DOI: 10.5061/dryad.3bk3j9kr2
title: Global lithospheric thickness reconstruction using machine learning

description: The lithosphere, as the outermost solid layer of our planet, preserves a
 progressively more fragmentary record of geological events and processes
 from Earth's history the further back in time one looks. Thus, the
 evolution of lithospheric thickness and its cascading impacts on Earth’s
 tectonic system are presently unknown. Herein, we track the lithospheric
 thickness history using machine learning based on lithogeochemical big
 data of basalt. Our results demonstrate that four dramatic lithospheric
 thinning events occurred during the Paleoarchean, early Paleoproterozoic,
 Neoproterozoic, and Phanerozoic with intermediate thickening scenarios.
 These events respectively correspond to supercontinent breakup and
 assembly periods. Causality investigation further indicates that crustal
 metamorphic and deformation styles are the feedback of lithospheric
 thickness. Cross-correlation between lithospheric thickness and
 metamorphic thermal gradients record the transition from intra-oceanic
 subduction systems to continental margin plus intra-oceanic in the
 Paleoarchean and Mesoarchean, and a progressive emergence of large thick
 continents that allow supercontinent growth, which promoted assembly of
 the first supercontinent during the Neoarchean.
description: Our compiled global basaltic rocks database (see data S1)
 contains 24,194 chronological and oxide analyses of whole-rock, aged
 between 3.8 Ga to the present. The chronological and oxide data were
 obtained from the EarthChem data repository (http://portal.earthchem.org/). The lithospheric thicknesses are caclulated by the method in the paper.  Our compiled experimental basaltic database (see data S2) contains 1,392 experimental basaltic liquid compositions in equilibrium with olivine and orthopyroxene, compiled from the LEPR database (https://lepr.earthchem.org/) and published literature. Samples were filtered to include only those basalts with SiO2 (43–55 wt%) and MgO (7–17 wt%). We also manually removed the samples with total oxides below 95 wt%, and missing pressure and temperature. Our compiled global metamorphic thermal gradient database (see data S3, n= 564) is after Brown et al. (2020, Geology). data S4 is the lithogeochemical data of the Cenozoic basalts (after Guo et al., 2020, Geology) and lithospheric thickness reconstruction results in eastern China. data S5 is the metamorphic thermal gradient after Neogene and corresponding lithospheric thickness obtained by Litho1.0. data S6 is the normalized reconstructed PF and T/P series, results for the global and local Pearson correlation coefficient (PCC), time-lagged cross-correlation coefficients (TLCC), and windowed TLCC analyses. Codes 1-7 are the computational source codes used in this paper.
description: Codes 1-6 are based on Python 3.8<br>   
 Requirements:<br>          scipy==1.5.0<br>         
 numpy==1.21.0<br>          pandas==1.0.5<br>         
 sklearn==1.3.0<br>          imblearn==0.6.2<br>         
 dowhy==0.6<br>          econml==0.13.0<br>         
 seaborn==0.11.2<br>          reg_resampler==2.1.1<br>         
 matplotlib==3.2.2 Code 7 is based on Matlab after http://www.glaciology.net/wavelet-coherence. data S1-S6 can be read by Excel.
description: # Global lithospheric thickness reconstruction: *** This is the code and
 dataset for： Zhen-Jie Zhang, Guo-Xiong Chen, Timothy Kusky, Jie Yang,
 Qiu-Ming Cheng. Lithospheric thickness records tectonic evolution by
 controlling crustal metamorphic and deformation conditions. Science
 Advances. Author @Zhenjie Zhang, China University of Geosciences (Beijing)
 zjzhang@cugb.eu.cn ## Description of the Data and file structure In this
 dataset, we include a data file. #### Supplementary Data.xlsx ``` data S1.
 Lithogeochemical database of global basalts and results for the
 lithospheric thickness reconstructions. data S2. Experimental database of
 basaltic liquid compositions to train the random forest (RF) regression
 model. data S3. Global metamorphic thermal gradient database. data S4.
 Lithogeochemical data of the Cenozoic basalts and lithospheric thickness
 reconstruction results in eastern China. data S5. Metamorphic thermal
 gradient after Neogene and corresponding lithospheric thickness obtained
 by Litho1.0. data S6. Normalized reconstructed PF and T/P series, results
 for the global and local Pearson correlation coefficient (PCC),
 time-lagged cross-correlation coefficients (TLCC), and windowed TLCC
 analyses. ``` #### Data description Our compiled global basaltic rocks
 database (see data S1) contains 24,194 chronological and oxide analyses of
 whole-rock, aged between 3.8 Ga to the present. The chronological and
 oxide data were obtained from the EarthChem data repository
 (http://portal.earthchem.org/). Our compiled experimental basaltic
 database (see data S2) contains 1,392 experimental basaltic liquid
 compositions in equilibrium with olivine and orthopyroxene, compiled from
 the LEPR database (https://lepr.earthchem.org/) and published literature.
 Our compiled global metamorphic thermal gradient database (see data S3, n
 = 564) is after Brown et al. (2020, Geology). Our compiled
 lithogeochemical data of the Cenozoic basalts (see data S4, n = 60) is
 after Guo et al. (2020, Geology). Our compiled global metamorphic thermal
 gradient after Neogene database (see data S5, n = 15) is after Brown et
 al. (2020, Geology). *The missing values or empty cells in all data sheets
 are "n/a" (not applicable).* The wavelet toolbox (Code 7) used
 in this study is from: http://www.glaciology.net/wavelet-coherence. ####
 Headers for all data sheets ##### Data S1: ``` Index: Sample index Source:
 Database for the data source Reference: Original data reference Latitude:
 Sample location latitude (degree) Longitude: Sample location longitude
 (degree) Age (Ma): Sample age Material: Analyzed materials Type: Sample
 type (e.g., volcanic, plutonic) SiO2: SiO2 content (%) TiO2: TiO2 content
 (%) Al2O3: Al2O3 content (%) FeOT: FeOT content (%) MnO: MnO content (%)
 MgO: MgO content (%) CaO: CaO content (%) Na2O: Na2O content (%) K2O: K2O
 content (%) P2O5: P2O5 content (%) LOI: Loss on ignition (%) Total: Total
 content (%) RF predicted PF: Predicted final pressure (GPa) Lithospheric
 thickness (km): Lithospheric thickness (km) Std (GPa): Standard deviation
 for predicted final pressure (GPa) Std (km): Standard deviation for
 lithospheric thickness (km) predict-i: The (i+1)th predicted lithospheric
 thickness (km) [Note: i = 0-99] Model precision: Model precision for each
 prediction average (km): Average lithospheric thickness for 100 times
 prediction std: Standard deviation for 100 times prediction of
 lithospheric thickness (km) SD (%): Standard deviation for 100 times
 prediction of lithospheric thickness (%) ``` ##### Data S2: ``` Index:
 Sample index Experiment: Experiment number in the original reference
 Citation: Original data reference Label: Type of the data source (LEPR
 Database or Literature) P (GPa): Experimental pressure (GPa) T (°C):
 Experimental temperature (Celsius degree) SiO2: SiO2 content (%) TiO2:
 TiO2 content (%) Al2O3: Al2O3 content (%) FeOT: FeOT content (%) MnO: MnO
 content (%) MgO: MgO content (%) CaO: CaO content (%) Na2O: Na2O content
 (%) K2O: K2O content (%) ``` ##### Data S3: ``` Age (Ga): Age of
 metamorphism (Billion years) Age (Ma): Age of metamorphism (Million years)
 T/P (°C/GPa): Ratio between temperature and pressure (°C/GPa) ``` #####
 Data S4: ``` Sample: Sample index Longitude (°E): Sample location
 longitude (degrees) SiO2: SiO2 content (%) TiO2: TiO2 content (%) Al2O3:
 Al2O3 content (%) FeOT: FeOT content (%) MnO: MnO content (%) MgO: MgO
 content (%) CaO: CaO content (%) Na2O: Na2O content (%) K2O: K2O content
 (%) P2O5: P2O5 content (%) RF predicted PF: Predicted final pressure (GPa)
