1. Paris Data Ladies#6
N I N A B E R T R A N D J U S T I N E D E S H A I S A D È L E G U I L L E T M A R I N E K R O L C H A R L O T T E L E D O U X A U R É L I A N È G R E
@parisdataladies
3. About me
Huikan XIANG
⬢ Data Engineer @ Deezer Core Data
⬢ 7 years experience on data related applications
⬢ Worked in startup environments
3
4. The Core-Data Team at Deezer
4
Recommendations/Search
Analytics
Royalties
CRM
Data WarehouseUsers app interactions
Serving all data related needs for the entire company
5. The Core-Data Team Operates on a Massive Scale
5
1900
Daily jobs
1.5
PB of historical data
2.5
TB/day
100
machines
50+
data engineers & scientists
14M
Active Users
12. Why is real-time data analysis important?
12
Recommendation & search
reactive content recommended and precise search ranking
BI & product
meteometer for product features, bug tracking
Fraud detection
fake fans detection as fast as possible
CRM
Fast emailing campaigns
16. Batch vs Streaming
16
Batch Processing Streaming Processing
Data scope All or most of the data
Most recent data managed by rolling
time window
Data size Larges batches > GB
Individual records or micro batches of
few records
Performance Latencies in minutes to hours Seconds or milliseconds
Analytics Complex analytics
Simple aggregates and rolling metrics
(Not true anymore for Spark
Streaming)
17. Why Spark Streaming
17
Tech Integration
Already our batch processing engine, reusable component
Multi-data sources integration
Kafka, tables, files and third-tier APIs etc.
Windowing Functions
Rich and adaptive functions
Distributed and Scalable
Data partitioned
18. Spark Streaming 101 exercises
18
Goal
Get your hands dirty
Official Example
NetCat and WordCount
More Fun !
Trending topic in Twitter
26. Deezer listen stream in real-time
26
Computing
dynamic alloc 3 executors, 2 cores, 4GB RAM each executor
Input rate
peak to 4K records/s, 6MB/s
Latency
queryable in less than 1 minute
27. Next steps
More applications
20% of our pipelines in streaming fashion
Sync with spark streaming release
State, structured API
Build an universal message driven system
27
30. DIFFUSION LIMITÉE –
MODÉLISATION DE RETARDS
POUR L’EXPLOITATION DES
GRANDES GARES
Marie de Faverges
CEDRIC – CNAM
DPF – SNCF Réseau
Marie.milliet-de-faverges@reseau.sncf.fr
35. –
RÉSOLUTION
Programmation linéaire en nombres entiers
+Données :
• Liste de trains qui arrivent ou partent de la gare
• Liste d’itinéraires traversant la gare
+Variables booléennes affectant un train à un itinéraire
+Contraintes d’infrastructure, contraintes commerciales, etc
Complexité
+En théorie : problème NP-complet
+En pratique : exple de Montparnasse :
• 800 arrivées et départs par jour
• 28 voies en gare
• 1000 itinéraires d’arrivée et 1000 itinéraires de départ
36. –
ROBUSTESSE
Mais les trains n’arrivent pas toujours à
l’heure !
+Le planning n’est plus valable
+Les retards se propagent et s’amplifient à
travers le réseau
!"
temps
A
!#
Comment rendre les GOV plus robustes
?
+Détecter les scenarios les moins
robustes
+Les pénaliser en objectif
+La solution finale tendra à absorber une
partie des retards
+MAIS paramétrage très complexe et
arbitraire
37. –
LES RETARDS
But
+Modéliser les retards de trains quelques jours
en avance
+Utiliser ces résultats pour paramétrer l’outil
Mais…
+Des retards plus exceptionnels qu’on ne le
pense
+Des causes trop variées et rares pour prédire
précisément
But de l’étude
+Estimer le risque de retard en prédisant sa loi
+Cibler uniquement les petits retards
(inférieurs à 20 minutes)
38. –
MÉTHODOLOGIE
38
Données
Brutes
• Historique des retards
• Grands travaux
• Météo
• Vacances
Création du
data set
•Encodage des données
•Troncature des retards
•Sélection des variables
Modélisation
•Modèles linéaires
généralisés
•Validation
Prédiction de
probabilités
de retards
39. –
MODÈLES LINÉAIRES GÉNÉRALISÉS
Trois composantes dans le modèle :
+La composante aléatoire : ! = ($%, … , $() suivant une loi de la famille exponentielle
+Un prédicteur linéaire : *+ = ∑-+.+
+Une fonction de lien / entre la composante aléatoire et le prédicteur linéaire : *+ =
/ 0+
Soit :
1 ~345678+91 :
; < =>?
