Susana Custódio

Understanding failed volcanic eruptions is key to mapping magma plumbing and forecasting hazards. Faults and fractures guide magma, but their mechanisms remain unclear due to the lack of precise earthquake locations and limited 3-D fault mapping in volcanic regions. The triple-junction setting of the Azores Archipelago, where volcanic systems and seismogenic faults coexist, offers a natural laboratory to study fault–magma interactions. We analysed ~18,000 earthquakes relocated to high precision using onshore and ocean-bottom seismometers, combined with geodetic data and seismic autocorrelation imaging, during a failed 2022 eruption on São Jorge Island. A magmatic dike ascended rapidly and mostly aseismically from the upper mantle, intruding a crustal fault before stalling ~1,600 m below the surface. Seismicity indicates that magma branching and lateral fluid escape along the fault triggered an intense, months-long swarm with rotated focal mechanisms. This study demonstrates the dual role of faults in facilitating and arresting magma ascent.

Nordland, Northern Norway, is a seismically active stable continental region. Along its coast, clusters of small earthquakes controlled by local extensional stresses are observed. In this study, we present a comparison between two adjacent seismically active areas along the Nordland coast: Jektvik and Rana, which have distinct spatiotemporal patterns. The seismicity in Jektvik, which presents a swarm-like behavior, shows outward activity progression from its center hinting at triggering between earthquake clusters. In contrast, the seismicity in Rana, where swarms are also observed, does not exhibit such pattern. Earthquakes in the Rana cluster occur within isolated spots and show repeating earthquake behavior. Singular spectrum analysis shows that seismicity in Jektvik has a dominant annual periodicity and is modulated by hydrological load, which is also observable on Global Navigation Satellite Systems stations. Although hydrological load changes also affect Rana, its seismicity does not exhibit an annual periodicity. We hypothesize that the Jektvik seismicity occurs within a fluid-rich fracture system that is affected by hydrological modulation, whereas the Rana seismicity occurs within fault irregularities, which accumulate stress and rupture repeatedly. This study presents a case where adjacent areas within an intraplate setting can have significantly different seismogenic behaviors.

Kinematic finite‐fault source inversions aim at resolving the spatio‐temporal evolution of slip on a fault given ground motion recorded on the Earth's surface. This type of inverse problem is inherently ill posed due to two main factors. First, the number of model parameters is typically greater than the number of independent observed data. Second, small singular values are generated by the discretization of the physical rupture process and amplify the effect of noise in the inversion. As a result, one can find different slip distributions that fit the data equally well. This ill posedness can be mitigated by decreasing the number of model parameters, hence improving their relationship to the observed data. In this study, we propose a fuzzy function approximation approach to describe the spatial slip function. In particular, we use an Adaptive Network‐based Fuzzy Inference System (ANFIS) to find the most adequate discretization for the spatial variation of slip on the fault. The fuzzy basis functions and their respective amplitudes are optimized through hybrid learning. We solve this earthquake source problem in the frequency domain, searching for optimal spatial slip distribution independently for each frequency. The approximated frequency‐dependent spatial slip functions are then used to compute the forward relationship between slip on the fault and ground motion. The method is constrained through Tikhonov regularization, requiring a smooth spatial slip variation. We discuss how the number of model parameters can be decreased, while keeping the inversion stable and achieving an adequate resolution. The proposed inversion method is tested using the SIV1‐benchmark exercise.

In this article, we validate the neuro‐fuzzy kinematic finite‐fault inversion method by studying the rupture process of the , Aug/24/2016, Amatrice, central Italy, earthquake. We jointly invert three different datasets to infer the spatio‐temporal slip distribution, namely static and high‐rate GNSS data (<= Hz) and strong‐motion data ( Hz). Each data set is used to constrain a different frequency range of the source model, depending on the sensitivity of the data set. The inferred slip shows a slow nucleation phase at shallow depths of 3–4 km, followed by a bilateral rupture that forms two asperities, one to the NW (Norcia) and another to the SE (Amatrice) of the hypocenter. Our inferred slip is compared with those previously obtained using well‐established methods. In order to select an adequate number of fuzzy basis functions, we propose two alternative procedures, which yield the same general slip features. The first approach consists of ensuring that the inverse problem is formally over‐determined and uses the same number of basis functions at all frequencies. The second approach is based on a maximum‐likelihood analysis of the model misfit and selects a different number of basis functions for each frequency. The maximum‐likelihood approach allows for more basis functions at high frequencies, where more detail in the spatial slip distribution is needed. The solution obtained with the maximum‐likelihood approach provides a more physically plausible source time function, which shows less back slip artifacts. The accurate prediction of high‐rate GNSS traces not used in the inversion attests the robustness of the inferred slip model.

