In the past few decades, the geophysics community has proposed a large number of new technologies for seismic exploration to meet the needs of high-resolution subsurface imaging. These new technologies have made great contributions to advances in seismic exploration and structural geology. For instance, the appearance of distributed optical fiber acoustic sensing (DAS) makes it possible to acquire seismic data with high spatial resolution at low cost. Advances have been made in full waveform inversion (FWI) and it is now considered the most robust approach for the reconstruction of subsurface velocity models. Multiples, which were originally regarded as a common noise, are now applied to seismic imaging and accordingly provide extra illumination, and least-square migration (LSM) greatly improves illumination and resolution of seismic imaging. Deep learning, especially the convolutional neural network (CNN), has shown remarkable performance in seismic noise attenuation, interpolation, velocity model reconstruction, arrival time picking, and interpretation.
Although these new technologies have solved certain real-world geophysical issues, they still have the following limitations. Firstly, fiber system noise reduces the quality of seismic data received by DAS, restricting its further applications. Secondly, slow convergence rate and huge computational cost are main bottlenecks faced by iterative seismic inversion approaches such as LSM and FWI. Moreover, the cycle-skipping problem is still a challenging issue in FWI. Thirdly, the weak generalization of trained models needs to be addressed before deep learning can be implemented widely to solve real-world problems. Forthly, the solution of the anisotropic elastic wave equation needs to be improved for its applications in practice.
This Research Topic aims to gather a collection of Original Research articles that propose or utilize advanced technologies to solve theoretical and practical problems in seismic exploration and structural geology as well as Review articles that summarize the development of new technologies and the challenges they confront.
We encourage colleagues from geophysics, signal processing, artificial intelligence, instrument science, applied mathematics, and seismic structural geology to participate in this Research Topic. Potential topics include, but are not limited to:
• New instruments and equipment for seismic exploration
• Deep learning methods and its applications in seismic exploration
• High-resolution seismic imaging
• FWI and its real application cases
• Applications of new imaging technologies in seismic structural geology
• Joint imaging of active and passive seismics
• Ambient noise tomography
• First-arrival seismic tomography
• Least-squares migration and its applications
• New algorithms of forward modeling and imaging
• Multi-component seismic data processing algorithms
• Seismic anisotropy
• Elastic wavefield imaging
In the past few decades, the geophysics community has proposed a large number of new technologies for seismic exploration to meet the needs of high-resolution subsurface imaging. These new technologies have made great contributions to advances in seismic exploration and structural geology. For instance, the appearance of distributed optical fiber acoustic sensing (DAS) makes it possible to acquire seismic data with high spatial resolution at low cost. Advances have been made in full waveform inversion (FWI) and it is now considered the most robust approach for the reconstruction of subsurface velocity models. Multiples, which were originally regarded as a common noise, are now applied to seismic imaging and accordingly provide extra illumination, and least-square migration (LSM) greatly improves illumination and resolution of seismic imaging. Deep learning, especially the convolutional neural network (CNN), has shown remarkable performance in seismic noise attenuation, interpolation, velocity model reconstruction, arrival time picking, and interpretation.
Although these new technologies have solved certain real-world geophysical issues, they still have the following limitations. Firstly, fiber system noise reduces the quality of seismic data received by DAS, restricting its further applications. Secondly, slow convergence rate and huge computational cost are main bottlenecks faced by iterative seismic inversion approaches such as LSM and FWI. Moreover, the cycle-skipping problem is still a challenging issue in FWI. Thirdly, the weak generalization of trained models needs to be addressed before deep learning can be implemented widely to solve real-world problems. Forthly, the solution of the anisotropic elastic wave equation needs to be improved for its applications in practice.
This Research Topic aims to gather a collection of Original Research articles that propose or utilize advanced technologies to solve theoretical and practical problems in seismic exploration and structural geology as well as Review articles that summarize the development of new technologies and the challenges they confront.
We encourage colleagues from geophysics, signal processing, artificial intelligence, instrument science, applied mathematics, and seismic structural geology to participate in this Research Topic. Potential topics include, but are not limited to:
• New instruments and equipment for seismic exploration
• Deep learning methods and its applications in seismic exploration
• High-resolution seismic imaging
• FWI and its real application cases
• Applications of new imaging technologies in seismic structural geology
• Joint imaging of active and passive seismics
• Ambient noise tomography
• First-arrival seismic tomography
• Least-squares migration and its applications
• New algorithms of forward modeling and imaging
• Multi-component seismic data processing algorithms
• Seismic anisotropy
• Elastic wavefield imaging