Data from: Role of tectonic rock damage in erosional processes: A global analysis
Data files
Feb 24, 2026 version files 32.39 MB
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2025_Kuhusubpasin_FaultRockDamage_211125.ipynb
32.14 MB
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Grid_E_20km_factors_weight.zip
238.50 KB
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README.md
8.99 KB
Abstract
The role of active faults in driving rock uplift is well-known, but their influence on rock damage and erosional efficiency remains unclear globally. Using 1744 10Be-derived erosion rates, we show that erosional efficiency is elevated on average within ~15 km of a fault trace and decreases with distance, up to ~100 km. Reverse faults, and those longer than 140 km, show the strongest effects. This length scale of decay suggests tectonic damage extends beyond fault-core pulverization on primary faults, possibly including fracturing or grain-to-grain contact weakening due to seismic shaking and distributed deformation on complex fault networks. Machine learning identifies fault proximity as a dominant control on erosional efficiency, exceeding precipitation and lithology, particularly when a measure of seismic shaking is included. These findings indicate that active tectonics are associated with erosion not only through uplift but also by enhancing erosional efficiency through long-range rock damage.
Dataset DOI: 10.5061/dryad.tht76hfb0
Description of the data and file structure
Repository for Role of Fault-Induced Rock Damage in Erosional Processes: A Global Analysis
Boontigan Kuhasubpasin, Seulgi Moon, Carolina Lithgow-Bertelloni
This repository contains the reproducible workflow and source code used to generate all figures and results presented in the associated manuscript. The code encompasses data preprocessing, statistical analyses, and visualization routines related to fault proximity, erosion efficiency, seismic shaking, and landscape metrics.
The directory structure mirrors the organization of the manuscript and its supplementary materials. All code cells are annotated to facilitate transparency, reproducibility, and ease of interpretation.
Required Files
1. 2025_Kuhusubpasin_FaultRockDamage_211125.ipynb
A fully documented Python Jupyter notebook containing the complete reproducible workflow used in the study. This notebook includes all data processing steps, statistical analyses, and figure-generation code required to reproduce the visualizations in the manuscript.
2. Grid_E_20km_factors_weight.zip
Equal-area (20 × 20 km) global grid dataset generated from E_OctopusV24_FaultRockDamage_1117.zip. This dataset contains basin-area–weighted interpolations of key physical variables for spatially uniform comparison.
| Field Name | Description |
|---|---|
lon, lat |
Longitude and latitude of grid cell center. |
PGA, PGA_STD |
Basin area-weighted mean and standard deviation of PGA. |
MAP, MAP_STD |
Basin area-weighted mean and standard deviation of MAP. |
Dist_km, Dist_km_ST |
Basin area-weighted mean and standard deviation of fault distance. |
F_reg, F_length |
Basin area-weighted mean fault parameters. |
CstRks, SedRks, Seds |
Basin area-weighted lithologic proportions. |
K, K_STD |
Basin area-weighted mean and standard deviation of K. |
Kq, Kq_STD |
Basin area-weighted mean and standard deviation of Kq. |
Eq_n, Eq_n_STD |
Basin area-weighted earthquake counts and standard deviation. |
Sum_Mw, Sum_Mw_STD |
Basin area-weighted cumulative moment magnitude and standard deviation. |
N_POLYS |
Number of basins intersecting each grid cell. |
geometry |
20 × 20 km grid cell polygon. |
3. E_OctopusV24_FaultRockDamage_1117.zip (hosted by Zenodo)
Primary global basin-scale dataset used in the fault-related rock damage and erosion-efficiency analysis. This archive contains processed observations including erosion rates, channel steepness metrics, lithology, fault attributes, seismicity, and climatic factors.
