About this Research Topic
With the continuous deepening of geoscience research, massive and complex geoscience data are constantly emerging. Traditional methods of data processing, interpretation, inversion, and imaging are facing many challenges when dealing with these data. Deep learning methods, as a powerful technical means, have achieved remarkable achievements in many fields. The combination of deep learning and geoscience data provides new ideas and methods for solving difficult problems in geoscience.
In recent years, an increasing number of studies have explored the application of deep learning to the processing, interpretation, inversion, and imaging of geoscience data, achieving a series of remarkable results. However, this field is still in a phase of rapid development with numerous unresolved issues and a continuous need for methodological improvements and innovations.
This Research Topic aims to gather the latest research results in this field, promote deep integration of geoscience and deep learning, and foster innovative developments in geoscience research. The specific goals include:
1. Showcasing the latest research progress and application achievements in the integration of geoscience data and deep learning.
2. Promoting interdisciplinary communication and cooperation, and attracting more researchers from the fields of geoscience and computer science to engage with this research direction.
3. Providing new theories and methods for resolving practical problems in geoscience.
This Research Topic welcomes research papers on the following themes:
• Deep learning-based forward and inverse methods for seismic data, including seismic wavefield simulation, seismic imaging, reservoir parameter inversion, and fault identification.
• Applications of deep learning in geological and mineral resource exploration, such as mineral identification and deposit prediction.
• Forward and inverse methods for non-seismic data using deep learning, including methods for gravity, magnetic, and electromagnetic data.
• Applications of deep learning in remote sensing image interpretation and geographic information systems, such as land use classification and ecological environment monitoring.
• Combining multi-source geoscience data fusion with deep learning techniques.
In recent years, an increasing number of studies have explored the application of deep learning to the processing, interpretation, inversion, and imaging of geoscience data, achieving a series of remarkable results. However, this field is still in a phase of rapid development with numerous unresolved issues and a continuous need for methodological improvements and innovations.
This Research Topic aims to gather the latest research results in this field, promote deep integration of geoscience and deep learning, and foster innovative developments in geoscience research. The specific goals include:
1. Showcasing the latest research progress and application achievements in the integration of geoscience data and deep learning.
2. Promoting interdisciplinary communication and cooperation, and attracting more researchers from the fields of geoscience and computer science to engage with this research direction.
3. Providing new theories and methods for resolving practical problems in geoscience.
This Research Topic welcomes research papers on the following themes:
• Deep learning-based forward and inverse methods for seismic data, including seismic wavefield simulation, seismic imaging, reservoir parameter inversion, and fault identification.
• Applications of deep learning in geological and mineral resource exploration, such as mineral identification and deposit prediction.
• Forward and inverse methods for non-seismic data using deep learning, including methods for gravity, magnetic, and electromagnetic data.
• Applications of deep learning in remote sensing image interpretation and geographic information systems, such as land use classification and ecological environment monitoring.
• Combining multi-source geoscience data fusion with deep learning techniques.
Keywords: Deep learning, Machine learning, Geophysical data acquisition, Geophysical data processing, Geophysical inversion method, Reservoir prediction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
