The inclusion of site-specific conditions is essential to adequately represent the seismic hazard and the seismic risk for a region. We acquired, gathered and organized a near surface shear-wave velocity database for Portugal, and applied a three-step methodological approach for developing a V S30 site-conditions map using extrapolation based on surface geology. The methodology includes: 1) defining a preliminary set of geologically defined units; 2) calculating the probability distribution of log V S30 for each unit; and 3) merging the units according to the results of statistical tests. The final model comprises three geologically defined units characterized by log V S30 distributions that are statistically significantly different from each other: F1-Igneous, metamorphic and old sedimentary rocks; F2-Neogene and Pleistocene formations; and F3-Holocene formations. The site conditions for F3 unit may be further refined using correlations with topographic slope based on the SRTM3 dataset. We analysed the performance site-conditions models based on correlations with exogenous data (topographic slope and surface geology analogues). The results show that the residual distributions between log V S30 values measured and estimated from those proxies are strongly biased for some geological units, emphasizing the need for acquiring regional V S data.
The West Iberia Lithosphere and Asthenosphere Structure (WILAS) project densely covered Portugal with broadband seismic stations for 2 yrs. Here we provide an overview of the deployment, and we characterize the network ambient noise and its sources. After explaining quality control, which includes the assessment of sensor orientation, we characterize the background noise in the short-period (SP), microseismic, and long-period (LP) bands. We observe daily variations of SP noise associated with anthropogenic activity. Temporary and permanent stations present very similar noise levels at all periods, except at horizontal LPs, where temporary stations record higher noise levels. We find that median noise levels are extremely homogeneous across the network in the microseismic band (3-20 s) but vary widely outside this range. The amplitudes of microseismic noise display a strong seasonal variation. The seasonality is dominated by very-long-period double-frequency microseisms (8 s), probably associated with winter storms. Stacks of ambient noise amplitudes show that some microseismic noise peaks are visible across the whole ground-motion spectrum, from 0.3 to 100 s. Periods of increased microseismic amplitudes generally correlate with ocean conditions offshore of Portugal. Some seismic records display an interesting 12 hr cycle of LP (100-s) noise, which might be related to atmospheric tides. Finally, we use plots of power spectral density versus time to monitor changes in LP instrumental response. The method allows the identification of the exact times at which LP response changes occur, which is required to improve the understanding of this instrumental artifact and to eventually correct data.
The spatial distribution of the physical properties of the first meters beneath the Earths surface is often complex due to its highly dynamic nature and small-scale heterogeneities resulting from both natural and anthropogenic processes. Therefore, obtaining numerical three-dimensional models that accurately describe the spatial distribution of these properties is often challenging, yet essential for different fields such as environmental assessment and remediation, geoarchaeological conservation, and precision agriculture. Frequency-domain electromagnetic (FDEM) induction methods have proven their potential to image these properties in high (spatial) detail as FDEM measurements are sensitive to two key soil properties: electrical conductivity; and magnetic susceptibility. Predicting subsurface properties from FDEM data requires solving an ill-posed and nonlinear inverse problem with multiple solutions. Recently, there has been a rapid growth of FDEM inversion methods, which may be broadly divided into probabilistic and deterministic methods. We compare two stochastic FDEM inversion approaches: the Kalman ensemble generator (KEG); and another one formulated as an iterative geostatistical FDEM inversion. Both methods are applied to a synthetic data set with spatially heterogeneous physical properties of interest, mimicking a real landfill mining site. The predicted models are compared to the reference models in terms of histogram and variogram models reproduction and in their ability to quantify spatial uncertainty. The results show the ability of both methods to predict the reference values. While the KEG is computationally efficient, it struggles to reproduce the extreme values. On the contrary, the geostatistical inversion approach ensures the reproduction of the imposed histograms and variogram models in the predicted models. As the prior information is included in both inversion methods in different ways, the pointwise variance models computed from all the posterior models has different information. The synthetic data set is available to the community so it can be used as benchmark for other FDEM inversion methods.
Direct control of doping in sports is based on the analysis of active substances and/or their metabolites in urine samples of the athletes by GC/MSn or LC/MSn. The World Anti-Doping Agency, WADA, defined criteria for the agreement between retention times, RT, or relative retention times, RRT, and abundance ratios, AR, of characteristic ions of the mass spectrum of the analyte in a calibrator (positive control) and the sample. Strict criteria for confirming analyte presence were defined to reduce false positive results rates, FP. However, these criteria can lead to high rates of false negative results, FN. This work presents a methodology to define statistically sound criteria for the agreement between RRT and AR that allow keeping the FN under control. This work also determined the FP of identifications. The statistical criteria were set from Monte Carlo simulations of correlated RT and ion abundances. The simulation of AR and signal noise was also used to estimate the FN and FP of identifications based on the criteria defined by WADA. The developed tools were successfully applied to the control of nine doping substances in urine samples by GC/MS/MS. The estimated FN were tested from independent experimental tests proving estimates are accurate. The criteria defined by WADA are associated with extremely low FP but, in some cases, associated with FN much larger than 50%. The statistically sound identification criteria allow a more convenient balance between FN and FP. The user-friendly spreadsheet used in this work is made available as Supporting Information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.