Les paramètres ? sont estimés par maximum de vraisemblance
Exemple
+Loi négative binomiale tronquée au-delà de 20 minutes
+Deux paramètres 0 et @
! ~ABC <, D
EF/ < = >G?G
EF/ D = >H?H
40. –
PREDICTIONS
R = 2 – Arrivée à 12h45 de La Rochelle un jeudi de vacances
R = 16 – Arrivée à 20h de la Rochelle un mercredi de vacances
R = 0 – Arrivée à 20h45 de Nantes un mercredi hors vacances
41. –
PROBLÈME DE VALIDATION
Difficulté
+Défaut d’homogénéité entre les prédictions et les observations
+Pas de méthode de validation adaptée au cas continu
Validation des modèles binaires
+Calibration : correspondance entre les probabilités prédites et les taux observés
+Discrimination : capacité à différencier les succès des échecs en se basant sur les
probabilités prédites
ON VA ADAPTER LES VALIDATIONS
BINAIRES À NOTRE MODÈLE CONTINU EN
TESTANT LA CDF EN PLUSIEURS POINTS
42. –
VALIDATION PAR CALIBRATION
Calibration plot :
+Pour un entier t on calcule pour chaque train ℙ(# ≥ % |') probabilité d’avoir un retard
supérieur à t
+On trie les trains par probabilités croissantes
+On crée g groupes de tailles égales
+On compare pour chaque groupe la probabilité prédite et la fréquence observée
APPROCHE GRAPHIQUE
43. –
VALIDATION PAR CALIBRATION
Test Hosmer-Lemeshow :
+Pour un entier t on calcule pour chaque train ℙ(# ≥ % |') probabilité d’avoir un retard
supérieur à t
+On trie les trains par probabilités croissantes
+On crée g groupes de tailles égales
+Statistique de Hosmer-Lemeshow :
) = +
,-.
/
0., − 2.,
3
2.,
+
05, − 25,
3
25,
+Si le modèle est le bon H suit une loi du chi-deux à g-2 degrés de liberté
+On veut que la p-valeur soit non significative pour ne pas pouvoir rejeter l’hypothèse
nulle
APPROCHE STATISTIQUE
45. –
VALIDATION PAR DISCRIMINATION
Sensitivité et spécificité
!"#!$%$&$%' =
)*
)*+,-
!."/$0$/$%' =
)-
)-+,*
La courbe roc représente la !"#!$%$&$%' et 1 − !."/$0$/$%' pour tout cutpoint % ∈ 0,1
Aire sous la courbe ROC
Pour un couple 67, 68 avec 67 = 1 et 68 = 0 de
probabilités de succès prédites 97 et 98 alors :
:;< = ℙ 97 > 98
46. –
RÉSULTATS
GLM avec une loi négative binomiale entraîné sur 17 000 arrivées de TGV à
Montparnasse
Le modèle est évalué sur un set de 6 000 retards
47. –
CONCLUSION
Objectif de la thèse
+Estimer le risque de retard de trains quelques jours en avance
+Utiliser ces résultats pour paramétrer un outil de routage des trains en gare
+But à terme : limiter la propagation des retards en gare
Approche
+Modèles linéaires généralisés pour prédire les lois de probabilité de retard
+Validation par calibration et discrimination
+Résultats : bonne calibration et discrimination stable
49. your.name@uwa.edu.auName et al., 201X
Visualizating the deep Australian continent
with advanced geophysical signal processing
Ph.D. Candidate
Sophie Monnier
Supervisors
Prof. David Lumley, A/Prof. Jeffrey Shragge, Dr. Rie Kamei
50. Paris Data Ladies #6Monnier, 2018
Outline
• Geophysics is cool!!
