Conventional methods for quantifying time-lapse seismic effects rely on a linear assumption that is easily violated. Therefore, more sophisticated methods are necessary. The full-waveform inversion (FWI) method is an inverse method that is able to reveal time-lapse changes in the image domain, in which the conventional methods break down. We investigated the behavior of FWI using different approaches for applying FWI on limited-offset time-lapse data. We compared acoustic and elastic inversion schemes. We introduced a method for constraining the model update for the monitor model to remove time-lapse artifacts. This method was based on migration of the residuals in the time-lapse data, which, in combination with a local contrast estimation algorithm, formed the update constraint. We found that for limited-offset data, elastic theory was necessary for the success of FWI and that FWI was able to quantify the time-lapse changes in the parameter models. The local migration regularization approach was able to remove time-lapse artifacts.
There is a consensus that exascale systems should operate within a power envelope of 20MW. Consequently, energy conservation is still considered as the most crucial constraint if such systems are to be realized. So far, most research on this topic focused on strategies such as power capping and dynamic power management. Although these approaches can reduce power consumption, we believe that they might not be sufficient to reach the exascale energy-efficiency goals. Hence, we aim to adopt techniques from embedded systems, where energy-efficiency has always been the fundamental objective. A successful energy-saving technique used in embedded systems is to integrate fine-grained autotuning with dynamic voltage and frequency scaling. In this paper, we apply a similar technique to a real-world HPC application. Our experimental results on a HPC cluster indicate that such an approach saves up to 20% of energy compared to the baseline configuration, with negligible performance loss.
In reverse time migration (RTM) or full-waveform inversion (FWI), forward and reverse time propagating wavefields are crosscorrelated in time to form either the image condition in RTM or the misfit gradient in FWI. The crosscorrelation condition requires both fields to be available at the same time instants. For large-scale 3D problems, it is not possible, in practice, to store snapshots of the wavefields during forward modeling due to extreme storage requirements. We have developed an approximate wavefield reconstruction method that uses particle velocity field recordings on the boundaries to reconstruct the forward wavefields during the computation of the reverse time wavefields. The method is computationally effective and requires less storage than similar methods. We have compared the reconstruction method to a boundary reconstruction method that uses particle velocity and stress fields at the boundaries and to the optimal checkpointing method. We have tested the methods on a 2D vertical transversely isotropic model and a large-scale 3D elastic FWI problem. Our results revealed that there are small differences in the results for the three methods.
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