 Lithospheric thickness (km): Lithospheric thickness (km) ``` ##### Data
 S5: ``` Index: Sample index Location: Location of sample Latitude: Sample
 location latitude (degrees) Longitude: Sample location longitude (degrees)
 Age (Ga): Age of metamorphism (Billion years) Peak P/P-T/peak T: Data are
 Peak P or P-T or peak T Type: Type of metamorphism (HAG or HUHT or I or
 LUHPOE or LHPCE or LHPOE or LUHPCE or LHPOB) Path: Path type (U or CW) T
 (°C): Experimental temperature (Celsius degree) P (GPa): Experimental
 pressure (GPa) T / P (°C/GPa): Ratio between temperature and pressure
 (°C/GPa) References: Data reference Lithospheric thickness (km) obtained
 by Litho1.0: Lithospheric thickness (km) obtained by Litho1.0 ``` #####
 Data S6: ``` Age (Ga): Age of metamorphism (Billion years) Normalized P
 (GPa): Normalized pressure Normalized T/P (°C/GPa): Normalized T/P ratio
 Global PCC R2: Global Pearson correlation coefficient Global PCC P-value:
 P-value for global Pearson correlation coefficient Local PCC R2
 (window=1.2Ga): Local Pearson correlation coefficient (window=1.2Ga) Local
 PCC R2 (window=0.6Ga): Local Pearson correlation coefficient
 (window=0.6Ga) TLCC offset (Ga): Offset time (Billion years) for
 time-lagged cross-correlation coefficients TLCC Pearson R2: Pearson
 correlation coefficient for time-lagged cross-correlation coefficients
 Windowed TLCC: Windowed time-lagged cross-correlation coefficients (offset
 from -1Ga to 1Ga with a step = 10 Ma) ``` Seven code files in our code.zip
 #### 1, DML\_Thickness\_a.py ``` Causality analysis by causal forests.
 treatment = 'Pressure' outcome = 'Temperature' ```
 #### 2, DML\_Thickness\_b.py ``` Causality analysis by causal forests.
 treatment = 'Temperature' outcome = 'Pressure' ```
 #### 3, RF\_for\_lid.py ``` Reconstructed lithospheric thickness based on
 random forest by using whole-rock oxides analyses of global basalts. ```
 #### 4, Lowess.py ``` Reconstructed PF and T/P series. ``` #### 5,
 Thickness-TP\_Pearson.py ``` Global and local Pearson correlation
 coefficients (PCCs) between PF and T/P. ``` #### 6,
 Time-lagged\_cross-correlation\_coefficients.py ``` Time-lagged
 cross-correlation coefficients (TLCC) between the PF and T/P ratio. ```
 #### 7, main\_wavelet.m ``` Wavelet analyze for PF and T/P. ``` #### Note:
 ``` Codes 1-6 are based on Python 3.8 Requirements: scipy==1.5.0
 numpy==1.21.0 pandas==1.0.5 sklearn==1.3.0 imblearn==0.6.2 dowhy==0.6
 econml==0.13.0 seaborn==0.11.2 reg_resampler==2.1.1 matplotlib==3.2.2 Code
 7 is based on Matlab. ``` ## Sharing/access Information Data processing
 steps were conducted in this study: (1) Causality analysis by causal
 forest demonstrates that the temperature variable can be disregarded in
 the estimation of final pressure (PF) (or lithospheric thickness) [by
 using Codes 1 and 2, and data S2] (2) The random forest (RF) regression
 model was trained using experimental basalt data [by using Code 3, and
 data S1 and S2] (3) Apply the trained RF regression model to calculate the
 PF for global basalts [by using Code 3, and data S1 and S2] (4)
 Reconstruction of normalized PF and T/P serials since 3.8 Ga [by using
 Code 4, and data S1 and S3] (5) Calculation for global and local Pearson
 correlation coefficients between PF and T/P serials [by using Code 5, and
 data S6] (6) Wavelet analysis for the PF and T/P serials [by using Code 7,
 and data S6] (7) Calculation for Time-lagged cross-correlation
 coefficients between the PF and T/P serials [by using Code 6, and data S6]
 All data sheets can be read directly using our code files. You can also
 easily fork my project by using the MyDDE platform at [Global lithospheric
 thickness
 reconstruction](https://mydde.deep-time.org/org-portal/MyDDE/project/654c6d05afe0567cf0c286af). No software and environment installation is required.