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Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016-2017 central Italy sequence

Yen Joe Tan, Felix Waldhauser, William L. Ellsworth, Miao Zhang, Weiqiang Zhu, Maddalena Michele, Lauro Chiaraluce, Gregory C. Beroza and Margarita Segou
Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016-2017 central Italy sequence
The Seismic Record (April 2021) 1 (1): 11-19

Abstract

The 2016-2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the M (sub w) 6.0 Amatrice event on 24 August 2016, followed by the M (sub w) 5.9 Visso event on 26 October and the M (sub w) 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of approximately 900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L'Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence.


EISSN: 2694-4006
Serial Title: The Seismic Record
Serial Volume: 1
Serial Issue: 1
Title: Machine-learning-based high-resolution earthquake catalog reveals how complex fault structures were activated during the 2016-2017 central Italy sequence
Affiliation: Chinese University of Hong Kong, Faculty of Science, Earth System Science Programme, Hong Kong, China
Pages: 11-19
Published: 202104
Text Language: English
Publisher: Seismological Society of America, Albany, CA, United States
References: 36
Accession Number: 2021-065839
Categories: Seismology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. sketch map, sects.
N42°19'60" - N43°10'00", E12°45'00" - E13°40'00"
Secondary Affiliation: Lamont-Doherty Earth Observatory, Palisades, NY, USA, United StatesStanford University, Department of Geophysics, Stanford, CA, USA, United StatesDalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, CAN, CanadaIstituto Nazionale di Geofisica e Vulcanologia, Rome, ITA, ItalyBritish Geological Survey, Lyell Center, Edinburgh, GBR, United Kingdom
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2021, American Geosciences Institute. Abstract, Copyright, Seismological Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 202121

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