Towards Smart Mining: An Integrated Process for High-Accuracy, High-Resolution, and Time-Lapse Geological Modeling

Smart mining integrates advanced geological, geophysical, and digital technologies—such as artificial intelligence (AI), the Internet of Things (IoT), robotics, and real-time monitoring—into traditional mining operations. This paradigm shift enhances efficiency, safety, and sustainability by enabling precise resource extraction, optimized resource management, and reduced environmental impact. As the mining industry faces challenges like resource depletion and environmental constraints, the adoption of smart mining methods becomes crucial for sustainable operations.

Central to smart mining is a high-accuracy, high-resolution, and time-lapse geological model (HHT geological model), which provides critical data for applications such as adaptive mining path planning, resource management, hazard assessment, and operational monitoring. Current geological models, while effective in some automated mining processes, lack dynamic coupling with mining equipment and disaster simulation tools, limiting their real-time applicability.

To address these limitations, we propose an integrated workflow to construct the HHT geological model: (1) Geophysical Exploration and Interpretation: Using multi-modal geophysical techniques (e.g., well logs, seismic surveys, transient electromagnetics), we invert geological properties (e.g., seismic impedance, wave speed, resistivity) and interpret structural features such as horizons, faults, voids, rock facies, and mineral boundaries. (2) Model Generation: Employing Triangulated Irregular Network (TIN) methods to create a detailed 3D geological framework. (3) Dynamic Updates via Continuous Monitoring: Utilizing data from seismic while mining (SWM), 4D seismic, and joint microseismic-electromagnetic monitoring to update the geological model as mining progresses.

The Key Innovations of our proposed workflow have three aspects: (1) we integrate geological, petrological, seismic, and electromagnetic data, combined with mining-induced seismic events, machinery running parameters, and video/image recognition technologies to enable high-resolution imaging and detection of coal seam thickness, fault zones, goaf areas, and subsidence columns, providing a comprehensive understanding of geological structures. (2) We apply Seismic While Mining (SWM) technology, which acquires continuous seismic data during mining operations, processed through reverse-time migration, cross-correlation, denoising, and source wavelet extraction, to dynamically image geological changes. A six-component seismometer further enhances constraints via virtual sonic well logging. (3) We apply the Real-time TIN regeneration method which incorporates the discrepancies between SWM-derived images and the prior model, ensuring accurate updates during mining.

We tested the platform in an underground coal mine near Erdos, Inner Mongolia, China, the SWM method successfully identified faults along a tunnel, later confirmed by mining reports. These results demonstrate the effectiveness of the integrated HHT geological model in revealing hidden geological features.

In conclusion, the HHT geological modeling is fundamental for realizing true smart mining. Merging multi-source geophysical data establishes a reliable seismic baseline, while the SWM system provides critical real-time monitoring of roof deformation, stress distribution, water infiltration, and rock bursts. The integration of these methods is essential to achieving a "transparent geological model" and advancing towards sustainable and intelligent mining practices.