3D gravity interface inversion constrained by a few points and its GPU acceleration

Chen, Zhaoxi; Meng, Xiaohong; Sheng, Zhang

doi:10.1016/j.cageo.2015.08.002

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…GPUs were initially used for computer games. Up to now, GPUs have been developed to provide computational power for scientific applications [21]. Parallel computing based on GPUs has been widely applied in geoscience [19][20][21][22][23][24].…”

Section: The Proposed Fast Methodsmentioning

confidence: 99%

“…For example, Chen et al [19] used GPU parallel computing and the CUDA platform to present a parallel program, GICUDA, a rapid inversion of large-scale gravity and gravity gradiometry data; Zhang et al [20] introduced a fast forward calculation method of gravity, which was further accelerated using GPU. Based on the platform of GPU, Chen et al [21] proposed a fast inversion of gravity interface. Liu and Li [22] used a GPU in CUDA to implement prestack Kirchhoff time migration, and so on [23,24].…”

Section: Introductionmentioning

confidence: 99%

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A Fast Method for Calculation of Marine Gravity Anomaly

et al. 2021

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Gravity data have been playing an important role in marine exploration and research. However, obtaining gravity data over an extensive marine area is expensive and inefficient. In reality, marine gravity anomalies are usually calculated from satellite altimetry data. Over the years, numerous methods have been presented for achieving this purpose, most of which are time-consuming due to the integral calculation over a global region and the singularity problem. This paper proposes a fast method for the calculation of marine gravity anomalies. The proposed method introduces a novel scheme to solve the singularity problem and implements the parallel technique based on a graphics processing unit (GPU) for fast calculation. The details for the implementation of the proposed method are described, and it is tested using the geoid height undulation from the Earth Gravitational Model 2008 (EGM2008). The accuracy of the presented method is evaluated by comparing it with marine shipboard gravity data. Its efficiency is demonstrated through comparison with the conventional sequential method. The tests demonstrate that the proposed method can be employed for accurately calculating marine gravity anomalies and provides an advantage on computational efficiency.

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Section: The Proposed Fast Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Method for Calculation of Marine Gravity Anomaly

et al. 2021

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“…The most commonly used techniques for the solution of this inverse problem are based on local optimization methods (see [ 47 ], for example) or even the sequential solution of the forward problem (see [ 48 ]). Global search methods have also been proposed for this particular problem in [ 49 ], although the uncertainty assessment of the solution was not performed.…”

Section: Uncertainty Analysis Using the Pso Sampling; Application mentioning

confidence: 99%

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems

Pallero

Fernández-Muñiz

Cernea

et al. 2018

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Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty.

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