Contents
Data Screening
Load and review the data, check that dates are loaded properly and filter data down to the events in La Palma only. The dataset itself contains all recent IGN earthquake data for Spanish territory.
import pandas as pd
import matplotlib.pyplot as pltdf = pd.read_csv('../data/catalogoComunSV_1663233588717.csv', sep=';')
df.columns = ['Event', 'Date', 'Time', 'Latitude', 'Longitude', 'Depth(km)', 'Intensity','Magnitude', 'Type Mag', 'Location']
df['Time'] = df['Time'].str.strip()
df['Date'] = df['Date'].str.strip()
df['DateTime'] = (df['Date'] + ' ' + df['Time']).apply(pd.to_datetime, format='%d/%m/%Y %H:%M:%S')
df['Timestamp'] = pd.to_numeric(pd.to_datetime(df['DateTime']))
df['Date'] = pd.to_datetime(df['Date'], format="%d/%m/%Y")
df = df.sort_values(by=['DateTime'], ascending=True)
df.head() Event Date Time Latitude Longitude Depth(km) \
0 es2017aaaga 2017-01-01 00:48:55 36.6765 -11.2736 0.0
1 es2017aaakn 2017-01-01 01:28:17 28.1119 -16.2225 21.0
2 es2017aaang 2017-01-01 01:49:08 42.0648 -7.8471 22.0
3 es2017aabdd 2017-01-01 02:36:20 38.3826 -9.3767 11.0
4 es2017aabkh 2017-01-01 03:35:03 36.2492 -7.8227 13.0
Intensity Magnitude Type Mag Location \
0 2.8 6 SW CABO DE SAN VICENTE
1 0.9 4 ATLÁNTICO-CANARIAS
2 2.0 4 SW RAIRIZ DE VEIGA.OU
3 2.2 4 ATLÁNTICO-PORTUGAL
4 1.9 4 GOLFO DE CÁDIZ
DateTime Timestamp
0 2017-01-01 00:48:55 1483231735000000000
1 2017-01-01 01:28:17 1483234097000000000
2 2017-01-01 01:49:08 1483235348000000000
3 2017-01-01 02:36:20 1483238180000000000
4 2017-01-01 03:35:03 1483241703000000000 df.describe() Latitude Longitude Depth(km) Magnitude Type Mag \
count 58036.000000 58036.000000 58036.000000 58036.000000 58036.000000
mean 34.806854 -8.356855 11.450831 1.881846 4.031153
std 5.263327 7.119341 11.111965 0.763577 0.291332
min 26.349300 -19.930400 0.000000 -0.500000 3.000000
25% 28.563800 -16.672325 3.000000 1.400000 4.000000
50% 35.989200 -4.785250 10.000000 1.800000 4.000000
75% 37.890700 -3.234500 14.300000 2.400000 4.000000
max 44.985000 5.996600 115.000000 6.000000 6.000000
Timestamp
count 5.803600e+04
mean 1.596182e+18
std 4.954865e+16
min 1.483232e+18
25% 1.559327e+18
50% 1.612319e+18
75% 1.635568e+18
max 1.663232e+18 df.plot.scatter(x="Longitude", y="Latitude", figsize=(12,12), grid="on");
Filter down to La Palma events only¶
Also some rows seem to be missing depth measurements, (see differences in the count for columns above) - take only rows with valid depths
df = df[(df['Latitude'] < 28.7) & (df['Latitude'] > 28.3) & (df['Longitude'] < -17.7) & (df['Longitude'] > -18)]
df = df[df['Depth(km)'].notna()]
df.describe() Latitude Longitude Depth(km) Magnitude Type Mag \
count 11347.000000 11347.000000 11347.000000 11347.000000 11347.0
mean 28.568893 -17.844062 14.089345 2.432423 4.0
std 0.021899 0.027345 8.624787 0.705282 0.0
min 28.310200 -17.995800 0.000000 0.200000 4.0
25% 28.558000 -17.854100 10.100000 1.900000 4.0
50% 28.564000 -17.838700 11.300000 2.600000 4.0
75% 28.574300 -17.829700 13.900000 2.900000 4.0
max 28.697800 -17.705700 46.900000 5.100000 4.0
Timestamp
count 1.134700e+04
mean 1.633359e+18
std 2.007970e+16
min 1.489103e+18
25% 1.633777e+18
50% 1.635671e+18
75% 1.638294e+18
max 1.663128e+18 plt.figure(figsize=(20,6))
plt.subplot(1,3,1)
df["Latitude"].plot.hist(bins=20, density=True)
df["Latitude"].plot.kde()
plt.subplot(1,3,2)
df["Longitude"].plot.hist(bins=20, density=True)
df["Longitude"].plot.kde()
plt.subplot(1,3,3)
df["Depth(km)"].plot.hist(bins=50, density=True)
_=df["Depth(km)"].plot.kde()
Spatial Plot¶
Scatter plot the spatial locations of events
from matplotlib import colormaps
cmap = colormaps['viridis_r']
ax = df.plot.scatter(x="Longitude", y="Latitude", s=40-df["Depth(km)"], c=df["Magnitude"], figsize=(12,10), grid="on", cmap=cmap)/opt/anaconda3/envs/py3/lib/python3.9/site-packages/matplotlib/collections.py:967: RuntimeWarning: invalid value encountered in sqrt
scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor
Timeline Plot¶
Scatter plot the event time series and look for any quantization issues. Have times & dates been loaded correctly?
ax = df.plot.scatter(x='DateTime', y='Depth(km)', figsize=(20,8))
ax.set_ylim(50,0);
df['Swarm'] = 0
df.loc[(df['Date'] >= '2017-10-01') & (df['Date'] <= '2017-10-31'), 'Swarm'] = 1
df.loc[(df['Date'] >= '2019-02-01') & (df['Date'] <= '2019-02-28'), 'Swarm'] = 2
df.loc[(df['Date'] >= '2020-07-21') & (df['Date'] <= '2020-08-05'), 'Swarm'] = 3
df.loc[(df['Date'] >= '2020-10-04') & (df['Date'] <= '2020-10-10'), 'Swarm'] = 4
df.loc[(df['Date'] >= '2020-10-11') & (df['Date'] <= '2020-10-22'), 'Swarm'] = 5
df.loc[(df['Date'] >= '2020-11-15') & (df['Date'] <= '2020-11-29'), 'Swarm'] = 6
df.loc[(df['Date'] >= '2020-12-10') & (df['Date'] <= '2020-12-29'), 'Swarm'] = 7
df.loc[(df['Date'] >= '2020-12-10') & (df['Date'] <= '2020-12-31'), 'Swarm'] = 8
df.loc[(df['Date'] >= '2021-01-15') & (df['Date'] <= '2021-02-07'), 'Swarm'] = 9
df.loc[(df['Date'] >= '2021-06-01') & (df['Date'] <= '2021-06-30'), 'Swarm'] = 10
df.loc[(df['Date'] >= '2021-07-01'), 'Swarm'] = None
df['Phase'] = 0;
df.loc[(df['Date'] >= '2021-09-11') & (df['Date'] <= '2021-09-30'), 'Phase'] = 1
df.loc[(df['Date'] >= '2021-10-01') & (df['Date'] <= '2021-11-30'), 'Phase'] = 2
df.loc[(df['Date'] >= '2021-12-01') & (df['Date'] <= '2021-12-31'), 'Phase'] = 3
df.loc[(df['Date'] >= '2021-12-31'), 'Phase'] = 4df.to_csv("../data/lapalma_ign.csv", index=False)
