Image analysis has been succesfully applied in core research, especially in studies from modern deposits, to enhance the visibility of ichnological features and characterize ichnoassemblages and ichnofabrics. Its application to ichnological research provides useful information for marine core studies, hence sedimentary geology, but also for hydrocarbon exploration. Here we develop a new methodology, using Python programming language, which significantly improve the ichnological analysis. The method automatizes the process of obtaining continuous ichnological information, in this case about the percentage of bioturbation as a key aspect of the ichnofabric approach. The method affords the possibility of automatically generating continuous percentage and other index records using pixel counts in previously treated images. The resulting data sets are easy to correlate with the information usually obtained from cores (e.g., geochemical and mineralogical data). Such an integration of different proxies for to the field of sedimentary geology especially in the use of ichnological analysis, making it easier for the researcher, less time consuming, and more likely to be undertaken. The coding and sharing of open software tools allow for great flexibility, giving researchers in ichnology or related fields the option to implement new features, develop more complex tools to improve the package, and share findings with the scientific community.

This study was funded by project CGL2015-66835-P (Secretaría de Estado de I+D+I, Spain), Research Group RNM-178 (Junta de Andalucía), and Scientific Excellence Unit UCE-2016-05 (Universidad de Granada). The work of Santiago Casanova is funded through a pre-doctoral grant from the Ministerio de Educación, Cultura y Deporte (Government of Spain). The research was conducted in the framework of the “Ichnology and Palaeoenvironment Research Group” (UGR).