Context COVID-19 caused a worldwide pandemic, and there are still many uncertainties about the disease. C-reactive protein (CRP) levels could be utilized as a prognosticator for disease severity in COVID-19 patients. Objectives This study aims to determine whether CRP levels are correlated with COVID-19 patient outcomes and length of stay (LoS). Methods A retrospective cohort study was conducted utilizing data obtained between March and May 2020. Data were collected by abstracting past medical records through electronic medical records at 10 hospitals within CommonSpirit Health. Patients were included if they had a positive COVID-19 test from a nasopharyngeal swab sample, and if they were admitted and then discharged alive or had in-hospital mortality and were ≥18 years. A total of 541 patients had CRP levels measured and were included in this report. Patient outcome and LoS were the endpoints measured. Results The 541 patients had their CRP levels measured, as well as the demographic and clinical data required for analysis. While controlling for body mass index (BMI), number of comorbidities, and age, the first CRP was significantly predictive of mortality (p<0.001). The odds ratio for first CRP indicates that for each one-unit increase in CRP, the odds of death increased by 0.007. For LoS, the first CRP was a significant predictor (p<0.001), along with age (p=0.002). The number of comorbidities also predicted LoS (p=0.007), but BMI did not. The coefficient for the first CRP indicates that, for each one-unit increase in CRP, LoS increased 0.003 days. Conclusions The results indicate that there is a positive correlation between the CRP levels of COVID-19 patients and their respective outcomes with regard to death and LoS.
Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.
We present a case study from the North West Shelf of Australia where the complexity of the overburden consists of several thin multi-level channel systems filled with a combination of anomalously high or low velocity sediments. Not accounting for these strong velocity variations accurately, can lead to subtle image distortions affecting the underlying section down to and including the reservoir level. This can have significant impact on the volumetric estimates of reserves in place. To resolve these complexities in the overburden, full waveform inversion (FWI) was utilized to generate an updated earth model exploiting both early arrivals and reflection events. One caveat to using full waveform inversion is the need for low frequencies to be present in the seismic data, or, the initial starting velocity model must contain the correct low wavenumber components. However, conventional seismic data acquired at shallow tow depths are usually band limited particularly at the very low frequencies. Our case study will discuss these issues along with other limitations that this "conventional data" presented along with the workflows and quality control methods adapted to this data in order to converge to a plausible, high resolution earth model. Introduction An accurate earth model is fundamental to any depth imaging project. Full waveform inversion is an advanced model building technique incorporating the full two way wave equation. Full waveform inversion produces an accurate high resolution earth model by simultaneously using the information of travel time, amplitude and phase contained in the full recorded seismic wavefield. One pre-requisite to full waveform inversion is an initial starting model. In this case study, the initial starting model was derived from a smooth version of a reflection travel time tomography velocity field derived from a depth migration workflow. The full waveform inversion process utilizes this model and a two-way wave equation finite difference acoustic wavefield propagator to generate modelled seismic data. These modelled shots are then compared to the acquired (observed) recorded seismic shots. The residual differences are backward propagated from time to depth domain, into velocity gradients and velocity changes required to obtain an updated model (see Figure 1). As with solving any non-linear inversion problem, it is an iterative process and is repeated as required until the residuals between the modelled shots and the actual observed seismic data are minimized. Iterations start at low frequencies and progress to higher frequencies.
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