The Industrial Source Complex 3 (ISC3) model, created by the United States Environmental Protection Agency (USEPA), is the best available model for estimating the dispersion of particulate matter less than 10 microns in diameter (PM₁₀) from surface mining operations; its use is required in applications for surface mining permits. However, it has been proven to over-predict actual PM₁₀ dispersion concentrations by a factor of 2 – 5. A newly developed model focuses on estimating PM₁₀ dispersion from haul trucks, since past research has shown that the majority (80 – 96%) of PM₁₀ emissions from surface mining operations are from mobile sources. This model is a promising improvement over the ISC3 model. Modeling exercises using the new model on actual surface mine hauling operations demonstrated a 77% improvement over the ISC3 model.

In recent years, engineering disciplines have begun to experience a revolution based on the use of information technology. In particular, the use of computer graphics and visualization have allowed engineers to gain unprecedented insight into both spatial and abstract datasets, resulting in better designs, more in-depth understanding, and more efficient analysis. In this paper, we argue that many engineering datasets are best visualized in three dimensions because of their inherent three-dimensional (3D) spatial properties. Traditional 3D visualizations are still limited, however. In order to mitigate these limitations, we describe two additional technologies that will further increase the benefits of 3D engineering visualizations. First, immersive virtual environments (VEs) allow an engineer to "get inside" the data and view it naturally, resulting in greater spatial understanding and potentially greater insight. Second, interactive visualizations allow the user to navigate within the 3D space, change the visualization parameters, query objects, and even test design ideas, clearly adding value to the visualization. When these two technologies are combined, a need arises for 3D user interfaces (3D UIs), which provide interactivity within an immersive VE. Throughout our discussion of these technologies, we draw upon our own experience with two interactive VEs for engineering — AMADEUS and Virtual-SAP.

The variations of seismic velocity prior to the occurrence of major seismic events are an indicator of the rock mass performance subjected to mining-induced stress. There have been no prior field-scale studies to examine stress change within the rockmass volume immediately prior to potentially damaging mining-induced seismicity. Monitoring stress change is critical for mine stability and operation safety and eventually improves production by optimizing mine designs and mining practices. In this study, five major seismic events that occurred in a narrow-vein mine were used as case studies in order to investigate any significant changes in P-wave velocity distribution, on a daily basis, within a week of seismic events with Mw > 1; if observed, such changes could provide a warning to mine engineers and workers. It was observed there was no consistent significant velocity change of more than 1% within 200 m of the hypocenters within 6 days prior to the events. Additionally, the influence of blasting in the week of the occurrence of events was investigated however no recognizable trend was observed between blasting and changes in the seismic velocity distribution within the rock mass on the day of a blast or the following day.

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments.