How are the environmental variables and marine evolution connected? Does astronomical forcing influence climate variation? Can we apply deep learning to classify index fossils?

1. Deep time Paleo-environmental
and Bio-diversity data mining
and deep learning classifcation
Abdullah Khan Zehady
Phd student @
Earth, Atmospheric & Planetary Science,
Purdue University

2. Research Projects
1. Macro and micro scale evolution of planktonic foraminifera and the potential drivers during Cenozoic era (66.04
Ma).
Hypothesis:
“Rates of evolution are correlated with rates of geochemical and sea-level change.”
- Can we find long term (2-5 myr) astronomical cycles?
2. Periodicities and other cause-effect relationships among pulses of evolution since Cambrian period (541 Ma).
Hypothesis:
(a) “The Earth has had semi-periodic episodes of unusual surface/biological change.”
- Abundance of events over time; verification of catastrophe models (20 – 60 myr periods); causation by impact and other disastrous events.
(b) “Pulses of biological evolution occur simultaneously with global changes in sediment facies.”
3. Automated fossil image recognition by feature extraction using deep neural network.
4. Effect of climate change on cultural turnover of last 2000 years of human civilization.
Hypothesis:
“A major factor in the rise and fall of human civilization in different continents is climate cooling.”
2

3. My papers on evolutionary tree visualization algorithm
3
To be sumbitted (Under review) at BMC
Evolutionary Biology
To be submitted at Nature Methods

4. Morphospecies and Lineage/Species evolution
(Fordham, Zehady et al 2018) 4

5. Species Phenon Integrated Tree
(Zehady et al 2019)

6. Mutual Learning using Integrated Tree
Comparison and learning between integrated tree and molecular tree

7. PaleoEnvironmental & Bio-Diversity Data
Dataset Significance
1. Marine genera ranges and subsequent turnover timeseries data
for whole Phanerozoic
Marine biodiversity, turnover
2. Cenozoic planktonic foraminifera evolution and turnover
timeseries. (Zehady, Fordham 2018, Aze et al 2011)
Foraminifer diversity, turnover
3. Oxygen-18 (δ18O) curves and events (Cramer 2009) Long-term cooling of the ocean interior
4. Carbon-13 (δ13C) curves and events (Cramer 2009) Terrestrial climate proxy record
(Gradual sinking of the organic matter)
5. Strontium isotope record (Sr87/Sr86) Tectonic evolution, continental spreading
origin and evolution of igneous rocks
(magmatic style)
6. Sulphur isotope record (δ34 Ssulphate) Link with LIP
7. Ages and volume extent of LIP(Large Igneous Province) Large volume gas release in the ocean-
atmospheric systems, link with magmatism,
mass extinction, extinction cyclicity (Melott
2012)
8. Sea-level synthesis curve (Haq et al 2014) Plate motion, changes in continent mass
distribution
9. Passive margin 7

8. Grand Cycles in Paleo-Meso-Cenzoic Era

9. Orbital forcing - Milankovitch Cycles
9
Amplitude modulation, 2.4 Ma eccentrici

10. Marine genera turnover data
Entire Phanerozoic (Prob) Cenozoic only (# turnover)
Raw turnover prob
= Raw speciation +
Raw extinction prob
To reduce stochastic noise:
Fit Hidden Markov Model
(HMM)
Markov property:
Only dependent on the
previous state
Parameter estimation
Using Baum-Welch
Algorithm.
AIC is used to estimate
Speciation and Extinction
State.

11. Multi-taper spectrum – Whole Phaneorozoic
* Number of significant F-test peaks
identified = 9
ID / Frequency / Period / Harmonic_CL / Red
noise_CL
1 0.1504065 6.648649 98.00066 69.56518
2 0.2524021 3.961933 96.46992 98.53005
3 0.27051 3.696721 93.19279 84.04573
4 0.2775314 3.603196 94.98532 97.86011
5 0.2852919 3.505181 93.26527 59.50705
6 0.2908352 3.438374 90.65819 99.49677
7 0.3510717 2.848421 98.56435 80.32858
8 0.4035477 2.478022 97.29846 68.29225
9 0.4903917 2.039186 95.83189 93.04155

12. Multi-taper spectrum – Only Cenozoic (0-67 Ma)
* Number of significant F-test peaks identified = 1
ID / Frequency / Period / Harmonic_CL /
Rednoise_CL
1 0.2787879 3.586957 99.95627 92.50397

13. Multi-taper spectrum – Only Mesozoic(67-252 Ma)
* Number of significant F-test peaks identified =
3
ID / Frequency / Period / Harmonic_CL /
Rednoise_CL
1 0.01079914 92.6 99.45956 67.96132
2 0.3401728 2.939683 99.03912 56.54744
3 0.3704104 2.699708 94.96196 88.81204

