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

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).

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

10.5194/egusphere-egu21-16045 ◽

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>&#160;</p><p>During the seismic sequence which followed the devastating L&#8217;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&#8804;M<sub>L</sub>&#8804;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&#8804;M<sub>L</sub>&#8804;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>

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Fluid injection induced seismicity reveals a NE dipping fault in the southeastern sector of the High Agri Valley (southern Italy)

Geophysical Research Letters ◽

10.1002/2014gl060948 ◽

2014 ◽

Vol 41 (16) ◽

pp. 5847-5854 ◽

Cited By ~ 36

Author(s):

T. A. Stabile ◽

A. Giocoli ◽

A. Perrone ◽

S. Piscitelli ◽

V. Lapenna

Keyword(s):

Induced Seismicity ◽

Southern Italy ◽

Fluid Injection

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Automating long-term glacier dynamics monitoring using single-station seismological observations and fuzzy logic classification: a case study from Spitsbergen

Journal of Glaciology ◽

10.1017/jog.2017.25 ◽

2017 ◽

Vol 63 (240) ◽

pp. 581-592 ◽

Cited By ~ 1

Author(s):

W. GAJEK ◽

J. TROJANOWSKI ◽

M. MALINOWSKI

Keyword(s):

Fuzzy Logic ◽

Induced Seismicity ◽

Flow Analysis ◽

Signal Frequency ◽

Steady Increase ◽

Seismic Events ◽

Dynamic Activity ◽

Glacial Origin ◽

Single Station

ABSTRACTRetreating glaciers are a consequence of a warming climate. Thus, numerous monitoring campaigns are being carried out to increase understanding of this on-going process. One phenomenon related to dynamic glacial changes is glacier-induced seismicity; however, weak seismic events are difficult to record due to the sparse seismological network in arctic areas. We have developed an automatic procedure capable of detecting glacier-induced seismic events using records from a single permanent seismological station. To distinguish between glacial and non-glacial signals, we developed a fuzzy logic algorithm based on the signal frequency and energy flow analysis. We studied the long-term changes in glacier-induced seismicity in Hornsund (southern Spitsbergen) and in Kongsfjorden (western Spitsbergen). We found that the number of detected glacial-origin events in the Hornsund dataset over the years 2013-14 has doubled. In the Kongsfjorden dataset, we observed a steady increase in the number of glacier-induced events with each year. We also observed that the seasonal event distribution correlates best with 1 month lagged temperatures, and that extreme rain events can intensify seismic emissions. Our study demonstrates the possibility of using long-term seismological observations from a single permanent station to automatically monitor the dynamic activity of nearby glaciers and retrieve its characteristic features.

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Deep geothermal-induced seismicity: controlling factors and hazard mitigation measures

10.5194/egusphere-egu21-14518 ◽

2021 ◽

Author(s):

Francesca De Santis ◽

Emmanuelle Klein ◽

Alain Thoraval

Keyword(s):

Induced Seismicity ◽

Tectonic Setting ◽

Direct Consequence ◽

Mitigation Measures ◽

Anthropogenic Factors ◽

Seismic Events ◽

Geothermal Systems ◽

Operational Parameters ◽

Spatio Temporal

<p>As many industrial activities impacting the underground, deep geothermal projects can be associated with the occurrence of induced seismic events. This seismicity is sometimes a direct consequence of stimulation operations needed to enhance the permeability of geothermal reservoirs, but, in other cases, it can also occur in different phases of geothermal projects, as during wells shut-in, after injection operations, or during the production phase, which generally implies lower flow rates and injection pressures. The intensity of this seismicity, in terms of magnitudes of seismic events, can be extremely variable, from microseismic events (M < 2), not felt at the surface, to large earthquakes (M > 5) that pose a serious risk to neighboring populations and may lead to the abandon of geothermal projects. In this context, it is of paramount importance to: i) better characterize and understand the interactions between natural and anthropogenic factors which may lead to geothermal-induced seismicity and ii) evaluate currently applied approaches to handle and minimize associated risks.</p><p>The objective of this work is to establish a state of the art about deep geothermal-induced seismicity, by describing factors that have a bearing on the generation of seismic events, as well as by discussing existing means to handle their occurrence. Based on a worldwide review of geothermal projects, we created a large database describing each selected case study in terms of geological properties and tectonic setting, operational parameters and type of geothermal systems, as well as spatio-temporal characteristics of the observed induced seismicity. Collected data are analyzed in order to better understand possible cause-effect relationships between induced seismicity and geothermal operations with the aim of identifying the most important preexisting and anthropogenic factors, as well as their interactions, which may have a key role on the occurrence of seismic activity. &#160;&#160;&#160;&#160;&#160;&#160;</p>

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Three-dimensional aseismic ruptures driven by fluid injection

10.5194/egusphere-egu21-13394 ◽

2021 ◽

Author(s):

Alexis Sáez ◽

Brice Lecampion ◽

Pathikrit Bhattacharya ◽

Robert C. Viesca

Keyword(s):

