Satellite Remote AI Intelligent Exploration AI-Driven · Deep Target Detection Technical Solution Service Provider
Integrating targeted ultra-weak NMR AI detection technology with AI + Geoscience cross-disciplinary innovation and a space-air-ground integrated application model, we identify and locate deep mineral resource targets to improve exploration efficiency through data intelligence.
About Us
Professional Team · Innovative Technology
Deep-Explor®（Deep Exploration） provides AI prospecting, satellite remote sensing analysis, and multi-modal geological data fusion for large and medium-sized mining-rights projects, mainly for early-stage target screening and exploration prioritization. Results must be combined with geological data, field surveys, or drilling verification and do not replace resource estimation.
Deep Exploration (Beijing) Technology Co., Ltd. was founded on November 8, 2019. As a pioneering technology enterprise exploring Earth from space, Deep Exploration combines multi-modal heterogeneous data AI intelligent prospecting and geophysical exploration technology, with targeted ultra-weak NMR detection technology, through an integrated AI + Geoscience space-air-ground application model.
Our multidisciplinary team brings together theoretical physics, geology, mineral exploration, and artificial intelligence — an internationally competitive AI-integrated exploration team. Core technology fuses particle physics, quantum mechanics, radiochemistry, mathematics, and AI to detect and interpret intrinsic material spectral information, pushing the company's 'deep detection' capability to new industry heights.
The company independently developed the Deep-Explor® OS multi-modal heterogeneous data processing platform, which integrates satellite remote sensing (VNIR / SWIR / TIR spectroscopy, SAR, DEM, etc.), geophysical survey (gravity, magnetic, electrical, seismic, radiometric, etc.), geochemical tracing, and geological structure interpretation into a unified high-dimensional digital representation. Through AI models such as Physics-Informed DeepONet, it mines synergistic patterns across multi-modal data, enabling engineering-scale technology deployment.
Government Recognition: Deep-Explor®（Deep Exploration） technology was recognized in 2023 by the Ministry of Natural Resources of China in official document [2023] No. 27, distributed to provincial natural resources departments as a recommended new technology.
Core Patents
Industry Recognition
![Ministry of Natural Resources Document [2023] No. 27](/img/recognition-document.png)
Solutions
Satellite-based remote non-invasive geological exploration — precise detection up to 2,000m for solid minerals and 8,000m for oil & gas
Multi-Modal AI Prospecting Solution
Built on a nonlinear systems cybernetics framework, this technology treats metallogenic systems as open dynamic geological systems. By fusing multi-modal heterogeneous data to construct geological process proxy variables and applying Physics-Informed DeepONet for joint inversion and feature extraction, AI drives the entire prospecting decision-making process, enabling probability distribution prediction of shallow/deep concealed ore bodies. Suitable for multi-mineral and large-area exploration target delineation.
- ◆ Fuses multi-source data including satellite RS, geophysics, geochemistry, geological structure, and lithological interpretation.
- ◆ Processes data with varying formats, scales, and noise patterns, constructing unified high-dimensional digital representations for AI model analysis.
- ◆ Outputs mineralization/petroleum probability distribution predictions.
Virtual Drilling
Based on multi-modal heterogeneous data AI mineralization prediction zones, virtual drilling technology detects high-potential areas using a grid distribution approach. Virtual drilling integrates targeted ultra-weak NMR and intelligent drilling layout algorithms, constructing a smart exploration closed-loop of 'simulate first, verify later'. Single-pixel virtual borehole tests extract mineral spectral features, and the POMDP-MCTS decision model predicts single-pixel ore discovery probability. Combined with AI inversion of anomaly distribution, tens of thousands of virtual drilling simulations are completed in-computer, ultimately outputting anomaly target interpretation results.
- ◆ Builds a 'spectral fingerprint' library of mineral element/compound samples, and uses targeted technology to detect and interpret target mineral signals at candidate drill positions in a laboratory setting.
- ◆ Builds POMDP-MCTS algorithm; through AI inversion, completes ore discovery probability prediction for candidate drill positions.
- ◆ Outputs Top-N recommended drill holes; after physical drilling verification, data is fed back for rolling optimization, forming a closed-loop intelligent exploration process.
Full-Process Resource Exploration Solution
Multi-source data acquisition → multi-modal heterogeneous data fusion & feature extraction → mineralization probability distribution output → targeted NMR detection & interpretation → deep target identification & positioning → field verification data feedback for optimization, forming a closed exploration loop.
- ◆ Anomaly target contour and coordinate data
- ◆ Mineral resource burial depth range
- ◆ Resource radiation intensity: I = W · V · G / D
Service Scenarios
Covers new resource assessment, concealed ore vein discovery, and reserve enhancement/production increase for existing mines; applicable to large-area exploration across multiple mineral types.
- ◆ Solid minerals (metals, non-metals)
- ◆ Energy resources (oil, natural gas)
- ◆ Groundwater (geothermal) resources
Service Capabilities
Short cycle, high accuracy — reducing uncertainty in traditional exploration, dramatically improving efficiency and accuracy
Case Studies
Over 70 projects completed as of August 2026. Selected case studies below:
12 km² Gold Project, Zimbabwe
The mining area borders a local residential area, making field work impractical in some blocks. To plan the next phase of exploration and mining, an international mining company signed a 12 km² remote AI detection service agreement with us. Upon delivery, 66 gold anomaly zones were delineated in two months...
Gold Mine Remote Exploration, Guinea, Africa
The client was unable to send field experts due to the pandemic. Through satellite remote scanning combined with AI data analysis, 12 potential gold anomaly zones were successfully identified.
Copper Mine Remote Exploration, Xizang, China
The mine had 20 years of traditional exploration history with ~10M tons of proven copper. Deep-Explor®（Deep Exploration） detected 18 anomaly zones with an estimated ~10M additional tons of copper resources.
News
Latest updates and industry insights
Deepening Roots in Papua New Guinea for Win-Win Cooperation | Deep Exploration Attends the 28th China Mining Congress
Deep Exploration attended the 2026 (28th) China Mining Convention in Tianjin, holding in-depth talks with Papua New Guinea's Mining Minister Solen Loifa and the official delegation. As of September 2026, all 9 of the company's copper-gold exploration licenses have been registered in PNG, with 3 core licenses completing all official legal hearing procedures.
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A Letter of Appreciation from Argentina: AI Exploration Results Confirmed by Drilling
Deep Exploration has received a letter of appreciation from Alto Grande Lithium S.A. of Argentina. Two years after Deep Exploration's satellite-based remote AI survey of the client's 4.35 km² block in the Arizaro salt flat — which delineated 17 polymetallic anomaly zones — the client's drilling confirmed the predicted locations with high accuracy.
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Deep Exploration and Ivory Coast Partner to Advance Oil & Gas Pilot Project Cooperation
Deep Exploration held an in-person cooperation meeting with oil trading clients from Ivory Coast. The two sides agreed to negotiate an MOU and launch pilot projects on one to two oil and gas blocks — a substantive step into the West African oil and gas market.
Read more →Partners
As of August 2026, 50+ clients across 15 countries and regions in Asia, Africa, Oceania, North America, and South America
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