Random-shot full-waveform inversion of shallow seismic surface waves = Zufallsschuss volle Wellenforminversion von flachseismischen Oberflächenwellen

Seismic acquisitions in urban areas represent a challenge due to the noisy environment during the recording of the data. This often prevents obtaining high-resolution images of the subsurface in the area of interest. One way to achieve better results, despite the challenging circumstances, is full-waveform inversion (FWI). This method iteratively updates a starting model until the simulated data and the observed field data matches to a satisfactory level. This eventually yields the desired subsurface model. However, FWI comes with some major limitations. Because each iteration requires a calculation of the wave propagation several times for each shot, it is highly computationally expensive. Furthermore, due to storage limitations, especially if high performance computing (HPC) clusters are not available, the model size is restricted as well. This work aims to find a solution for these limitations in an elastic 2D case by implementing the random-shot workflow (RSW). In a conventional FWI workflow which is called full-shot workflow (FSW) in this work, each iteration uses all shots of the recorded data before updating the whole model at once which leads to the high computational and storage costs. ... mehrRSW randomly picks only one shot, applies FWI within the area of the shot and its corresponding receivers and then updates the model only within this subset. This should, in my expectation, split the workload into several smaller subsets without sacrificing the effectiveness of FWI. After the implementation, I compare RSW to FSW in a synthetic benchmark where I verify the performance. Afterwards, the workflows are compared to each other by using a fraction of a field data set that was acquired in Salt Lake City. These data represents a large urban seismic land data set with a low signal-to-noise ratio. They also have suboptimal conditions such as the end-on spread geometry due to the use of a land streamer and occasionally bad coupling of the geophones. This turns out to be a big challenge for the FWI algorithm. The starting model used for the field data inversion is based on the multichannel analysis of surface waves which deals with the inversion of surface-wave dispersion curves. It is a cost-efficient way to obtain an initial model for FWI.With a subset of this model and the data, I investigate the influence of traces near the sources which suffer heavily from the suboptimal acquisition conditions, and if more iterations can improve the results. Removing such traces improve the major part of the results, while more iterations are not beneficial with this particular data set. At the end I show a final comparison of both workflows by applying them to the complete Salt Lake City field data set. An application of FWI on such a large-scale land streamer acquisition with such a large number of shots has, to the best of my knowledge, not been done yet, most likely because of the large computational costs, even in 2D. The comparisons and investigations show that while RSW has a major computational advantage in storage usage and per-CPU performance, it is too sensitive to bad traces and shots which significantly degrade the final model. Therefore, it is unsuitable for field data with a low signal-to-noise ratio or that have not been preprocessed well. Furthermore, RSW cannot utilise the whole power of HPC clusters while FSW is able to do so by optimal shot parallelisation. Thus, FSW could potentially outperform RSW by raw computing power if such excessive power is available.