Geophysical Journal International

SummaryIn February-March 2025 a seismic sequence occurred in the western sector of the Aeolian Archipelago (Southern Tyrrhenian Sea, Italy), a seismotectonic complex region located along the Africa-Eurasia plate boundary and mainly controlled by their NW-trending convergence. The seismicity, located ∼20 km south of Alicudi Island and ∼40 km north of the coast of Sicily, started on February 7 with an earthquake of magnitude Mw 4.7 that was followed in the next month by 42 events with local magnitudes between 1.2 and 3.4. Notwithstanding its moderate energy, this recent seismicity offers a unique opportunity to investigate seismogenic processes in a region for which a seismic potential of ∼M7 or even more has been suggested and a relevant data paucity mainly related to its offshore location was widely recognized. We tackle the limitations of not-optimal network configuration, by designing an ad-hoc approach, which integrates different advanced techniques. Specifically, we combine Bayesian methodology for accurate absolute hypocenter locations, machine learning techniques for detection of weaker events, Distance Geometry Solvers for relative locations, and a probabilistic inversion tool for source mechanism estimation. Our analysis led us to strongly enrich the dataset of detected earthquakes, and to define the causative source of the 2025 sequence as a NE-SW trending N-dipping thrust faulting structure. The proposed source agrees with the regional seismogenic stress field and with the structural architecture of the southern Tyrrhenian portion of the Africa-Eurasia plate margin by also adding new constraints in a sector where no known fault segments were previously reported. This study provides new insights on seismogenic processes in the investigated area, while proving the effectiveness of the employed combined approach for characterizing seismogenic sources in poor network configurations.

SummaryDecomposing spectral responses in induced polarization on the basis of elementary Debye relaxation kernels with a distribution of time constants (Relaxation Time Distribution) is a powerful tool for analysing observations in this low-frequency electromagnetic method. Notably, it enables the estimation of the sizes of polarisation sites, particularly in the presence of metallic particles, as well as facilitating environmental studies. These decompositions generalise a plethora of historical models, some of which can be considered equivalent to each other in the sense of mathematical equivalence classes. Here, we explicit several types of these equivalence relations, which we recall in their definition in relation to a common property, the elements of a given class belong to a given set. For example, we present a class of models that fall under the same differential equation, meaning this is the class of models that belong to the set of distributions that verify the differential equation. We also exhibit another class of models where we can pass from one to the other by an elementary calculation. Among all the possibilities, a particular class often interests us in IP: RTD classes such as spectra are practically indistinguishable as they are so close according to a defined criterion. In this particular case, we study here the equivalence (or non-equivalence) of certain classical models. We confirm that two models play major roles: the lognormal distribution (because it is the most natural) and the Cole-Cole distribution, which is empirical but also often used for its simplicity (and the associated RTD is analytical, unlike that of the lognormal which requires numerical evaluations). It turns out that these two distributions are equivalent in terms of their quasi-equal spectra, a fact known since Cole and Cole (1941), but whose scope is extended here by an in-depth study of the objective function which separates them in the least squares sense.