| Field Name | Description |
|---|---|
OBSID1 |
Observation ID of the basin (Codilean et al., 2018). |
EBE_MMYR, EBE_MMYRE |
10Be-derived erosion rate (mm/yr) and associated uncertainty (Codilean et al., 2018). |
ksn045 |
Channel steepness index (concavity = 0.45). |
r045, std045 |
Correlation coefficient and standard deviation for channel steepness fits. |
ksnq045 |
Rainfall-weighted channel steepness index. |
rq045, stdq045 |
Correlation coefficient and standard deviation for rainfall-weighted fits. |
PGA, PGA_STD |
Peak ground acceleration (g) with 10% exceedance probability in 50 years and standard deviation. |
MAP, MAP_STD |
Mean annual precipitation (mm/yr) and standard deviation. |
Dist_km |
Distance to nearest mapped active fault (km), calculated in this study. |
F_reg, F_length, F_sliprate, F_dip |
Fault region and geometric/slip parameters. |
Lithology |
Dominant lithologic class. |
CstRks, SedRks, Seds |
Proportions of crystalline rock, sedimentary rock, and unconsolidated sediments. |
K, Kq |
Erosional efficiency coefficients (uniform and rainfall-weighted). |
log_D |
Log10-transformed fault distance. |
Eq_n |
Number of earthquakes (Mw > 4) within 1° of basin centroid. |
Sum_M0, Sum_Mw |
Cumulative seismic moment and moment magnitude within 1° of basin centroid. |
geometry |
Basin centroid geometry used in this study (derived from Codilean et al., 2018). |
4. SoCal_Database.zip: Southern California Subset (hosted by Zenodo)
Regional dataset for Southern California including basin-scale variables and additional region-specific data.
4a. E_OctopusV24_FaultRockDamage_socal.shp
Southern California subset of the basin-scale dataset. All fields are identical to the global dataset, with one additional variable:
| Field Name | Description |
|---|---|
Vs_SoCal |
Shear-wave velocity (Vs) in km/s at 500 m depth from Rayleigh-wave ambient noise tomography (Zigone et al., 2015). |
4b. Socal_Eq.csv
Derived earthquake dataset used for regional seismic-distance analysis.
| Field Name | Description |
|---|---|
Magn |
Earthquake moment magnitude (Mw) obtained from the GCMT. |
Dist_km |
Distance (km) from each earthquake to the nearest mapped active fault, calculated in this study. |
4c. vs_socal_fault.csv
Shear-wave velocity sampling dataset used in regional fault-distance analysis.
| Field Name | Description |
|---|---|
Vs_SoCal |
Shear-wave velocity (Vs) in km/s at 500 m depth from Rayleigh-wave ambient noise tomography (Zigone et al., 2015). |
Dist_km |
Distance (km) from each Vs sampling location to the nearest mapped active fault, calculated in this study. |
Code/software
All analyses were performed using Python.
Access information
Data were derived from the following publicly available repositories:
- Basin-averaged erosion rates from Octopus (https://octopusdata.org: Codilean et al., 2018)
- Active fault map from GEM global active faults (https://www.globalquakemodel.org/product/active-faults-database: Styron et sl., 2020)
- Digital elevation model from HydroSHEDs (https://www.hydrosheds.org/products/hydrosheds: Lehner et al., 2008)
- Mean annual precipitation from World Clim (https://www.worldclim.org/data/worldclim21.html: Fick et al., 2017)
- Lithologic map from GLiM (https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html: Hartmann et al., 2012)
- Peak ground acceleration from GEM global hazard map (https://www.globalquakemodel.org/product/global-seismic-hazard-map: Johnson et al., 2023)
- Earthquake catalog from ISC (https://www.isc.ac.uk/event_bibliography/eventindex.php: Di et al., 2014)
- Earthquake magnitudes were obtained from the GCMT (https://www.globalcmt.org/CMTcite.html: Dziewonski et al.,1981 and Ekstrom et al., 2012)
- Shear-wave velocity from the Southern California ambient noise tomography model (https://doi.org/10.1007/s00024-014-0872-1: Zigone et al., 2015)
