Data Screening

Screening and filtering data from the IGN catalog down to the relevant events for La Palma

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.

In [2]:

import pandas as pd
import matplotlib.pyplot as plt

In [3]:

df = 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)	Intensity	Magnitude	Type Mag	Location	DateTime	Timestamp
0	es2017aaaga	2017-01-01	00:48:55	36.6765	-11.2736	0.0		2.8	6	SW CABO DE SAN VICENTE	2017-01-01 00:48:55	1483231735000000000
1	es2017aaakn	2017-01-01	01:28:17	28.1119	-16.2225	21.0		0.9	4	ATLÁNTICO-CANARIAS	2017-01-01 01:28:17	1483234097000000000
2	es2017aaang	2017-01-01	01:49:08	42.0648	-7.8471	22.0		2.0	4	SW RAIRIZ DE VEIGA.OU	2017-01-01 01:49:08	1483235348000000000
3	es2017aabdd	2017-01-01	02:36:20	38.3826	-9.3767	11.0		2.2	4	ATLÁNTICO-PORTUGAL	2017-01-01 02:36:20	1483238180000000000
4	es2017aabkh	2017-01-01	03:35:03	36.2492	-7.8227	13.0		1.9	4	GOLFO DE CÁDIZ	2017-01-01 03:35:03	1483241703000000000

In [4]:

df.describe()

	Latitude	Longitude	Depth(km)	Magnitude	Type Mag	Timestamp
count	58036.000000	58036.000000	58036.000000	58036.000000	58036.000000	5.803600e+04
mean	34.806854	-8.356855	11.450831	1.881846	4.031153	1.596182e+18
std	5.263327	7.119341	11.111965	0.763577	0.291332	4.954865e+16
min	26.349300	-19.930400	0.000000	-0.500000	3.000000	1.483232e+18
25%	28.563800	-16.672325	3.000000	1.400000	4.000000	1.559327e+18
50%	35.989200	-4.785250	10.000000	1.800000	4.000000	1.612319e+18
75%	37.890700	-3.234500	14.300000	2.400000	4.000000	1.635568e+18
max	44.985000	5.996600	115.000000	6.000000	6.000000	1.663232e+18

In [5]:

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

In [6]:

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	Timestamp
count	11347.000000	11347.000000	11347.000000	11347.000000	11347.0	1.134700e+04
mean	28.568893	-17.844062	14.089345	2.432423	4.0	1.633359e+18
std	0.021899	0.027345	8.624787	0.705282	0.0	2.007970e+16
min	28.310200	-17.995800	0.000000	0.200000	4.0	1.489103e+18
25%	28.558000	-17.854100	10.100000	1.900000	4.0	1.633777e+18
50%	28.564000	-17.838700	11.300000	2.600000	4.0	1.635671e+18
75%	28.574300	-17.829700	13.900000	2.900000	4.0	1.638294e+18
max	28.697800	-17.705700	46.900000	5.100000	4.0	1.663128e+18

In [7]:

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

In [14]:

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)
colorbar = ax.collections[0].colorbar
colorbar.set_label("Magnitude")

plt.show()

Figure 1: Locations of earthquakes on La Palma since 2017.

Timeline Plot

Scatter plot the event time series and look for any quantization issues. Have times & dates been loaded correctly?

In [10]:

ax = df.plot.scatter(x='DateTime', y='Depth(km)', figsize=(20,8))
ax.set_ylim(50,0);

In [11]:

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'] = 4

In [11]:

df.to_csv("../data/lapalma_ign.csv", index=False)