[Part-2] Earthquake Data Analysis

I recommend that you go over Part-1 of the earthquake data analysis before moving on to the next level. You can check out the code here.

Fig 1: Destruction caused by an earthquake

1. Earthquakes and tectonic plates

A tectonic plate (also known as a lithospheric plate) is a massive slab of solid rock with an uneven shape[1]. I’d use the Tectonic Plate Boundaries dataset to plot the position of several tectonic plates. This data contains latitude and longitude data that completely encloses 56 tectonic plates.

df = pd.read_csv('/content/tectonic_plates.csv')
df.head()

Fig 2: Tectonic Plate Boundary Data

import pandas as pd
df = pd.read_csv('/content/tectonic_plates.csv')
from keplergl import KeplerGl
map_1 = KeplerGl(height = 600)
map_1.add_data(data=df, name = 'Plates Visualization')
map_1

Fig 3: Different tectonic plates across the globe

Fig 4: Tectonic plates and their names

Now that we know the location of different tectonic plates, I will be plotting the earthquake data as another layer on the top of this dataset. The cluster’s color is determined by the magnitude of the earthquake, with yellow denoting high magnitude and red denoting low magnitude. The size of the cluster is based on the number of earthquakes in that cluster. I have used the ‘maximum’ instead of the average as a statistic to represent each cluster.

df2 = pd.read_csv('/content/database.csv')
map_1.add_data(data = df2, name = 'Earthquakes')
map_1

Fig 5: Significant earthquakes (1965–2016) and different tectonic plates

The tectonic plates near the Pacific Ocean, as seen above, are the ones with the most earthquakes of high magnitude; this is where the pacific plate is being subducted beneath the surrounding plates. The plates witnessing the most numbers of quakes are OK, AM, YZ, PS, PA, SU, CR, CO, and NZ.

2. Comparing earthquake magnitude of India, Japan, and the World

# Importing libraries
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
plt.title('Earthquake Magnitude Distribution')
plt.tight_layout()
# Preparing data
df = pd.read_csv('/content/Locations.csv') 
(#This file was created in Part-1) 
df1 = df.loc[df.Country.isin(['IN', 'JP'])]
filt1 = (df1['Country'] == 'IN')
filt2 = (df1['Country'] == 'JP')
India = df1[filt1]
Japan = df1[filt2]
# Plot
kwargs = dict(hist_kws={'alpha':.1}, kde_kws={'linewidth':2})
sns.distplot(df['Magnitude'], color="dodgerblue", label="World", **kwargs)
sns.distplot(India['Magnitude'], color="orange", label="India", **kwargs)
sns.distplot(Japan['Magnitude'], color="red", label="Japan", **kwargs)
plt.legend();
plt.savefig('2a.png')

Fig 6: Comparing magnitude distributions for India, Japan, and the World

The world (including India and Japan) observes the mean at around 5.5 whereas Japan and India achieved it at around 5.6. Note: The difference in the peak length is attributable to the fact that each category has a distinct amount of observations.

However, the distribution after magnitude > 5.8 is almost similar for the three categories. If the graph for magnitude <5.5 is extended as above, India converges at the lowest rate, implying that India has a higher chance of observing an earthquake of less than 5.5 magnitude than Japan or the world.

3. India: Where do most earthquakes occur?

filt = (df2['Country'] == 'IN')
Indian_EQ= df2[filt]
map_2 = KeplerGl(height = 1300)
map_2.add_data(data=Indian_EQ, name = 'Earthquakes Visualization')
map_2

Fig 7: Earthquakes in India (1965–2016)

In the above graph, yellow represents a high magnitude earthquake while red represents a low one. The Andaman-Nicobar Islands, the Himalayan belt, and India’s north-eastern states are home to the high magnitude earthquakes.

Fig 8: Seismic zone graph of India for the year 2022

India is vulnerable to earthquakes of varying magnitudes due to its unique geophysical location. A table shows distinct seismic zones, the number of earthquakes in each, and the predicted return period for those earthquakes.

Table 1: Region-wise frequency of occurrence of earthquakes for the last 110 years

The collision of the Indian and Eurasian plates in the north, as well as subduction tectonics (when one edge of a plate is forced below the edge of another) along with the Indo-Myanmar range (IMR) in the east, are responsible for the high seismicity in the North-Eastern states [2].

Seismologists have classified 59 percent of India’s landmass as being prone to earthquakes of various magnitudes — 11 percent in very high-risk zone V, 18 percent in high-risk zone IV, and 30 percent in moderate risk zone III [3].

4. India: Can we do better?

Fig 9: The earthquake-resilient building

Now that with the help of data, we know which areas are likely to witness the earthquake along with their return period, the following things can be done:

1. The budget for natural disasters can be allocated per the seismic zone level. The higher the vulnerability, the more the government should be prepared.
2. In his Budget Speech (GOI 2015; The Hindu 2015), the Finance Minister announced housing for all by 2022 with basic facilities like tap water, toilets, and connectivity. With these basic facilities, housing in high seismic zone areas can include ‘safety’ as a basic necessity.
3. The government has to build a comprehensive earthquake mitigation plan. This would entail, among other things, the construction of robust infrastructure. The Delhi Metro pillar, for example, can readily withstand a magnitude nine earthquake. The GOI can opt for a private-public partnership (PPP) to build better infrastructure.
4. More Rapid Action Forces (RAF) and disaster monitoring centers can be established in the medium to high seismic zone areas so that, in the event of an earthquake, the situation can be dealt with quickly.
5. Mandatory earthquake preparedness classes should be implemented into Zone-IV and V school curricula. The public must be aware of what to do in an emergency.
6. The Indian zone map’s seismic zones IV and V include some of India’s most densely inhabited areas, including Delhi, the world’s second-most populous city. In addition to being densely populated, Delhi’s population is expected to increase from 25 million to 36 million by 2030, making it even more vulnerable (United Nations 2014). With the increase in the number of residents, the chances of more fatalities become high. The government should tighten the building standards and make environmental impact assessments necessary before initiating mining or dam construction. Also, the population density of some high seismic zone areas should not be allowed to go up further or at least be well monitored.
7. Construction of water discharge tunnel in Andaman and Nicobar island. For instance, Tokyo’s Water Discharge Tunnel collects floodwaters caused by natural disasters like cyclones and tsunamis and safely redistributes the water into the Edo River. If the area is hit by an earthquake and triggers a tsunami, the city should be spared any major flooding [4].

References

1. https://pubs.usgs.gov/gip/dynamic/tectonic.html
2. https://blog.studyiq.com/why-north-east-india-is-prone-to-earthquakes-burning-issues-free-pdf-download/#:~:text=The%20high%20seismicity%20in%20the,(IMR)%20in%20the%20east.
3. https://nidm.gov.in/safety_earthquake.asp
4. https://theculturetrip.com/asia/japan/articles/8-ways-japan-prepares-for-earthquakes/