We present a new complex network-based study focused on intraplate earthquakes recorded in southern Norway during the period 1980–2020. One of the most recognized limitations of spatial complex network procedures and analyses concerns the definition of adequate cell size, which is the focus of this approach. In the present study, we analyze the influence of observational errors of hypocentral and epicentral locations of seismic events in the construction of a complex network, looking for the best cell size to build it and to develop a basis for interpreting the results in terms of the structure of the complex network in this seismic region. We focus the analysis on the degree distribution of the complex networks. We observed a strong result of the cell size for the slope of the degree distribution of the nodes, called the critical exponent $\gamma$. Based on the Abe–Suzuki method, the slope ($\gamma$) showed a negligible variation between the construction of 3- and 2-dimensional complex networks. The results were also very similar for a complex network built with subsets of seismic events. These results suggest a weak influence of observational errors measured for the coordinates latitude, longitude, and depth in the outcomes obtained with this particular methodology and for this high-quality dataset. These results imply stable behavior of the complex network, which shows a structure of hubs for small values of the cell size and a more homogeneous degree distribution when the cell size increases. In all the analyses, the $\gamma$ parameter showed smaller values of the error bars for greater values of the cell size. To keep the structure of hubs and small error bars, a better range of the side sizes was determined to be between 8 to 16 km. From now on, these values can be used as the most stable cell sizes to perform any kind of study concerning complex network studies in southern Norway.

The fractal formalisms are well known for providing new understandings regarding the geometrical, spatial, and temporal behaviour of seismicity. Particularly, the fractal dimensions give information about the seismic events self‐organization and self‐similarity. On the other hand, the Gutenberg–Richter value, known as the b‐value, has shown through the years to give handy information regarding the statistical distribution of earthquakes, on‐site physical parameters, and geomechanical inputs. The Gutenberg–Richter value (b) and the capacity and correlation fractal dimensions, (D₀ and D₂), of the spatial distribution of earthquake hypocentres interact mathematically for micro‐ and macro‐events. From this interaction, it is possible to obtain new insights into the fracture network development and the microseismicity source characterization in terms of single fractures, fault planes, or densely fractured volumetric spaces. Here we show this interaction for the open‐source Decatur CO₂ project seismicity catalogue, comparing it with the results obtained for a natural earthquake catalogue of Illinois, in the United States. The fractal dimension D₀ is calculated using two different methodologies: box‐counting and correlation integral partitioning. This last method is also used to calculate D₂. The results presented in this study allow us to describe how the fracture network geometry influences the earthquake complexity. Together with the calculation of the b‐value, we present clear indications which show that seismicity recorded in the Illinois tectonic environment partially follows the Aki relationship D₀ ∼ 2b, which is not the case for induced events. In addition, the induced earthquake dataset shows that D₂ > D₀, an anomalous behaviour in terms of the fractal formalisms. All these facts might be used to establish spatial fracture network control techniques and seismicity‐type distinctions in CO₂ injection sites located in highly active tectonic areas, respectively.

We present an updated and validated seismic catalog for the northern Fennoscandian region, focusing on postglacial faults from the Merasjärvi fault system in the southwest to the Iešjávri fault system in the northeast. This work involved a comprehensive review of continuous waveforms derived from open datasets from 2007 to 2015 and processed using the Regressive ESTimator algorithm. The primary objective was to refine the delineation of seismicity along the above-mentioned postglacial faults and highlight their seismic potential. Our analysis revealed distinct waveform patterns originating primarily from two main sources: approximately 15% were associated with areas mapped as postglacial faults, and the remainder of the events outside these areas, 89%, were concentrated in areas with active mines. Compared to previously reported events in the Fennoscandian Earthquake Catalogue (FENCAT), we observed a 22% increase in seismic activity within postglacial fault zones. These results demonstrate that the Regressive ESTimator algorithm not only improves the detection of tectonic seismicity but also effectively identifies seismic signals resulting from mining activities in the study area. The Merasjärvi, Lainio–Suijavaara, Palojärvi, and Maze and Iešjávri fault systems appear to form a continuous deformation complex of approximately 300 km long, which we propose naming the Merasjärvi–Stuoragurra fault complex.

Distributed Acoustic Sensing (DAS) has become a powerful technique for high-resolution, continuous monitoring of near- and subsurface earth phenomena, with increasing applications in geohazards, seismology, and industry applications such as CO₂ storage monitoring. However, the sensitivity of DAS measurements to atmospheric forcing, particularly during extreme weather events, remains poorly understood. In this study, we investigate the response of a permanent, 1.2 km long straight fibre-optic array installed at the Svelvik CO₂ Field Laboratory (Norway), to intense wind conditions associated with the Amy Storm, which hit Norway from October 3-6, 2025. As part of efforts to understand passive methods to monitor CO2 migration in the subsurface, an Alcatel Submarine Networks (ASN) DAS system continuously recorded strain-rate data along a buried fibre that includes both near surface-installed sections and borehole down- and up-going segments reaching depths of approximately 100 m. The near-surface sections were installed inside protective pipes and were therefore not directly coupled to the surrounding ground. To characterise wind-induced seismic signatures, we analyse downsampled recordings using band-limited root-mean-square (RMS) amplitudes and spectral methods across three frequency ranges (0.1–1 Hz, 1–3 Hz, and 3–10 Hz) and time averages over 1 hr intervals. Time–frequency characteristics are examined using group-averaged spectrograms, and a Spectral Energy Index (SEI) is derived by integrating power spectral density within each frequency band. These seismic metrics are compared with near located meteorological observations, including mean wind speed, maximum mean wind speed, and maximum wind gusts. The results reveal a pronounced increase in DAS energy coincident with the maximum speed gusts of storm Amy, with the strongest responses observed at frequencies below 3 Hz. Correlation and lag analyses show that seismic energy variations are closely associated with periods of enhanced wind activity, particularly wind gusts, indicating a strong coupling between transient atmospheric forcing and ground vibrations. Importantly, the response differs significantly between surface and depth segments of the fibre. Surface-installed channels exhibit broadband amplitude increases correlated with direct wind–ground interaction, while depth channels display coherent low-frequency spectral patterns, suggesting excitation by wind-generated surface waves or distant secondary sources (e.g., waves from neighbouring fjord) rather than direct aerodynamic loading. These findings demonstrate that DAS arrays deployed at wells (abandoned or active) are sensitive to extreme meteorological forcing, which can imprint distinct and depth-dependent seismic signatures. Quantifying and distinguishing wind-induced signals is therefore critical for the robust interpretation of DAS data in long-term passive monitoring applications, particularly when subtle subsurface signals related to CO₂ injection, migration, or leakage must be detected in the presence of strong environmental noise. At the same time, this sensitivity highlights an additional benefit of such fibre-optic installations: DAS infrastructure deployed in future abandoned wells in the context of Oil & Gas industry and their reutilization for CO2 capture and storage, can also provide valuable information for national seismic and environmental monitoring networks, extending their utility beyond site-specific applications.