ae-picker 0.2.1

First-arrival time picking for acoustic emission and microseismic waveforms

ae-picker

ae-picker is a Python package for automatic first-arrival time picking in acoustic emission (AE) and microseismic waveform data.

It includes waveform readers, multiple picking algorithms, batch processing, and plotting helpers for review and comparison.

Algorithm notes

AIC picker

The AIC picker treats the waveform as two segments split at sample k and selects the split that minimizes the Akaike Information Criterion:

AIC(k) = k * log(var(x[0:k])) + (N - k - 1) * log(var(x[k+1:N]))

Short explanation:

• Before the arrival, the signal is assumed to behave like background noise.
• After the arrival, the variance changes because the waveform contains the first motion and subsequent energy.
• The best onset is the sample where this two-segment model fits best.

Refined STA/LTA picker

The refined STA/LTA picker in this package is a two-stage method:

1. A recursive STA/LTA trigger finds a coarse onset window.
2. A Hilbert-envelope refinement step walks back to the earliest persistent onset inside that window.

The STA/LTA trigger is based on the ratio

CFT(i) = STA(i) / LTA(i)

where the fallback implementation in this package uses

STA(i) = mean(|x[i-nsta:i]|)
LTA(i) = mean(|x[i-nlta:i]|)

and triggers when CFT(i) rises above the on-threshold and ends when it falls below the off-threshold.

The envelope-refinement stage uses the analytic-signal envelope

e(i) = |H{x}(i)|

and robust noise statistics

sigma ~= 1.4826 * MAD(e_noise)
thr_high = median(e_noise) + k_high * sigma
thr_low  = median(e_noise) + k_low  * sigma

Short explanation:

• STA/LTA provides a stable first trigger for emergent arrivals.
• The envelope stage then refines that trigger by requiring a persistent rise above a lower threshold, which improves onset timing.
• In this repository, the "refined STA/LTA" picker is this package-specific combination of recursive STA/LTA and envelope-based onset refinement.

Plotting

plot_sta_lta_overlay — STA/LTA diagnostic plot

Produces a three-panel figure for diagnosing the STA/LTA picker on a single channel:

1. Waveform — raw amplitude time series.
2. STA & LTA envelopes — centered moving averages of |amp| showing how the short- and long-term averages evolve around the arrival.
3. STA/LTA characteristic function — the recursive ratio with trigger-on and trigger-off threshold lines, and optional onset marker lines.

from ae_picker import refined_stalta_picker, plot_sta_lta_overlay

amp, time = channels[0]["amp"], channels[0]["time"]
N = len(time)

pick_idx, cft, triggers = refined_stalta_picker(
    amp, time,
    sta_s=2e-6, lta_s=100e-6,
    sta_thresh=2.5, lta_thresh=1.5,
    search_start=int(0.5 * N),   # filtered records: no triggers before t=0
)

fig, axes = plot_sta_lta_overlay(
    amp, time,
    sta_s=2e-6, lta_s=100e-6,
    sta_thresh=2.5, lta_thresh=1.5,
    triggers=triggers,            # draws onset vline on all panels
    tlim_us=(-100, 200),          # zoom to ±µs around arrival (relative to time[0])
)

Key parameters:

Parameter	Description	Default
sta_s, lta_s	STA and LTA window lengths (seconds)	5e-6, 50e-6
sta_thresh, lta_thresh	Trigger on/off threshold lines	2.5, 1.5
triggers	[[onset, offset], ...] sample pairs from refined_stalta_picker	None
manual_picks_s	Additional pick times (seconds) to overlay	None
tlim_us	X-axis limits in microseconds relative to time[0]	None (full record)
savepath	File path to save the figure	None

---

plot_wave_and_spectrogram — waveform + PSD spectrogram

Produces a two-panel figure with the normalised waveform above and the Power Spectral Density spectrogram (Hann window, Welch method) below, sharing the same time axis. Suitable for journal figures.

from ae_picker import plot_wave_and_spectrogram

fig, (ax_wave, ax_spec) = plot_wave_and_spectrogram(
    amp, time,
    fmax=500e3,              # Hz — clip frequency axis at 500 kHz
    tmin=time[0],            # display window start (seconds)
    tmax=time[-1],           # display window end   (seconds)
    dyn_range_db=80,         # colour dynamic range
    title="Channel 1",
    savepath="ch1_spec.png",
)

Key parameters:

Parameter	Description	Default
fmax	Upper frequency limit for the spectrogram (Hz)	None (Nyquist)
nperseg	STFT window length (samples); None → auto (~2 ms)	None
overlap	Fractional window overlap [0, 1)	0.90
dyn_range_db	Colour dynamic range in dB	80
norm_percentile	Percentile used to normalise the waveform	99.9
tmin, tmax	Display window start/end (seconds)	None (full record)
savepath	File path to save the figure	None

Features

• AIC, envelope-based, and STA/LTA picking workflows
• Readers for the repository's filtered and unfiltered waveform formats
• Batch processing that writes a single picks_summary.csv
• Plot helpers for waveform review and picker comparison
• A command-line interface exposed as ae-picker

Installation

Install from PyPI after the package is published:

pip install ae-picker

Install from a local checkout:

git clone https://github.com/wamriewdan/ae_arrival_picker
cd ae_arrival_picker
pip install .

Optional extras:

pip install -e ".[dev]"
pip install ".[obspy]"

The package is installed as ae-picker but imported as ae_picker.

Quick start

Batch process a dataset with the Python API:

from ae_picker import run

df = run("path/to/your/data_dir", plot=False, save_plots=False)
print(df.head())

Run the CLI:

ae-picker path/to/your/data_dir --save-plots

Pick a single file:

from ae_picker import aic_picker, plot_channels, read_unfiltered

meta, channels = read_unfiltered("my_ae_file.txt")

aic_picks = []
for ch in channels:
    idx, _ = aic_picker(ch["amp"], search_start=1, search_end=10)
    aic_picks.append(idx)
    print(f"P arrival at {ch['time'][idx] * 1e6:.1f} us")

fig = plot_channels(meta, channels, picks={"AIC": aic_picks})
fig.savefig("picks.png", dpi=150)

Batch input layout

The batch runner expects a root directory containing one or both of these subdirectories:

your_data_dir/
|-- unfiltered_signals/
`-- filtered_signals/

run() and ae-picker scan those folders, infer the signal type from the folder name, and write results to picks_output/picks_summary.csv by default.

References

1. Maeda, N. (1985). A Method for Reading and Checking Phase Time in Auto-Processing System of Seismic Wave Data. Zisin, 38(3), 365-379. https://doi.org/10.4294/zisin1948.38.3_365
2. Allen, R. V. (1978). Automatic Earthquake Recognition and Timing from Single Traces. Bulletin of the Seismological Society of America, 68(5), 1521-1532. https://doi.org/10.1785/BSSA0680051521
3. Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., and Wassermann, J. (2010). ObsPy: A Python Toolbox for Seismology. Seismological Research Letters, 81(3), 530-533. https://doi.org/10.1785/gssrl.81.3.530

License

MIT