Taiwan Earthquake Data Analysis

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Project Description

Recently, Taiwan experienced a magnitude 7.4 earthquake, and another magnitude 6.1 earthquake occurred at 3:13 a.m. on April 27th. With an increase in seismic activity, concerns about the region’s vulnerability to earthquakes and the potential impact on its population, infrastructure, and economy are rising. Situated in an active seismic zone, earthquake analysis is crucial for assessing risks and disaster management. This project aims to conduct an analysis of earthquake activity in Taiwan, including its frequency and magnitude distribution, with the goal of enhancing seismic resilience and preparedness measures.

Dataset Introduction

The dataset I used is obtained from the Taiwan Central Weather Administration’s Taiwan Geophysical Database Management System, covering earthquake records from March 22, 2023, to March 21, 2024. This dataset included fifteen attributes: date, time, lat, lon, depth, ML, nstn, dmin, gap, arms, ERH, ERZ, fixed, nph, and quality. According to the Taiwan Geophysical Database Management System, the following is the detailed description of each attribute:

date: Year-Month-Day
time: hour:minute:sec.ss
lat: Latitude (North).
lon: Longitude (East).
depth: Focal Depth (km).
ML: Earthquake magnitude.
nstn: Station number.
dmin: Epicentral distance to the nearest station.
gap: Largest azimuthal separation in degrees between stations.
trms: Root-mean-square error of the time residuals.
ERH: Standard error of the epicenter in km.
ERZ: Standard error of the depth in km.
fixed: Focal depth control.
1. F: free run, means the solution is automatically converged to minimal error.
2. X: fixed depth, means if the solution is not good, then the depth will be given an empirical value to reach the minimal error.
nph: Number of phases picked.

quality: Solution quality of the hypocenter based on the nature of station distribution and RMS. It is classified into four ranks: A, B, C, D, according to the following scheme:

quality	Number of stations	Gap	Minimal of epicentral distance of station
A	>= 6	<= 90	<= Depth or 5 km
B	>= 6	<= 135	<= 2 Depth or 10 km
C	>= 6	<= 180	<= 50 km
D	others	-	-

Limitations: The limitation is that this dataset only has attributes of longitude and latitude and thus is difficult in geospatial analysis. Without city or county information, it's challenging to perform geospatial analysis to identify regions with higher seismic activity. Analyzing earthquakes based solely on latitude and longitude coordinates may not provide sufficient details to pinpoint specific areas prone to frequent earthquakes.

Tasks

The following are the two tasks I want to explore with my earthquake dataset:

What time in the day does the earthquake occur most frequently?
In which month did the earthquake that had the highest magnitude occur?
Where do earthquakes of higher magnitude or depth tend to occur more frequently? In which city or county in Taiwan? In which region of Taiwan?
Is there any correlation between the magnitude of earthquakes and their focal depth?
How do the frequency and magnitude of earthquakes vary over time?

Task 1: Frequency of Earthquakes

Plot 1: Histogram (2-hour Intervals)

This histogram displays the frequency of earthquakes occurring during different 2-hour intervals in a day (12 time periods in total). It helps to identify the time period when earthquakes are most frequent.

Plot 2: Histogram (Group by Earthquake Quality)

This histogram categorizes earthquakes based on their quality, providing a comparison of earthquake frequencies across different quality categories. It helps to understand the relationship between earthquake occurrence and quality and find the time period within a day when earthquakes are most frequent.

Task 2: Distribution of Earthquake Magnitude / Max Magnitude

Plot 1: Violin Plot

This violin plot shows the distribution of earthquake magnitudes across different months. It helps to identify the month with the highest magnitude earthquake and find the pattern of the magnitude distribution density across different months.

Plot 2: Ridgeline Plot

Implemented this plot by referring to this page.

This ridgeline plot visualizes the distribution of earthquake magnitudes for each month using Kernel Density Estimation plots. It helps to find the month with the highest magnitude earthquake and compare the magnitude distribution density across different months, with the y-axis representing the density of earthquake distribution for magnitudes.