The map service (WMS group) presents the digital map basics from the field of geology of the Saarland.:The map of near-surface raw materials 1: 50 000 (KOR 50) mining areas represent the distribution of usable industrial minerals, stones and earths and form the data basis for the map of near-surface raw materials 1:200 000 of the Federal Republic of Germany.

This service provides data implemented for the INSPIRE topic Geology from the near-surface raw material mining areas, the State Office for Environmental and Occupational Health and Safety.:This layer visualizes the spatial MappedFeature objects of the Saarland geological data (near-surface raw material mining areas), whose specification property is of the type GeomorphologicFeature. The data base complies with the INSPIRE data specification.

Under the Act of June 9, 2011, Geological and Mining Law (Journal of Laws No. 163, item 981, as amended) Polish Geological Institute – National Research Institute (PIG-PIB) performs the role of Polish Geological Survey. One of the tasks of PIG-PIB as Geological Survey is to create and maintain geological databases, in this including the system MIDAS (the System of management and protection of mineral resources in Poland – MIDAS). System MIDAS is the primary source of information on mineral resources of Poland, the exploitation of deposits and it is the source of data for the project Mintell4eu. The System contains information on deposits (and its spatial location), raw materials in deposits, raw materials resources and on the raw materials volumes of exploitation. In addition, it also contains data on mining areas and exploatation permits (concessions) as well as their spatial location. The owner of the colected data is the State Treasury represented by the proper minister resposible for geology. The original MIDAS database structure has been modified and adapted to the structure given in Mintell4eu specification.

Since the end of the 1980ies the geological, areal and production data of operating mining sites have been collected systematically by LGRB. The periodic update of this information is carried out every four or five years. Main reasons are 1) the preparation of the periodic follow-up of the 12 regional development plans, 2) the work on the near-surface mineral raw material maps published by LGRB, and 3) the periodical editing of the state report for near-surface mineral raw materials published by LGRB at the start of each new election period. The geological data include a detailed documentation of the thickness, petrography and quality of mined rock(s) and the overburden as well as geochemical data gained from rock samples. The areal data refer both to the permitted mining area (zones of recultivation, work and expansion) and to possible areas for the mine expansion (the latter are confidential). Due to the quick spatiotemporal variability of these data, here all mining sites are shown as point data. The confidential annual production data are the basis for the periodic raw material report. In addition, another data are collected, e.g. for the mining permission, the delivery area and the subsequent land use. All these data are stored in the mining site database of the LGRB (Rohstoffgewinnungs-stellendatenbank = RGDB). This one comprises also the data for abandoned mining sites and mines. In total, actual (2021) about 14.000 data records are stored. The name of each mining site (e.g. RG 6826-3) consists of three parts. RG is the abbreviation for "Rohstoffgewinnungsstelle". the following four-digit number means the number of the relevant topographic map 1 : 25.000. The last number means the serial number of the mining site; serial numbers 1-99 mark operating mining sites gathered since the end of the 1980ies ( (today partially already closed) , such > 100 mark abandoned mining sites collected before 1980 and such > 300 mark data of mining sites and mines collected in the course of actual raw material mapping.
The mintell4eu data set comprises all mining sites with serial numbers 1-99. In addition, the most important abandoned mines of former or probably still ongoing economic importance.

This service provides data for the INSPIRE topic geology from the near-surface raw material mining areas, the State Office for Environmental and Occupational Health and Safety.:A spatial representation of a GeologicFeature.

Open dataset contains basic information on all registered reserved mineral deposits (B) and approved prognosticated mineral resources of reserved (P) and non-reserved (R) minerals in the territory of the Czech Republic. The Ministry of the Environment issues a certificate of reserved deposit if a reserved mineral is found in quantity and quality that allow its accumulation to be reasonably expected. The data of subregisters B, P, R are part of the Raw Materials Information System (SurIS) of the Czech Geological Survey.

