This article provides a statistical visualization of what ** moon positions and phases
** past earthquakes have occurred.
It was Schuster (1897) [1] more than 100 years ago who first discussed the effects of the tides from the moon. After that, regarding the relationship between earth tides and earthquakes, there are studies such as Tanaka (2012) [4] and Ide et al. (2016) [5] as studies targeting specific epicenters (groups). In addition, Tsuruoka and Ohtake (1995) [2] discussed the relationship with earth tides for earthquakes that occurred in the world, but it is difficult for the general public in non-academic fields. Therefore, after all, the relationship between the moon and the earthquake is not very clear to many people in the world.
Therefore, the purpose of this report is to make it possible to recall the probability of an earthquake by looking at the moon and the sun.
2.1 Data Source
The earthquake catalog to be visualized was (probably) provided by the United States Geological Survey (USGS), Significant Earthquakes, 1965-2016. earthquake-database) is used. This data is only cataloged with a magnitude M of 5.5 or higher.
Not only USGS but also Japan Meteorological Agency and Disaster Prevention Research Institute publish past earthquake catalogs. However, it is not a simple list and is very difficult to use. Therefore, the above data is used.
Originally, earthquakes are caused by plate tectonics at each point, so there are always regional characteristics. This time, since we targeted seismic activity over a wide area of the earth, we did not consider any regional characteristics. Therefore, please be aware that the content may make you feel uncomfortable when viewed from a professional researcher.
2.2 Data Overview
First, read the data and check the first few lines. Use python for analysis and visualization.
import math
import datetime
import os, sys
import numpy as np
import pandas as pd
DATA_DIR = "/kaggle/input/earthquake-database/" + os.sep
# read data file
earthquake = pd.read_csv(
DATA_DIR+"database.csv",
sep=",",
parse_dates={'datetime':['Date', 'Time']},
encoding="utf-8",
error_bad_lines=False,
)
# treating irregular data
for idx in [3378,7512,20650]:
earthquake.at[idx, "datetime"] = earthquake.at[idx, "datetime"].split(" ")[0]
earthquake["datetime"] = pd.to_datetime(earthquake["datetime"], utc=True)
earthquake.set_index(["datetime"], inplace=True)
earthquake.head()
""" Output
Latitude Longitude Type Depth Depth Error Depth Seismic Stations Magnitude Magnitude Type Magnitude Error Magnitude Seismic Stations ... Horizontal Error Root Mean Square ID Source Location Source Magnitude Source Status inner moon_dist sun_dist
datetime
1965-01-02 13:44:18 19.246 145.616 Earthquake 131.6 NaN NaN 6.0 MW NaN NaN ... NaN NaN ISCGEM860706 ISCGEM ISCGEM ISCGEM Automatic 0.364867 4.128686e+08 1.471089e+11
1965-01-04 11:29:49 1.863 127.352 Earthquake 80.0 NaN NaN 5.8 MW NaN NaN ... NaN NaN ISCGEM860737 ISCGEM ISCGEM ISCGEM Automatic -0.996429 4.065270e+08 1.471063e+11
1965-01-05 18:05:58 -20.579 -173.972 Earthquake 20.0 NaN NaN 6.2 MW NaN NaN ... NaN NaN ISCGEM860762 ISCGEM ISCGEM ISCGEM Automatic 0.947831 4.052391e+08 1.471037e+11
1965-01-08 18:49:43 -59.076 -23.557 Earthquake 15.0 NaN NaN 5.8 MW NaN NaN ... NaN NaN ISCGEM860856 ISCGEM ISCGEM ISCGEM Automatic 0.248578 3.896846e+08 1.471106e+11
1965-01-09 13:32:50 11.938 126.427 Earthquake 15.0 NaN NaN 5.8 MW NaN NaN ... NaN NaN ISCGEM860890 ISCGEM ISCGEM ISCGEM Automatic -0.988605 3.882323e+08 1.471218e+11
"""
The data was like this.
2.3 World Map
Check the epicenter distribution of the earthquake.
