基于CUDA架构并行算法的带地形AMT二维反演实现与应用

并行计算是提高音频大地电磁（audio-frequency magnetotelluric method，AMT）数据反演效率的有效途径。本文在统一计算设备架构(compute unified device architecture,CUDA) 下开展带地形的AMT数据二维反演并行算法研究，旨在利用GPU强大的计算能力及并行计算技术实现高精度、快速度的AMT数据二维反演。首先利用有限元和自适应正则化反演算法实现AMT数据二维反演的串行化计算；然后在PGI Visual Fortran+ CUDA5.5环境下编写基于CPU+GPU的CUDA并行代码，将正演中的频率循环、反演中的模型灵敏度矩阵计算和反演方程正则化求解部分进行并行化处理；通过不同复杂程度的理论模型正反演模拟验证了该并行算法的有效性和准确性。不同模型和不同模式下的数值模拟结果对比表明，基于CPU+GPU的CUDA并行算法相较于传统的CPU串行算法，在灵敏度矩阵计算和反演方程正则化方面耗时更少，加速比最高可达10倍以上。最后将该并行算法应用于某矿区实测AMT数据的二维反演中，取得了较好的应用效果。

Implementation and Application of 2D Audio-frequency Magnetotelluric Method Inversion with Topography Based on Compute Unified Device Architecture Parallel Algorithm

Parallel computation is an effective way to improve the efficiency of audio-frequency magnetotelluric (AMT) data inversion. In order to realize 2D AMT data inversion with high accuracy and high speed by using the powerful computing ability of GPU and parallel computing technology,the parallel algorithm of 2D AMT data inversion based on compute unified device architecture (CUDA) was studied. Firstly, the serial computation of 2D AMT data inversion was realized by using finite element method and adaptive regularization inversion algorithm, and then the parallel code based on CPU + GPU was written in the environment of PGI Visual FORTRAN + CUDA 5.5. The frequency cycle in forward modeling, the sensitivity matrix calculation in inversion and the regularized solution of inverse equations were paralleled. The validity and accuracy of the parallel algorithm were verified by forward and inverse simulation of theoretical models with different complexity. The comparison of numerical simulation results under different model scales and different modes shows that the parallel CUDA algorithm based on CPU + GPU is less time-consuming in sensitivity matrix calculation and inversion equation regularization than the traditional CPU serial algorithm, the maximum acceleration ratio can be more than 10 times. Finally, the parallel algorithm is applied to the 2D inversion of the measured AMT data in a mining area, and good results are obtained.