Earthquakes are one of the most devastating natural disasters that can cause massive damage and loss of lives. Predicting when and where an earthquake will occur is a challenging task that has been pursued by many researchers for decades.
However, traditional methods of earthquake prediction are often limited by the complexity and uncertainty of the seismic processes.
In recent years, ML has also shown promising results in earthquake prediction, using different techniques and data sources.
ML techniques for earthquake prediction
ML techniques can be broadly classified into two categories: supervised and unsupervised. Supervised ML involves training a model with labeled data, where the input features and the output labels are known.
The model then learns to map the input features to the output labels, and can be used to make predictions on new data. Unsupervised ML involves finding patterns or structures in unlabeled data, where the output labels are unknown.
The model then learns to cluster or classify the data based on some criteria, and can be used to discover hidden information or anomalies.
Supervised ML techniques have been widely used for earthquake prediction, especially for predicting the magnitude, location, and occurrence of earthquakes. Some of the common supervised ML algorithms include linear regression, logistic regression, support vector machines (SVMs), decision trees, random forests, neural networks, and deep learning.
These algorithms can be trained with various types of seismic data, such as seismic waveforms, seismic catalogs, geodetic measurements, electromagnetic signals, and satellite images.
For example, a study by Md Ridzwan and Md. Yusoff (2023) reviewed 31 studies on earthquake prediction using supervised ML algorithms from 2017 to 2021. They found that most of the models focused on predicting the earthquake magnitude, trend, and occurrence, using different types of seismic indicators based on Gutenberg Richter’s law and Omori’s law.
They also compared the performance of different algorithms and found that neural networks and deep learning had the highest accuracy for earthquake magnitude prediction.
Unsupervised ML techniques have been less explored for earthquake prediction, but they have the potential to reveal new insights into the seismic processes and patterns. Some of the common unsupervised ML algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), independent component analysis (ICA), self-organizing maps (SOMs), and generative adversarial networks (GANs).
These algorithms can be applied to analyze the more complete and detailed expression of seismicity in the next-generation earthquake catalogs that are developed through supervised ML.
For example, a study by Beroza et al. (2021) suggested that applying unsupervised ML to analyze these catalogs may be the fastest route to improving earthquake forecasting.
They argued that unsupervised ML can help identify features that are predictive of future seismicity, such as stress accumulation and release patterns, fault geometry and interactions, seismic cycle phases, and precursory signals.
Advantages and limitations of ML-based earthquake prediction
ML-based earthquake prediction has several advantages over traditional methods. First, ML can handle large and complex data sets that are beyond human capabilities. ML can also extract useful features from raw data without requiring prior knowledge or assumptions about the underlying physical mechanisms.
Second, ML can learn from historical data and adapt to new data without requiring manual intervention or retraining. ML can also generalize to unseen situations and cope with uncertainty and noise in the data. Third, ML can provide probabilistic predictions that quantify the confidence and uncertainty of the outcomes. ML can also provide interpretable predictions that explain the rationale and evidence behind the decisions.
However, ML-based earthquake prediction also faces several challenges and limitations. First, ML requires high-quality and representative data sets that cover a wide range of scenarios and conditions.
However, seismic data are often incomplete, imbalanced, noisy, or corrupted by human errors or environmental factors. Moreover, seismic data are not always available or accessible due to technical or ethical issues.