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Accurate identification and understanding of various metallic minerals are crucial for deciphering geological formations, structures, and ages. Giving their pivotal role as essential natural resources, a microscopic exploration of metallic minerals becomes imperative. Traditional analytical methods, while helpful, exhibit certain limitations. However, terahertz time-domain spectroscopy, distinguished by its high signal-to-noise ratio, expansive frequency band, and low incident wave energy, is a promising complement to conventional techniques in characterizing metallic minerals. This study employs terahertz time-domain spectroscopy to examine samples of Stibnite, Sphalerite, Galena, and Pyrite originating from diverse geological conditions. The vibrations of molecules within these metallic minerals induce discernible changes in the terahertz spectra. Our findings untiate the extensive potential of terahertz time-domain spectroscopy in the characterization of metallic minerals, affirming its considerable practical value in mineral resource exploration.

To efficiently address the challenge of thermoelastic coupling in functionally graded materials, we propose an approach that combines the radial integral boundary element method (RIBEM) with proper orthogonal decomposition (POD). This integration establishes a swift reduced-order model to transform the high-dimensional system of equations into a more manageable, low-dimensional counterpart. The implementation of this reduced-order model offers the potential for rapid numerical simulations of functionally graded materials (FGMs) under thermal shock loading. Initially, the RIBEM is utilized to resolve the thermal coupling issue within the FGMs. From these solutions, a snapshot matrix is constructed, capturing the solved temperature and displacement fields. Subsequently, the POD modes are established and a POD reduced-order model is constructed for the boundary element format of the thermally coupled problem. Finally, a system of low-order discrete differential equations is solved. Numerical experiments demonstrate that the results obtained from the reduced-order model closely align with those of the full-order model, even when considering variations in structural parameters or impact loads. Thus, the introduction of the reduced-order model not only guarantees solution accuracy but also significantly enhances computational efficiency.