Seismic Imaging: An Overview and Parallel Implementation of Poststack Depth Migration
November 02, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ahmad Shawahna, Syed Abdul Salam, Mayez Al-Mouhamed
arXiv ID
1912.05529
Category
physics.geo-ph
Cross-listed
cs.DC,
cs.PF,
eess.IV,
eess.SP
Citations
0
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Seismic migration is the core step of seismic data processing which is important for oil exploration. Poststack depth migration in frequency-space (f-x) domain is one of commonly used algorithms. The wave-equation solution can be approximated as FIR filtering process to extrapolate the raw data and extract the subsurface image. Because of its computational complexity, its parallel implementation is encouraged. For calculating the next depth level, previous depth level is required. So, this part cannot be parallelized because of data dependence. But at each depth level there is plenty of roam for parallelism and can be parallelized. In case of CUDA programming, each thread calculate a single pixel on the next depth plan. After calculating the next depth plan, we can calculate the depth row by summing over all the frequencies and calculating all the depth rows results in the final migrated image. The poststack depth migration is implemented in CUDA and its performance is evaluated with the sequential code with different problem sizes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.geo-ph
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Machine-Learning Approach for Earthquake Magnitude Estimation
R.I.P.
π»
Ghosted
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
R.I.P.
π»
Ghosted
Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations
R.I.P.
π»
Ghosted
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data
R.I.P.
π»
Ghosted
Seismic data interpolation based on U-net with texture loss
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted