Pattern recognition methodologies or the spacetime identification of strong earthquakes: the case of - PowerPoint PPT Presentation

1 / 62
About This Presentation
Title:

Pattern recognition methodologies or the spacetime identification of strong earthquakes: the case of

Description:

The non-random nature of this phenomenon has been proven by an especially ... An alarm is declared for a CI only if the overwhelming majority (more than 75 ... – PowerPoint PPT presentation

Number of Views:41
Avg rating:3.0/5.0
Slides: 63
Provided by: dst101
Category:

less

Transcript and Presenter's Notes

Title: Pattern recognition methodologies or the spacetime identification of strong earthquakes: the case of


1
Pattern recognition methodologies or the
space-time identification of strong earthquakes
the case of Italy
A. Peresan G.F. Panza
.
Dipartimento di Scienze della Terra Università di
Trieste
2
Outline
  • Pattern recognition of earthquake prone-areas
  • Algorithm M8 in Italy
  • Algorithm CN in Italy
  • CN algorithm and long lasting changes in reported
    magnitudes
  • Stability of CN predictions with respect to
    random errors in reported magnitudes

3
Pattern-Recognition of Earthquake Prone Areas
4
Pattern Recognition of Earthquake Prone areas
  • Pattern recognition technique is used to
    identify, independently from seismicity
    information, the sites where strong earthquakes
    are likely to occur
  • Assumption strong events nucleate at the nodes,
    specific structures that are formed around
    intersections of fault zones.

5
Pattern Recognition of Earthquake Prone areas
  • The nodes are defined by the Morphostructural
    Zonation Method
  • delineates a hierarchical block structure of
    the studied region, based on
  • topography
  • tectonic data
  • geological data

6
Pattern Recognition of Earthquake Prone areas
  • The Earthquake Prone Areas are identified
    evaluating the following topographic
    characteristics
  • Elevation and its variations in mountain belts
    and watershed areas
  • Orientation and density of linear topographic
    features
  • Type and density of drainage pattern.
  • These features indicate higher intensity in the
    neotectonic movements and increased fragmentation
    of the crust at the nodes.

7
Pattern Recognition of Earthquake Prone areas
GELFAND I., Guberman Sh., Izvekova M.,
KEILIS-BOROK V F. RANTSMAN E. (1972) - Criteria
of high seismicity determined by pattern
recognition. Tectonophysics, 13 (1/4),
415-422. Gvishiani A., and Soloviev A. (1980). On
the concentration of the major earthquakes around
the intersections of morphostructural lineaments
in South America. In V.I.Keilis-Borok and
A.L.Levshuin (eds), Methods and Algorithms for
Interpretation of Seismological Data. Nauka,
Moscow, 46-50 (Computational Seismology, Iss.13,
in Russian).
  • The fact that earthquakes are nucleated at the
    nodes was first established from observations in
    the Pamirs and Tien Shan (Gelfand et al., 1972).
  • The non-random nature of this phenomenon has been
    proven by an especially designed statistical test
    (Gvishiani Soloviev, 1980)

8
Pattern Recognition of Earthquake Prone areas
GELFAND I., Guberman Sh., Izvekova M.,
KEILIS-BOROK V F. RANTSMAN E. (1972) - Criteria
of high seismicity determined by pattern
recognition. Tectonophysics, 13 (1/4),
415-422. Gvishiani A., and Soloviev A. (1980). On
the concentration of the major earthquakes around
the intersections of morphostructural lineaments
in South America. In V.I.Keilis-Borok and
A.L.Levshuin (eds), Methods and Algorithms for
Interpretation of Seismological Data. Nauka,
Moscow, 46-50 (Computational Seismology, Iss.13,
in Russian).
This approach has been applied to many regions of
the world. The predictions made in the last 3
decades have been followed by many events in the
analysed areas, 84 of which occurred in some of
the nodes previously recognized to be the
potential sites for the occurrence of strong
events.
9
Morphostructural zonation of the Italian area
  • The morphostructural map of peninsular Italy,
    Alps and Dinarides (at a scale 11,000,000) has
    been compiled based on
  • topographic maps
  • tectonic maps
  • satellite photos
  • relevant publications
  • Special attention was paid to the present-day
    topography.

