Abstract Knowledge graph (KG) is a topic of great interests to geoscientists as it can be
deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless …

Modern geological practices, in both industry and academia, rely largely on a legacy of
observational data at a range of scales. However, widespread ambiguities in the …

Artificial intelligence promotes the development of mineral/rock automatic recognition
technology to reduce labor costs and reliance on personal experience. With instrument …

This study investigates the application of machine learning techniques—specifically
convolutional neural networks, multilayer perceptrons and cascaded forward neural …

An intelligent lithology identification method is proposed based on deep learning of the rock
microscopic images. Based on the characteristics of rock images in the dataset, we used …

Carbonate rocks are important archives of past ocean conditions as well as hosts of
economic resources such as hydrocarbons, water, and minerals. Geologists typically …

Shear wave velocity (S-wave velocity) has great significance for reservoir characterization
and can effectively reduce the ambiguity in seismic interpretation. However, owning to its …

A growing number of studies in the spatial estimation of geological features use machine
learning (ML) models, as these models promise to provide efficient solutions for estimation …

Zircon is a widely-used heavy mineral in geochronological and geochemical research
because it can extract important information to understand the history and genesis of rocks …

Segmentation and analysis of individual pores and grains of mudrocks from scanning
electron microscope images is non-trivial because of imaging artifacts, variation in pixel …