalt discovery in Zambia -- described as one of the largest new copper discoveries of the decade -- and attributes the find to its ML-driven targeting methodology. The discovery, made by a company backed by BHP, Rio Tinto, Bill Gates, and Jeff Bezos, is the most prominent demonstration that AI can guide geoscientists to ore deposits that traditional methods missed. It accelerates the adoption of AI targeting platforms across the mineral exploration industry and the energy transition demand for exploration geoscientists.
Will AI replace geologists and geoscientists? What the research says
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.
Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.
What's changing day to day for geologists and geoscientists
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is sitting alongside you hereInterpret 3D seismic reflection datasets for subsurface structural and stratigraphic mapping, running AI-assisted horizon autotracking and fault detection algorithms in SLB Petrel or OpendTect to extract continuous horizon surfaces across a seismic volume
Interpret 3D seismic reflection datasets for subsurface structural and stratigraphic mapping, running AI-assisted horizon autotracking and fault detection algorithms in SLB Petrel or OpendTect to extract continuous horizon surfaces across a seismic volume; evaluating AI-generated fault networks against known geological constraints (regional stress orientation, fault spacing, offsets); computing seismic attributes (amplitude, impedance, curvature) with ML-derived attribute volumes for facies and lithology prediction; and QC-checking AI outputs against well ties and checkshot data before using AI-extracted horizons to define drill targets.[11],[6],[12]
SLB's seismic foundation model and OpendTect's ML plugin can compress horizon autotracking and fault interpretation from weeks of manual work to hours: S&P Global (2024) estimates 30-60% cycle time reduction across major operators. But AI seismic interpretation models are trained primarily on passive margin stratigraphy and perform poorly in structurally complex settings: fold-thrust belt geometries, sub-salt imaging, and steeply dipping formations with multiples all confound standard autotracking. Your irreplaceable contribution is the geological context check: does the AI-extracted horizon topology make sense given what you know about regional deformation history, salt geometry, and depositional systems? Validate every AI-assisted interpretation against well control before committing to a drill decision, and invest in understanding the specific failure modes of the AI tool for your basin type.
AI is sitting alongside you hereConstruct 3D geological models for mineral exploration and mine planning using implicit surface modeling, importing borehole collar and downhole lithological, geochemical, and geophysical data into Leapfrog Geo
Construct 3D geological models for mineral exploration and mine planning using implicit surface modeling, importing borehole collar and downhole lithological, geochemical, and geophysical data into Leapfrog Geo; using ML-assisted lithological classification from drill core scan images to automate interval logging for high-volume drill programs; building continuous implicit surfaces for geological units, alteration zones, and mineralized domains without manual triangulation; validating model domains against structural geology interpretations and applying probabilistic uncertainty quantification to resource block model inputs.[7],[4],[1]
Leapfrog Geo's implicit modeling dramatically reduces the time to build a geologically consistent 3D model from a drill dataset: what previously required weeks of manual polygonization can be done in days. The geoscientist's irreplaceable judgment is in model geology: deciding which structural interpretations the implicit engine's algorithm should honor (fault truncations, unconformities, intrusive contacts), specifying the anisotropy settings that reflect the real geological grain of the mineralized system, and evaluating whether the model domain boundaries match the mineralization style and structural controls observed in the field and core. A Leapfrog model built without sound geological input is a smooth mathematical surface that may have no relationship to the actual orebody geometry: the geological input is the intellectual product.
AI is sitting alongside you hereApply AI-driven mineral prospectivity mapping to prioritize drill targets in greenfields critical minerals exploration, integrating regional airborne electromagnetic (AEM), gravity, magnetics, and radiometric survey data with geochemical stream sediment and soil sampling data and satellite remote sensing in a machine learning platform (KoBold Metals, Goldspot Discoveries)
Apply AI-driven mineral prospectivity mapping to prioritize drill targets in greenfields critical minerals exploration, integrating regional airborne electromagnetic (AEM), gravity, magnetics, and radiometric survey data with geochemical stream sediment and soil sampling data and satellite remote sensing in a machine learning platform (KoBold Metals, Goldspot Discoveries); evaluating probabilistic prospectivity maps against geological conceptual models (lithological host rocks, structural controls, alteration systems); ranking drill targets by expected value and logistics feasibility; and presenting targeting rationale to exploration boards for drill program approval.[4],[13],[8]
KoBold Metals' Mingomba discovery (2024) demonstrated that ML-driven targeting can identify large deposits at regional scale that traditional geological analysis missed: a genuine expansion of exploration reach. But AI prospectivity models are trained on historical exploration datasets that systematically underrepresent unexplored terranes, commodity types without rich historical analogues (lithium brines, carbonatite REE systems), and structural controls that are interpreted differently by different geologists. The geoscientist's role is to specify the geological conceptual model that constrains the ML prediction, if you let an unconstrained algorithm pick from data alone without a mineralization model, it will overweight proximity to past discoveries. Your expertise in recognizing the ore system you are targeting and translating that into ML feature engineering and model constraints is what makes the platform's outputs geologically meaningful.