10
GORSHKOV A.I., Panza G.F., Soloviev A.A. Aoudia
A. (2002) - Morphostructural zoning and
preliminary recognition of seismogenic nodes
around the Adria margin in peninsular Italy and
Sicily. JSEE Spring 2002, 4, No.1, 1-24.
Lineaments and Nodes in peninsular Italy and
Sicily
  • Lineaments
  • First rank
  • Second rank
  • Third rank

Gorshkov et al. (2002)
11
Observed epicentres and Nodes Prone to
Earthquakes with M?6.0 in peninsular Italy and
Sicily
Published in Gorshkov, Panza, Soloviev, Aoudia ,
JSEE, 7 (2002) http//www.iiees.ac.ir/English/Publ
ication/abs/jsee7_1.html
First presented at IUGG 1999 - Birmingham, UK
12
Observed epicentres and Nodes Prone to
Earthquakes with M?6.5 in peninsular Italy and
Sicily
Published in Gorshkov, Panza, Soloviev, Aoudia ,
JSEE, 7 (2002) http//www.iiees.ac.ir/English/Publ
ication/abs/jsee7_1.html
First presented at IUGG 1999 - Birmingham, UK
13
GORSHKOV A.I., Panza G.F., Soloviev A.A. Aoudia
A. (2003) - Identification of seismogenic nodes
around the Adria margin case study for the Alps
and Dinarides
Morphostructural zonation of the Alps
  • Lineaments
  • First rank
  • Second rank
  • Third rank

Gorshkov et al. (2003)
14
Observed epicentres and Nodes Prone to
Earthquakes with M?6.0 in the Alps
Nodes circles with 25 Km radius The five
parameters that compose the decision rule are
essential for the recognition. They are the
distance, L, between the points with Hmax
(maximum topogaphic altitude) and Hmin (minimum
topographic altitude), the relief gradient
(DH/L), the minimum value of the Bouguer anomaly
(Bmin), the distance to the nearest node (Dn),
the difference between maximum and minimum values
of the Bouguer anomaly (DB), observed inside the
node
Gorshkov et al. (2003)
15
Algorithm M8
16
Algorithm M8 in Italy

Algorithm M8 was designed for the prediction of
the strongest earthquakes worldwide, with
magnitude 8.0 and above (Keilis-Borok and
Kossobokov, 1987), and was later adapted for the
prediction of earthquakes with smaller magnitudes
(Keilis-Borok and Kossobokov, 1990).
17
Algorithm M8 in Italy

The M8 algorithm analyses the seismic activity
inside a set of Circles of Investigation, CIs,
with radius normalized by the linear size of the
events to be predicted, i.e. proportional to
magnitude threshold M0. A hierarchy of
predictions is usually delivered for different
magnitude ranges M0, considering values of M0
with an increment of 0.5 (i.e. M0 indicates the
magnitude range M0 ? M ? M00.5).
18
Algorithm M8 in Italy
For the monitoring of seismicity in the Italian
territory, a new spatially stabilized variant of
the algorithm M8, namely M8s algorithm, is used,
where the seismicity is analysed within a dense
set of overlapping circles covering the monitored
area (Kossobokov et al., JSEE 2002). In Italy
predictions are performed in the three different
magnitude ranges defined by M6.5, M6.0 and
M5.5.

19
Algorithm M8s in Italy steps of the analysis
  • The territory is scanned with a set of small
    circles distributed over a fine grid, with the
    radius of the small circles approximately equal
    the grid spacing and to the linear dimensions of
    the source of target events.
  • The seismically active grid points are then
    selected by the condition that the average annual
    rate of seismic activity, within the small
    circle, is above a given threshold.
  • The grid points where data are insufficient for
    the application of M8 algorithm and isolated grid
    points are excluded.


