Seminar

Extensive exploration of novel machine learning (ML) technologies within the domain of geoscience has been actively pursued. This pursuit is driven by two primary factors: Firstly, there exists a wealth of concepts within geoscience that lend themselves to mathematical formulation, enabling more robust quantitative analyses. Secondly, the field of geoscience is inherently data-rich, offering fertile ground for the development of novel ML models tailored to geoscience. However, the integration of ML with geoscience is in its early stages and unevenly advancing. This study aims to contribute to this interdisciplinary research field by taking a modest step forward. The study concentrates on two main research subjects: (1) natural hazard data analyses and (2) data-driven geotechnical engineering applications. These two subjects encompass a total of five specific research topics: (1) classic ML for classification, (2) classic ML for regression, (3) supervised learning by convolutional neural network (CNN), (4) unsupervised learning by CNN, and (5) deep learning (DL) with 3D input data. These five research topics cover a wide range of contents, including (1) boulder fall volume range prediction, (2) seismic source localization, (3) rock type classification, (4) low-light rock image enhancement, and (5) 3D point cloud filtering. They collectively contribute to the integration of ML in geoscience.

Additional information: Mr. ZHOU Yimeng, zhouym52@connect.hku.hk