Data Screening

In [1]:

import pandas as pd
import matplotlib.pyplot as plt

Import la-palma.csv - a clean subset of catalogoComunSV_1663233588717.csv:

In [2]:

df = pd.read_csv('la-palma.csv')
df['DateTime'] = pd.to_datetime(df['DateTime'])
df = df.sort_values(by=['DateTime'], ascending=True)
df.head()

	Longitude	Latitude	Depth(km)	Magnitude	DateTime
11346	-17.8349	28.5346	26.0	1.6	2017-03-09 23:44:06+00:00
11343	-17.8459	28.5491	27.0	2.0	2017-03-10 00:16:10+00:00
11344	-17.8863	28.5008	20.0	2.1	2017-03-10 00:16:11+00:00
11345	-17.8657	28.5204	30.0	1.6	2017-03-10 03:20:26+00:00
11342	-17.7156	28.5985	0.0	1.6	2017-08-21 02:06:55+00:00

In [3]:

df.describe()

	Longitude	Latitude	Depth(km)	Magnitude
count	11347.000000	11347.000000	11347.000000	11347.000000
mean	-17.844062	28.568893	14.089345	2.432423
std	0.027345	0.021899	8.624787	0.705282
min	-17.995800	28.310200	0.000000	0.200000
25%	-17.854100	28.558000	10.100000	1.900000
50%	-17.838700	28.564000	11.300000	2.600000
75%	-17.829700	28.574300	13.900000	2.900000
max	-17.705700	28.697800	46.900000	5.100000

In [4]:

df.plot.scatter(x="Longitude", y="Latitude", figsize=(12,12), grid="on");

Filter

Also some rows seem to be missing depth measurements, (see differences in the count for columns above) - take only rows with valid depths

In [5]:

df = df[df['Depth(km)'].notna()]
df.describe()

	Longitude	Latitude	Depth(km)	Magnitude
count	11347.000000	11347.000000	11347.000000	11347.000000
mean	-17.844062	28.568893	14.089345	2.432423
std	0.027345	0.021899	8.624787	0.705282
min	-17.995800	28.310200	0.000000	0.200000
25%	-17.854100	28.558000	10.100000	1.900000
50%	-17.838700	28.564000	11.300000	2.600000
75%	-17.829700	28.574300	13.900000	2.900000
max	-17.705700	28.697800	46.900000	5.100000

In [6]:

plt.figure(figsize=(20,6))
plt.subplot(1,3,1)
df["Latitude"].plot.hist(bins=20, density=True)
plt.subplot(1,3,2)
df["Longitude"].plot.hist(bins=20, density=True)
plt.subplot(1,3,3)
df["Depth(km)"].plot.hist(bins=50, density=True)

<Axes: ylabel='Frequency'>

Spatial Plot

Scatter plot the spatial locations of events

In [7]:

#| label: fig-spatial-plot
#| fig-cap: Locations of earthquakes on La Palma since 2017.
#| fig-alt: A scatterplot of earthquake locations plotting latitude against longitude.
from matplotlib import colormaps
cmap = colormaps['viridis_r']
ax = df.plot.scatter(x="Longitude", y="Latitude", 
                     s=47-df["Depth(km)"], c=df["Magnitude"], 
                     figsize=(12,10), grid="on", cmap=cmap)
colorbar = ax.collections[0].colorbar
colorbar.set_label("Magnitude")

plt.show()

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?

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

Locations of earthquakes on La Palma since 2017.