20
Algorithm M8s in Italy steps of the analysis
  • The M8 algorithm is then applied with the circles
    of investigations, CIs, centred at each of the
    selected grid points.
  • An alarm is declared for a CI only if the
    overwhelming majority (more than 75) of the CIs
    centred at the neighbouring grid points are also
    in state of alarm.


21
M8s algorithm in Italy
  • Magnitude
  • M? 6.5
  • Radius of CI
  • 192 Km
  • Monitored region
  • Alerted region

Predictions as on 1-7-2003 (subject to update
on 1-1-2004)
22
M8s algorithm in Italy
  • Magnitude
  • M? 6.0
  • Radius of CI
  • 138 Km
  • Monitored region
  • Alerted region

Predictions as on 1-7-2003 (subject to update
on 1-1-2004)
23
M8s algorithm in Italy
  • Magnitude
  • M? 5.5
  • Radius of CI
  • 106 Km

14/09/2003 Mmax5.6
  • Monitored region
  • Alerted region

Predictions as on 1-7-2003 (subject to update
on 1-1-2004)
24
  • All together there are 22 strong events (sum over
    the three M ranges) 14 of them are predicted
    (about 64)
  • The space time volume occupied by alarms is
    around 40, in each of the three magnitude ranges

Space-time volume of alarm in M8 application in
Italy
Algorithm M8 predicted 64 of the events occurred
in the monitored zones in Italy, i.e. 14 out of
18 events occurred within the area alerted for
the corresponding magnitude range
25
Algorithm CN
26
Seismotectonic zoning of Italy
  • Seismotectonic model defined by GNDT (Gruppo
    Nazionale per la Difesa dai Terremoti)
  • (Meletti et al., 2000)

27
Rules for the definition of CN regions according
to the seismotectonic model
A single region includes 1.   adjacent zones
with the same seismogenic characteristics (e.g.
only compressive or only extensive) 2.   zones
with transitional properties.   A transitional
zone is included in a region if 1.   it is
between zones of the same kind 2.   it is at the
edges of the region and the space distribution of
the aftershocks reveals a possible
connection. (Peresan, Costa Panza., 1999)
28
NorthernRegion
14/09/2003 M5.5
  • Alerted area
  • Alarm Periods

TIPs
Strong Earthquakes predicted
  • Prediction of the events with M?5.4
  • Updated to January 2004
  • (next update March 2004)
  • TIP 29.4 of total time

29
Central Region
  • Alerted area
  • Alarm Periods

TIPs
Strong Earthquakes predicted
  • Prediction of the events with M?5.6
  • Updated to January 2004
  • (next update March 2004)
  • TIP 23.6 of total time

30
SouthernRegion
  • Alerted area
  • Alarm Periods

TIPs
Strong Earthquakes predicted
Failure to predict
  • Prediction of the events with M?5.6
  • Updated to January 2004
  • (next update March 2004)
  • TIP 29.0 of total time

31
The regionalization based on the seismotectonic
model
  • The regionalization of the Italian territory
    strictly based on the seismotectonic zoning
    permits to reduce the spatial uncertainty of
    predictions. A general reduction of the
    percentage of total TIPs has been observed as
    well.
  • The hypothesis, involved by the regionalization
    based on the seismotectonic model, that
    precursors can be found inside seismogenically
    homogeneous areas associated to a dominating
    geodynamic process, seems supported by the
    corresponding improvement of results.

32
The regionalization based on the seismotectonic
model
  • The seismotectonic model, supported by kinematic
    arguments, can be viewed as a useful tool that
    permits to optimise the selection of the fault
    systems involved in the generation of strong
    earthquakes.

