Type
Conference PaperKAUST Department
Earth Science and Engineering ProgramKing Abdullah University of Science and Technology
Physical Science and Engineering (PSE) Division
Date
2022Permanent link to this record
http://hdl.handle.net/10754/678304
Metadata
Show full item recordAbstract
Multi-Dimensional Deconvolution (MDD) is a versatile technique used in seismic processing and imaging to create ideal datasets deprived of overburden effects. Whilst, the forward problem is well defined for a single source, stable inversion of the MDD equations relies on the availability of a large number of sources, this being independent on the domain where the problem is solved, frequency or time. In this work, we reinterpret the cost function of time-domain MDD as a finite-sum functional, and solve the associated problem by means of stochastic gradient descent algorithms, where gradients at each step are computed using a small subset of randomly selected sources. Through synthetic and field data examples, we show that the proposed method converges more stably than the conventional approach based on full gradients. Therefore, it represents a novel, efficient, and robust approach to deconvolve seismic wavefields in a multi-dimensional fashion.Citation
Ravasi, M., Pandurangan, T. S., & Luiken, N. (2022). Multi-Dimensional Deconvolution with Stochastic Gradient Descent. 83rd EAGE Annual Conference & Exhibition. https://doi.org/10.3997/2214-4609.202210234Sponsors
The authors thank KAUST for supporting this research. We are also grateful to Equinor and partners for releasing the Volve dataset.Conference/Event name
83rd EAGE Annual Conference & ExhibitionAdditional Links
https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210234ae974a485f413a2113503eed53cd6c53
10.3997/2214-4609.202210234