Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application
  to Demystify Image Recognition
Jin-Jian Xu Hao Zhang Chao-Sheng Tang Lin Li Bin Shi
As Earth science enters the era of big data, artificial intelligence (AI) not
only offers great potential for solving geoscience problems, but also plays a
critical role in accelerating the understanding of the complex, interactive,
and multiscale processes of Earth's behavior. As geoscience AI models are
progressively utilized for significant predictions in crucial situations,
geoscience researchers are increasingly demanding their interpretability and
versatility. This study proposes an interpretable geoscience artificial
intelligence (XGeoS-AI) framework to unravel the mystery of image recognition
in the Earth sciences, and its effectiveness and versatility is demonstrated by
taking computed tomography (CT) image recognition as an example. Inspired by
the mechanism of human vision, the proposed XGeoS-AI framework generates a
threshold value from a local region within the whole image to complete the
recognition. Different kinds of artificial intelligence (AI) methods, such as
Support Vector Regression (SVR), Multilayer Perceptron (MLP), Convolutional
Neural Network (CNN), can be adopted as the AI engines of the proposed XGeoS-AI
framework to efficiently complete geoscience image recognition tasks.
Experimental results demonstrate that the effectiveness, versatility, and
heuristics of the proposed framework have great potential in solving geoscience
image recognition problems. Interpretable AI should receive more and more
attention in the field of the Earth sciences, which is the key to promoting
more rational and wider applications of AI in the field of Earth sciences. In
addition, the proposed interpretable framework may be the forerunner of
technological innovation in the Earth sciences.