33
AdriaticRegion
Alarm Periods
Alerted Area
TIPs
Strong Earthquakes predicted
Failure to predict
  • Prediction of the events with M?5.4
  • Updated to September 2003
  • (next update November 2003)

34
Space-time volume of alarm in CN application in
Italy
Algorithm CN predicted all of the events
occurred in the monitored zones in Italy, i.e.
each event was preceded by alarms in at least one
region.
35
Updated predictions, at national and global
scale, are available at the following web
site http//www.ictp.trieste.it/www_users/sand/pr
ediction/prediction.htm To avoid misuse, the
access to current predictions is restricted to
experts (password required).
36
CN algorithm and long lasting changes in
reported magnitudes the case of Italy
37
CN algorithm and long lasting changes in
reported magnitudes
38
CN algorithm and long lasting changes in
reported magnitudes
Time diagrams of the standard CN functions
obtained for the Central region(Peresan et al.,
1999) Functions Sigma, Smax and Zmax and are
evaluated for 4.2?M?4.6, functions K, G, N3, q
for M?4.5 and functions N2 for M ?5.0
39
Catalogues comparison
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

CCI1996
Common events ?T?1min ?Lat,
?Lon?1(Storchak, Bird and Adams, 1998)
40
Magnitude comparison Central Region
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Duration Magnitude
Md3.0
Yearly Average differencesNEIC-ING
ML3.0
Local Magnitude
41
Magnitude comparison Central Region
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Local MagnitudeDifferences
42
CN algorithm and long lasting changes in
reported magnitudes
Time diagrams of the standard CN functions
obtained for the Central region(Peresan et al.,
1999) MML(ING)0.5
43
Magnitude comparison Northern Region
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Yearly Average differencesNEIC-ING
Local Magnitude ?MLML(NEIC)- ML(ING)
Average difference
44
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Events used for Md analysisYearly number of
common events used for the comparison between ING
and NEIC catalogues Events used for ML
analysis
45
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

?Md
Frequency scatter-plots of ?Md and ?ML versus the
corresponding NEIC magnitude
?ML
46
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

?Md
?ML
47
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

?ML0
All
48
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Duration MagnitudeNEIC - ING
LocalMagnitudeNEIC - ING
49
Magnitude comparison Italian territory
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

LDG - ING
ML3.0
Local MagnitudeYearly Average differences
ML3.0
NEIC - LDG
50
CN algorithm and long lasting changes in
reported magnitudes
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, reported
    by ING and NEIC, indicates, since 1987, an
    average underestimation of about 0.5 in the Local
    Magnitude provided by ING.

51
CN algorithm and long lasting changes in
reported magnitudes
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.
  • The presence of a general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • (Peresan, Panza Costa, GJI 2000)
  • (Gasperini, Vannucci Orlanducci
    Rivalutazione della magnitudo per i terremoti
    italiani nel periodo post 1980. In Catalogo
    strumentale dei terremoti Italiani dal 1981 al
    1996 2001)

52
Compilation of a homogeneous updated catalogue
for CN monitoring in Italy
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

Databases available to us CCI1996 PFG
revisedING bulletins (Italian catalogue,
available up to July 1997) Priority ML, Md,
MI NEIC PDE Preliminary Determinations of
Epicenters from NEIC (global catalogue). Priority
to be defined (available M mb, MS, M1,
M2) ALPOR Catalogo delle Alpi Orientali (local
catalogue for eastern Alps) Priority ML, MI
53
Compilation of a homogeneous updated catalogue
for CN monitoring in Italy
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.
  • Procedure
  • Study of the completeness of PDE catalogue
  • Study of the relations between different kind of
    magnitudes reported in the CCI1996 and PDE
    catalogues
  • Formulation of a rule for the choice of magnitude
    priority in PDE, similar to the priority used for
    CCI1996
  • Construction of the Updated catalogue,
    integrating CCI1996, ALPOR and NEIC data
    (compatibly with the completeness of NEIC).

