Accelerating Time and Depth Seismic Migration by CPU and GPU Cooperation

Panetta, Jairo; Teixeira, Thiago; Filho, P.R.P. de Souza; Filho, Carlos Alves da Cunha; Sotelo, David; Motta, Fernando M. Roxo da; Pinheiro, Silvio Sinedino; Rosa, Andre Luiz Romanelli; Monnerat, Luiz; Carneiro, L.; Albrecht, Carlos

doi:10.1007/s10766-011-0185-2

Cited by 5 publications

(9 citation statements)

References 14 publications

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“…Kirchhoff migration is primarily used as an industrial seismic exploration tool, and as such, implementations are usually developed with a view to industrial competitiveness, and publications of implementation or performance details are few (Panetta et al 2007(Panetta et al , 2012. Having performed some initial optimisation for x86 architectures, we now discuss some salient points.…”

Section: O M P U T a T I O N A L I M P L E M E N T A T I O Nmentioning

confidence: 99%

“…As a result, a naive implementation could easily give rise to "thrashing", failing to reuse cached data before they are evicted, in favour of uncached data mandated by the algorithm's access pattern. It is possible and critical for efficient use of hardware to achieve cache reuse by strategically choosing the order of data updates and resulting access pattern, as mentioned in the work of Panetta et al (2012).…”

Section: O M P U T a T I O N A L I M P L E M E N T A T I O Nmentioning

confidence: 99%

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Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm

O’Brien¹,

Delaney²,

Igoe³

et al. 2018

Geophysical Prospecting

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Extracting true amplitude versus angle common image gathers is one of the key objectives in seismic processing and imaging. This is achievable to different degrees using different migration techniques (e.g., Kirchhoff, wavefield extrapolation, and reverse time migration techniques) and is a common tool in exploration, but the costs can vary depending on the selected migration algorithm and the desired accuracy. Here, we investigate the possibility of combining the local‐shift imaging condition, specifically the time‐shift extended imaging condition, for angle gathers with a Kirchhoff migration. The aims are not to replace the more accurate full‐wavefield migration but to offer a cheaper alternative where ray‐based methods are applicable and to use Kirchhoff time‐lag common image gathers to help bridge the gap between the traditional offset common image gathers and reverse time migration angle gathers; finally, given the higher level of summation inside the extended imaging migration, we wish to understand the impact on the amplitude versus angle response. The implementation of the time‐shift imaging condition along with the computational cost is discussed, and results of four different datasets are presented. The four example datasets, two synthetic, one land acquisition, and a marine dataset, have been migrated using a Kirchhoff offset method, a Kirchhoff time‐shift method, and, for comparison, a reverse time migration algorithm. The results show that the time‐shift imaging condition at zero time lag is equivalent to the full offset stack as expected. The output gathers are cleaner and more consistent in the time‐lag‐derived angle gathers, but the conversion from time lag to angle can be considered a post‐processing step. The main difference arises in the amplitude versus offset/angle distribution where the responses are different and dramatically so for the land data. The results from the synthetics and real data show that a Kirchhoff migration with an extended imaging condition is capable of generating subsurface angle gathers. The same disadvantages with a ray‐based approach will apply using the extended imaging condition relative to a wave equation angle gather solution. Nevertheless, using this approach allows one to explore the relationship between the velocity model and focusing of the reflected energy, to use the Radon transformation to remove noise and multiples, and to generate consistent products from a ray‐based migration and a full‐wave equation migration, which can then be interchanged depending on the process under study.

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Section: O M P U T a T I O N A L I M P L E M E N T A T I O Nmentioning

confidence: 99%

Section: O M P U T a T I O N A L I M P L E M E N T A T I O Nmentioning

confidence: 99%

Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm

O’Brien¹,

Delaney²,

Igoe³

et al. 2018

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…Only few works use CPU and GPU to solve PKTM. Panetta et al , presented a combined solution where both CPU and GPU were used to perform meaningful computations. In such proposed strategy, the computational power provided by CPU was employed to execute operations of reading and filtering input trace, besides performing traditional I/O and control operations.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, parallel and distributed computing techniques have been applied in PKTM aiming to reduce its execution time . Because of data parallelism characteristics of PKTM algorithm, many works, for example and , have proposed the use of computational accelerators such as Graphical Processing Units (GPU) or Field‐programable Gate Array (FPGA) to reduce PKTM execution time. In this strategy, generally the computation is accomplished just by GPUs and FPGAs, and CPU is used only for performing control tasks such as moving data from memory to these devices.…”

Section: Introductionmentioning

confidence: 99%

“…In this strategy, generally the computation is accomplished just by GPUs and FPGAs, and CPU is used only for performing control tasks such as moving data from memory to these devices. Although this approach with GPUs and FPGAs achieves satisfactory results, some works, as the one presented by Panetta et al , , have shown that a new strategy combining computational power of conventional multicore processors and these accelerators can significantly improve PKTM performance.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating Pre‐stack Kirchhoff Time Migration by Manual Vectorization

Alves

Pestana

Silva

et al. 2016

Concurrency and Computation

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Summary The Pre‐stack Kirchhoff Time Migration (PKTM) is a central process in petroleum exploration. As PKTM is computationally intensive, many works have proposed the use of accelerators like GPU and FPGA to improve its execution time. On the other hand, although many off‐the‐shelf processors are endowed with a set of SIMD vector instructions, few papers tackle the problem considering vectorization and all of them consider that compilers can successfully vectorize the code. In this paper, we show that programming PKTM by using SIMD vector instructions manually is more efficient than the automatically and semi‐automatically vectorized codes, provided by a hardware specific compiler and library. Experiments considering both real and synthetic datasets showed that our solution is more than four times faster than the traditional code. It also outperformed automatically vectorized codes in all tests. We believe that the proposed strategy can be used together with the other ones to accelerate seismic migration methods in general without new investments in hardware. Copyright © 2016 John Wiley & Sons, Ltd.

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Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data

2020

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Massive computation of seismic traveltimes is widely used in seismic processing, e.g. for the Kirchhoff migration of seismic and microseismic data. Implementation of the Kirchhoff migration operators utilizes large pre-computed traveltime tables (for all sources, receivers and densely sampled imaging points). We test the idea of using Artificial Neural Networks for approximating these traveltime tables. The neural network has to be trained for each velocity model, but then the whole traveltime table can be compressed by several orders of magnitude (up to six orders) to the size of less than one megabyte. This makes it convenient to store, share, and use such approximation for processing large data volumes. We discuss some aspects of choosing the neural-network architecture, training procedure, and optimal hyperparameters. On synthetic tests, we demonstrate reasonably accurate approximation of traveltimes by neural networks for various velocity models. Final synthetic test shows that using the neural-network traveltime approximation results in good accuracy of microseismic event localization (within the grid step) in 3D case.

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Accelerating Time and Depth Seismic Migration by CPU and GPU Cooperation

Cited by 5 publications

References 14 publications

Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm

Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm

Accelerating Pre‐stack Kirchhoff Time Migration by Manual Vectorization

Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data

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