Seismic inversion by multi-dimensional Newtonian machine learning
Type
Conference PaperKAUST Department
Center for Subsurface Imaging and Fluid ModelingEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2020-09-30Online Publication Date
2020-09-30Print Publication Date
2020-09-30Permanent link to this record
http://hdl.handle.net/10754/665468
Metadata
Show full item recordAbstract
Newtonian machine learning (NML) inversion has been shown to accurately recover the low-to-intermediate wavenumber information of subsurface velocity models. This method uses the wave-equation inversion kernel to invert the skeletonized data that is automatically learned by an autoencoder. The skeletonised data is a one-dimensional latent-space representation of the seismic trace. However, for a complicated dataset, the decoded waveform could lose some details if the latent space dimension is set to one, which leads to a low-resolution NML tomogram. To mitigate this problem, an autoencoder with a higher dimensional latent space is needed to encode and decode the seismic data. In this paper, we present a wave equation inversion that inverts the multi-dimensional latent variables of an autoencoder for the subsurface velocity model. The multi-variable implicit function theorem is used to determine the perturbation of the multi-dimensional skeletonised data with respect to the velocity perturbations. In this case, each dimension of the latent variable is characterized one gradient and the velocity model is updated by the weighted sum of all these gradients. Numerical results suggest that the multidimensional NML inverted result can achieve a higher resolution in the tomogram compared to the conventional single dimensional NML inversion.Citation
Chen, Y., Saygin, E., & Schuster, G. T. (2020). Seismic inversion by multi-dimensional Newtonian machine learning. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425975.1Sponsors
We thanks to the Deep Earth Imaging Future Science Platformof CSIRO for funding and computing resources of CSIRO.Publisher
Society of Exploration GeophysicistsAdditional Links
https://library.seg.org/doi/10.1190/segam2020-3425975.1ae974a485f413a2113503eed53cd6c53
10.1190/segam2020-3425975.1