Geologic Core Sample Images
Buy and sell geologic core sample images data. Photographed drill cores showing rock layers, mineral composition, and formation boundaries. Mining and oil exploration AI analyzes core imagery.
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What Is Geologic Core Sample Images?
Geologic core sample images are photographs of drill cores extracted during oil, gas, and mining exploration operations. These images capture rock layers, mineral composition, formation boundaries, and sedimentary structures in high resolution, typically ranging from 2129 × 2969 pixels with variable resolution depending on lighting and imaging protocols. Core imagery serves as a visual record of subsurface geology and contains rich compositional and textural information valuable to geologists and geotechnical engineers. The data is particularly important for understanding reservoir characteristics, guiding unconventional play development, and training machine learning models used in mineral and hydrocarbon exploration.
Market Data
20 cents per photo; 4-5 cents per meter
Average Collection Cost
Source: Datarock
400 box core images in Dataset 1; 100 images in Dataset 2
Typical Dataset Size (Academic Example)
Source: PubMed Central
2129 × 2969 pixels average; 72–2400 dpi variable
Standard Image Resolution
Source: PubMed Central
Who Uses This Data
What AI models do with it.do with it.
Oil & Gas Exploration
Operators analyze core imagery to guide reservoir evaluation and unconventional play development. Core-derived imagery reveals details critical for hydrocarbon potential assessment and well placement decisions.
Mining & Mineral Exploration
Mining engineers use core images to assess mineral deposits, evaluate rock quality designation (RQD), and classify lithologies and formations for resource estimation.
Machine Learning & AI Training
ML models are trained on core imagery for automated rock classification, fracture detection, mineral distribution analysis, and depth registration. Core photos provide training data that captures compositional and textural variability.
Geotechnical Engineering & Academic Research
Universities and research institutions use core images for hands-on geoscience training, core description, structural analysis, and environmental studies. Core photos provide foundational data with high spatial coverage.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
High-Resolution Imagery
Images must be captured with consistent lighting and camera setup to ensure clarity of mineral composition, fracture patterns, and sedimentary layers. Variable dpi (72–2400 range) is acceptable if documented; consistency within datasets improves ML training outcomes.
Detailed Metadata & Cataloging
Core images require comprehensive documentation including depth registration, formation name, lithology classification, depositional environment (estuary, shoreface, offshore, delta), geographic location, and well information for reproducibility and searchability.
Spatial Coverage & Representativeness
Datasets should capture geological variability across lithologies (mudstone, sandstone, conglomerate, etc.) and depositional environments. High spatial coverage and long-lived datasets are valued as foundational data for ML training and exploration programs.
Condition & Handling Standards
Core imagery should follow consistent photography protocols. Even lower-quality historical images retain analytics value if properly documented. Images must be artifact-free and depth-registered for automated analysis and segmentation.
Potential applications and organizations
Who's buying.buying.
Provides publicly accessible core imagery datasets (Price River formations) for industry research, drilling analysis, and fluvial/shoreface depositional studies.
Specializes in processing and analyzing core imagery for geotechnical variables, color analysis, and ML-based rock classification. Extracts compositional and textural data from historical core photos.
Maintains one of the largest public core repositories (9,800 rock cores, 53,000 well cuttings, 25,000+ thin sections) and provides cataloged core data and access for research and industry.
Supplies geological core samples for research, testing, and training. Supports oil & gas operators, universities, and CT scanning labs with precision-prepared cores meeting exact tolerances.
FAQ
Common questions.questions.
What formats are geologic core sample images sold in?
Core images are typically provided as high-resolution digital photographs (JPG, PNG, TIFF) with variable resolutions ranging from 72–2400 dpi. Metadata, depth registration information, and formation cataloging are delivered alongside raw imagery. Some datasets include 3D visualization files or depth-registered strip imagery derived from processed core photos.
Who owns historical core imagery, and can it be monetized?
Historical core photos are often held by exploration companies, mining operations, and government repositories like the USGS Core Research Center. Private operators and exploration companies can monetize proprietary core collections through licensing agreements with AI/ML firms, research institutions, and competing exploration companies. Public repositories require appointment-based access and may have restrictions on commercial use.
What quality issues reduce the value of core imagery?
Poor lighting, inconsistent camera angles, variable resolution, and lack of depth registration can reduce ML training utility. However, even lower-quality historical images retain value if they are well-documented and represent rare formations or geological variability. Missing metadata, contaminated or fragmented samples, and images without formation context significantly diminish buyer interest.
How are core images used in AI and machine learning?
Core imagery datasets are used to train models for automated rock classification, lithology identification, fracture detection, mineral distribution mapping, and RQD (rock quality designation) analysis. ML pipelines segment individual rock fragments, detect structural features, and register depth information. Large, diverse datasets representing multiple formations and depositional environments are most valuable for generalizable model development.
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