14. Multi-taper spectrum – Only Paleozoic(252- 541 Ma)
* Number of significant F-test peaks identified =
7
ID / Frequency / Period / Harmonic_CL /
Rednoise_CL
1 0.004149378 241 93.49593 78.74621
2 0.01521438 65.72727 91.12898 93.60384
3 0.1500692 6.663594 91.39851 88.02085
4 0.2524205 3.961644 94.03579 98.97888
5 0.2904564 3.442857 93.06378 89.31391
6 0.3506224 2.852071 96.671 84.24726
7 0.4439834 2.252336 90.65441 92.18804

15. Ocean-Atmosphere System
How marine and terrestrial environment are connected?
MaGIC model – Geochemistry based modeling
(Arvidson et al 2006)
f[1]: Organic Phosphorus (P)
f[21]: Terrestrial Organic matter(C) burial flux
f[50]: Sulfate(S) reduction
f[54]: Organic Carbon(C) sedimentation
f[71], f[72]: Carbonate precipitation

16. PaleoEnvironmental Data – Oxygen & Carbon
16
Global warming/cooling
Organic carbon
abundance

17. PaleoEnvironmental Data : Strontium + Passive Margin
17
Continental/Sea floor Spreading
Transition between
oceanic and continental
lithosphere via
sedimentation on
passive margin

18. Large Igneous Province
18
Number of Large Igneous Provinces and
the volume since 140 Ma

19. PaleoEnvironmental Data – Oxygen & Carbon
19
Regional impact events
(Crater > 50 km, 5-10km)
Sulphur isotope data

20. Biodiversity Data
20
Speciation and extinction
of 18,000 marine genera
Number of marine genera

21. PaleoEnvironmental & Bio-Diversity Data
What am I looking for?
Synchronous anomalies
Long-term oscillations/Periodicities
Changes in rates

22. Correlation matrix of timeseries data
22
genera_ts genera_prokoph oxy_18 carbon_13 sr87_86 s34 LIP LIP_volume1 LIP_volume2 impact passive_margin sea level
genera_ts 1 0.018355819 -0.0340282 0.083035629 0.0297944 -0.0373417 0.04421 0.344736229 0.344635849 -0.04627802 0.104789606 -0.08349
genera_prokoph 0.018355819 1 -0.530246 -0.465936957 0.48878332 0.742836318 -0.083082 -0.43421673 -0.437467491 0.14855874 -0.101226221 -0.41527
oxy_18 -0.034028212 -0.530246 1 0.237833917 -0.8404705 -0.62891252 -0.0204 0.281461527 0.285603444 -0.15390199 0.118727266 0.849575
carbon_13 0.083035629 -0.4659369570.23783392 1 -0.1983475 -0.37694399 0.1964381 0.17408195 0.181388307 -0.11451248 -0.0444354 0.178451
sr87_86 0.029794405 0.48878332 -0.8404705 -0.19834753 1 0.453619955 -0.08259 -0.19061375 -0.194846829 0.17851698 -0.218495446 -0.9094
s34 -0.037341696 0.742836318 -0.6289125-0.376943992 0.45361995 1 -0.106022 -0.45775826 -0.464132734 0.10039699 -0.170888249 -0.46339
LIP 0.04420999 -0.083082314 -0.0204002 0.196438071 -0.0825905 -0.10602187 1 0.118074692 0.121020407 -0.12452535 -0.042236671 0.072872
LIP_volume1 0.344736229 -0.4342167280.28146153 0.17408195 -0.1906138 -0.45775826 0.1180747 1 0.999783534 0.04981638 -0.005744232 0.202309
LIP_volume2 0.344635849 -0.4374674910.28560344 0.181388307 -0.1948468 -0.46413273 0.1210204 0.999783534 1 0.04848648 -0.00962115 0.207276
impact -0.046278015 0.148558742 -0.153902-0.114512478 0.17851698 0.100396987 -0.124525 0.049816378 0.04848648 1 0.061153414 -0.25373
passive_margin 0.104789606 -0.1012262210.11872727 -0.0444354 -0.2184954 -0.17088825 -0.042237 -0.00574423 -0.00962115 0.06115341 1 0.117477
sea level -0.083488878 -0.4152675890.84957453 0.178450982 -0.9093977 -0.46338895 0.0728716 0.202309075 0.207276424-0.25373116 0.1174771 1

23. Principal Component Analysis of Cenozoic data
23
PCA transforms correlated data into a new co-ordinate
such that the new variables are uncorrelated.
The goal of PCA is to find components
Z = [Z_1, Z_2, …, Z_p]
which are linear combination of
u = [u_1, u_2, …, u_p]’ of the
Original variable
X = [X_1, X_2, …, X_p] that achieve maximum
variance.
For Cenozoic, we have no missing values for all 12
variables/parameters.
X: A matrix with dimension 69 x 12, n = 69, p =12
Y: Normalized matrix of X
C: Covariance matrix where C = t(X) * X / (p -1) ,
Eigen value decomposition in R
E = eigen(C) where t(E) * E = I
EOF (Empirical Orthogonal Function) : Orthogonal basis
function, basically the eigen vectors
Ev : Eigen vector matrix

24. Principal Component Analysis - loadings
24
Loadings table is composed of principal component vectors

25. Principal Component Analysis of Cenozoic data
25
Principal Component Vector 1 Principal Component Vector 2

26. Principal Component Analysis of Cenozoic data
26
Principal Component Vector 3 Principal Component Vector 4

27. Principal Component Analysis of Cenozoic data
27
Principal Component Vector 5 Principal Component Vector 6

28. Automated Fossil Genus Classification
Hedbergella Sigali Globigerinoides Altiaperturus
Binomial nomenclature system: Genus Species
Can we extract features from the species to detect its Genus?

