A crucial aspect of seismotectonic studies is accurately identifying active faults and reconstructing their geometry, kinematics, and deformation rates using geological, seismological, and geodetic data to the fullest extent possible within the current deformation field. This task is challenging, often complicated by the scarcity of clear evidence or quantitative data, both at the near-surface and at seismogenic depths. Developing a reliable seismotectonic model is, therefore, subject to uncertainties stemming from data limitations and errors, which can hinder the precise characterization of fault geometry, kinematics, and associated stress and deformation fields.
To overcome these challenges, it has become essential to integrate various methodologies both cutting-edge in their technologies and complementary in their resolution scales, depth, and dimensions (from 3D to 4D). The multidisciplinary nature of seismotectonics, which synthesises structural-geological, morphological, seismological, geophysical, remote-sensing, and geodetic data alongside numerical and analogue modelling, offers a comprehensive approach to identifying active tectonic signals. Additionally, the increasing availability of big data and the application of deep learning techniques in geosciences present a unique opportunity to bridge data gaps and improve the accuracy and reliability of seismotectonic models.
This session invites studies focused on the following themes: i) field-based geological and structural surveys of active faults, including those in volcanic regions; ii) classical and innovative multiscale and multidisciplinary approaches in geology, seismology, and geophysics; iii) the development and analysis of new or updated seismological, geophysical, and field- or remotely-collected datasets; iv) fault imaging, tectonic setting definitions, and the creation of 3D seismotectonic models; v) numerical and analogue modelling; vi) studies that explore the alignment or discrepancies between known fault characteristics, seismotectonic models, and seismic events; vii) novel insights aimed at advancing seismotectonic modelling.
Our goal is to stimulate significant scientific interest and debate on advancing our understanding of active faulting, aiming to produce robust seismotectonic models. We particularly encourage submissions that combine classical and innovative methodologies, including big data, deep learning, and other forms of artificial intelligence.