Since 1999, the Geologic Survey of Baden-Württemberg publishes a statewide geological map series 1 : 50 000 "Karte der mineralischen Rohstoffe 1 : 50 000 (KMR 50)". On it, the distribution of near-surface mineral raw material prospects and occurrences (mainly) and deposits (subordinate) is shown. This continuously completed and updated map currently covers around 60% of the federal state. It is the base for the regional associations in the task of mineral planning.

The prospects and occurrences are classified according to different raw material groups (e.g. raw material for crushed stone (limestone, igneous rocks, metamorphic rocks, sand and gravel), raw materials for cement, dimension stone, high purity limestone, gypsum ...). Their spatial delineation is based on various group-specific criteria such as minimum workable thickness, minimum resources, ratio overburden/workable thickness, and so on. It is assumed that they contain deposits as a whole or in parts. In the vast majority of cases, the data is not sufficient for the immediate planning of mining projects, but it does facilitate the selection of exploration areas.

The name of each area (e.g. L 6926-3) consists of three parts. L = roman rnumeral fo 50, 6926 = sheet number of the topographic map 1 : 50 000, 3 = number of the area/mineral occurrence shown on this sheet.

Co-occurring land-use conflicts, e.g. water protection areas and nature conservation areas, forestry and agriculture, are not taken into account in the processing of KMR 50. Their assessment is the task of land use planning, the licensing authorities and the companies interested in mining.

The data is stored in the statewide raw material area database "olan-db" of the LGRB.

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

MIN4EU DB consists of two parts: Minerals Inventory data and Minerals Yearbook data. Minerals Inventory covers mineral occurrences and mines in Europe (onshore). The European Union has identified security of supply, improving environmental management and resource efficiency as key challenges for the raw materials sector. Data on the location and spatial distribution of primary and secondary raw materials in relation to exploration, exploitation, production and trading activities form the basis for decision-making in government and industry. Given the dynamic nature of such data, regular updates of comprehensive, reliable and harmonized information across borders are required, as there are several sources of non-harmonized data with different coverages developed over the last decades by national and international projects for different purposes. Data have been prepared and collected in the projects Minerals4EU, EURare, ProSUM, ORAMA, RESEERVE and MINTELL4EU, and others and are shared in the European Geological Data Infrastructure (EGDI).

Supplementary Materials for Specimens 360 Degree Rotation

Under the Act of June 9, 2011, Geological and Mining Law (Journal of Laws No. 163, item 981, as amended) Polish Geological Institute – National Research Institute (PIG-PIB) performs the role of Polish Geological Survey. One of the tasks of PIG-PIB as Geological Survey is to create and maintain geological databases, in this including the system MIDAS (the System of management and protection of mineral resources in Poland – MIDAS). System MIDAS is the primary source of information on mineral resources of Poland, the exploitation of deposits and it is the source of data for the project Mintell4eu. The System contains information on deposits (and its spatial location), raw materials in deposits, raw materials resources and on the raw materials volumes of exploitation. In addition, it also contains data on mining areas and exploatation permits (concessions) as well as their spatial location. The owner of the colected data is the State Treasury represented by the proper minister resposible for geology.

From the site: "Abandoned mine features compiled by the Office of Abandoned Mine Lands and Reclamation (AMLR) of the West Virginia Department of Environmental Protection. The AMLR eliminates damage that occurred from mining operations prior to August 3, 1977 and is funded by the AML fund. It corrects hazardous conditions and reclaims abandoned and forfeited mine sites. Typical AML features include highwalls, portals, refuse piles, and mining structures such as tipples.

AML features were digitized from AMLR source materials by the WVU Department of Geology and Geography and the WVU Natural Resource Analysis Center. Published in 1996."

This upload includes the data set as part of CERN's FCCIS "Mining The Future" competition measured at Université de Genève in Geneva, Switzerland.

Data were collected by Faculty of Mining and Geology, Serbia, in frame of RESEERVE project.

