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Project Title: SEISMIC LANDSCAPE OF THE PHILIPPINES: A DATA ANALYSIS

(1800-Present)

INTRODUCTION

As a resident of one of the islands of the Philippines, I have always been keenly aware of the region's susceptibility to seismic hazards. Living in an area prone to earthquakes and potentially tsunamis, I embarked on this case study to assess the seismic risks faced by my community. The insights gained from this analysis are not only crucial for individuals residing on the island, especially those near coastal areas vulnerable to tsunamis, but also for those living in proximity to volcanoes.

Having personally experienced living near a volcano, I understand the importance of being prepared and informed about potential geological hazards. Therefore, this analysis aims to provide valuable information that can benefit communities living near both earthquake-prone areas and volcanic regions.

The Philippines, nestled within the Pacific Ring of Fire, is no stranger to significant seismic activity. This analysis project delves into the significant earthquakes data spanning from 1800 to the present, seeking to uncover patterns and trends in seismicity. Through comprehensive exploration of temporal, spatial, and geological factors influencing earthquake occurrence, including magnitude distribution, depth, and correlation with geological features, we aim to shed light on the seismic landscape of the Philippines.

Furthermore, by assessing the socio-economic impact of earthquakes, this analysis endeavors to offer insights for disaster preparedness and resilience-building efforts. It is my hope that the knowledge generated from this study will contribute to a deeper understanding of seismic risks in the Philippines and empower communities to better mitigate and adapt to these natural hazards.

DATA PREPARATION AND METHODS

In this section, we outline the methodologies employed to analyze earthquake data from the Philippines spanning 1800 to the present. Our approach encompasses data preprocessing, exploratory data analysis, and statistical modeling techniques. We detail our strategies for temporal and spatial analysis, magnitude distribution examination, and correlation assessments with geological features. Additionally, we describe our methods for assessing earthquake impact and forecasting future seismic activity. Through these robust methodologies, we aim to extract meaningful insights and contribute to a comprehensive understanding of seismic dynamics in the Philippines.

The data used in this project was collected from National Centers for Environmental Information NCEI's database.

Using the present data, we were only able to answer/asses the following:

  1. How has the frequency of earthquakes in the Philippines changed over the past decade?
  2. Are there certain years or decades with increased seismic activity? Has there been an increase or decrease in earthquake frequency or magnitude over time?
  3. Which regions of the Philippines are most prone to high-magnitude earthquakes?
  4. Are there more frequent small earthquakes or occasional large earthquakes?
  5. Are earthquakes predominantly shallow, intermediate, or deep-focus earthquakes?
  6. What is the relationship between earthquake magnitude and tsunami occurrence/intensity?
  7. Are earthquakes associated with volcanic eruptions in the Philippines?

Additional questions:

  1. Do earthquakes tend to cluster around certain geological features?
  2. What is the relationship between earthquake occurrence and geological features such as fault lines, plate boundaries, and geological formations?
  3. How can predictive models be developed to forecast future earthquake occurrence and assess the likelihood of seismic events in different regions of the Philippines?
  4. What additional datasets, such as the precise locations of fault lines and plate boundaries, would be beneficial for the development of predictive models?
  5. How can an analysis of earthquakes, including less significant events, improve the proposed machine learning model in predicting future earthquake occurrence?

EXPLORATORY DATA ANALYSIS: EARTHQUAKES

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

edata = 'earthquakes.csv'
tdata = 'Tsunamis.csv'
vdata = 'Volcano.csv'
edf = pd.read_csv(edata)
tdf = pd.read_csv(tdata)
vdf = pd.read_csv(vdata)
fl_df = pd.read_csv('faultlines.csv')
edf[['Year', 'Month', 'Day', 'Hour', 'Min', 'Sec']] = edf[['Year', 'Month', 'Day', 'Hour', 'Min', 'Sec']].fillna(0)
edf['Date'] = pd.to_datetime(edf['Year'].astype(int).astype(str) + '-' + 
                            edf['Month'].astype(int).astype(str).str.zfill(2) + '-' + 
                            edf['Day'].astype(int).astype(str).str.zfill(2) + ' ' + 
                            edf['Hour'].astype(int).astype(str).str.zfill(2) + ':' + 
                            edf['Min'].astype(int).astype(str).str.zfill(2) + ':' + 
                            edf['Sec'].astype(int).astype(str).str.zfill(2), 
                            format='%Y-%m-%d %H:%M:%S', errors='coerce')
edf.drop(['Month', 'Day', 'Hour', 'Min', 'Sec'], axis=1, inplace=True)
cols = ['Date'] + [col for col in edf if col != 'Date']
edf = edf[cols]

General Statistics: Earthquakes

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As the magnitude increases, the number of deaths tends to increase as well. However, there is significant variability in the data. Some large earthquakes result in relatively few casualties, while others cause substantial loss of life. Factors such as building infrastructure, population density, and preparedness play a crucial role in determining casualties.

There is a clear trend: as the intensity increases, so does the damage (expressed in monetary terms). Higher intensity earthquakes cause more severe damage to buildings, infrastructure, and the economy. Mitigation strategies should focus on strengthening structures, land-use planning, and disaster risk reduction.

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Temporal Analysis

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