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  5. Microseismicity recorded in the geothermal areas of Mt. Amiata (Italy)
 
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Microseismicity recorded in the geothermal areas of Mt. Amiata (Italy)

Author(s)
Braun, Thomas  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia  
Cesca, Simone  
GFZ Potsdam  
Caciagli, Marco  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Bologna, Bologna, Italia  
Famiani, Daniela  
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Roma1, Roma, Italia  
Dahm, Torsten  
GFZ-Potsdam  
Type
Conference paper
Language
English
Obiettivo Specifico
4T. Sismicità dell'Italia
Status
Published
Journal
Seismological Research Letters  
Date Issued
April 2019
Conference Location
Seattle (WA)
DOI
10.1785/0220190061
URI
https://www.earth-prints.org/handle/2122/13065
Subjects

Geothermal energy

Mt Amiata

Abstract
Mt. Amiata (Tuscany, Italy) is an extinct volcano whose last eruptive activity was dated about
200 ky ago. Today, its underlying crustal volume is still characterized by a high geothermal
gradient, which makes the area particularly suitable for geothermal exploitation. Seismicity in the
Tuscan Geothermal Areas is generally observed within the upper crust and is confined in depth by
the so called K-horizon, a strong seismic reflector located in between 4-8 km b.s.l., often
interpreted as the 400°C isotherme. The overlaying structure presents permeable layers of highly
fractured volcanic rocks, saturated with hot water and steam. Geothermal exploitation from
these layers started in the 1960's. Since then, shallow earthquakes have been occasionally
observed close to the geothermal wells, and the question is whether these event are of natural
origin or related to the exploitation of heat.
To monitor the seismic activity inside the geothermal field of Mt. Amiata, we installed in 2015 a
dedicated 8-station seismic network in the vicinity of the productive geothermal power plants for
a 3-years recording period. The main challenges of our experiment are to automatically detect and
locate the local microseismicity, trying to discriminate from natural seismicity those events
caused by human operations. Due to the strong regional seismic activity of the 2016 Central Italy
sequence, the automatic detection of local seismic events resulted challenging. We therefore use
a waveform based detector (Lassie, developed at GFZ) to quickly scan the large dataset and
automatically detect weak events in the target volume. Lassie provides preliminary event
locations, which are then refined in a second step, using standard and waveform based
techniques. For those hypocenters that are located close to the geothermal power plants, at a
similar depth as the production level (3500 m b.s.l.), it remains very challenging to discriminate
between natural and anthropogenic events.
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