• Seismic Data and Model Inputs
• Seismic inversion method: Full Waveform Inversion
• Results and Impacts
51. Paris Data Ladies #6Monnier, 2018
Geophysics: a tool to probe the Earth’s Interior
Crust
Mantle
Outer Core
Inner Core
~ 6400 km
We live on the surface of planet
Earth, but we cannot penetrate
its depths…
Earth’s deepest drilling:
• Kola superdeep borehole
• 12 km (0.1% Earth’s radius)
52. Paris Data Ladies #6Monnier, 2018
Geophysics: a tool to probe the Earth’s Interior
Crust
Mantle
Outer Core
Inner Core
We live on the surface of planet
Earth, but we cannot penetrate
its depths…
We cannot physically
penetrate the Earth!!!
~ 6400 km
Earth’s deepest drilling:
• Kola superdeep borehole
• 12 km (0.1% Earth’s radius)
53. Paris Data Ladies #6Monnier, 2018
So how do we know what we know?
The study of seismic waves gives us a picture of the interior of the earth
1. A seismic source propagates in the
earth…
2. …is recorded at seismic stations
3. …Gathering information shows
seismic discontinuities in the Earth
Earthquake seismology: deep earth scale (100 – 6000 km)
54. Paris Data Ladies #6Monnier, 2018
Exploration seismology: shallow earth scale (0.05 – 6 km)
Seismology not only is cool but useful!
2D seismic image of buried geological layersFoetal ecography
55. Paris Data Ladies #6Monnier, 2018
"
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!(
!(
114°0'0"E
114°0'0"E
112°0'0"E
112°0'0"E
110°0'0"E
110°0'0"E
20°0'0"S 20°0'0"S
22°0'0"S 22°0'0"S
Ü
0 100 km
-100-3000-6000
Seafloor elevation (m)
Bart 2D
line
Gascoyne
abyssal plain
Exm
outhPlateau
110 E 112 E 114 E
20 S
22 S
Cuvier abyssal
plain
New developments in Australian OBS technology
New Australian OBS fleet:
• 18 State-of-the-art seismic sensors
• Inaugural survey
• Acquisition year: 2014 – 2015
• Investigate deep WA structure
56. Paris Data Ladies #6Monnier, 2018
PhD Research Objectives
receivers
(OBS)
ØObtain a high-resolution image of Australia’s deep
continental structure offshore Western Australia
ØInvestigate the performance of state-of-the-art seismic
inversion methods on an innovative dataset
ØHybrid project: crustal-scale seismology (5–30 km)
57. Paris Data Ladies #6Monnier, 2018
Outline
• Geophysics is cool!!
• Seismic Data and Model Inputs
• Seismic inversion method: Full Waveform Inversion
• Results and Impacts
58. Paris Data Ladies #6Monnier, 2018
!"
!#
!$
!%
Moho
Crust
Mantle
Source
Receivers (OBS)
Controlled-source acquisition of seismic data
Crust
Mantle
Outer Core
Inner Core
~ 12 800 km
Ø Crustal-scale seismology: [10"
,10#
] km
~ 30km
59. Paris Data Ladies #6Monnier, 2018
!"
!#
!$
!%
Crust
Mantle
Receivers (OBS)
Controlled-source acquisition of seismic data
Ø Crustal-scale seismology: [10#
,10"
] km
()*) = ,(!", !#, !$, !%)Source
Moho
Crust
Mantle
Outer Core
Inner Core
~ 12 800 km
~ 20km
60. Paris Data Ladies #6Monnier, 2018
Modelling and inversion of seismic data
!"#" = %('(, '*, '+, ',)
Forward modelling
Inversion
• Seismic modelling
“Mathematically simulate the propagation of seismic waves from a
geological model of the subsurface” (Stekl et al., 1998)
61. Paris Data Ladies #6Monnier, 2018
!"#" = %('(, '*, '+, ',)
Forward modelling
Inversion
• Seismic inversion
'(
'*
'+
',
“Estimate a 2D or 3D Velocity model of the Earth”
(Lailly, 1983)
Modelling and inversion of seismic data
62. Paris Data Ladies #6Monnier, 2018
!
"
#
!
"
$
What does seismic data look like?
