How can we identify active faults when surface evidence is limited or ambiguous? Which strategies best capture their geometry and kinematics from the surface to seismogenic depths? How can present-day deformation be linked to long-term tectonics, and how can emerging technologies and big data reduce uncertainties in seismotectonic models?
These key questions guide this session. Characterizing active faults structurally and dynamically is challenging because geological, seismological, geophysical, and geodetic observations are often fragmented, scale-dependent, or indirect. This leads to major uncertainties in fault geometry, slip behavior, and related stress and strain fields, especially where direct evidence is scarce. To address these challenges, we invite researchers engaged in fieldwork, seismological and geophysical imaging, geodetic monitoring, modelling, and data-driven approaches to share insights. Bringing together diverse expertise will foster cross-disciplinary discussion and highlight strategies for advancing seismotectonic models. High-resolution field investigations, geophysical and seismological imaging, satellite-based deformation monitoring, and numerical or analogue modelling provide complementary perspectives. Alongside these, artificial intelligence—including machine learning and generative models—offers powerful ways to identify patterns, bridge data gaps, and improve the reliability of seismotectonic interpretations.
We welcome contributions on (but not limited to):
-geological and structural investigations of active faults, including volcanic settings;
-innovative, multidisciplinary approaches integrating geology, seismology, and geophysics;
-development and integration of new or updated datasets, from field observations to remote sensing;
-fault imaging, tectonic analysis, and construction of 3D/4D seismotectonic models;
-numerical and analogue modelling of fault systems and tectonic processes;
-studies comparing seismicity, fault characteristics, and seismotectonic interpretations;
-applications of big data, artificial intelligence, and deep learning in tectonic and seismic research, including advances using AI and generative models to extract, simulate, or enhance seismotectonic signals.
By encouraging open, collaborative exchange, this session aims to advance our capacity to recognize, model, and understand active fault systems, ultimately supporting the development of robust, integrative seismotectonic frameworks.