Browsing by Author "Zhang, Julin"

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    The applications of data science to petrology and geochemistry
    (2020-12-04) Zhang, Julin; Lee, Cin-Ty
    Data science has been widely applied to a lot of disciplines in recent years. However, few relevant works have been done in the field of geology. Therefore, this thesis aims to discuss the application and potential of data science in petrology and geochemistry. The most common data in petrology and geochemistry are rock images and geochemical compositions of rocks. The color and texture of igneous rocks are closely related to their diagenetic process while geochemical compositions can be used to decipher the source of rocks and the physical and chemical processes they undergo before they solidify. The development of large geochemical databases such as EarthChem and GeoRoc provides the geochemists with convenience for accessing the big data and studying the origin and evolution of Earth. However, the lack of a large rock image database hinders the potential of using data science to better understand the formation of rocks. Therefore, in this thesis, I propose an image processing workflow that allows geologists to easily acquire and report standardized rock images with smartphones, contributing to the buildup of the rock image database. I also use image analysis methods to discuss the origin of orbicular granitoids from Mount Magnet, western Australia. Moreover, I use machine learning tools to explore the relationship between heat-producing elements and major elements of the igneous rocks, providing the implication on the thermal state of the lower crust during the Archean.
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    Using computer-aided image processing to estimate chemical composition of igneous rocks: A potential tool for large-scale compositional mapping
    (Elsevier, 2021) Zhang, Julin; Lee, Cin-Ty A.; Farner, Michael; Earth, Environmental and Planetary Sciences
    Digital cameras, particularly on smartphones, have led to the proliferation of amateur photographers. Of interest here is the use of smartphone cameras to conduct rapid, low-cost compositional mapping of geologic bedrock, such as plutons and batholiths, in combination with chemical analyses of rocks in the laboratory. This paper discusses some of the challenges in geochemical mapping using image analysis. We discuss methods for color calibration through a series of experiments under different light intensities and conditions (spectra). All indoor and outdoor experiments show good reproducibility, but suffer from biases imparted by different light intensities, light conditions, and camera exposure times. These biases can be greatly reduced with a linear color calibration method. Over-exposed and under-exposed images, however, cannot be fully calibrated, so we discuss methods that ensure images are properly exposed. We applied our method to 59 natural granitoid and mafic enclave samples of known chemical composition. Multivariate linear regression has been explored for relating calibrated rock images with chemical compositions. Among all the chromatic and texture features of rock images, we show that average gray levels strongly correlate with major oxide concentrations. Subtle variations in bulk composition can potentially be rapidly assessed using calibrated photographs of outcrops.