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Probabilistic models of earthquake location and velocity structure beneath Preston New Road, UK

The models are probabilistic inferences of earth structure and earthquake location based around travel times of P and S waves recorded at seismic stations above Preston New Road, Lancashire, UK. For each point in the subsurface, we present many different values of the P- and S-wave velocity which are compatible with the data. Likewise, we present many event locations for each earthquake included in the dataset. These different models represent the posterior probability distribution respectively for the subsurface velocity and earthquake locations.

Default

Identification info

Metadata Language
English (en)
Dataset Reference Date ()
2022-10-28
Identifier

http://data.bgs.ac.uk/id/dataHolding/13607992

 

University of Leeds

-

Andy Nowacki


 

British Geological Survey

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Enquiries


 

British Geological Survey

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Enquiries


Maintenance and update frequency
notApplicable
GEMET - INSPIRE themes
  • Geology
BGS Thesaurus of Geosciences
  • Monte Carlo method

  • Induced seismicity

  • Earthquake magnitude

Keywords
  • NERC_DDC

Limitations on Public Access
otherRestrictions
Other constraints
licenceOGL
Other constraints
Available under the Open Government Licence subject to the following acknowledgement accompanying the reproduced NERC materials "Contains NERC materials ©NERC [year]"
Use constraints
otherRestrictions
Other constraints

The copyright of materials derived from the British Geological Survey's work is vested in the Natural Environment Research Council [NERC]. No part of this work may be reproduced or transmitted in any form or by any means, or stored in a retrieval system of any nature, without the prior permission of the copyright holder, via the BGS Intellectual Property Rights Manager. Use by customers of information provided by the BGS, is at the customer's own risk. In view of the disparate sources of information at BGS's disposal, including such material donated to BGS, that BGS accepts in good faith as being accurate, the Natural Environment Research Council (NERC) gives no warranty, expressed or implied, as to the quality or accuracy of the information supplied, or to the information's suitability for any use. NERC/BGS accepts no liability whatever in respect of loss, damage, injury or other occurence however caused.

Other constraints

Available under the Open Government Licence subject to the following acknowledgement accompanying the reproduced NERC materials "Contains NERC materials ©NERC [year]"

Topic category
  • Geoscientific information
Extent

Lancashire County [id=5156]

Dataset Reference Date ()
2009

N
S
E
W


Begin date
2018-10-01
End date
2018-12-31
 

Spatial Reference System

No information provided.

Distribution Information

Data format
  • NetCDF

    ()

Resource Locator
Data
Resource Locator
Digital Object Identifier (DOI)
Resource Locator
Data
Resource Locator
Digital Object Identifier (DOI)
 
Quality Scope
model
Other

model

Report

Dataset Reference Date ()
2011
Explanation

See the referenced specification

Degree

Report

Dataset Reference Date ()
2010-12-08
Explanation

See http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2010:323:0011:0102:EN:PDF

Degree
Statement

The suite of models are created by sampling the posterior probability distribution using a Markov chain Monte Carlo approach. The methods are described in the open-access paper below: Roy, C., Nowacki, A., Zhang, X., Curtis, A., Baptie, B., 2021. Accounting for natural uncertainty within monitoring systems for induced seismicity based on earthquake magnitudes. Front. Earth Sci. 9, 634688. https://doi.org/10.3389/feart.2021.634688

Metadata

File identifier
ec6ade01-2017-0ba2-e053-0937940a5172 XML
Metadata Language
English (en)
Resource type
model
Hierarchy level name

model

Metadata Date
2023-03-19
Metadata standard name
UK GEMINI
Metadata standard version

2.3

 

British Geological Survey


Environmental Science Centre,Keyworth

,

NOTTINGHAM

,

NOTTINGHAMSHIRE

,

NG12 5GG

,

United Kingdom

+44 115 936 3100
 
 

Overviews

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