Enahancing the detail on low-level seismicity and swarms in central-southern Italy by template matching

2021 ◽  
Author(s):  
Luca Carbone ◽  
Rita de Nardis ◽  
Giusy Lavecchia ◽  
Laura Peruzza ◽  
Enrico Priolo ◽  
...  
Keyword(s):  
Template Matching ◽  
Nearest Neighbor ◽  
Matched Filter ◽  
Central Italy ◽  
Total Duration ◽  
Space Time ◽  
Risk Area ◽  
Low Level ◽  
Seismic Catalogue ◽  
Completeness Magnitude

<p> </p><p>During the seismic sequence which followed the devastating L’Aquila 2009 earthquake, on 27 May 2009 the OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) and the GeosisLab (Laboratorio di Geodinamica e Sismogenesi, Chieti-Pescara University) installed a temporary seismometric network around the Sulmona Basin, a high seismic risk area of Central Italy located right at SE of the epicentral one. This area of the central Apennines is generally characterized by low level seismicity organized in low energy clusters, but it experienced destructive earthquakes both in historical and in early instrumental time (Fucino 1915 =XI MCS, Majella 1706 =X-XI MCS, Barrea 1984 =VIII MCS).</p><p>From the 27 May 2009 to 22 November 2011, the temporary network provided a huge amount of continuous seismic recordings, and a seismic catalogue covering the first seven months of network operation (-1.5≤M<sub>L</sub>≤3.7, with a completeness magnitude of 1.1) and a spatial area that stretches from the Sulmona Basin to Marsica-Sora area. Aiming to enhance the detection of microearthquakes reported in this catalogue, we applied the matched-filter technique (MFT) to continuous waveforms properly integrated with data from permanent stations belonging to the national seismic network. Specifically, we used the open-source seismological package PyMPA to detect microseismicity from the cross-correlation of continuous data and templates. As templates we used only the best relocated events of the available seismic catalogue. Starting from 366 well located earthquakes<strong> </strong>we obtain a new seismic catalogue of 6084 new events (-2<M<sub>L</sub><4) lowering the completeness magnitude to 0.2. To these new seismic locations, we applied a declustering method to separate background seismicity from clustered seismicity in the area. All the seismicity shows a bimodal behaviour in term of distribution of the nearest-neighbor distance/time with the presence of two statistically distinct earthquake populations. We focused the attention on two of these clusters (C1 and C2) that numerically represent the 60% of the catalogue. They consist in 2619 and 995 events, respectively, with magnitude -2.0<M<sub>L</sub><3.6 and -0.5<M<sub>L</sub><3.2 occurred in Marsica-Sora area. C1 shows the typical characteristics of a seismic swarm, without a clear mainshock, but with 8 more energetic events (3.0≤M<sub>L</sub>≤3.5); the temporal evolution is very articulated with a total duration of one month with different bursts of seismicity and characteristic time extension of approximately one week. C2 instead has a different space-time evolution and consists of different swarm-like seismic sequences more discontinuous in comparison with C1. These swarms are described in greater detail to investigate the influence of overpressurized fluids and their space-time distribution.</p>

scholarly journals Analysis of the 2016–2018 fluid-injection induced seismicity in the High Agri Valley (Southern Italy) from improved detections using template matching

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
T. A. Stabile ◽  
Josef Vlček ◽  
Milosz Wcisło ◽  
Vincenzo Serlenga
Keyword(s):  
Induced Seismicity ◽  
Template Matching ◽  
Southern Italy ◽  
Damage Zone ◽  
Fluid Injection ◽  
Seismic Events ◽  
Operational Parameters ◽  
Seismic Catalogue ◽  
Single Station ◽  
Completeness Magnitude

AbstractImproving the capability of seismic network to detect weak seismic events is one of the timeless challenges in seismology: the greater is the number of detected and locatable seismic events, the greater insights on the mechanisms responsible for seismic activation may be gained. Here we implement and apply a single-station template matching algorithm to detect events belonging to the fluid-injection induced seismicity cluster located in the High Agri Valley, Southern Italy, using the continuous seismic data stream of the closest station of the INSIEME network. To take into account the diversity of waveforms, albeit belonging to the same seismic cluster, eight different master templates were adopted. Afterwards, using all the stations of the network, we provide a seismic catalogue consisting of 196 located earthquakes, in the magnitude range − 1.2 ≤ Ml ≤ 1.2, with a completeness magnitude Mc = − 0.5 ± 0.1. This rich seismic catalogue allows us to describe the damage zone of a SW dipping fault, characterized by a variety of fractures critically stressed in the dip range between ~ 45° and ~ 75°. The time-evolution of seismicity clearly shows seismic swarm distribution characteristics with many events of similar magnitude, and the seismicity well correlates with injection operational parameters (i.e. injected volumes and injection pressures).

scholarly journals Retrospective short-term forecasting experiment in Italy based on the occurrence of strong (fore) shocks

2020 ◽  
Author(s):  
P Gasperini ◽  
E Biondini ◽  
B Lolli ◽  
A Petruccelli ◽  
G Vannucci
Keyword(s):  
Total Duration ◽  
Space Time ◽  
Time Interval ◽  
Good Reliability ◽  
Seismic Catalogue ◽  
Main Shocks ◽  
Short Term Forecasting ◽  
Set Up ◽  
Italian Territory ◽  
Better Than