Regionally significant basic raw materials 1:100 000 contains geoscientific data relating to basic raw material resources including clay, limesand, limestone, hard rock aggregate, sand and gravel. The data are held in GDA 94

License: Creative Commons Attribution

Tags:100 000, Aggregate, Basic Raw Materials, Clay, Geology, Geosciences, Gravel, Industry, Limesand, Limestone, Minerals, Mining, Sand

Contact: landuseplanning@dmirs.wa.gov.au

The preparation of this map was preceded by the preparation of the Mineral Resources Cadastre of the Republic of Srpska. The process of creating the cadastre itself is an extensive multi-year work that includes a field work as well as electronic data processing. The data from the cadaster are ploted on the previously prepared Geological map and the result of this process is the Mineral Resources map of the Republic of Srpska.

Metadata and data derived from Chouteau Mine Documents. This collection contains mining documents for properties and claims within Chouteau County in southwest Montana. These documents include assays, correspondence, maps, news clippings, photos, publications, reports, surveys, or other material related to any property. This webpage allows viewers to browse and search by claim or district on the navigation tabs above. Or you can search any keywords into the search bar. The 'Map' or 'Timeline' tabs may also aid in research and finding material using different contexts. Please note that this collection is incomplete as it is a work in progress. If you do not find what you are looking for here, please contact an MBMG Data Preservation staff member.

AI Mineral Exploration Systems Market Outlook 2025-2034

The global AI Mineral Exploration Systems market was valued at $1.2 billion in 2025 and is projected to reach $5.4 billion by 2034, registering a robust compound annual growth rate (CAGR) of 17.0% over the forecast period 2026-2034. AI-powered mineral exploration systems are fundamentally transforming how the mining and resource sector identifies, maps, and evaluates subsurface deposits of critical and strategic minerals. By integrating machine learning algorithms, computer vision, autonomous sensor arrays, and high-resolution satellite imagery with traditional geological datasets, these systems dramatically reduce the time, cost, and environmental footprint of mineral discovery campaigns. The convergence of urgent global demand for battery-grade lithium, cobalt, nickel, rare earth elements, and copper - driven by the accelerating energy transition - with the maturation of AI inference hardware and cloud-based geospatial analytics platforms has created an ideal commercial environment for these technologies to achieve rapid mainstream adoption across exploration-stage juniors, mid-tier miners, and major diversified mining corporations alike.

The primary growth engine for AI Mineral Exploration Systems through 2034 is the global surge in critical minerals demand tied to electric vehicle (EV) battery manufacturing, grid-scale energy storage deployment, and advanced defense electronics production. Governments across North America, Europe, and the Asia Pacific have enacted strategic minerals policies - including the U.S. Critical Minerals Strategy, the EU Critical Raw Materials Act, and Australia's Critical Minerals Strategy - that channel significant public funding toward exploration innovation. Traditional exploration programs are notoriously capital-intensive and uncertain, with discovery-to-production timelines often spanning 10-20 years and greenfield discovery rates declining year over year as surface-exposed deposits are exhausted. AI Mineral Exploration Systems address this structural challenge by processing terabytes of geophysical, geochemical, and remote sensing data to identify buried ore systems at depths and in geological settings that were previously impractical to target using conventional techniques. Leading platforms now integrate AI subsurface geology modeling with real-time downhole data from autonomous drilling systems, compressing early-stage target generation cycles from 18-24 months to as little as 4-6 months. In 2026, the deployment of super-resolution satellite imaging systems capable of detecting subtle surface expression signatures associated with deep-seated mineral systems is expected to unlock exploration activity across historically underexplored terrains in Africa, Central Asia, and South America. The market is further supported by falling AI compute costs, improved open-source geoscience foundation models, and a growing cohort of specialized exploration AI vendors competing with major geoscience software providers on the basis of discovery success rate, interpretability, and integration with existing mine planning workflows.

The data set for the Porcupine Wash quadrangle has been prepared by the Southern California Areal Mapping Project (SCAMP), a cooperative project sponsored jointly by the U.S. Geological Survey and the California Division of Mines and Geology. The Porcupine Wash data set represents part of an ongoing effort to create a regional GIS geologic database for southern California. This regional digital database, in turn, is being developed as a contribution to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS. The Porcupine Wash database has been prepared in cooperation with the National Park Service as part of an ongoing project to provide Joshua Tree National Park with a geologic map base for use in managing Park resources and developing interpretive materials.