54
Operating magnitude selection
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

M(CCI1996) mb(PDE)
M(CCI1996) M1(PDE)
55
Operating magnitude selection
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

M(CCI1996) MS(PDE)
M(CCI1996) M2(PDE)
56
Operating magnitude selection
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

57
Operating magnitude selection
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

Central Italy
M(CCI1996) M2(PDE)
Southern Italy
58
The Updated Catalogue of ItalyUCI2001
59
The Updated Catalogue of Italy
60
Stability of intermediate-term earthquake
predictions with respect to random errors in
magnitude the case of Central Italy
61
CN is essentially based on the information
contained in the earthquake catalogues. It
analyses the seismic flow using origin time,
epicentral coordinates and magnitudes of
earthquakes. All catalogues are inevitably
affected by errors. Magnitudes are characterised
by the most significant errors, conditioning both
aftershocks removal and seismic flow.
Systematic Errors
Random Errors
How random errors on reported magnitudes affect
CN prediction results?
62
Randomised Magnitude
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.

Mc operating magnitude ?MI measurement error
?MD error of discretisation
Measurement random errors
P(?MI) FTR(?MI) Truncated normal probability
distribution with FTR(?MI) F(?MI)/2F(?Mmax)-1
Mmax maximum assumed error on magnitudes
Errors of magnitude discretisation
P(?MD) Uniform probability distribution
Interval -d/2 d/2) ddiscretization
step0.1
63
Magnitude randomisation
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Discretization step
Discretisation step d0.1
Distribution of the number of events versus
magnitude for the original and the randomised
catalogues
64
Results obtained with the original catalogue
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

TIPs obtained with the original catalogue with a
different length of the thresholds setting
period (Learning)
  • Prediction of the events with M?5.6

65
Results obtained with the randomised catalogue
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.
  • ? Original catalogue
  • ? All randomised catalogues
  • Average of randomised

Long Learning
Short Learning
66
Results obtained with the randomised catalogue
 
  • N is the number of strong earthquakes occurred
    during the time period T covered by predictions
  • The alarms cover altogether the time t and they
    have missed n strong events

?n/N the rate of failures-to-predict ?t/T
the rate of time of alarms
67
Results obtained with the randomised
cataloguevariation of target events
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

 
List of earthquakes with Central Italy
1950-1999
68
Results obtained with the randomised catalogue
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

 
Average differences in prediction errors
OC Original Catalogue RCs Randomised Catalogues
69
Stability of TIPs diagnosis
  • DST
  • In order to perform the magnitude comparison, the
    events common to the different catalogues are
    identified according to the following rules a)
    time difference 1 minute b) epicentral
    distance 1 for the comparison with the global
    catalogue (Storchak, Bird and Adams, 1998). No
    limitation is imposed to magnitude or depth
    differences.

Central Italy (Long threshold setting period)
? percentage of tests for which the recognition
of the time t does not change with respect to its
average value  0???50
?(t) percentage of tests for which the time t
belongs to a TIP
70
Stability of CN predictions with respect to
random errors in magnitude
  • DST
  • The analysis of CN functions in Central Italy
    allowed us to detect a relevant long lasting
    change in the reported magnitudes.
  • The comparison of individual magnitudes, and ,
    reported by ING and NEIC catalogues indicates,
    since 1987, an average underestimation of about
    0.5 in the provided by ING.
  • The hypothesis of general local magnitude
    underestimation in the Italian ING bulletins is
    substantiated by the cross-comparison performed
    between ING, LDG and NEIC catalogues.
  • The presence of the evidenced magnitude change
    prevents the use of ING bulletins for CN
    algorithm application and makes necessary to use
    global data like NEIC.
  • The results of prediction remain stable for
    ?Mmaxlt0.3.
  • To guarantee the stability of the results, the
    thresholds setting period must be long enough to
    include a significant sample of dangerous and non
    dangerous intervals of time.
  • The quality of predictions is mainly controlled
    by the percentage of failures to predict, which
    depends on the changes in the number of strong
    earthquakes.
  • The identification of TIPs is very stable during
    most of the time and the randomisation does not
    introduce spurious alarming patterns associated
    with the occasionally strong events.
Write a Comment
User Comments (0)
About PowerShow.com