FoldApp: A MATLAB application for area-depth-strain (ADS) analysis of detachment folds

The Area-Depth-Strain (ADS) method is a mass-balance approach to quantify shortening, depth to detachment, and strain in both contractional and extensional systems We introduce FoldApp , a MATLAB-based, cross-platform app that automates ADS calculations to asses the deformation in detachment folds. The software is organized into three modules that guide users through the necessary steps required by the method: (i) import a seismic interpretation, (ii) set the location of pinning lines in adjacent synclines, (iii) compute the area above the syncline regionals, and (iv) find the best-fit to changes in folded area as a function of stratigraphic thickness. FoldApp also estimates uncertainties in strain and detachment depth, improves reproducibility as users can test the sensitivity of the seismic interpretation to noise, and integrates corrections for shortening caused by tectonic compaction and pure shear layer thickening. Although these effects are rarely considered in mass-balance restorations, they give rise to significant mismatches between the estimated and actual shortening. We tested FoldApp on a representative detachment fold from the Mexican Ridges Fold Belt, western Gulf of Mexico, obtaining robust estimates of tectonic transport, consistent with previously published manual calculations including compaction effects in the transport direction, with quantified uncertainties. FoldApp thus provides a reliable and accessible platform for structural geologists, bridging the gap between traditional interpretation workflows and quantitative analysis. • FoldApp automates excess-area calculations and ADS regression in fold analysis. • Software quantifies shortening, detachment depth, and strain with uncertainty bounds. • Corrections incorporate tectonic compaction and pure shear thickening. • Case study from Mexican Ridges Fold Belt validates the methodology. • Open-source tool that improves reproducibility and reduces interpretation bias.