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 $VS30$ site‐condition 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 $logVS30$ 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 $logVS30$ 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 the F3 unit may be further refined using correlations with topographic slope based on the Shuttle Radar Topography Mission at 3 arcsec resolution (SRTM3) dataset. We analyzed the performance site‐condition models based on correlations with exogenous data (topographic slope and surface‐geology analogs). The results show that the residual distributions between $logVS30$ values measured and estimated from those proxies are strongly biased for some geological units, emphasizing the need for acquiring regional $VS$ data.

© 2014, Bulletin of the Seismological Society of America. All rights reserved. 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.This research was supported by Fundação para a Ciência e Tecnologia through projects WILAS (PTDC/CTE-GIX/097946/2008), QuakeLoc-PT (PTDC/GEO-FIQ/3522/2012), and SCENE (PTDC/CTE-GIX/103032/2008), and by the European Commission FP7 project NERA (Network of European Research Infrastructures for Earthquake Risk Assessment and Mitigation, GA-262330). This is a contribution to PEST-OE/CTE/LA0019/2011 (IDL). The first author acknowledges a Marie Curie International Reintegration Grant from the European Commission, FP7 (PIRG03-GA-2008-230922)Peer Reviewe

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 natural and anthropogenic processes. Therefore, obtaining numerical 3D 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 because 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 with the reference models in terms of histogram and variogram models reproduction and in their ability to quantify spatial uncertainty. The results indicate the ability of both methods to predict the reference values. Although the KEG is computationally efficient, it struggles to reproduce the extreme values. In contrast, 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 of the posterior models have different information. The synthetic data set is available to the community, so it can be used as a benchmark for other FDEM inversion methods.

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.