Near fault ground motions may contain impulse behavior on velocity records. Such signals have a particular indicator which makes it possible to distinguish them from non-impulsive signals. These signals have significant effects on structures; therefore, they have been investigated for more than 20 years. In this study, we used Ricker and Morlet wavelets in order to analyze the wavelet power spectrum of the strong motion signals to investigate the impulsiveness. Both the area around the PGV and the area that exceeds the minimum threshold for the energy function are used in order to determine the position of the pulse. On both of these cases, particular criteria are used in order to characterize the signal. Then, we calculate the pulse period of the pulse region. Ricker and Morlet wavelets are also used to mimic the pulse signal. This method provides advanced information about the position of the maximum energy of the pulse part of the signal. We found that the impulsive part of the signal is frequently at the position where PGV occurs and the Ricker wavelet is better than the Morlet wavelet on mimicking the pulse part of the waveform. Spectral responses of strong motion waveform and the wavelets have strong correlation at around pulse period. Results show consistency with previous studies; hence, it can be used as a solid alternative on pulse shape signal investigations.

A strong motion monitoring network records data that provide an excellent way to study how source, path, and site effects influence the ground motion, specifically in the near-source area. Such data are essential for updating seismic hazard maps and consequently building codes and earthquake-resistant design. This paper aims to present the Italian Strong Motion Network (RAN), describing its current status, employment, and further developments. It has 648 stations and is the result of a fruitful co-operation between the Italian government, regions, and local authorities. In fact, the network can be divided into three sub-networks: the Friuli Venezia Giulia Accelerometric Network, the Irpinia Seismic Network, and all the other stations. The Antelope software automatically collects, processes, and archives data in the data acquisition centre in Rome (Italy). The efficiency of the network on a daily basis is today more than 97%. The automatic and fast procedures that run in Antelope for the real-time strong motion data analysis are continuously improved at the University of Trieste: a large set of strong motion parameters and correspondent Ground Motion Prediction Equations allow ground shaking intensity maps to be provided for moderate to strong earthquakes occurring within the Italian territory. These maps and strong motion parameters are included in automatic reports generated for civil protection purposes.

On the 6th of February 2023, a large magnitude earthquake (Pazarcık earthquake), $$M_{w}=$$ M w = 7.7, occurred in southeast Türkiye, which caused significant destruction in Türkiye and Syria. Relatively large magnitude aftershocks followed the main shock, and after 9 hours of the main event, another large magnitude earthquake (Elbistan earthquake) occurred, $$M_{w}=$$ M w = 7.6, on a nearby fault. This study analyzes the near-fault seismic signals from earthquakes larger than 5.5 recorded between the main shock and the 31st of March 2023. More than 60 impulsive motions are detected in 3 earthquakes, mostly concentrated in the Pazarcık and Elbistan earthquakes. In the Pazarcık earthquake, many impulsive motions are recorded in near-fault stations with periods of up to 14 s. In contrast, in the Elbistan earthquake, impulsive motions are spatially distributed, with pulse periods of up to 11 s and at distances greater than 150 km. Pulse periods mostly correlate with the magnitude of the earthquake, but pulse probability models do not predict impulsive motions over long distances. The presence of strong impulsive motions in vertical components is also observed. For both earthquakes, peak ground velocities (PGVs) are larger than predicted by ground motion prediction equations. The observation of long-period, large amplitude signals may indicate the presence of a directivity effect for both earthquakes. In some stations, spectral periods exceed the 2018 Turkish building design codes for long periods ( $$\ge$$ ≥ 1 s).

Near-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

A massive landslide (1–1.2 Mm3) struck the Çöpler Gold Mine in Erzincan, Türkiye, on 13 February 2024 (∼11:30:10 UTC), and killed nine buried miners under a heap leach pad. The landslide was recorded at seismic stations up to 400 km away, with two distinct pulses 48 s apart. These pulses were analyzed using a single-force model with Bayesian bootstrap-based probabilistic inversion, providing insight into the complex forces driving the landslide. This landslide consisted of two mass movements: (F1) westward detachment on a steep slope and (F2) north-northeast movement on a gentler slope, with significant impacts consistent with field observations and optical imagery. Interferometric Synthetic Aperture Radar detected continuous slow deformation over four years at mean rates of up to 60 mm/yr. The cyanide leach pond exhibits critical 85 mm/yr deformation with the risk of environmental contamination because it contains poisonous waste and is located close to the Euphrates River. This study stresses that seismological monitoring and infrastructure resilience are crucial factors in active mining areas.

Separation of seismic sources of seismic events such as earthquakes and quarry blasts is a complex task and, in most cases, require manual inspection. In this study, artificial neural network models are developed to automatically identify the events that occurred in North-East Italy, where earthquakes and quarry blasts may share the same area. Due to the proximity of the locations of the active fault lines and mining sites, many blasts are registered as earthquakes that can contaminate earthquake catalogues. To be able to differentiate various sources of seismic events 11,821 seismic records from 1463 earthquakes detected by various seismic networks and 9822 seismic records of 727 blasts manually labelled by the Slovenian Environment Agency are used. Three-component seismic records with 90 s length and their frequency contents are used as an input. Ten different models are created by changing various features of the neural networks. Regardless of the features of the created models, results show that accuracy rates are always around 99 %. The performance of our models is compared with a previous study that also used artificial neural networks. It is found that our models show significantly better performance with respect to the models developed by the previous study which performs badly due to differences in the data. Our models perform slightly better than the new model created by using our dataset, but with the previous study’s architecture. Developed model can be useful for the discrimination of the earthquakes from quarry blasts in North-East Italy, which may help us to monitor seismic events in the region.