Abstract
Abstract 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.
Highlights
In seismic reflectivity inversion, a seismic profile can be treated as a number of layers and a reflection represents an interface separating layers of the subsurface media (Wang & Wang 2016)
In the basis-pursuit method, since the dictionary used to represent the seismic reflectivity is composed by large amounts of even and odd dipoles, matrices used in this method are huge and the matrix operations are tedious
We have developed a Graphics Processing Unit (GPU)-accelerated basispursuit algorithm using CUDA, to improve computational efficiency
Summary
A seismic profile can be treated as a number of layers and a reflection represents an interface separating layers of the subsurface media (Wang & Wang 2016). We develop seismic reflectivity inversion algorithm by using the basis-pursuit method to decompose reflectors and the L1-norm constraint for the sparsity of the solution. In seismic geophysics, the GPU-based algorithm has been developed for solving 3D wavefield simulations (Weiss & Shragge 2013; Xu & Liu 2018), 3D reverse-time migration (Shi & Wang 2016; Li et al 2018; Zhou et al 2018) and geostatistical inversion (Liu & Grana 2019). These implementations have employed multiple GPUs to get a satisfactory result with much lower time consumption. We shall demonstrate that the computational time of the basis-pursuit method is drastically reduced, while precision of inversion is maintained
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