Summary In a recent work we computed the relative frequencies with which strong shocks (4.0 ≤ Mw < 5.0), widely felt by the population were followed in the same area by potentially destructive main shocks (Mw ≥ 5.0) in Italy. Assuming the stationarity of the seismic release properties, such frequencies can be tentatively used to estimate the probabilities of potentially destructive shocks after the occurrence of future strong shocks. This allows us to set up an alarm-based forecasting hypothesis related to strong foreshocks occurrence. Such hypothesis is tested retrospectively on the data of a homogenized seismic catalogue of the Italian area against a purely random hypothesis that simply forecasts the target main shocks proportionally to the space-time fraction occupied by the alarms. We compute the latter fraction in two ways a) as the ratio between the average time covered by the alarms in each area and the total duration of the forecasting experiment (60 years) and b) as the same ratio but weighted by the past frequency of occurrence of earthquakes in each area. In both cases the overall retrospective performance of our forecasting algorithm is definitely better than the random case. Considering an alarm duration of three months, the algorithm retrospectively forecasts more than 70 per cent of all shocks with Mw ≥ 5.5 occurred in Italy from 1960 to 2019 with a total space-time fraction covered by the alarms of the order of 2 per cent. Considering the same space-time coverage, the algorithm is also able to retrospectively forecasts more than 40 per cent of the first main shocks with Mw ≥ 5.5 of the seismic sequences occurred in the same time interval. Given the good reliability of our results, the forecasting algorithm is set and ready to be tested also prospectively, in parallel to other ongoing procedures operating on the Italian territory.

Markov modulated Poisson processes for stochastic modelling of background seismicity

2021 ◽  
Author(s):  
Elisa Varini ◽  
Antonella Peresan ◽  
Amel Benali
Keyword(s):  
Poisson Process ◽  
Case Studies ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Central Italy ◽  
Space Time ◽  
Poisson Processes ◽  
Suitable Alternative ◽  
Background Seismicity ◽  
Markov Modulated

<p>We investigate the statistical properties of declustered catalogs as obtained from the application of two different data-driven declustering algorithms, namely the nearest-neighbor method and the stochastic declustering method (Benali et al., Stoch. Environ Res Risk Assess, 2020). The nearest-neighbor method partitions earthquakes into background and clustered components, based on nearest-neighbor distances between earthquakes in the space-time-magnitude domain (Zaliapin and Ben-Zion, J Geophys Res, 2013); the stochastic declustering method classifies earthquakes into background and clustered components through a probabilistic procedure based on the estimation of the space-time ETAS model (Zhuang et al., J Geophys Res, 2004).</p><p>Two Italian case studies are considered: North-Eastern Italy (data from OGS Bulletins) and Central Italy (data from ISIDe catalog). For both case studies, the time series of background seismicity are obtained from the two declustering methods. Then we investigate the general assumption according to which the temporal sequence of background seismicity is suitably modelled by the stationary Poisson model. For this purpose, several features and statistical tests are considered to verify the main properties that characterize Poisson processes (e.g. events are independent, exponential inter-arrival times, etc.). </p><p>Whenever the Poissonian hypothesis is rejected, we get evidence of certain heterogeneity in the background sequence, which leads us to rule out the simple Poisson process for background seismicity modeling. As a simple and more suitable alternative, we consider here the Markov Modulated Poisson Process (MMPP model), which allows the Poisson seismicity rate to change over time according to a finite (unknown) number of states of the system. The MMPP model turns out suitable for identifying and quantifying heterogeneities in background seismicity, as well as for comparing them against the two considered declustering algorithms.</p>

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

Space–time variability of denudation rates at the catchment and hillslope scales on the Tyrrhenian side of Central Italy

Geomorphology ◽  
2009 ◽  
Vol 107 (3-4) ◽  
pp. 161-177 ◽  
Author(s):  
M. Della Seta ◽  
M. Del Monte ◽  
P. Fredi ◽  
E. Lupia Palmieri
Keyword(s):  
Central Italy ◽  
Space Time ◽  
Time Variability ◽  
Denudation Rates

An Improved Matched-Filter Based Detection Algorithm for Space-Time Shift Keying Systems

2012 ◽  
Vol 19 (5) ◽  
pp. 271-274 ◽  
Author(s):  
Ping Yang ◽  
Yue Xiao ◽  
Lei Li ◽  
Qian Tang ◽  
Shaoqian Li
Keyword(s):  
Matched Filter ◽  
Detection Algorithm ◽  
Space Time ◽  
Time Shift ◽  
Shift Keying

Cardiotoxicity in rabbits after a low-level exposure to diazinon, propoxur, and chlorpyrifos

2014 ◽  
Vol 33 (12) ◽  
pp. 1241-1252 ◽  
Author(s):  
A Zafiropoulos ◽  
K Tsarouhas ◽  
C Tsitsimpikou ◽  
P Fragkiadaki ◽  
I Germanakis ◽  
...  
Keyword(s):  
Cardiac Tissue ◽  
Total Duration ◽  
Cardiac Complications ◽  
Long Term Effects ◽  
Serum Samples ◽  
Cardiac Tissues ◽  
Low Level ◽  
Experimental Scheme ◽  
Echocardiographic Evaluation

Lethal cardiac complications leading to death and various arrhythmias have been reported after organophosphate and/or carbamate poisonings. The present study focuses on the long-term effects of repeated low-level exposure to diazinon, propoxur, and chlorpyrifos (CPF) on cardiac function in rabbits. The yearly based experimental scheme of exposure consisted of two oral administration periods, lasting 3 months and 1 month each, interrupted by an 8-month washout period (total duration 12 months). At the end of the experimental scheme, the rabbits underwent an echocardiographic evaluation under sedation, after which they were killed and the tissue and serum samples were collected. A mild localized cardiotoxic effect was established by echocardiography for the three pesticides tested. Severe histological alterations were identified, especially in the diazinon-treated animals in agreement with increased persistence of this pesticide established in the cardiac tissue. In addition, all pesticides tested increased the oxidative stress and oxidative modifications in the genomic DNA content of the cardiac tissues, each one following a distinct mechanism.

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