The Canary and Madeira provinces in the Central‐East Atlantic Ocean are characterized by an irregular spatio‐temporal distribution of volcanism along the hotspot tracks, and several alternative scenarios have been suggested to explain it. Here, we combine results from seismic tomography, shear‐wave splitting and gravity along with plate reconstruction constraints to investigate the mantle structure and dynamics beneath those provinces. We find that the Central‐East Atlantic Anomaly (CEAA), which rises from the core‐mantle boundary and stalls in the topmost lower mantle, is the deep source of distinct upper‐mantle upwellings beneath the region. The upwellings detach intermittently from the top of the CEAA and appear to be at different evolutionary stages. We argue that the accumulation of plume material in the topmost lower mantle can play a key role in governing the first‐order spatio‐temporal irregularities in the distribution of hotspot volcanism.

Kinematic finite-fault source inversions aim at resolving the spatio-temporal evolution of slip on a fault given ground motion recorded on the Earth’s surface. This type of inverse problem is inherently ill-posed due to two main factors. First, the number of model parameters is typically greater than the number of independent observed data. Second, small singular-values are generated by the discretization of the physical rupture process and amplify the effect of noise in the inversion. As a result, one can find different slip distributions that fit the data equally well. This ill-posedness can be mitigated by decreasing the number of model parameters, hence improving their relationship to the observed data. In this article, we propose a fuzzy function approximation approach to describe the spatial slip function. In particular, we use an Adaptive Network-based Fuzzy Inference System (ANFIS) to find the most adequate discretization for the spatial variation of slip on the fault. The fuzzy basis functions and their respective amplitudes are optimized through hybrid learning. We solve this earthquake source problem in the frequency domain, searching for independent optimal spatial slip distributions for each frequency. The approximated frequency-dependent spatial slip functions are then used to compute the forward relationship between slip on the fault and ground motion. The method is constrained through Tikhonov regularization, requiring a smooth spatial slip variation. We discuss how the number of model parameters can be decreased while keeping the inversion stable and achieving an adequate resolution. The proposed inversion method is tested using the SIV1-benchmark exercise.

Finite-fault kinematic source inversions aim at resolving the spatio-temporal evolution of slip on a fault plane given ground motion recorded on the Earth’s surface. This type of inverse problem is inherently ill-posed and rank-deficient due to two main factors. First, the number of model parameters is typically greater than the number of observed data. The second issue is that small singular values result from the discretization of the physical rupture process, amplifying the effect of noise in the inversion. Consequently, we can find different slip distributions that fit the data equally well. This ill-posedness can be mitigated by decreasing the number of model parameters, hence improving their relationship to the observed data. In this article, we propose a fuzzy function approximation approach to describe the slip function. In particular, we use Adaptive Network-based Fuzzy Inference System (ANFIS) to find the most adequate discretization for the spatial variation of slip on the fault. The fuzzy basis functions and their respective amplitudes are optimized through hybrid-learning. We solve this earthquake source problem in the frequency domain, searching for independent optimal spatial slip distributions for each frequency. The approximated frequency-dependent spatial slip functions are then used to compute the forward relationship between slip on the fault and ground motion. The method is constrained through Tikhonov regularization, requiring a smooth spatial slip variation. We discuss how the number of model parameters can be decreased while keeping the inversion stable and achieving an adequate resolution. The proposed inversion methodology is tested using the SIV1-benchmark exercise.

In typical subduction systems, plate convergence is subperpendicular to the trench. The Gibraltar Arc System is exceptional, with its narrow subduction arc oriented N‐S and laterally “squeezed” by the NNW‐SSE tectonic convergence between Nubia and Iberia. The extent to which the slab is still coupled to the surface and how it interacts actively with the surrounding mantle is a matter of ongoing debate. Here, we analyze new densely spaced GPS data, together with crustal and mantle observations, to better understand the slab kinematics, plate dynamics, and mantle flow. In light of previous and current research, we find that subduction below the Gibraltar Arc is currently in the middle of a disruption process, with parts of it already detached and others yet coupled to the surface. In particular, the slab seems to be detached to the north of the Gibraltar Strait, with a small portion still attached to the surface or in the process of detaching below the western Betics. South of Gibraltar, the slab is still coupled to the overriding plate, although the subduction seems to be very slow or stopped. Flow of mantle material around the detached portions of the slab causes most of the surface uplift and a positive residual topography anomaly. Our findings show that the interplay between slab dynamics, mantle flow, and plate convergence explains much of the observed residual topography, surface motion, seismicity, and mantle structure.