Application of monitoring guidelines to induced seismicity in Italy

Braun, T.; Danesi, Stefania; Morelli, A.

doi:10.1007/s10950-019-09901-7

Cited by 16 publications

(7 citation statements)

References 38 publications

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“…5 c: when fluid injection is operated with injection rate up to 1800 m 3 /day the average daily seismic moments of events occurred above and below the given threshold remain constant to values of about 3 × 10 9 N m/day and about 2 × 10 8 N m/day, respectively; conversely, the average daily seismic moment of events occurred above and below the given threshold follows an exponential growth if injection rates greater or equal than 1900 m 3 /day are applied. We can argue that the 2016–2018 fluid-induced microseismicity is mainly activated after acidification operations and for injection rates above 1900 m 3 /day, but never exceeding the magnitude threshold of M = 1.5 that triggers the level of attention of the four-level traffic light system introduced in the Italian guidelines 58 , 59 . When acid treatment is not executed, injection rates of about 2000 m 3 /day do not affect significantly the earthquake production; this is in agreement with rate–state simulations recently provided by Hager et al 50 .…”

Section: Discussionmentioning

confidence: 95%

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

Stabile

Vlček

Wcisło

et al. 2021

Sci Rep

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Improving 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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Section: Discussionmentioning

confidence: 95%

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

Stabile

Vlček

Wcisło

et al. 2021

Sci Rep

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“…Notice that no case of anthropogenically-induced seismicity has been documented so far in the study area (Braun et al, 2020).…”

Section: Introductionmentioning

confidence: 89%

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Livani

Petracchini

Benetatos

et al. 2023

Preprint

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Abstract. The Po Plain (Italy) is one of the most densely populated and productive regions of Europe, characterized by a flourishing economy (also linked to strategic subsurface resources) and several world cultural and natural heritage sites. The coupling of social-economic interests with geological hazards (i.e., seismic, subsidence and flooding hazards) in this area requires accurate knowledge of the subsurface geology, active geological processes, and impact of human activities on natural environments to mitigate the potential natural and anthropic risks. Most data unveiling the subsurface geology of this region were produced by the hydrocarbon exploration industry. Po Plain hosts indeed many hydrocarbon fields that were discovered since the early 1950s giving rise to the subsurface exploration through extensive seismic reflection surveys and drilling of numerous deep wells. In this work, geological-geophysical data from 160 deep wells drilled for hydrocarbon exploration/exploitation purposes in the Po Plain and in the facing northern Adriatic Sea have been collected and digitized along with several published geological cross-sections and maps. These data have been used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units. The digitized data are suitable to be imported into geo-software environments so to derive the geophysical-mechanical properties of the geological units for a wealth of applied and scientific studies such as geomechanical, geophysical and seismological studies. The integrated dataset may represent a useful tool in defining strategies to ensure the safety of the urbanized areas and human activities and to reduce natural and anthropic risks that may affect this crucial region of Europe. Nowadays, such issues are particularly relevant for the underground industry development related to the increasing interest on possible CO2 and hydrogen underground storage, which can play a fundamental role in the energy transition process towards the decarbonisation goals.

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“…A TLS consists of decision variables (e.g., peak ground acceleration, peak ground velocity, earthquake magnitude and other parameters) and thresholds (Mignan et al, 2017). When the earthquake magnitude or ground shaking exceed the threshold, the alert levels will be automatically activated and relevant actions must be taken (e.g., stopping operations, reducing injection rate or volume) (Braun et al, 2020).…”

Section: Working Mechanisms and Application Casesmentioning

confidence: 99%

Injection-Induced Seismic Risk Management Using Machine Learning Methodology – A Perspective Study

2020

Front. Earth Sci.

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Effective identification of induced seismicity and real-time management of seismic risks are hot topics due to increasing induced seismicity in areas related to energy exploitation. Existing decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is essential. In recent years, machine learning, an advanced inductive and analytical method, has been widely used in seismology. In this context, research gaps associated with the identification and management of induced seismicity, as well as the current achievements of machine learning in addressing induced seismicity problems, are reviewed. A basic framework of using machine learning method to optimize the traffic light system in the industrial production process is first proposed. Then, its feasibility and rationality are demonstrated by similar cases. This framework may provide a reference for the development of a risk-based adaptive traffic light management system.

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Application of monitoring guidelines to induced seismicity in Italy

Cited by 16 publications

References 38 publications

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

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

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Injection-Induced Seismic Risk Management Using Machine Learning Methodology – A Perspective Study

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