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 Earth’s 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.

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<p>The characterization and monitoring of landfills has become a major concern, not only for assessing the associated environmental impact (e.g., groundwater contamination) but also for evaluating the potential for recovery of secondary resources, in particular for the production of raw materials and energy. For both objectives, it is crucial to have knowledge of the waste composition and the current landfill conditions (e.g. water saturation level). Near-surface geophysical surveys have been proven effective for the non-invasive investigation of landfills, in which different methods have been used depending on the specific survey targets.  Because of its sensitivity to two subsurface physical properties, electrical conductivity (EC) and magnetic susceptibility (MS), frequency-domain electromagnetic (FDEM) induction has been successfully applied to the qualitative characterization of urban and industrial landfills, including mine tailings. Yet, due to the generally complex composition and strongly heterogeneous spatial distribution of waste deposits, reconstructing a reliable landfill model from surface geophysical measurements remains challenging. Geostatistical inversion emerges as powerful tool to improve the landfill modelling from geophysical data, allowing for a more detailed description of the spatial distribution of the properties of interest and the associated uncertainty. Additionally, it provides a flexible framework for integrating data from geophysical surveys and conventional sampling from drilling or trenching.</p><p>In this work, we present a new geostatistical inversion technique able for the simultaneous inversion of FDEM data for EC and MS, which optimize the landfill modelling procedure and is sensitive towards change on the physical properties of interest. This method is based on an iterative procedure where ensembles of subsurface models of EC and MS are generated with stochastic sequential simulation and co-simulation. These simulated models are conditioned locally by existing borehole data for these properties and by a spatial continuity pattern imposed by a variogram model. Synthetic instrument response data, including both the in-phase and quadrature-phase components of the FDEM response, are generated from each model using a forward model connecting the data domain (FDEM data) with the model domain (subsurface physical properties). The misfit between the observed and forward-modelled FDEM data, weighted according to the depth sensitivity of the FDEM response toward changes in EC and MS, is used to drive the generation of a new set of models in the next iteration. We illustrate the inversion procedure with synthetic landfill example data sets which were created based on real data collected at a mine tailing in Portugal and a municipal solid waste landfill in Belgium.</p>

The capital city of Lisbon and the Lower Tagus Valley region of central Portugal mainland are located in the Eurasian plate about 350 Km from the approximately E-W oriented Eurasia-Africa plate boundary. It is characterized by low slip-rates (<0.4 mm/year) and a moderate seismicity, occasionally shaken by some important historical earthquakes causing significant damages and economical losses. The most well know damaging earthquakes occurred in 1344, 1531, 1755, 1909 and 1969. The seismic hazard evaluation and mitigation of the area is therefore of great importance to this densely populated area. This paper focuses the evaluation of Pwave and S-wave seismic velocities of the shallowest surface using seismic refraction data interpretation and insitu lithostratigraphic studies to obtain geotechnical parameters such as Vp/Vs ratios and the Poisson coefficient, estimated to provide information for future site effect studies and preliminary VS30 and soil classification maps. The information will also be used to correct earthquake records since this information was also collected close to the location of seismological stations. The soil classification is based upon the European Code 8 for civil engineering which was carried out for land use planning and design of critical facilities. Hundreds of available boreholes drilled for engineering (with SPT data) and water supply were used to confirm layer thicknesses and lithologies at depth together with a detailed geological survey of each profile area. It is the first time VS30 maps and a soil classification based on geophysical and geotechnical parameters is attempted for this highly populated region.

In the geostatistical modeling and characterization of natural resources, the traditional approach for determining the spatial distribution of a given deposit using stochastic sequential simulation is to use the existing experimental data (i.e., direct measurements) of the property of interest as if there is no uncertainty involved in the data. However, any measurement is prone to error from different sources, for example from the equipment, the sampling method, or the human factor. It is also common to have distinct measurements for the same property with different levels of resolution and uncertainty. There is a need to assess the uncertainty associated with the experimental data and integrate it during the modeling procedure. This process is not straightforward and is often overlooked. For the reliable modeling and characterization of a given ore deposit, measurement uncertainties should be included as an intrinsic part of the geo-modeling procedure. This work proposes the use of a geostatistical simulation algorithm to integrate uncertain experimental data through the use of stochastic sequential simulations with local probability functions. The methodology is applied to the stochastic modeling of a benchmark mineral deposit, where certain and uncertain experimental data co-exist. The uncertain data is modeled by assigning individual probability distribution functions to each sample location. Different strategies are proposed to build these local probability distributions. Each scenario represents variable degrees of uncertainty. The impacts of the different modeling approaches on the final deposit model are discussed. The resulting models of these proposed scenarios are also compared against those retrieved from previous studies that use conventional geostatistical simulation. The results from the proposed approaches showed that using stochastic sequential simulation with local probability functions to represent local uncertainties decreased the estimation error of the resulting model, producing fewer misclassified ore blocks.