Data-driven geophysics: from dictionary learning to deep learning
- URL: http://arxiv.org/abs/2007.06183v2
- Date: Tue, 29 Sep 2020 04:12:34 GMT
- Title: Data-driven geophysics: from dictionary learning to deep learning
- Authors: Siwei Yu and Jianwei Ma
- Abstract summary: "Model-driven" approaches to geophysics suffer from the curse of dimensionality and may inaccurately model the subsurface.
"Data-driven" techniques may overcome these issues with increasingly available geophysical data.
- Score: 3.6713387874278247
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding the principles of geophysical phenomena is an essential and
challenging task. "Model-driven" approaches have supported the development of
geophysics for a long time; however, such methods suffer from the curse of
dimensionality and may inaccurately model the subsurface. "Data-driven"
techniques may overcome these issues with increasingly available geophysical
data. In this article, we review the basic concepts of and recent advances in
data-driven approaches from dictionary learning to deep learning in a variety
of geophysical scenarios. Explorational geophysics including data processing,
inversion and interpretation will be mainly focused. Artificial intelligence
applications on geoscience involving deep Earth, earthquake, water resource,
atmospheric science, satellite remoe sensing and space sciences are also
reviewed. We present a coding tutorial and a summary of tips for beginners and
interested geophysical readers to rapidly explore deep learning. Some promising
directions are provided for future research involving deep learning in
geophysics, such as unsupervised learning, transfer learning, multimodal deep
learning, federated learning, uncertainty estimation, and activate learning.
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