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
%matplotlib inline
ti = "Map of Earthquake's epicenter duaring 1965-2016"
fig = plt.figure(figsize=(18, 18), dpi=96)
plt.rcParams["font.size"] = 24
m = Basemap(projection='robin', lat_0=0, lon_0=-170, resolution='c')
m.drawcoastlines()
m.fillcontinents(color='#606060', zorder = 1)
for i in range(5,10,1):
#print(i)
tmp = earthquake[(earthquake["Magnitude"]>=i)&(earthquake["Magnitude"]<i+1)&(earthquake["Type"]=="Earthquake")]
x, y = m(list(tmp.Longitude), list(tmp.Latitude))
points = m.plot(x, y, "o", label=f"Mag.: {i}.x", markersize=0.02*float(i)**3.2, alpha=0.55+0.1*float(i-5))
plt.title(f"{ti}", fontsize=22)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0, fontsize=18)
plt.show()
It is clear that earthquakes are concentrated in the Pacific Ring of Fire. I mean, Japan is dangerous.
2.4 Distribution of the Depth
Check the depth distribution of the epicenter.
ti = "Distribution of Earthquake's Depth"
plt.figure(figsize=(20, 12), dpi=96)
plt.rcParams["font.size"] = 24
for i in range(5,9,1):
#print(i)
tmp = earthquake[(earthquake["Magnitude"]>=i)&(earthquake["Magnitude"]<i+1)&(earthquake["Type"]=="Earthquake")]
plt.hist(tmp["Depth"], bins=50, density=True, histtype='step', linewidth=2.5, label=f"Mag.: {i}.x")
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0, fontsize=18)
plt.xlabel("Depth, km")
plt.ylabel("Count of Earthquake (Normarized at Total surface=1)")
plt.title(f"{ti}")
plt.show()
Most earthquakes
You can see that. Although it is not clear from this graph alone, earthquakes with a depth of more than 550 km are probably not plate-boundary, but earthquakes that occurred inside the continental crust in the central part of the continent.
The seismic intensity of earthquakes caused by tidal stress and ocean tide is up to about 70-80km due to its mechanism. From now on, the data to be visualized will be the ones shallower than 80km.
earthquake = earthquake[earthquake["Depth"]<80]
earthquake = earthquake[earthquake["Type"]=="Earthquake"]
The tidal stress from the moon on the surface of the earth increases when:
Therefore, for each time in the acquired earthquake list
I will ask for.
3.1 Library
Use Astropy, which is a convenient library for calculating the position of celestial bodies.
3.2 Ephemeris for Astronomical position calculation
As an ephemeris for calculating the position of celestial bodies, JPL (Jet Propulsion Laboratory) published ephemeris Of these, use the latest DE432s. The DE432s are shorter (1950-2050) than the DE432, but have a smaller file size (~ 10MB) and are easier to handle. (Reference)
3.3 Obtaining of Phase Angle between the Sun and the Moon
Please see here for the detail.
Please see here for the detail.
The positional relationship between the moon and the sun at the time of the earthquake was investigated. As a result, the following was found in a large earthquake (M> 5.5) when viewed on the entire earth.
Furthermore, it was found that there are the following tendencies.
Originally, it is necessary to scrutinize whether or not the earthquake is really caused by the lunar tide, based on the characteristics of the epicenter of each earthquake. For example, there are studies such as Tanaka (2012) [4] and Ide et al. (2016) [5] as studies targeting specific epicenters. However, I entrust it to a professional researcher, and here I will only talk about statistical trends toward earthquakes in the world.
Needless to say, Japan is an earthquake-prone country. As a result, major disasters have frequently occurred in the past, including tsunamis and fires caused by earthquakes. Therefore, he has a great interest in earthquake prediction, and an organization called Earthquake Prediction Liaison Committee is operated under the initiative of the government.
However, most of the earthquakes are plate-destroying earthquakes, which account for the majority of all earthquakes, including fault-type earthquakes indirectly caused by the same plate tectonics (source is forgotten). In any case, both occur due to the sabotage of the Earth plate, and it is generally physically impossible to predict the future of sabotage in the first place. (It's not impossible under very special conditions, but it's unlikely that those special conditions will come naturally).
Therefore, ** "earthquake prediction is impossible in principle" **, but recently the government is shifting its focus to ** disaster mitigation ** instead of disaster prevention. From the perspective of disaster mitigation, if the probability of occurrence can be handled like a weather forecast, it may contribute to improving QOL (for example, since the probability of a major earthquake is high this week, let's go out again next week. Such).
Future Work
I would like to make similar visualizations for a specific area within a certain range. In some areas, the influence of the moon may be strong.
Reference