Adaptive Inversion Optimization of the Static Factor of Safety for Newmark-Based Earthquake-Induced Landslide Susceptibility Assessment
- 1School of Civil Engineering, Lanzhou Institute of Technology, Lanzhou 730050, China
- 2Geological Engineering and Environmental Investigation Institute, Sichuan Provincial Geological Engineering Co., Ltd., Chengdu 610072, China
- 3School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Abstract
Rapid regional evaluation of earthquake-induced landslide susceptibility via the traditional Newmark model is often constrained by geotechnical parameter spatial variability and subjective empirical adjustments in static factor of safety (Fs) calculations. To resolve this bottleneck, this study develops an inversion-based stability factor determination (ISFD) method for adaptive parameter configuration. Integrating sparse post-earthquake observations with multi-source geo-environmental factors, a LightGBM inversion model targets and corrects unphysical anomalies (Fs < 1.0) from traditional limit equilibrium calculations while retaining native dynamic analytical solutions for valid mechanical units (Fs ≥ 1.0), achieving a physically consistent parameter calibration within the Newmark framework. Validation in the Jiuzhaigou earthquake-affected region demonstrates that the ISFD method minimizes parameter uncertainty interferences. The model’s AUC increases from 0.727 to 0.786 (an 8.116% relative improvement), yielding high-susceptibility zones with superior spatial alignment with actual landslide inventories and mitigating localized under-predictions. Data sensitivity analysis confirms that model performance exhibits rapid initial gains followed by high-level saturation as the sample size increases, maintaining robust stability under ultra-low-sample conditions and accurately capturing critical risks along the Minjiang fault zone. The ISFD method delivers a mechanistically self-consistent, highly regionally adaptive technical workflow for rapid post-earthquake landslide susceptibility evaluation under data-scarce scenarios.
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BibTeX
@article{sun2026adaptive,
title = {{Adaptive Inversion Optimization of the Static Factor of Safety for Newmark-Based Earthquake-Induced Landslide Susceptibility Assessment}},
author = {Bingyang Sun and Xin Feng and Jun Luo and Zhen Zhou},
journal = {Geosciences},
year = {2026},
month = sep,
doi = {10.3390/geosciences16100386},
url = {https://doi.org/10.3390/geosciences16100386},
note = {CC BY},
}
RIS
TY - JOUR
TI - Adaptive Inversion Optimization of the Static Factor of Safety for Newmark-Based Earthquake-Induced Landslide Susceptibility Assessment
AU - Bingyang Sun
AU - Xin Feng
AU - Jun Luo
AU - Zhen Zhou
PY - 2026
DA - 2026/09/01
JO - Geosciences
DO - 10.3390/geosciences16100386
UR - https://doi.org/10.3390/geosciences16100386
AB - Rapid regional evaluation of earthquake-induced landslide susceptibility via the traditional Newmark model is often constrained by geotechnical parameter spatial variability and subjective empirical adjustments in static factor of safety (Fs) calculations. To resolve this bottleneck, this study develops an inversion-based stability factor determination (ISFD) method for adaptive parameter configuration. Integrating sparse post-earthquake observations with multi-source geo-environmental factors, a LightGBM inversion model targets and corrects unphysical anomalies (Fs < 1.0) from traditional limit equilibrium calculations while retaining native dynamic analytical solutions for valid mechanical units (Fs ≥ 1.0), achieving a physically consistent parameter calibration within the Newmark framework. Validation in the Jiuzhaigou earthquake-affected region demonstrates that the ISFD method minimizes parameter uncertainty interferences. The model’s AUC increases from 0.727 to 0.786 (an 8.116% relative improvement), yielding high-susceptibility zones with superior spatial alignment with actual landslide inventories and mitigating localized under-predictions. Data sensitivity analysis confirms that model performance exhibits rapid initial gains followed by high-level saturation as the sample size increases, maintaining robust stability under ultra-low-sample conditions and accurately capturing critical risks along the Minjiang fault zone. The ISFD method delivers a mechanistically self-consistent, highly regionally adaptive technical workflow for rapid post-earthquake landslide susceptibility evaluation under data-scarce scenarios.
SN - 2076-3263
ER -