Analytical Solution ◽

Pore Pressure ◽

Induced Seismicity ◽

Amplification Factor ◽

Three Dimensional ◽

Fluid Injection ◽

Seismic Events ◽

Aseismic Slip ◽

Rupture Area ◽

Self Similar

<div> <div> <div> <p>Injection-induced seismicity is usually observed as an enlarging cloud of seismic events that grows in a diffusive manner around the injection zone. These observations are commonly interpreted as the triggering of instabilities in pre-existing fractures and faults due to the direct effect of pore pressure increase (Shapiro, 2015), whereas poroelastic stressing is usually associated with the occurrence of seismic events beyond the plausible zone affected by pore pressure diffusion (Segall and Lu, 2015). However, an alternative triggering mechanism based on the elastic transfer of stress due to injection- induced aseismic slip has been recently proposed (Viesca, 2015; Guglielmi et al, 2015). Previous studies have shown that in critically stressed faults, the aseismic rupture front can outpace fluid diffusion (Garagash and Germanovich, 2012; Bhattacharya and Viesca, 2019), and in turn be the primary cause that controls the evolution of seismicity as it has been recently inferred from in-situ experiments of fluid injection (Duboeuf et al., 2017) and recent cases of injection-induced earthquakes (Eyre et al, 2019).</p> <div> <div> <div> <p>Despite the great relevance of aseismic slip on injection-induced seismicity, the conditions that control the three-dimensional propagation of aseismic ruptures are still poorly constrained. This is in part due to the challenge of solving such a 3D moving boundary problem in which both fault slip and rupture shape are unknown. Here, we study the mechanics of injection-induced aseismic ruptures on a planar fault characterized by a strength equal to the product of a constant friction coefficient and the effective normal stress. We systematically track the temporal evolution of the rupture area relative to the evolution of the pressurized zone and focus on the effect of the initial stress state and injection scenario. For injection at constant flux, we derive a semi-analytical solution for circular ruptures (for a Poisson&#8217;s ratio equal to zero), which gives the ratio between the rupture radius and a nominal pore pressure front location, which we named as amplification factor &#955;. This amplification factor is a function of a unique dimensionless parameter that depends on the initial fault stress criticality and the fluid-induced overpressure. Then, we generalize the semi-analytical solution to the case of non-circular ruptures (for any value of the Poisson&#8217;s ratio) by solving numerically for the spatiotemporal evolution of fault slip using a fully implicit boundary-element-based solver with quadratic triangular elements. We show that the rupture front is nearly elliptical and the rupture area A<sub>r</sub> evolves in a self-similar diffusive manner such that A<sub>r</sub>(t) = 4&#960;&#945;&#955;<sup>2</sup>t, where &#945; is the fault hydraulic diffusivity and &#955; is the amplification factor for circular ruptures. The rupture area is greater than the nominal pressurized area if &#955; > 1. The semi-analytical solution for the rupture area provides a unique opportunity for verifying numerical hydro-mechanical solvers. After, we investigate numerically the case of circular and non-circular ruptures driven by injection at constant pressure instead of constant flux. We show that the self-similar property of the rupture growth is lost under this injection scenario.</p> </div> </div> </div> </div> </div> </div>

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INDUCED SEISMICITY ASSOCIATED WITH WASTE FLUID INJECTION WELLS

10.1130/abs/2017ne-290461 ◽

2017 ◽

Author(s):

Megan MacDonald ◽

◽

John E. Ebel

Keyword(s):

Induced Seismicity ◽

Fluid Injection ◽

Injection Wells

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GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Sensors ◽

10.3390/s21238080 ◽

2021 ◽

Vol 21 (23) ◽

pp. 8080

Author(s):

Ahmed Shaheen ◽

Umair bin Waheed ◽

Michael Fehler ◽

Lubos Sokol ◽

Sherif Hanafy

Keyword(s):

Deep Learning ◽

Template Matching ◽

Carbon Capture And Storage ◽

Microseismic Monitoring ◽

Seismic Events ◽

Learning Approaches ◽

Monitoring Networks ◽

Level Sensor ◽

Multi Level ◽

Low Magnitude

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

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Regional Seismic Characterization of Shallow Subsoil of Northern Apulia (Southern Italy)

Geosciences ◽

10.3390/geosciences11100416 ◽

2021 ◽

Vol 11 (10) ◽

pp. 416

Author(s):

Enrico Paolucci ◽

Giuseppe Cavuoto ◽

Giuseppe Cosentino ◽

Monia Coltella ◽

Maurizio Simionato ◽

...

Keyword(s):

Southern Italy ◽

Carbonate Platform ◽

Low Cost ◽

Principal Component ◽

Ambient Vibration ◽

Resonance Effects ◽

Resonance Phenomena ◽

Single Station ◽

Seismic Characterization

A first-order seismic characterization of Northern Apulia (Southern Italy) has been provided by considering geological information and outcomes of a low-cost geophysical survey. In particular, 403 single-station ambient vibration measurements (HVSR techniques) distributed within the main settlements of the area have been considered to extract representative patterns deduced by Principal Component Analysis. The joint interpretation of these pieces of information allows the identification of three main domains (Gargano Promontory, Bradanic Through and Southern Apennines Fold and Thrust Belt), each characterized by specific seismic resonance phenomena. In particular, the Bradanic Through is homogeneously characterized by low frequency (<1 Hz) resonance effects associated with relatively deep (>100 m) seismic impedance, which is contrasting corresponding to the buried Apulian carbonate platform and/or sandy horizons located within the Plio-Pleistocene deposits. In the remaining ones, relatively high frequency (>1 Hz) resonance phenomena are ubiquitous due to the presence of shallower impedance contrasts (<100 m), which do not always correspond to the top of the geological bedrock. These general indications may be useful for a preliminary regional characterization of seismic response in the study area, which can be helpful for an effective planning of more detailed studies targeted to engineering purposes.

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