Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data
Abstract
1. Introduction
- Feature Extraction with an Autoencoder: An encoder has used ReLU activations to compress complex microtremor data and give more amplitude to the subtle pattern related to liquefaction.
- Robust Learning with Regularization: Batch normalization ascertains that the model will not overfit features, focusing only on the rare signal of liquefaction.
- Latent Space Embeddings: Critical indirect factors of liquefaction are caught by a latent 16-dimensional space, showing inner patterns of data.
- Stacked Classifier Ensemble: A stacked classified enseble combines the complex relationships identified by MLPwith the handling of class imbalance treated using XGBoost to make sure that the diversity within the data is reflected.
- Intelligent Meta-Model Aggregation: The final meta-model of MLP refines base model outputs, providing high priority to the rare events of liquefaction that may be missed in the individual models.
- Bias Mitigation by Representation Learning: The strengths of an autoencoder with embeddings and a stacked classifier are combined to avoid biases in concentrated datasets to represent liquefaction occurrence for rare data distributions.
2. Geographical Setting
3. Methodology
3.1. Data Acquisition and Preprocessing
3.2. Bandpass Filtering
3.3. HVSR Computation
3.4. Feature Extraction
- The amplitude spectra are obtained via Fast Fourier Transform (FFT):
- The HVSR (hvsr_ratio) is the ratio of the horizontal spectral components (East–West and North–South) to the vertical component.- is the amplitude spectrum of the North–South component;- is the amplitude spectrum of the East–West component;- is the amplitude spectrum of the vertical component.Then, this ratio is smoothed using a moving average with a kernel of size 10:
- Mean and Standard Deviation of HVSR: Shapes and variability in the overall HVSR curve provide critical information on resonance and amplification phenomena of the site.The mean of the smoothed HVSR ratio is expressed as follows:The standard deviation of the smoothed HVSR is expressed as follows:
- Alpha and gamma are parameters derived as the logarithmic and direct ratio, respectively, between the amplification factor and the fundamental frequency of the site; these are important in interpreting the dynamic resonance characteristics of the site.
- Z represents the energy distribution between horizontal and vertical components of microseismic waves to provide an integrated view of wave characteristics.
- The vulnerability index () is derived from the amplitude factor () and the resonant frequency (), which provides more detail for the determination of spectral properties of the site.
3.5. Model Description
4. Experiments
- Accuracy compares the total accuracy of the model as a ratio of true predictions to the total number of predictions.
- Precision estimates the number of predicted positives that are actually positive:
- Recall estimates how many actual positives were correctly identified by the model:
- The F1 score is the mean of precision and recall, offering a balance between the two metrics:
4.1. Data Description
4.2. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Class | Precision | Recall | F1 Score | Support |
---|---|---|---|---|---|
MLP | 0 | 0.92 | 1.00 | 0.96 | 22 |
1 | 0.00 | 0.00 | 0.00 | 2 | |
Accuracy | 0.92 | 24 | |||
Macro Avg | 0.46 | 0.50 | 0.48 | 24 | |
Weighted Avg | 0.84 | 0.92 | 0.88 | 24 | |
XGBoost | 0 | 0.96 | 1.00 | 0.98 | 22 |
1 | 1.00 | 0.50 | 0.67 | 2 | |
Accuracy | 0.96 | 24 | |||
Macro Avg | 0.98 | 0.75 | 0.82 | 24 | |
Weighted Avg | 0.96 | 0.96 | 0.95 | 24 | |
Stacked Model | 0 | 1.00 | 1.00 | 1.00 | 22 |
1 | 1.00 | 1.00 | 1.00 | 2 | |
Accuracy | 1.00 | 24 | |||
Macro Avg | 1.00 | 1.00 | 1.00 | 24 | |
Weighted Avg | 1.00 | 1.00 | 1.00 | 24 |
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Arab, O.; Mekouar, S.; Mastere, M.; Cabieces, R.; Collantes, D.R. Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data. Appl. Sci. 2025, 15, 6614. https://doi.org/10.3390/app15126614
Arab O, Mekouar S, Mastere M, Cabieces R, Collantes DR. Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data. Applied Sciences. 2025; 15(12):6614. https://doi.org/10.3390/app15126614
Chicago/Turabian StyleArab, Oussama, Soufiana Mekouar, Mohamed Mastere, Roberto Cabieces, and David Rodríguez Collantes. 2025. "Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data" Applied Sciences 15, no. 12: 6614. https://doi.org/10.3390/app15126614
APA StyleArab, O., Mekouar, S., Mastere, M., Cabieces, R., & Collantes, D. R. (2025). Improved Liquefaction Hazard Assessment via Deep Feature Extraction and Stacked Ensemble Learning on Microtremor Data. Applied Sciences, 15(12), 6614. https://doi.org/10.3390/app15126614