The common-reflection surface (CRS) method is a sophisticated alternative to the traditional common-midpoint stacking because its traveltime approximation allows for the use of more traces than the normal moveout. This in turn requires more parameters for the moveout description, thus increasing the computational burden of the parameter estimation. In the literature, a suboptimal strategy is often used, which decreases the complexity but, as we found in this work, compromises the accuracy of the parameters in some cases. To cope with this problem, in this work, we have devised detailed information for efficient estimation of the CRS parameters using the differential evolution (DE) global optimization algorithm. Because we used data sets with low fold and low signal-to-noise ratio, from which no reliable velocity analysis could be easily performed, we applied this algorithm in a fully automatic global search, i.e., without any velocity guide. The results for a 2D real data set from Brazil indicated that the global strategy yielded good results, both in terms of image quality as in the quality of the parameter volumes, especially the stacking velocity estimates, while keeping the computational costs relatively low. We also developed a convergence and a sensitivity analysis of the DE that shows its computational efficiency and the robustness of the optimization method with respect to the choice of the control parameters of the algorithm.

Reverse time migration (RTM) is a prominent technique in seismic imaging. Its resulting subsurface images are used in the industry to investigate with higher confidence the existence and the conditions of oil and gas reservoirs. Because of its high computational cost, RTM must make use of parallel computers. Balancing the workload distribution of an RTM is a growing challenge in distributed computing systems. The competition for shared resources and the differently-sized tasks of the RTM are some of the possible sources of load imbalance. Although many load balancing techniques exist, scaling up for large problems and large systems remains a challenge because synchronization overhead also scales. This paper proposes a cyclic token-based work-stealing (CTWS) algorithm for distributed memory systems applied to RTM. The novel cyclic token approach reduces the number of failed steals, avoids communication overhead, and simplifies the victim selection and the termination strategy. The proposed method is implemented as a C library using the one-sided communication feature of the message passing interface (MPI) standard. Results obtained by applying the proposed technique to balance the workload of a 3D RTM system present a factor of 14.1 % speedup and reductions of the load imbalance of 78.4 % when compared to the conventional static distribution.

Reverse time migration (RTM) is an algorithm widely used in the oil and gas industry to process seismic data. It is a computationally intensive task that suits well in parallel computers. Methods such as RTM can be parallelized in shared memory systems through scheduling iterations of parallel loops to threads. However, several aspects, such as memory size and hierarchy, number of cores, and input size, make optimal scheduling very challenging. In this paper, we introduce a run-time strategy to automatically tune the dynamic scheduling of parallel loops iterations in iterative applications, such as the RTM, in multicore systems. The proposed method aims to reduce the execution time of such applications. To find the optimal granularity, we propose a coupled simulated annealing (CSA) based auto-tuning strategy that adjusts the chunk size of work that OpenMP parallel loops assign dynamically to worker threads during the initialization of a 3D RTM application. Experiments performed with different computational systems and input sizes show that the proposed method is consistently better than the default OpenMP schedulers, static, auto, and guided, causing the application to be up to 33% faster. We show that the possible reason for this performance is the reduction of cache misses, mainly level L3, and low overhead, inferior to 2%. Having shown to be robust and scalable for the 3D RTM, the proposed method could also improve the performance of similar wave-based algorithms, such as full-waveform inversion (FWI) and other iterative applications.