Seismic reflectivity inversion using an L1-norm basis-pursuit method and GPU parallelisation
Seismic reflectivity inversion using an L1-norm basis-pursuit method and GPU parallelisation
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DOI：
10.1093/jge/gxaa029
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发表时间：
2020-06
影响因子：
1.4
通讯作者：
Ruo Wang;Yanghua Wang;Y. Rao
中科院分区：
文献类型：
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作者：
Ruo Wang;Yanghua Wang;Y. Rao
地震反射率反演问题可以用追踪基方法来描述，其目的是生成地下介质的稀疏反射率序列。在追赶基方法中，反射率序列由大量的偶偶极子和奇偶极子组成，因此地震响应矩阵的规模很大，地震反演中涉及的矩阵运算非常耗时。为了加速矩阵运算，在图形处理器(GPU)上实现了一种基于基追踪法的地震反演算法。基于L1范数的稀疏模型约束下的基寻踪反演算法，利用线性规划方法对L1范数的基寻踪反演问题进行了重新描述。反演的核心问题是大规模线性系统，采用并行共轭梯度法进行求解。评估了这种完全并行化实现的性能，并将其与传统的串行编码进行了比较。具体来说，使用不同大小的现场地震数据集进行的研究表明，基于GPU的并行可以显著减少计算时间，总计算量高达145倍。这种效率的提高显示了基寻踪反演方法在大规模地震反射率反演问题的实际应用中的巨大潜力。
Seismic reflectivity inversion problem can be formulated using a basis-pursuit method, aiming to generate a sparse reflectivity series of the subsurface media. In the basis-pursuit method, the reflectivity series is composed by large amounts of even and odd dipoles, thus the size of the seismic response matrix is huge and the matrix operations involved in seismic inversion are very time-consuming. In order to accelerate the matrix computation, a basis-pursuit method-based seismic inversion algorithm is implemented on Graphics Processing Unit (GPU). In the basis-persuit inversion algorithm, the problem is imposed with a L1-norm model constraint for sparsity, and this L1-norm basis-pursuit inversion problem is reformulated using a linear programming method. The core problems in the inversion are large-scale linear systems, which are resolved by a parallelised conjugate gradient method. The performance of this fully parallelised implementation is evaluated and compared to the conventional serial coding. Specifically, the investigation using several field seismic data sets with different sizes indicates that GPU-based parallelisation can significantly reduce the computational time with an overall factor up to 145. This efficiency improvement demonstrates a great potential of the basis-pursuit inversion method in practical application to large-scale seismic reflectivity inversion problems.