# Skill 150

> Model and predict earthquake occurrences using historical seismic data and machine learning techniques. Use when assessing risks and preparing for potential earthquake events.

- Skill: `legendtkl/skill-150` (Agent Skill)
- Install (CLI): `npx skillmds@latest add legendtkl/skill-150`
- Raw SKILL.md: https://api.skillmd.com/api/skills/legendtkl/skill-150/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: legendtkl (https://skillmd.com/u/legendtkl)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/legendtkl/skill-150

---


# Earthquake Forecasting Using Machine Learning

## Overview

Forecasting earthquakes is a complex task that involves analyzing historical seismic data to identify patterns that may indicate future events. This guide covers the process of building and training models to provide forecasts.

## Key Concepts

### Data Sources for Earthquake Forecasting

1. **USGS Earthquake Catalog**: Provides a comprehensive archive of seismic events.
2. **Seismic Sensors**: Real-time data collection from seismic networks.

### Machine Learning Approaches
- **Supervised Learning**: Use labeled data to train models on past earthquake occurrences.
- **Unsupervised Learning**: Identify clusters and patterns in seismic activity without labeled outcomes.

## Data Preparation

### Loading Earthquake Data

```python
import pandas as pd

# Load earthquake data from CSV

df = pd.read_csv('earthquake_data.csv')
print(df.head())
```

### Feature Engineering

Transform raw data into features suitable for machine learning:

- **Magnitude**: The size of the earthquake.
- **Depth**: Distance below the Earth's surface.
- **Location**: Latitude and longitude coordinates.

```python
# Creating features from the data

df['depth_bins'] = pd.cut(df['depth'], bins=[0, 10, 30, 50, 100, 300], labels=[1, 2, 3, 4, 5])
```

## Model Development

### Splitting Data

```python
from sklearn.model_selection import train_test_split

X = df[['magnitude', 'depth_bins']]
y = df['occurred']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```

### Choosing a Model

You can use various models, such as:
- **Random Forest**: Good for handling complex interactions.
- **Support Vector Machines**: Effective in high-dimensional spaces.
- **Neural Networks**: Suitable for capturing nonlinear relationships.

### Training the Model

```python
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model.fit(X_train, y_train)
```

## Model Evaluation

### Predicting Earthquake Occurrences

```python
predictions = model.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, predictions))
```

## Conclusion

Earthquake forecasting is a challenging but rewarding task. By utilizing machine learning techniques on historical seismic data, we can improve our understanding of potential earthquake risks and enhance disaster preparedness.