An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

Xu, Jincheng; Liu, Wei; Wang, Jin; Liu, Linong; Zhang, Jianfeng

doi:10.1016/j.cageo.2017.11.020

Cited by 19 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, it takes 115 and 2218 s to calculate the simple 2D profile in Figures 3 and 4, respectively. Even when using GPUs, several months may be required to obtain the high-resolution results for a real 3D field dataset [3]. This prevents the application of this approach to large-scale problems.…”

Section: High-resolution Imaging Using Qpstmmentioning

confidence: 99%

“…However, in contrast to the conventional PSTM, the QPSTM approach involves performing an additional integral over all the frequencies, which is time consuming and prevents the application of this approach to large-scale problems. To improve the computational efficiency, Xu [3] distributed the heavy workload to graphics processing units (GPUs) and optimized the calculation at the programming level. Although the use of the GPUs is beneficial, the approach remains less efficient for solving realistic problems; for instance, performing a realistic 3D QPSTM can take several months even when using advanced GPU clusters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

The emerging applications of deep learning in solving geophysical problems have attracted increasing attention. In particular, it is of significance to enhance the computational efficiency of the computationally intensive geophysical algorithms. In this paper, we accelerate deabsorption prestack time migration (QPSTM), which can yield higher-resolution seismic imaging by compensating absorption and correcting dispersion through deep learning. This is implemented by training a neural network with pairs of small-sized patches of the stacked migrated results obtained by conventional PSTM and deabsorption QPSTM and then yielding the high-resolution imaging volume by prediction with the migrated results of conventional PSTM. We use an encoder-decoder network to highlight the features related to high-resolution migrated results in a high-order dimension space. The training data set of small-sized patches not only reduces the required high-resolution migrated result (for instance, only several inline is required) but leads to a fast convergence in training. The proposed deep-learning approach accelerates the high-resolution imaging by more than 100 times. Field data is used to demonstrate the effectiveness of the proposed method.

show abstract

Section: High-resolution Imaging Using Qpstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Time‐domain imaging, especially prestack time migration (PSTM), has long been the standard imaging output of seismic data processing (Fomel, 2014; Garabito et al., 2022; Zhang et al., 2012). In particular, the implementation of GPU parallel processing (Shi et al., 2011; Xu et al., 2018) has allowed PSTM to become the most practical and effective tool for real large‐scale 3D seismic imaging. The key for the PSTM to generate satisfactory migration results, that is, the results that accurately reflect the true subsurface structure, is the velocity model, which is obtained primarily by migration velocity analysis (MVA).…”

Section: Introductionmentioning

confidence: 99%

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

Zhang

2023

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

An accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.

show abstract

Redesigning elastic full‐waveform inversion on the new Sunway architecture

Hua,

Wan,

Sun

et al. 2023

Engineering Reports

View full text Add to dashboard Cite

IFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59 and an overall program speedup of about 14. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.

show abstract

An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

Cited by 19 publications

References 18 publications

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

Accelerating High-Resolution Seismic Imaging by Using Deep Learning

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

Redesigning elastic full‐waveform inversion on the new Sunway architecture

Contact Info

Product

Resources

About