Massively parallel regularized 3D inversion of potential fields on CPUs and GPUs

C̆uma, Martin; Zhdanov, Michael S.

doi:10.1016/j.cageo.2013.10.004

Cited by 54 publications

(22 citation statements)

References 28 publications

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“…For the size of forward problems that we aim to solve, the sensitivity matrix, with dimensions of the number of data points no times the number of 3D volume cells (nx+1)(ny+1)(nz +1) , is too large to keep in memory. The solution is to parallelize over the number of grid cells (nx+1)(ny+1)(nz +1) , i.e., a single row of the matrix G. This is especially important for the accelerators, which have limited memory capacity, e.g., 1023 MB onboard memory in a GTX 650Ti GPU, and high cost of data transfer between the host and the accelerator (Moorkamp et al, 2010;Čuma and Zhdanov, 2014). Figure 6 shows the core algorithm of this step using CUDA FORTRAN.…”

Section: Calculate the Sensitivity Matrix Gmentioning

confidence: 99%

“…When the inversion extends from 2D to 3D, it is in general very computationally demanding, and parallel computational resources or even supercomputers are often used to obtain a result (Yao et al, 2003;Moorkamp et al, 2010;Čuma and Zhdanov, 2014;Chen et al, 2012a and2012b).…”

Section: Introductionmentioning

confidence: 97%

“…One way is to optimize the forward algorithm, for example by the information reduction approach (Portniaguine and Zhdanov, 2002;Li and Oldenburg, 2003;Yao et al, 2003;Davis and Li, 2011;Čuma and Zhdanov, 2014), which has achieved a speedup ratio of several or dozens of times compared to the original method.…”

Section: Introductionmentioning

confidence: 99%

“…Another efficient way to accelerate the forward procedure is to use parallel computing with specific hardware (Moorkamp et al, 2010;Chen et al, 2012a and2012b;Liu et al, 2012;Čuma and Zhdanov, 2014), such as multi-core CPUs and general-purpose graphics processing units (GP-GPUs), which have been used as co-processors of CPU to achieve high-performance computing (NVIDIA Corporation, 2010;Greg, 2011;PGI Corporation, 2011) and has been used successfully in many geophysical parallel computing applications (Zhang et al, 2009;Liu, 2009Liu, , 2012Moorkamp et al, 2010;. In a potential field application, Moorkamp et al (2010), Chen et al (2012a and2012b), and Čuma and Zhdanov (2014) use a GPU accelerator in their forward/inversion program and achieve greatly improved efficiency compared to the serial code on CPU.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rapid calculation of gravity anomalies based on residual node densities and its GPU implementation

Sheng

Meng

Chen

et al. 2015

Computers & Geosciences

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Section: Calculate the Sensitivity Matrix Gmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rapid calculation of gravity anomalies based on residual node densities and its GPU implementation

Sheng

Meng

Chen

et al. 2015

Computers & Geosciences

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“…However, the efficiency of inversion method can also restrict the development of the potential field. Over the last decade, most of the computer acceleration technology has been achieved and applied by hardware parallelization according to the more and more processor cores into the Central Processing Unit (CPU) and Graphics Processing Units (CPUs) (Chen et al, 2012;Čuma and Zhdanov, 2014). These inversion methods can obviously enhance the inversion efficiency by the hardware parallelization.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of 3D potential field data inversion using a BB iterative algorithm

Meng¹,

Li²,

Yu³

et al. 2018

ASEG Extended Abstracts

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SUMMARYAs the big-data age arrives, the efficiency of three dimensional inversions of potential field data can be paid enough attention by scholars. A new inversion method is considered to deal with the large potential field data with a fast rate of convergence. Hence, in this paper, a fast inversion method is researched. And the gravity data is used as an example of potential field data to test the efficiency of our inversion method. To achieve this aim, the study region will be divided into huge amounts of rectangular prisms with unknown constant physical properties of rock. The traditional smooth inversion method is the main principle, and a new Barzilai-Borwein iterative algorithm is applied to ensure the rapid rate of convergence of the inversion method. To compare the rate of convergence of the BB (Barzilai-Borwein) iterative algorithm, the iterative gradient descent algorithm and the iterative conjugate gradient algorithm are used as the compared algorithms. To test high efficiency of the new fast developed inversion method, the large synthetic gravity data are performed. The contrast analysis results can easily reflect the high efficiency of our new inversion method. The great practical value of our inversion method is expounded by a real gravity data. Therefore, the new inversion method may have a great influence on the potential field data inversion.

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The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales

Binley

Hubbard

Huisman

et al. 2015

Water Resources Research

559

379

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Geophysics provides a multidimensional suite of investigative methods that are transforming our ability to see into the very fabric of the subsurface environment, and monitor the dynamics of its fluids and the biogeochemical reactions that occur within it. Here we document how geophysical methods have emerged as valuable tools for investigating shallow subsurface processes over the past two decades and offer a vision for future developments relevant to hydrology and also ecosystem science. The field of “hydrogeophysics” arose in the late 1990s, prompted, in part, by the wealth of studies on stochastic subsurface hydrology that argued for better field‐based investigative techniques. These new hydrogeophysical approaches benefited from the emergence of practical and robust data inversion techniques, in many cases with a view to quantify shallow subsurface heterogeneity and the associated dynamics of subsurface fluids. Furthermore, the need for quantitative characterization stimulated a wealth of new investigations into petrophysical relationships that link hydrologically relevant properties to measurable geophysical parameters. Development of time‐lapse approaches provided a new suite of tools for hydrological investigation, enhanced further with the realization that some geophysical properties may be sensitive to biogeochemical transformations in the subsurface environment, thus opening up the new field of “biogeophysics.” Early hydrogeophysical studies often concentrated on relatively small “plot‐scale” experiments. More recently, however, the translation to larger‐scale characterization has been the focus of a number of studies. Geophysical technologies continue to develop, driven, in part, by the increasing need to understand and quantify key processes controlling sustainable water resources and ecosystem services.

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Massively parallel regularized 3D inversion of potential fields on CPUs and GPUs

Cited by 54 publications

References 28 publications

Rapid calculation of gravity anomalies based on residual node densities and its GPU implementation

Rapid calculation of gravity anomalies based on residual node densities and its GPU implementation

Acceleration of 3D potential field data inversion using a BB iterative algorithm

The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales

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