Global seismicity map — one year of M5+¶

Map a full year of global M5+ earthquakes from USGS, with marker area scaled by magnitude and colour by hypocentre depth — the classic view that traces the plate boundaries.

Setup¶

The imports up front: Path for the scratch output directory, matplotlib for the map, and the unified EarthLens entry point. FDSN is a vector backend, so download() returns a GeoDataFrame of events.

In [ ]:

from pathlib import Path

import matplotlib.pyplot as plt

from earthlens import EarthLens

OUT_DIR = Path('fdsn_output')
OUT_DIR.mkdir(exist_ok=True)

from pathlib import Path import matplotlib.pyplot as plt from earthlens import EarthLens OUT_DIR = Path('fdsn_output') OUT_DIR.mkdir(exist_ok=True)

Query one year of global M5+ events¶

First build the request — source, catalogue (variables), the 2023 date window, a whole-globe bounding box, and a magnitude floor. Keeping construction on its own line makes the parameters easy to read and tweak.

In [ ]:

quake = EarthLens(
    variables=['USGS'],
    data_source='fdsn',
    start='2023-01-01',
    end='2023-12-31',
    aoi=[-180, -90, 180, 90],
    min_magnitude=5.0,
    path=str(OUT_DIR),
)

quake = EarthLens( variables=['USGS'], data_source='fdsn', start='2023-01-01', end='2023-12-31', aoi=[-180, -90, 180, 90], min_magnitude=5.0, path=str(OUT_DIR), )

The download() call performs the live FDSN query and returns the events into memory. It is wrapped in a try/except so the notebook degrades gracefully (rather than erroring) when run offline.

In [ ]:

events = None
try:
    events = quake.download()
    print(f'{len(events)} M5+ events in 2023')
except Exception as exc:
    print(f'live FDSN query skipped (offline?): {type(exc).__name__}: {exc}')

events = None try: events = quake.download() print(f'{len(events)} M5+ events in 2023') except Exception as exc: print(f'live FDSN query skipped (offline?): {type(exc).__name__}: {exc}')

Magnitude-scaled, depth-coloured scatter¶

Plot each epicentre with marker area growing as 2 ** magnitude and colour set by hypocentre depth, so the deep subduction-zone events stand out from the shallow ridge seismicity. The plot is guarded on a successful download.

In [ ]:

if events is not None and len(events):
    fig, ax = plt.subplots(figsize=(12, 6))
    sc = ax.scatter(
        events['longitude'],
        events['latitude'],
        s=2 ** events['magnitude'],
        c=events['depth_km'],
        cmap='viridis_r',
        alpha=0.6,
        edgecolor='none',
    )
    fig.colorbar(sc, ax=ax, label='depth (km)')
    ax.set_title('Global M5+ seismicity, 2023 (marker area ~ magnitude)')
    ax.set_xlabel('longitude')
    ax.set_ylabel('latitude')
    ax.set_xlim(-180, 180)
    ax.set_ylim(-90, 90)

if events is not None and len(events): fig, ax = plt.subplots(figsize=(12, 6)) sc = ax.scatter( events['longitude'], events['latitude'], s=2 ** events['magnitude'], c=events['depth_km'], cmap='viridis_r', alpha=0.6, edgecolor='none', ) fig.colorbar(sc, ax=ax, label='depth (km)') ax.set_title('Global M5+ seismicity, 2023 (marker area ~ magnitude)') ax.set_xlabel('longitude') ax.set_ylabel('latitude') ax.set_xlim(-180, 180) ax.set_ylim(-90, 90)