63. Paris Data Ladies #6Monnier, 2018
What does a velocity model look like?
Critical acquisition parameters:
Ø Source Frequency content
Ø Source-Receiver distance (offset)
Ø Source/Receiver sampling
Ø Choice of Objective Function
!
2
=
$
2%
(Kamei et al. 2013)
Full Waveform Inversion (FWI)
Depth(km)
Distance (km)
km/s
FWI Resolution:
64. Paris Data Ladies #6Monnier, 2018
Outline
• Geophysics is cool!!
• Seismic Data and Model Inputs
• Seismic inversion method: Full Waveform Inversion
• Results and Impacts
65. Paris Data Ladies #6Monnier, 2018
Full Waveform inversion: a local optimization
approach
Starting Model !"
≤ $ ?
No:
Update model
!&'( = !& + +!
Synthetic DataPreprocessed Field Data
Data Misfit
Yes:
Convergence
Raw Field Data
66. Paris Data Ladies #6Monnier, 2018
Full Waveform Inversion Pipeline
1. Build initial
velocity model
2. Prepare and
model data
3. Inversion
parameter tuning
4. Interpret results
67. Paris Data Ladies #6Monnier, 2018
AuSREM, crustal component
(Salmon et al., 2013)
AusMoho
(Kennett et al., 2011)
1D velocity profiles
(Goncharov et al., 2016)
Maurice Ewing cruise
(Geoscience Australia, 2001)
Synthetic Model
"
"
!(
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!(
!(
114°0'0"E
114°0'0"E
112°0'0"E
112°0'0"E
110°0'0"E
110°0'0"E
20°0'0"S 20°0'0"S
22°0'0"S 22°0'0"S
Ü
0 100 km
Bart 2D
line
Velocity Model Building
1. Build initial
velocity model
2. Prepare and
Model Data
3. Inversion
parameter tuning
4. Interpret
results
68. Paris Data Ladies #6Monnier, 2018
AuSREM Crustal model below Bart 2D line
Distance from first shot point
(km)
Distance from first shot point (km)
Depth(km)
Bart 2D line
Ewing (2001)
Moho contour from Maurice
Ewing velocity model
Moho depth from AusMoho
model
Moho depth estimated from
Goncharov et al. 2016
Velocity Model Building
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
69. Paris Data Ladies #6Monnier, 2018
Field Data
"
"
!(
!(
!(
!(
!(
!(
!(
!(
!(
!(
!(
!(
!(
!(
114°0'0"E
114°0'0"E
112°0'0"E
112°0'0"E
110°0'0"E
110°0'0"E
20°0'0"S 20°0'0"S
22°0'0"S 22°0'0"S
Ü
0 100 km
?
Bart 2 Field Receiver Gather, near-offset traces muted & AGC applied (window length: 30 s)
Bart 2
Ø Processed field data
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
70. Paris Data Ladies #6Monnier, 2018
Bart 2 Synthetic Receiver Gather, with AGC applied (window length: 30s)
Ø Bart 2 Synthetic
"
"
!(
!(
!(
!(
!(
!(
!(
!(
!(
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!(
!(
!(
!(
114°0'0"E
114°0'0"E
112°0'0"E
112°0'0"E
110°0'0"E
110°0'0"E
20°0'0"S 20°0'0"S
22°0'0"S 22°0'0"S
Ü
0 100 km
Bart 2
2D Synthetic Modelling
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
71. Paris Data Ladies #6Monnier, 2018
Ø Frequency and Offset for Subsurface Illumination
Inversion Parameter Tuning
Low frequency High frequency
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
S
R
72. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Ø Offset and Source-Receiver Sampling for Resolution
1 km 22 km
Sampling Illumination
Inversion Parameter Tuning
73. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Ø Offset and Source-Receiver Sampling for Resolution
1 km
6 km
18 km
22 km
18 km
9 km
Sampling Illumination
Inversion Parameter Tuning
74. Paris Data Ladies #6Monnier, 2018
1 km
6 km
18 km
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
22 km
18 km
9 km
Sampling Illumination
Inversion Parameter Tuning
Ø Offset and Source-Receiver Sampling for Resolution
75. Paris Data Ladies #6Monnier, 2018
Outline
• Geophysics is cool!!
