A new 3D seismotectonic model of the Northern Apennines Pedeapenninic margin for seismic hazard assessment.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Giacomo Carloni, Luca Martelli, Alberto Martini, Thomas Gusmeo, Gianluca Vignaroli, Giulio Viola
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引用次数: 0

Abstract

Faults have the potential to generate earthquakes causing significant damage to societal infrastructure and life losses. Innovative methodologies and multidisciplinary research approaches are essential for assessing seismic hazard in areas where the study and parametrization of earthquake-generating faults present significant challenges, for example due to the scarcity of fault exposures. This is the case of the Northern Apennines front (Italy), where active and seismogenic faults are concealed beneath thick sedimentary deposits. To study the local seismotectonic framework we present a new 3D model for a sector of the Northern Apennines Pedeapenninic margin between Parma and Bologna. The model was generated by integrating geological, geophysical and seismological datasets in the software Leapfrog Works. It assembles eight primary geological units from the surface down to ∼15 km depth and allowed for the reconstruction of 54 active faults, including 12 seismogenic faults. The main aim of this study is to introduce a 3D seismotectonic database that serves scientific, educational and practical applications and can be used for seismic hazard analytical assessment.

北亚平宁地区地震危险性评价新三维地震构造模型。
断层有可能引发地震,对社会基础设施造成重大破坏和生命损失。在地震发生断层的研究和参数化面临重大挑战的地区,创新的方法和多学科研究方法对于评估地震危害至关重要,例如由于断层暴露的稀缺性。这就是北亚平宁锋面(意大利)的情况,在那里,活跃的和诱发地震的断层隐藏在厚厚的沉积层之下。为了研究当地的地震构造格局,我们提出了一个新的三维模型,用于帕尔马和博洛尼亚之间的亚平宁北部的一个板块。该模型是在Leapfrog Works软件中整合地质、地球物理和地震数据集生成的。从地表到深度约15公里,它汇集了8个主要地质单元,并允许重建54个活动断层,其中包括12个发震断层。本研究的主要目的是建立一个服务于科学、教育和实际应用的三维地震构造数据库,并可用于地震灾害分析评估。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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