Interactive Example¶

In [1]:

from gradio_client import Client
import obspy
import numpy as np
import json
import pandas as pd
import matplotlib.pyplot as plt
import json

from gradio_client import Client import obspy import numpy as np import json import pandas as pd import matplotlib.pyplot as plt import json

/Users/weiqiang/.local/miniconda3/envs/gradio/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

2. Prepare seismic waveforms¶

Find more details in obspy's tutorials:

FDSN web service client for ObsPy

Mass Downloader for FDSN Compliant Web Services

In [2]:

import obspy
stream = obspy.read()
stream.plot();

import obspy stream = obspy.read() stream.plot();

In [3]:

stream.write("data.mseed", format="MSEED")

stream.write("data.mseed", format="MSEED")

In [4]:

## Extract 3-component data
stream = stream.sort()
assert(len(stream) == 3)
data = []
for trace in stream:
    data.append(trace.data)
data = np.array(data).T
assert(data.shape[-1] == 3)

data_id = stream[0].get_id()[:-1]
timestamp = stream[0].stats.starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]

## Extract 3-component data stream = stream.sort() assert(len(stream) == 3) data = [] for trace in stream: data.append(trace.data) data = np.array(data).T assert(data.shape[-1] == 3) data_id = stream[0].get_id()[:-1] timestamp = stream[0].stats.starttime.datetime.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]

3. Predict P/S-phase picks using PhaseNet¶

In [5]:

model = "ai4eps/phasenet"
# model = "http://127.0.0.1:7860"
client = Client(model, serialize=True)
_, picks_csv, picks_json = client.predict(["data.mseed"], json.dumps(data.tolist()), json.dumps([data_id]))

model = "ai4eps/phasenet" # model = "http://127.0.0.1:7860" client = Client(model, serialize=True) _, picks_csv, picks_json = client.predict(["data.mseed"], json.dumps(data.tolist()), json.dumps([data_id]))

Loaded as API: http://127.0.0.1:7860/ ✔

In [7]:

picks = pd.read_json(picks_json)
# picks = pd.read_csv(csv, parse_dates=["phase_time"])
# print(picks)

picks = pd.read_json(picks_json) # picks = pd.read_csv(csv, parse_dates=["phase_time"]) # print(picks)

In [8]:

fig, ax = plt.subplots(len(stream), 1, figsize=(10, 10))
for i, tr in enumerate(stream):
    ax[i].plot(tr.times(), tr.data, label=tr.stats.channel, c="k")
    for _, pick in picks.iterrows():
        c = "blue" if pick["phase_type"] == "P" else "red"
        label = pick["phase_type"] if i == 0 else None
        ax[i].axvline((pick["phase_time"]-tr.stats.starttime.datetime).total_seconds(), c=c, label=label, alpha=pick["phase_score"])
    ax[i].legend()

fig, ax = plt.subplots(len(stream), 1, figsize=(10, 10)) for i, tr in enumerate(stream): ax[i].plot(tr.times(), tr.data, label=tr.stats.channel, c="k") for _, pick in picks.iterrows(): c = "blue" if pick["phase_type"] == "P" else "red" label = pick["phase_type"] if i == 0 else None ax[i].axvline((pick["phase_time"]-tr.stats.starttime.datetime).total_seconds(), c=c, label=label, alpha=pick["phase_score"]) ax[i].legend()

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