29. Automated Fossil Genus Classification
Image Data Number of Images Accuracy with Best model so far
Training (Transformed image) 1947 ~ 74%
Validation 649 ~ 55%
Test (Previously Seen Species) 236 ~ 90%
Unseen Test (Totally new species) 37 ~ 43%
Multi class classification
How many different Genus class we have? --> 79
Model Comparison of 3 CNN (VGG19) models “Cross Entropy” loss minimization with Adam Optimizer

30. Automated Fossil Genus Classification
Paragloborotalia predicted as Turborotalia
Most Likely cause of misclassification
Turborotalia Training Images

31. Misclassification Analysis - 1
Globigerina
as
Globigerinoides

32. Misclassification Analysis – 2
Globigerinoides
as
Globorotalia

33. Misclassification Analysis – 3 (Reverse of 2)
Globorotalia
as
Globigerinoides
The Black hole in the center!!

34. Misclassification Analysis – 4
Globorotalia
as
Globoturborotalita

35. Misclassification Analysis – 5
Globoturborotalita
as
Globigerina

36. Misclassification Analysis – 6
Globuligerina
as
Pseudohastigerina

37. What each CNN layer is learning?
Layer2 : Block2_Conv2
Mostly directional
Layer3 : Block3_Conv2
10 random filters in each layer

38. What each CNN layer is learning?
Layer2 : Block2_Conv2
Mostly directional
Layer3 : Block3_Conv2
10 random filters in each layer

39. How does VGG19 CNN model classify between genus?
What are the final abstraction/specific output categories for each of the 79 classes?
Unique features to classify.. With categorical cross-entropy
Globigerina Globigerinoides Globorotalia Sigalia

40. Month/Year Prioritized Item
Jan 2019 ~ April 2019 • Submission of Evolutionary tree visualization paper
• Submission of Species-Phenon Integrated tree paper
• Completion of culture paper
Feb 2019 • NSF proposal resubmission.
Jan 2019 ~ May 2019 • Phanerozoic data extraction, Visualization
• Correlation/causality and spectral analysis for Phanerozoic (500 myr) data.
• Learning on Cross phase, phase lag analysis, Causality analysis
• Automatic fossil image detection project
May 2019 ~ Aug 2019 Internship at Cisco Systems in their Data Center
Nov 2019 • Poster, paper presentation on Machine-learning and artificial-intelligence application in
the geosciences (GSA Annual meeting)
• Other Machine learning journals..
Timeline
40

41. Suggestions,
Recommendations..
41

42. Extra Slides

43. Multi-taper spectrum – first 300 myr of Phaneorozoic
* Number of significant F-test peaks identified =
9
ID / Frequency / Period / Harmonic_CL /
Rednoise_CL
1 0.01136364 88 96.39839 95.99728
2 0.1557487 6.420601 94.82795 86.59931
3 0.1657754 6.032258 98.66033 94.32486
4 0.2159091 4.631579 97.50714 82.3273
5 0.2332888 4.286533 97.22287 68.10389
6 0.3709893 2.695495 98.40121 86.48012
7 0.3923797 2.548552 93.06953 89.62598
8 0.3977273 2.514286 93.58799 91.69948
9 0.4946524 2.021622 96.30716 82.51252

44. Multi-taper spectrum – 300-600 myr of Phaneorozoic
* Number of significant F-test peaks identified =
8
ID / Frequency / Period / Harmonic_CL /
Rednoise_CL
1 0.01570248 63.68421 90.13648 92.73254
2 0.1429752 6.99422 91.96099 97.77526
3 0.246281 4.060403 91.6426 95.21544
4 0.2520661 3.967213 93.09128 97.57965
5 0.2917355 3.427762 90.01668 84.54075
6 0.3504132 2.853774 95.51884 82.62293
7 0.3628099 2.756264 97.1726 71.84603
8 0.4438017 2.253259 97.93826 87.35393

45. Principal Component Analysis of Cenozoic data
45
Principal Component Vector 7 Principal Component Vector 8

46. Principal Component Analysis of Cenozoic data
46
Principal Component Vector 9 Principal Component Vector 10

47. Principal Component Analysis of Cenozoic data
47
Principal Component Vector 11 Principal Component Vector 12