Fast Marching Method in Seismic Ray Tracing on Parallel GPU Devices

Monsegny, Jorge; Monsalve, Jonathan; León, Kareth; Duarte, Maria Claudia Quimbayo; Becerra, Sandra; Agudelo, William; Argüello, Henry

doi:10.1007/978-3-030-16205-4_8

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Monsegny et al . (2019) propose an algorithm for computing raytracing shortest path on GPU that improves the computing time up to three times, an approach that could be potentially implemented to this work's scheme.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…Real-time use of the method could probably be achieved by carefully implementing the method in a compiled language such as C++ after profiling and optimization, and by implementing critical parts such as raytracing on GPUs. Monsegny et al (2019) propose an algorithm for computing raytracing shortest path on GPU that improves the computing time up to three times, an approach that could be potentially implemented to this work's scheme.…”

Section: Real Casementioning

confidence: 99%

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Microseismic monitoring of rockbursts with the ensemble Kalman filter

Dip

Giroux

Gloaguen

2021

Near Surface Geophysics

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We introduce an algorithm to monitor seismic velocity changes associated with rockbursts in mines, through microseismic monitoring. Rockbursts are extreme events resulting from the complex interaction between mining activities and geology, and represent a significant threat to mines. In recent years, the use of passive seismic monitoring for mine safety and productivity has progressed substantially, aiming to understand and predict this hazard. In this work, additional value is given to microseismic monitoring, using it to map temporal changes of seismic velocity in the rock mass that can potentially be associated with stress changes leading to rockbursts. An application of the ensemble Kalman filter is presented for assimilating travel times of seismic P and S waves in a fast and efficient way in order to update the mine's velocity model. Combining sequential Gaussian simulation and ensemble Kalman filter techniques, we were able to monitor the occurrence of velocity changes underground. The proposed approach allows zones to be highlighted where the rock mass is under stress and where potential risk can be expected. The performance of the method was first tested on several synthetic scenarios and, subsequently, on a real 3D case of a deep mine in Canada. The application on the real data set allowed mapping a change in velocity in the area where a rockburst occurred four hours afterwards.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Section: Real Casementioning

confidence: 99%

Microseismic monitoring of rockbursts with the ensemble Kalman filter

Dip

Giroux

Gloaguen

2021

Near Surface Geophysics

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“…Such parallel implementations of seismic ray tracers on GPUs exist, see e.g. [14,15,16]. All of them achieve faster computation times than comparable implementations on a central processing unit (CPU).…”

Section: Introductionmentioning

confidence: 99%

Parallel 2D Seismic Ray Tracing Using Cuda on a Jetson Nano

Shin

Wientgens

Shutin

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present a parallel implementation of a 2D seismic ray tracer on a graphics processing unit of the compact Jetson Nano by Nvidia. Ray tracing is commonly used in seismic imaging as an intermediate step in reconstructing subsurface structures. We employ a gradient-based ray tracer that requires a travel time map. Here, we make use of the fast iterative method that computes a travel time map in a parallel fashion. Since a ray path is independent from any other ray path, the tracing can be implemented in parallel as well. To this end, we use a graphics processing unit and implement ray tracing using the CUDA programming language. For performance evaluations we compare our implementation on the Jetson Nano to a state-of-the-art sequential seismic ray tracer on a desktop CPU and show that speedups with factors of up to three can be achieved. The results indicate that edge devices such as the Jetson Nano can play a relevant role for tomographic applications particularly in scenarios where mobility of processing devices and compactness are important.

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“…The fast-marching method saw great interest and development in the subsequent period. This included extension of the method to improve traveltime accuracy [17,18,19], incorporating anisotropy [20,21,22], parallelization for computational speedup using multiple CPUs [23], and even acceleration using GPUs [24].…”

Section: Introductionmentioning

confidence: 99%

A holistic approach to computing first-arrival traveltimes using neural networks

Waheed¹,

Alkhalifah²,

Haghighat³

et al. 2021

Preprint

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Since the original algorithm by John Vidale in 1988 to numerically solve the isotropic eikonal equation, there has been tremendous progress on the topic addressing an array of challenges including improvement of the solution accuracy, incorporation of surface topography, adding more accurate physics by accounting for anisotropy/attenuation in the medium, and speeding up computations using multiple CPUs and GPUs. Despite these advances, there is no mechanism in these algorithms to carry on information gained by solving one problem to the next. Moreover, these approaches may breakdown for certain complex forms of the eikonal equation, requiring approximation methods to estimate the solution. Therefore, we seek an alternate approach to address the challenge in a holistic manner, i.e., a method that not only makes it simpler to incorporate topography, allow accounting for any level of complexity in physics, benefiting from computational speedup due to the availability of multiple CPUs or GPUs, but also able to transfer knowledge gained from solving one problem to the next. We develop an algorithm based on the emerging paradigm of physics-informed neural network to solve various forms of the eikonal equation. We show how transfer learning and surrogate modeling can be used to speed up computations by utilizing information gained from prior solutions. We also propose a two-stage optimization scheme to expedite the training process in presence of sharper heterogeneity in the

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Fast Marching Method in Seismic Ray Tracing on Parallel GPU Devices

Cited by 3 publications

References 6 publications

Microseismic monitoring of rockbursts with the ensemble Kalman filter

Microseismic monitoring of rockbursts with the ensemble Kalman filter

Parallel 2D Seismic Ray Tracing Using Cuda on a Jetson Nano

A holistic approach to computing first-arrival traveltimes using neural networks

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