The digital geologic map database for the Porcupine Wash quadrangle has been created as a general-purpose data set that is applicable to land-related investigations in the earth and biological sciences. Along with geologic map databases in preparation for adjoining quadrangles, the Porcupine Wash database has been generated to further our understanding of bedrock and surficial processes at work in the region and to document evidence for seismotectonic activity in the eastern Transverse Ranges. The database is designed to serve as a base layer suitable for ecosystem and mineral resource assessment and for building a hydrogeologic framework for Pinto Basin.

This data set maps and describes the geology of the Porcupine Wash 7.5 minute quadrangle, Riverside County, southern California. The quadrangle, situated in Joshua Tree National Park in the eastern Transverse Ranges physiographic and structural province, encompasses parts of the Hexie Mountains, Cottonwood Mountains, northern Eagle Mountains, and south flank of Pinto Basin. It is underlain by a basement terrane comprising Proterozoic metamorphic rocks, Mesozoic plutonic rocks, and Mesozoic and Mesozoic or Cenozoic hypabyssal dikes. The basement terrane is capped by a widespread Tertiary erosion surface preserved in remnants in the Eagle and Cottonwood Mountains and buried beneath Cenozoic deposits in Pinto Basin. Locally, Miocene basalt overlies the erosion surface. A sequence of at least three Quaternary pediments is planed into the north piedmont of the Eagle and Hexie Mountains, each in turn overlain by successively younger residual and alluvial deposits.

The Tertiary erosion surface is deformed and broken by north-northwest-trending, high-angle, dip-slip faults and an east-west trending system of high-angle dip- and left-slip faults. East-west trending faults are younger than and perhaps in part coeval with faults of the northwest-trending set.

The Porcupine Wash database was created using ARCVIEW and ARC/INFO, which are geographical information system (GIS) software products of Environmental Systems Research Institute (ESRI). The database consists of the following items: (1) a map coverage showing faults and geologic contacts and units, (2) a separate coverage showing dikes, (3) a coverage showing structural data, (4) a scanned topographic base at a scale of 1:24,000, and (5) attribute tables for geologic units (polygons and regions), contacts (arcs), and site-specific data (points). The database, accompanied by a pamphlet file and this metadata file, also includes the following graphic and text products: (1) A portable document file (.pdf) containing a navigable graphic of the geologic map on a 1:24,000 topographic base. The map is accompanied by a marginal explanation consisting of a Description of Map and Database Units (DMU), a Correlation of Map and Database Units (CMU), and a key to point-and line-symbols. (2) Separate .pdf files of the DMU and CMU, individually. (3) A PostScript graphic-file containing the geologic map on a 1:24,000 topographic base accompanied by the marginal explanation. (4) A pamphlet that describes the database and how to access it. Within the database, geologic contacts , faults, and dikes are represented as lines (arcs), geologic units as polygons and regions, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum and link it to other tables (.rel) that provide more detailed geologic information.

Map nomenclature and symbols

Within the geologic map database, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. The authors have attempted to adhere to the stratigraphic nomenclature of the U.S. Geological Survey and the North American Stratigraphic Code, but the database has not received a formal editorial review of geologic names.

Special symbols are associated with some map units. Question marks have been added to the unit symbol (e.g., QTs?, Prpgd?) and unit name where unit assignment based on interpretation of aerial photographs is uncertain. Question marks are plotted as part of the map unit symbol for those polygons to which they apply, but they are not shown in the CMU or DMU unless all polygons of a given unit are queried. To locate queried map-unit polygons in a search of database, the question mark must be included as part of the unit symbol.

Geologic map unit labels entered in database items LABL and PLABL contain substitute characters for conventional stratigraphic age symbols: Proterozoic appears as 'Pr' in LABL and as '<' in PLABL, Triassic appears as 'Tr' in LABL and as '^' in PLABL. The substitute characters in PLABL invoke their corresponding symbols from the GeoAge font group to generate map unit labels with conventional stratigraphic symbols.