• Seismic Data and Model Inputs
• Seismic inversion method: Full Waveform Inversion
• Results and Impacts
76. Paris Data Ladies #6Monnier, 2018
Starting Model
Distance from first shot point
(km)
Distance from first shot point (km)
Depth(km)
Bart 2D line
Ewing (2001)
Moho contour from Maurice
Ewing velocity model
Moho depth from AusMoho
model
Moho depth estimated from
Goncharov et al. 2016
Initial Model
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
77. Paris Data Ladies #6Monnier, 2018
Final Model
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Bart 2D line
Ewing (2001)
Moho contour from Maurice
Ewing velocity model
Moho depth from AusMoho
model
Moho depth estimated from
Goncharov et al. 2016
Distance from first shot point
(km)
Final Model
Depth(km)
Distance from first shot point (km)
78. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Seismic Inversion Results
Before After
• Crustal visibility: < 10 km
• Moho not imaged
• Tomographic resolution: 5-10 km
• Crustal visibility: down to 25 km
• Moho imaged at 20-25 km
• FWI resolution: 1-2 km
• Sensitivity analysis:
• Subsurface Illumination
• Resolution
• Acquisition Design
• Sensitivity analysis used for second
OBS deployment over Lord Howe
Rise Plateau
79. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Seismic Inversion Results
Before After
• Crustal visibility: < 10 km
• Moho not imaged
• Tomographic resolution: 5-10 km
• Crustal visibility: down to 25 km
• Moho imaged at 20-25 km
• FWI resolution: 1-2 km
• Sensitivity analysis:
• Subsurface Illumination
• Resolution
• Acquisition Design
• Sensitivity analysis used for second
OBS deployment over Lord Howe
Rise Plateau
80. Paris Data Ladies #6Monnier, 2018
Conclusion
Ø The deep structure of the WA continent was imaged in high
resolution thanks to high-quality seismic data, advanced geophysical
processing and state-of-the-art seismic inversion methods
Ø My analysis enabled better planning of seismic acquisition surveys
offshore Australia
Ø Geophysical Data Analysis and Machine Learning are meeting
halfway!
o Automatized geological interpretation
o CNNs for induced seismicity prediction
o Automatic detection of ore minerals
81. Paris Data Ladies #6Monnier, 2018
Conclusion
Ø The deep structure of the WA continent was imaged in high
resolution thanks to high-quality seismic data, advanced geophysical
processing and state-of-the-art seismic inversion methods
Ø My analysis enabled better planning of seismic acquisition surveys
offshore Australia
Ø Geophysical Data Analysis and Machine Learning are meeting
halfway!
o Automatized geological interpretation
o CNNs for induced seismicity prediction
o Automatic detection of ore minerals
82. Paris Data Ladies #6Monnier, 2018
Conclusion
Ø The deep structure of the WA continent was imaged in high
resolution thanks to high-quality seismic data, advanced geophysical
processing and state-of-the-art seismic inversion methods
Ø My analysis enabled better planning of seismic acquisition surveys
offshore Australia
Ø Geophysical Data Analysis and Machine Learning are meeting
halfway!
o Automatized geological interpretation
o CNNs for induced seismicity prediction
o Automatic detection of ore minerals
83. Paris Data Ladies #6Monnier, 2018
Conclusion
Ø The deep structure of the WA continent was imaged in high
resolution thanks to high-quality seismic data, advanced geophysical
processing and state-of-the-art seismic inversion methods
Ø My analysis enabled better planning of seismic acquisition surveys
offshore Australia
Ø Geophysical Data Analysis and Machine Learning are meeting
halfway!
o Automatized geological interpretation
o CNNs for induced seismicity prediction
o Automatic detection of ore minerals
84. Paris Data Ladies #6Monnier, 2018
Conclusion
Ø The deep structure of the WA continent was imaged in high
resolution thanks to high-quality seismic data, advanced geophysical
processing and state-of-the-art seismic inversion methods
Ø My analysis enabled better planning of seismic acquisition surveys
offshore Australia
Ø Geophysical Data Analysis and Machine Learning are meeting
halfway!
o Automatized geological interpretation
o CNNs for induced seismicity prediction
o Automatic detection of ore minerals
85. Paris Data Ladies #6Monnier, 2018
References
• Alcock, M. B., Stagg, H. M. J., Colwell, J. B., Borissova, I., Symonds, P. A. and Bernardel, G., 2006. “Seismic
Transects of Australia’s Frontier Continental Margins”, Geoscience Australia Record
• Goncharov, A., Cooper, A. Chia,P. and O’Neil, P., 2016. “A New Dawn for Australian Ocean-Bottom Seismography.”
