A python-based semi-automated workflow for morphometry of pockmark populations

Submarine pockmarks are among the most ubiquitous geomorphological expressions of seafloor fluid escape, commonly occurring in high-density fields that may include hundreds to tens of thousands of individual features. Under these conditions, traditional manual morphometric analyses become time-consuming and impractical, limiting the robustness and reproducibility of pockmark-based studies. In this work, we present an open-source Python-based semi-automated workflow designed to systematically analyse the morphometry of pockmark populations using Digital Elevation Models (DEMs) and polygonal vectors as primary inputs. The workflow enables extraction of a comprehensive set of planimetric, elevation-based, geometric, and volumetric measurements, thereby minimizing operator-dependent biases. The approach has been tested on two contrasting geological case studies: the tectonically and volcanically active Graham Bank region in the Sicily Channel, mapped using high-resolution multibeam bathymetry, and the passive north-western Sardinian continental margin, investigated using regional-scale bathymetric grids from EMODnet Bathymetry. These datasets allow evaluation of the workflow performance across different geological settings, spatial resolutions, and pockmark size classes. Results demonstrate that the workflow provides robust and internally consistent morphometric outputs, suitable for large-scale statistical analyses and comparative studies. Although developed and validated on submarine pockmarks, the method is inherently general and can be applied to any negative landform that can be represented as a closed polygon and for which a DEM is available, such as karstic sinkholes or other collapse-related depressions. The proposed workflow therefore represents a flexible and reproducible methodological framework with broad applicability across geomorphology, environmental sciences, and hazard-related studies.