Geophysical inversion and interpretation are the crucial means to realize the value of the geophysical data obtained at a high cost. Geophysical inversion and interpretation are one with two sides. Inversion is the means of interpretation, while interpretation is the purpose of inversion. Geophysical inversion inverts the physical property parameter of the geological targets based on the geophysical model function, such as velocity, density, resistivity, and so on. The interpretation infers the property of the geological target based on its physical property parameter obtained by the inversion and judges whether it is the target object looking for, such as a gas reservoir or a mineral ore body.
Geophysical inversion and interpretation is a comprehensive discipline involving physics, mathematics, geology, computational technology, and engineering techniques. Geophysical inversion is basically a nonlinear mapping problem. Geophysical interpretation is essentially a classification problem guided by geological knowledge. Being enslaved to the incompleteness and the inaccuracy of the data, the complexity, and diversity of the geological targets, and the approximation of the physical and mathematical models, reliable inversion and interpretation in complex cases is the most challenging field in the inversion community and the geoscience community.
The new generation artificial intelligence represented by deep learning has both powerful statistical nonlinear modeling and classification capabilities. So, it is believed that deep learning could play an essential role in solving complex geophysical inversion and interpretation problems. Deep learning has been successfully applied to many single geophysical inversion and interpretation problems, such as first arrival travel time picking, fault identification, earthquake event auto-detection, fault identification, seismic facies identification and classification, seismic wave impedance inversion, electromagnetic wave inversion, seismic velocity modeling, etc. However, the powerful potential of the new generation artificial intelligence in solving complex geophysical inversion and interpretation problems is far from being realized.
To promote the application of the new generation artificial intelligence in solving the complex geophysical inversion and interpretation problems, and to promote the progress of geophysical inversion and interpretation methods and techniques, we initiated this research topic.
The research topic aims to serve as a forum for researchers in related fields to contribute state-of-the-art ideas and approaches to deal with the complex geophysical inversion and interpretation problems using new generation artificial intelligence and machine learning. We invite researchers to submit novel technical papers covering theoretical aspects, methodology, algorithms, tutorials, case studies, and review papers with prospective analysis include, but not limited to
• Intelligent identification of very weak geophysical signals
• Intelligent target recognizes with minimal physical differences
• Complex structure imaging
• Complex reservoir identification
• Simultaneous inversion of multimodal geophysical data
• Comprehensive interpretation of multimodal geological and geophysical data
Geophysical inversion and interpretation are the crucial means to realize the value of the geophysical data obtained at a high cost. Geophysical inversion and interpretation are one with two sides. Inversion is the means of interpretation, while interpretation is the purpose of inversion. Geophysical inversion inverts the physical property parameter of the geological targets based on the geophysical model function, such as velocity, density, resistivity, and so on. The interpretation infers the property of the geological target based on its physical property parameter obtained by the inversion and judges whether it is the target object looking for, such as a gas reservoir or a mineral ore body.
Geophysical inversion and interpretation is a comprehensive discipline involving physics, mathematics, geology, computational technology, and engineering techniques. Geophysical inversion is basically a nonlinear mapping problem. Geophysical interpretation is essentially a classification problem guided by geological knowledge. Being enslaved to the incompleteness and the inaccuracy of the data, the complexity, and diversity of the geological targets, and the approximation of the physical and mathematical models, reliable inversion and interpretation in complex cases is the most challenging field in the inversion community and the geoscience community.
The new generation artificial intelligence represented by deep learning has both powerful statistical nonlinear modeling and classification capabilities. So, it is believed that deep learning could play an essential role in solving complex geophysical inversion and interpretation problems. Deep learning has been successfully applied to many single geophysical inversion and interpretation problems, such as first arrival travel time picking, fault identification, earthquake event auto-detection, fault identification, seismic facies identification and classification, seismic wave impedance inversion, electromagnetic wave inversion, seismic velocity modeling, etc. However, the powerful potential of the new generation artificial intelligence in solving complex geophysical inversion and interpretation problems is far from being realized.
To promote the application of the new generation artificial intelligence in solving the complex geophysical inversion and interpretation problems, and to promote the progress of geophysical inversion and interpretation methods and techniques, we initiated this research topic.
The research topic aims to serve as a forum for researchers in related fields to contribute state-of-the-art ideas and approaches to deal with the complex geophysical inversion and interpretation problems using new generation artificial intelligence and machine learning. We invite researchers to submit novel technical papers covering theoretical aspects, methodology, algorithms, tutorials, case studies, and review papers with prospective analysis include, but not limited to
• Intelligent identification of very weak geophysical signals
• Intelligent target recognizes with minimal physical differences
• Complex structure imaging
• Complex reservoir identification
• Simultaneous inversion of multimodal geophysical data
• Comprehensive interpretation of multimodal geological and geophysical data
