Paleoenvironmental changes during the late Miocene (Messinian)–Pliocene transition (Bajo Segura Basin, southeastern Spain): sedimentological and ichnological evidence

A detailed sedimentological and paleontological analysis of the uppermost Miocene (Messinian)–Pliocene boundary at the northern border of the Bajo Segura Basin, southeastern Spain, was carried out in order to describe the evolution of the regional paleocoastline during the Pliocene reflooding of the Mediterranean immediately after the sea-level fall related to the Messinian Salinity Crisis. Multiple trace fossils typical of firm- and hardgrounds were recognized, allowing identification of Glossifungites (two different types), Entobia, and Gnathichnus ichnofacies. Trace-fossil analysis showed that lithology and media consistency exerted considerable control on the development of the different ichnocoenoses and that there was a clear decrease in hydrodynamic energy from a coastal to a shallow-water shelf environment related to progressive sea-level rise. Ichnological and sedimentological data provide evidence that the definitive flooding of the Mediterranean was rapid and synchronous throughout the northern margin of the Bajo Segura Basin. The following model for the Pliocene transgression in the study area is therefore proposed: (1) the marine ingression penetrated along the incised paleovalleys carved as a consequence of the fall in sea level, where the first two Pliocene systems were deposited (P0–P1); (2) during the maximum flooding surface of the transgression, the sea overflowed the margins of the paleovalleys and extended throughout the entire northern margin of the basin; and (3) the third Pliocene system was deposited, forming the lower part of a highstand systems tract (P2).

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.