Rafael da Costa Silva

Tying seismic data to well logs is a critical step in seismic amplitude processing and interpretation. This process is often repeated throughout the exploration phase to enhance the understanding of well characteristics, such as the time–depth relationship. However, the presence of data noise and inherent uncertainties makes the well-tie task time-consuming and complex. It requires the estimation and careful control of several parameters to ensure reliable results. A workflow was proposed based on segmented global optimization that partially automates the well-seismic tying process by estimating the matching position between the synthetic and seismic traces and automatically aligning them. The matching position was estimated using time-weighted dynamic time warping (TWDTW), which accounts for the maximum allowable perturbation in the velocity log. In conjunction with TWDTW, a global Bayesian optimizer estimated the tie-related parameters. Following this step, the alignment was performed through an optimization-based method that incorporated segmentation to estimate the velocity log perturbation and wavelet phase, constrained by a predefined tolerance. The well logs and seismic traces were segmented, and each segment was aligned using constrained dynamic time warping. This segmentation increased the number of knots available for interpolating the velocity perturbation curve, resulting in a more detailed and refined alignment. The proposed partial automation improved the correlation in all six wells evaluated, with four wells achieving correlation values above 70%, without introducing physically unrealistic distortions.

This work aimed to apply the ARIMA model to predict the under-reporting of new Hansen’s disease cases during the COVID-19 pandemic in Palmas, Tocantins, Brazil. This is an ecological time series study of Hansen’s disease indicators in the city of Palmas between 2001 and 2020 using the autoregressive integrated moving averages method. Data from the Notifiable Injuries Information System and population estimates from the Brazilian Institute of Geography and Statistics were collected. A total of 7035 new reported cases of Hansen’s disease were analyzed. The ARIMA model (4,0,3) presented the lowest values for the two tested information criteria and was the one that best fit the data, as AIC = 431.30 and BIC = 462.28, using a statistical significance level of 0.05 and showing the differences between the predicted values and those recorded in the notifications, indicating a large number of under-reporting of Hansen’s disease new cases during the period from April to December 2020. The ARIMA model reported that 177% of new cases of Hansen’s disease were not reported in Palmas during the period of the COVID-19 pandemic in 2020. This study shows the need for the municipal control program to undertake immediate actions in terms of actively searching for cases and reducing their hidden prevalence.