Where geologists and geoscientists go next
Natural next steps for someone with your foundation: not exits, evolutions.
- Data Scientists
- US median pay
- $120,230 (May 2025)
- BLS outlook 2025-35
- +34.6%
- Mining and Geological Engineers, Including Mining Safety Engineers
- US median pay
- $106,220 (May 2025)
- BLS outlook 2025-35
- +3.8%
- Urban and Regional Planners
- US median pay
- $89,320 (May 2025)
- BLS outlook 2025-35
- +3.9%
What each move takes
Data Scientists
Geoscientists with strong quantitative skills and Python/R experience are increasingly transitioning to Data Scientist roles at exploration AI companies (KoBold Metals, Goldspot Discoveries, EarthAI, ION Geophysical) and at energy companies building AI-driven subsurface workflows. These companies need data scientists who understand the physical meaning of geophysical and geochemical datasets (the failure modes of ML models applied to geological data [spatial autocorrelation, class imbalance from drill data density bias, non-stationarity of geological features]) that pure data scientists from other domains do not recognize. The transition is High difficulty because it requires acquiring production ML engineering skills (MLOps, model deployment, feature engineering pipelines) well beyond the Python/R analysis scripts typical of geoscience workflows. The return is high: geoscience-domain ML engineers command premium compensation at exploration AI companies and energy majors building digital subsurface teams.
- · Production ML stack: PyTorch or scikit-learn for model training, XGBoost and gradient boosting for geochemical/geophysical feature classification, SHAP for geological feature importance interpretation; MLflow or Weights & Biases for experiment tracking
- · Geospatial ML: spatial cross-validation techniques to handle spatial autocorrelation in geological training data (standard k-fold CV produces over-optimistic results when nearby samples are correlated); geostatistical simulation (Sequential Gaussian Simulation, MPS) for uncertainty quantification in resource models
- · Deep learning for geoscience: convolutional neural networks for seismic facies classification and core image analysis; graph neural networks for geological knowledge graphs; transformer models for multi-physics dataset integration
- · MLOps and software engineering: Docker containerization, FastAPI for inference API serving, CI/CD pipelines for model retraining, version control for ML experiments; transitioning from Jupyter-notebook geoscience analysis to production-grade Python package development
- · Communication to non-geological stakeholders: translating probabilistic ML targeting outputs (prospectivity maps, drill success probabilities) into capital allocation recommendations for exploration investment committees; framing model uncertainty in terms investors and boards understand
How has the work of geologists and geoscientists changed since 1879?
The tools that defined the work, one line per era. Select an era to see how it reshaped the work.
Field notebook, plane table, and Brunton compass (systematic geological mapping era)
The founding of the USGS in 1879 established systematic geological mapping as the core professional practice of the geoscientist. The Brunton pocket transit compass, invented by David Brunton in 1894, became the emblematic field instrument of the profession -- a device that measured strike and dip of rock formations that geologists still carry today. The plane table, alidade, and field notebook were the complete toolkit: the geologist walked the terrain, measured structural orientations, described rock units, and returned to sketch geological maps by hand. The output was a geological map, a cross-section, and a written report. No computation was involved beyond simple trigonometry; the tool was the trained human eye.
