Crustal structure of northern Italy from the ellipticity of Rayleigh waves

Northern Italy is a diverse geological region, including the wide and thick Po Plain sedimentary basin, which is bounded by the Alps and the Apennines. The seismically slow shallow structure of the Po Plain is difficult to retrieve with classical seismic measurements such as surface wave dispersion, yet the detailed structure of the region greatly affects seismic wave propagation and hence seismic ground shaking. Here we invert Rayleigh wave ellipticity measurements in the period range 10–60 s for 95 stations in northern Italy using a fully non linear approach to constrain vertical vS,vPvS,vP and density profiles of the crust beneath each station. The ellipticity of Rayleigh wave ground motion is primarily sensitive to shear-wave velocity beneath the recording station, which reduces along-path contamination effects. We use the 3D layering structure in MAMBo, a previous model based on a compilation of geological and geophysical information for the Po Plain and surrounding regions of northern Italy, and employ ellipticity data to constrain vS,vPvS,vP and density within its layers. We show that ellipticity data from ballistic teleseismic wave trains alone constrain the crustal structure well. This leads to MAMBo-E, an updated seismic model of the region’s crust that inherits information available from previous seismic prospection and geological studies, while fitting new seismic data well. MAMBo-E brings new insights into lateral heterogeneity in the region’s subsurface. Compared to MAMBo, it shows overall faster seismic anomalies in the region’s Quaternary, Pliocene and Oligo-Miocene layers and better delineates the seismic structures of the Po Plain at depth. Two low velocity regions are mapped in the Mesozoic layer in the western and eastern parts of the Plain, which seem to correspond to the Monferrato sedimentary basin and to the Ferrara-Romagna thrust system, respectively

Additional file 1: of BCM: toolkit for Bayesian analysis of Computational Models using samplers

Supplementary Information. Description: Supplementary information describing the methodological details and all settings that were used for each inference. (DOCX 49 kb

Additional file 2: Figure S1. of BCM: toolkit for Bayesian analysis of Computational Models using samplers

Description: Overview of the sampling results of each inference of the comparison with existing software packages. (PDF 1 mb

Number of CISs per Scale Parameter

<p>Number of CISs for Various Scale Parameters (Corrected and Uncorrected), the csCISs, the Background-Corrected csCISs, and the CISs from the RTCGD. Background correction only has effect at larger scales.</p

Example of Novel CIS and Background Corrected CIS

<div><p>(A) Venn diagram comparing the csCISs and the CISs in the RTCGD. For reasons explained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020166#pcbi-0020166-g007" target="_blank">Figure 7</a>, the intersection shows two counts.</p><p>(B) An example of a CIS that consists of three insertions from three independent screens, and therefore is only detected when integrally analyzing the data.</p><p>(C) Venn diagram comparing the csCISs with and without applying background correction.</p><p>(D) An example of a csCIS, that was also included in the RTCGD, and is rejected based on the background-corrected threshold. The small vertical bars (red) in the genes denote the 5′ ends of genes, and a star denotes a corrected CIS. Since we are only interested in correcting regions that are putative CISs, a background-corrected threshold is only computed for peaks in the estimated number of insertions. The corrected threshold is given by the horizontal dotted line above the peak.</p></div

Comparison with Previous CIS Definition

<div><p>(A) Plot of the increase of the error as a function of the screen size, when using the definition from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020166#pcbi-0020166-b008" target="_blank">8</a>], computed using the Poisson distribution, or a permutation approach. Also the results from the two individual windows used are given. Since the errors made by the two windows individually are not mutually exclusive, the Poisson estimate is an overestimate of the true error.</p><p>(B) Venn diagram comparing three different CIS definitions: a) the definition from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020166#pcbi-0020166-b008" target="_blank">8</a>] applied to the complete dataset, b) the csCISs resulting from the GKC, and c) the published CISs from the RTCGD. The intersection between sets shows three counts (and corresponding percentages), indicating the count for set a, b, and c, respectively. This is because the three sets of CISs used different definitions (at different scales) for a CIS, so that some CISs are split up, and hence are counted twice.</p></div

Schematic Depiction of the Significance Analysis of the Density Estimate of the Insertion Data

<div><p>(A) The position of the <i>N</i> insertions is permuted.</p><p>(B) The convolution method is applied to the resulting permuted insertion profile. The heights of all peaks are recorded.</p><p>(C) Step A and B are repeated. A distribution of the peaks in random data results.</p><p>(D) The threshold is computed by determining the <i>α</i>-level in the empirical CDF of the null-distribution. This threshold is applied to the insertion estimate of the real insertion data, resulting in a series of significant peaks.</p></div

Schematic Depiction of the Computation of a Background-Corrected Threshold

<div><p>(A) The density of TSSs (the 5′ ends of the genes) is computed using a fixed kernel width <i>h</i>_bg.</p><p>(B) A new realization of insertions is generated using the density from step A.</p><p>(C) The GKC method is applied to the resulting insertion profile, yielding one realization of the background density estimate. Steps (A) and (B) and applying the GKC are repeated N times to yield a distribution of background realizations. For every position on the genome, a CDF of these realizations is computed and the threshold is determined based on the <i>α</i>-level.</p><p>(D) The location-dependent threshold is combined with the threshold based on uniform background. Finally, the smoothed insertion estimate of the real data is thresholded with the resulting threshold.</p></div

The <i>Myc</i> Locus on Chromosome 15

<div><p>(A) The blue line represents the estimated number of insertions as a function of position for a certain region. The red line depicts the threshold associated with an <i>α</i>-level of 0.05.</p><p>(B) CISs are depicted by means of vertical lines. From top to bottom these represent: the CISs for the current scale (30k), the csCISs, the CISs from the RTCGD, the insertions, and the genes (top and bottom strand separated).</p><p>(C) Scale space diagram. The vertical axes of the scale space has a logarithmic scale and indicates the scale for which the CIS was detected (only a subset of scales was actually evaluated: [50 100 250 500 1 k 2.5 k 5 k 10 k 30 k 50 k 100 k 150 k] bp).</p><p>(D) Evaluation of the insertion distribution over four small scale CISs, identified by scale space analysis. Per screen we list the number of insertions that fall within the small scale CIS. The screens are labeled consistent with RTCGD nomenclature.</p></div

Results from Simulation Experiments—True and False Positives

<div><p>(A,B) Results for the GKC applied to artificial data.</p><p>(C,D) Results for the TKC.</p><p>(E,F) Results for the RKC.</p><p>The horizontal solid lines in (A), (C), and (E) show the 5% significance level, the dotted lines show the average number of csFPs. The legend shows the different simulated CISs, stating the number of insertions <i>N</i>_CIS that fall within the CIS of width <i>W</i>_CIS.</p></div