Active fault characterization: Improving fault scaling laws by drone technology and advanced statistics

Fault geometric characterization includes the analysis of fault attributes such as length, throw, core thickness and damage zone width. These attributes are correlated through scaling laws, which allow predicting one from another and understanding the fault growth mechanisms. Fault geometric investigations are challenged by (i) limited access to fault 3D structure; (ii) resolution limits of methods and (iii) gaps in the global fault datasets. We introduce a new dataset of active faults from Baza, Galera, and Padul fault zones (Betic Cordillera, Spain) acquired by drone photogrammetry, which addressed some of these challenges. Compiling the published data and our new dataset, we refined the fault global dataset and the fault scaling laws, using truncated-piecewise power laws for bivariate and univariate analyses. We found specific break points in the trendlines of the combined data sets that are critical for the fault scaling laws. Results indicate that maximum geometrical attributes localize in the narrower sections of fault zones. Additionally, syn-sedimetary active faults in poorly-lithified sediments can exhibit atypical scaling relationships, due to their susceptibility to alteration. These active faults are overdisplaced and showcase thicker fault cores, compared to the global dataset. Finally, we revisited the fault growth models, highlighting that a hybrid model between the fault length and displacement better accounts for the heterogeneity observed in the fault zones. • We updated and refined global fault scaling laws with piecewise power laws. • Drone imagery reduced the existing information gaps in the fault global dataset. • Maximum geometrical attributes localize in the narrower section of the fault zones. • Active faults in young sediments are overdisplaced and have a thicker fault core. • We support a hybrid fault growth model between fault length and displacement .