Reflection seismology (geophone arrays and paper seismograms)
The seismic reflection survey -- detonating dynamite in a shallow shot hole and recording the echo of sound waves off subsurface rock layers with an array of geophones -- was first used commercially for oil exploration in 1924 (Orchard Salt Dome, Texas) and became the dominant exploration method for the oil industry through the 1920s-1960s. It created an entirely new specialization: the exploration geophysicist, who could infer subsurface structure without drilling. The output was a paper seismogram that geoscientists interpreted by hand, drawing reflectors in pencil on paper to construct cross-sections of the subsurface. The method expanded the geoscientist's reach from surface outcrops to depths of thousands of feet, making petroleum geology viable across broad basin areas without extensive drilling.
Effect on the workReflection seismology drove the growth of the exploration geophysics workforce from near-zero in the 1920s to tens of thousands by the mid-20th century, and made the petroleum industry the dominant employer of geoscientists.
Digital seismic recording, 3D seismic, and mainframe processing
The transition from analog paper seismograms to magnetic tape digital recording in the mid-1960s, followed by the first 3D seismic surveys in the 1970s (pioneered by Exxon Production Research), transformed seismic interpretation from a qualitative art into a quantitative practice. Geoscientists now worked with printed computer-generated seismic sections annotated by hand, and progressively with CRT workstations in the 1980s. The 1970s oil boom accelerated adoption of digital seismic processing across the industry. The workload of interpretation grew with the data volume: a 3D seismic survey covering about a hundred square miles produced thousands of cross-sections to interpret, requiring large geoscience teams in corporate oil company exploration departments.
Effect on the workDigital seismic interpretation expanded the petroleum geoscientist workforce substantially through the 1970s-early 1980s oil boom. The combination of high oil prices and new 3D seismic technology drove corporate geology department headcounts to their historical peak.
Seismic interpretation workstations (Landmark, GeoQuest) and GIS geological mapping
The introduction of interactive seismic interpretation workstations -- Landmark Graphics' first workstation in 1982, followed by Schlumberger's GeoQuest (now SLB Petrel's predecessor) -- moved geoscientists off paper seismograms and onto screen-based digital interpretation for the first time. A geoscientist could now scroll through a 3D seismic cube, pick horizons interactively with a mouse, and automatically generate maps of subsurface structure. The workflow compressed but the scale of interpretation work exploded: 3D seismic surveys grew from tens to hundreds to thousands of square miles, and the productivity gain from workstations was partially absorbed by the increased data volume. GIS software (early ESRI ArcInfo, introduced in 1981) reached geological mapping workflows through the 1990s, replacing hand-drawn geological maps with digitized GIS datasets.
SLB Petrel + integrated subsurface modeling (seismic, wells, and reservoir in one platform)
SLB's Petrel, launched commercially around 2001 and adopted industry-wide through the 2000s, unified seismic interpretation, well correlation, facies modeling, and reservoir simulation into a single platform. Before Petrel, a petroleum geoscientist worked in separate specialized tools and transferred data between them manually. Petrel's integration made subsurface models more internally consistent and reduced data transfer errors, but the key productivity impact was the ability to iterate quickly: a geoscientist could update a geological interpretation, propagate it through the reservoir model, and immediately see the impact on volumetric estimates within hours rather than weeks. The shale revolution of 2008-2015 drove enormous Petrel adoption as unconventional plays required high-volume well planning and multi-well geological correlation across thousands of horizontal wells.
Effect on the workPetrel-era geoscientists became substantially more productive per individual but the technology also raised the skill bar: a Petrel-illiterate geoscientist was effectively unemployable in the petroleum sector from the 2000s onward.
Leapfrog implicit geological modeling (mineral exploration)
Seequent's Leapfrog Geo, first released in 2004 and reaching wide adoption from around 2006-2010, did for mineral exploration what Petrel had done for oil and gas: it unified 3D geological model building from borehole data into a single workflow. The distinguishing innovation was implicit surface modeling, which builds continuous geological surfaces from sparse drill hole data automatically without requiring manual triangulation. A mining geologist who previously spent weeks or months hand-drawing 2D cross-sections and manually constructing 3D wireframes could now iterate a full 3D geological model in days. Leapfrog reached 80% of major mining companies by the 2010s. The productivity gain compressed exploration timelines and allowed smaller companies to run more sophisticated geological analyses.
