He B, et al. (2018) Normalized nonzero-lag crosscorrelation elastic full-waveform inversion.ABSTRACT Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.
Many full-waveform inversion schemes are based on the iterative perturbation theory to fit the observed waveforms. When the observed waveforms lack low frequencies, those schemes may encounter convergence problems due to cycle skipping when the initial velocity model is far from the true model. To mitigate this difficulty, we have developed a new objective function that fits the seismic-waveform intensity, so the dependence of the starting model can be reduced. The waveform intensity is proportional to the square of its amplitude. Forming the intensity using the waveform is a nonlinear operation, which separates the original waveform spectrum into an ultra-low-frequency part and a higher frequency part, even for data that originally do not have low-frequency contents. Therefore, conducting multiscale inversions starting from ultra-low-frequency intensity data can largely avoid the cycle-skipping problem. We formulate the intensity objective function, the minimization process, and the gradient. Using numerical examples, we determine that the proposed method was very promising and could invert for the model using data lacking low-frequency information.
Global Fishing Watch (GFW) provides global open-source data collected via automated monitoring of vessels to help with sustainable management of fisheries. Limited previous global fishing effort analyses, based on Automatic Identification System (AIS) data (2017–2020), suggest economic and environmental factors have less influence on fisheries than cultural and political events, such as holidays and closures, respectively. As such, restrictions from COVID-19 during 2020 provided an unprecedented opportunity to explore added impacts from COVID-19 restrictions on fishing effort. We analyzed global fishing effort and fishing gear changes (2017–2019) for policy and cultural impacts, and then compared impacts of COVID-19 lockdowns across several countries (i.e., China, Spain, the US, and Japan) in 2020. Our findings showed global fishing effort increased from 2017 to 2019 but decreased by 5.2% in 2020. We found policy had a greater impact on monthly global fishing effort than culture, with Chinese longlines decreasing annually. During the lockdown in 2020, trawling activities dropped sharply, particularly in the coastal areas of China and Spain. Although Japan did not implement an official lockdown, its fishing effort in the coastal areas also decreased sharply. In contrast, fishing in the Gulf of Mexico, not subject to lockdown, reduced its scope of fishing activities, but fishing effort was higher. Our study demonstrates, by including the dimensions of policy and culture in fisheries, that large data may materially assist decision-makers to understand factors influencing fisheries’ efforts, and encourage further marine interdisciplinary research. We recommend the lack of data for small-scale Southeast Asian fisheries be addressed to enable future studies of fishing drivers and impacts in this region.
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