The Leading Edge 35 (1): 99–104.
• Kamei, R., Pratt, R. G. and Tsuji, T., 2013. “On Acoustic Waveform Tomography of Wide-Angle OBS Data--
Strategies for Pre-Conditioning and Inversion.” Geophysical Journal International 194 (2): 1250–80.
• Kennett, B.L.N., Salmon, M., Saygin, E. & AusMoho Working Group,2011. “AusMoho: the variation of Moho depth
in Australia.” Geophysical Journal International 187: 946-958.
• Lailly, P. 1983. “The Seismic Inverse Problem as a Sequence of Before Stack Migrations.” In Conference on Inverse
Scattering, Theory and Applications, Society for Industrial and Applied Mathematics, 206–20.
• Salmon, M., B. L. N. Kennett, and E. Saygin. 2012. “Australian Seismological Reference Model (AuSREM): Crustal
Component.” Geophysical Journal International 192 (1): 190–206.
• Stagg, H.M.J., Alcock, M.B., Bernardel, G., Moore, A.M.G., Symonds, P.A. & Exon, N.F., 2004. “Geological
framework of the outer Exmouth Plateau and adjacent ocean basins.” Geoscience Australia Record
• Štekl, I., and Pratt, R. G., 1998. “Accurate visco-elastic modelling by frequency-domain finiti differences using
rotated operators”, Geophysics, 63, 796-809
• Tortopoglu, B., 2015. “The Structural Evolution of the Northern Carnarvon Basin, Northwest Australia”, PhD
Thesis
• Zelt, C. A., Smith, R. B., 1992. “Seismic Traveltime Inversion for 2-D Crustal Velocity Structure.” Geophysical
Journal International 108 (1): 16–34.
89. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Resolution
90. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Imaging
91. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Imaging
92. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Imaging
93. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Imaging
94. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Resolution
95. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Resolution
96. Paris Data Ladies #6Monnier, 2018
1. Build initial
velocity model
3. Inversion
parameter tuning
4. Interpret
results
2. Prepare and
Model Data
Inversion Parameter Tuning
Ø Receiver Sampling and Objective Function for Imaging
97. L’association créée en 1987
Objectif principal favoriser la présence des femmes en
mathématiques.
Actions
• Journées « filles et maths : une équation lumineuse »,
• Forum des jeunes mathématiciennes,
• Interventions dans les classes,
• Statistiques, ….
Elle est également un lieu de rencontre et de promotion de la
contribution des femmes à la recherche et à l’enseignement des
mathématiques. femmes et mathématiques
98. femmes et mathématiques
Depuis 2009, en partenariat avec Animath,
Journées organisées autour de 4 temps forts:
• Promenade mathématique ,
• Atelier sur les stéréotypes liés aux maths
et découverte des métiers des maths,
• Speed-meeting,
• Pièce de théâtre-forum Dérivée
Les 2 associations ont déjà organisé 76 journées
En 2017:
Ø 10 villes
Ø 15 journées
Ø 1250 lycéennes
Ø 420 collégiennes
En 2018, nous visons:
Ø 15 villes
Ø 20 journées
Ø 1700 lycéennes
Ø 550 collégiennes
99. femmes et mathématiques
Nous avons besoin de vous !
Vous pouvez nous aider.
1) en nous faisant un don
2) en participant à des speed-meetings*
3) via du bénévolat de compétences
4) en organisant une journée dans vos locaux….
Vous pouvez nous joindre à fetm@ihp.fr
Prochaines dates: 18 mai à Pau, 25 mai au Salon de la culture et des jeux
mathématiques Place Saint Sulpice, 30 mai au Lycée la Mare Carré à Moissy-
Cramayel