AI seismic interpretation and ML mineral targeting (SLB DELFI, OpendTect AI, KoBold Metals)
The 2018-2024 wave of AI tools in geoscience represents the most significant productivity shift since the seismic workstation. SLB's seismic foundation model (2024) -- pretrained on terabytes of seismic reflection data -- enables AI-assisted horizon picking and fault detection that compresses weeks of manual seismic interpretation to hours, with S&P Global estimating 30-60% cycle time reduction across major oil company operators. Leapfrog Geo's ML lithological classification from drill core scan images automates high-volume drill logging. For the geoscientist, these tools are powerful accelerants -- not replacements. AI seismic models fail in complex geological settings (fold-thrust belts, salt basins, basement-hosted mineral systems) not well represented in training data; geological QC remains mandatory for capital decisions; and the QP regulatory accountability under SEC SK-1300 and NI 43-101 is structurally irreplaceable by any AI system.
Effect on the workAI tools are compressing the desk-interpretation cycle substantially while demand for geoscientists grows for the tasks AI cannot perform: field work, QP sign-off, geological judgment in novel settings, and the critical minerals exploration needed for the energy transition.
See the same long-arc view for your own profession.
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Browse all rolesGeoscientists, Except Hydrologists and Geographers employment and pay by year
View all 30 years of cited US data, each linked to its source
| Year | US employment | Annual pay (median unless marked) | Source | Report |
|---|---|---|---|---|
| 1870 | 1,100 | n/a | Est., Scholarly estimate of ~1,100… Researched context | Report |
| 1890 | 3,500 | n/a | Est., Estimated ~3,500 professional… Researched context | Report |
| 1930 | 12,000 | n/a | Est., Estimated ~12,000 professional… Researched context | Report |
| 1970 | 28,000 | $13,500 | Est., Estimated ~28,000 US geologists and… (employment) Researched contextEst., University of Missouri Libraries (pay) Researched context | Report |
| 1982 | 48,000 | n/a | Est., SERC Carleton Researched context | Report |
| 1985 | n/a | $38,000 | Est., Augsburg University Researched context | Report |
| 2000 | 21,810 | n/a | BLS OEWS 2000 Official statistic | Report |
| 2003 | 26,090 | $68,460 | BLS OEWS May 2003 Official statistic | Report |
| 2004 | 25,100 | $68,730 | BLS OEWS May 2004 Official statistic | Report |
| 2005 | 27,430 | $71,640 | BLS OEWS May 2005 Official statistic | Report |
| 2006 | 28,980 | $72,660 | BLS OEWS May 2006 Official statistic | Report |
| 2007 | 31,390 | $75,800 | BLS OEWS May 2007 Official statistic | Report |
| 2008 | 31,260 | $79,160 | BLS OEWS May 2008 Official statistic | Report |
| 2009 | 31,860 | $81,220 | BLS OEWS May 2009 Official statistic | Report |
| 2010 | 30,830 | $82,500 | BLS OEWS May 2010 Official statistic | Report |
| 2011 | 32,490 | $84,470 | BLS OEWS May 2011 Official statistic | Report |
| 2012 | 35,180 | $90,890 | BLS OEWS May 2012 Official statistic | Report |
| 2013 | 34,690 | $91,920 | BLS OEWS May 2013 Official statistic | Report |
| 2014 | 34,000 | $89,910 | BLS OEWS May 2014 Official statistic | Report |
| 2015 | 31,800 | $89,700 | BLS OEWS May 2015 Official statistic | Report |
| 2016 | 30,420 | $89,780 | BLS OEWS May 2016 Official statistic | Report |
| 2017 | 28,520 | $89,850 | BLS OEWS May 2017 Official statistic | Report |
| 2018 | 29,260 | $91,130 | BLS OEWS May 2018 Official statistic | Report |
| 2019 | 29,200 | $92,040 | BLS OEWS May 2019 Official statistic | Report |
| 2020 | 27,890 | $93,580 | BLS OEWS May 2020 Official statistic | Report |
| 2021 | 23,620 | $83,680 | BLS OEWS May 2021 Official statistic | Report |
| 2022 | 25,230 | $87,480 | BLS OEWS May 2022 Official statistic | Report |
| 2023 | 24,620 | $92,580 | BLS OEWS May 2023 Official statistic | Report |
| 2024 | 22,510 | $99,240 | BLS OEWS May 2024 (employment) Official statisticBLS OEWS May 2024 (pay) Official statistic | Report |
| 2025 | 23,470 | $101,920 | BLS OEWS May 2025 Official statistic | Report |
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