RT info:eu-repo/semantics/article T1 pySCu: A new python code for analyzing remagnetizations directions by means of small circle utilities A1 Calvín Ballester, Pablo A1 Villalaín Santamaria, Juan José A1 Casas Sainz, Antonio M. A1 Tauxe, Lisa . A1 Torres López, Sara K1 Small circle K1 SCI method K1 Fold-test K1 Remagnetization K1 Synfolding K1 pySCu K1 Physics K1 Paleontology K1 Física K1 Paleontología AB The Small Circle (SC) methods are founded upon two main starting hypotheses: (i) the analyzed sites were remagnetized contemporarily, acquiring the same paleomagnetic direction. (ii) The deviation of the acquired paleomagnetic signal from its original direction is only due to tilting around the bedding strike and therefore the remagnetization direction must be located on a small circle (SC) whose axis is the strike of bedding and contains the in situ paleomagnetic direction. Therefore, if we analyze several sites (with different bedding strikes) their SCs will intersect in the remagnetization direction.The SC methods have two applications: (1) the Small Circle Intersection (SCI) method is capable of providing adequate approximations to the expected paleomagnetic direction when dealing with synfolding remagnetizations. By comparing the SCI direction with that predicted from an apparent polar wander path, the (re)magnetization can be dated. (2) Once the remagnetization direction is known, the attitude of the beds (at each site) can be restored to the moment of the acquisition of the remagnetization, showing a palinspastic reconstructuion of the structure. Some caveats are necessary under more complex tectonic scenarios, in which SC-based methods can lead to erroneous interpretations. However, the graphical output of the methods tries to avoid ‘black-box’ effects and can minimize misleading interpretations or even help, for example, to identify local or regional vertical axis rotations. In any case, the methods must be used with caution and always considering the knowledge of the tectonic frame.In this paper, some utilities for SCs analysis are automatized by means of a new Python code and a new technique for defining the uncertainty of the solution is proposed. With pySCu the SCs methods can be easily and quickly applied, obtaining firstly a set of text files containing all calculated information and subsequently generating a graphical output on the fly. PB Elsevier SN 0098-3004 YR 2017 FD 2017-12 LK http://hdl.handle.net/10259/4677 UL http://hdl.handle.net/10259/4677 LA eng NO CGL2012-38481 andCGL2016-77560 of the MINECO (Spanish Ministry of Economy andCompetitiveness) with also FEDER founding (European Union). PC acknowledgesthe MINECO for the F.P.I. research grant BES-2013-062988.LT acknowledges support from National Science Foundation grant # EAR1345003. DS Repositorio Institucional de la Universidad de Burgos RD 29-mar-2024