Skip to content

Exporting Results

Overview

Any snapshot can be exported as a structured JSON document using aimbat snapshot results. The output contains everything needed to identify the snapshot, the source event, and the per-station arrival-time picks — including quality metrics from ICCS and, if MCCC has been run, formal timing standard errors.

Exporting does not require MCCC to have been run. ICCS picks alone are sufficient for many workflows. MCCC adds formal per-station timing uncertainties and is worth running when those are required, but the output format is the same either way — MCCC fields are simply null in snapshots that pre-date any MCCC run.

Running the export

aimbat snapshot results <SNAPSHOT_ID>                    # print to stdout
aimbat snapshot results <SNAPSHOT_ID> --output out.json  # save to file

snapshot results <SNAPSHOT_ID>
snapshot results <SNAPSHOT_ID> --output out.json

Press Enter on a snapshot row in the Snapshots tab and choose Save results to JSON. A file-picker dialog opens; the suggested filename is results_<short_id>.json.

Pass --alias to use camelCase field names (e.g. snapshotId, eventTime, mcccRmse).

Output format

The output is a JSON object with two parts: an envelope containing event-level information, and a seismograms list with one entry per station.

{
  "snapshot_id": "3f1a2b4c-...",
  "snapshot_time": "2025-03-01T14:22:00Z",
  "snapshot_comment": "post-MCCC final",
  "event_id": "6a4a...",
  "event_time": "2024-11-15T08:43:12Z",
  "event_latitude": 37.2,
  "event_longitude": 141.8,
  "event_depth_km": 35.0,
  "mccc_rmse": 0.021,
  "seismograms": [
    {
      "seismogram_id": "...",
      "name": "II.MAJO",
      "channel": "BHZ",
      "select": true,
      "flip": false,
      "t1": "2024-11-15T08:43:47.312Z",
      "iccs_cc": 0.94,
      "mccc_cc_mean": 0.91,
      "mccc_cc_std": 0.03,
      "mccc_error": 0.018
    }
  ]
}

Envelope fields

Event-level information appears once in the envelope, rather than being repeated on every seismogram row.

Field Type Always present Description
snapshot_id UUID string Yes Snapshot this export came from
snapshot_time ISO 8601 Yes When the snapshot was taken
snapshot_comment string | null Yes Optional label from snapshot creation
event_id UUID string Yes Event this snapshot belongs to
event_time ISO 8601 Yes Seismic event origin time
event_latitude float Yes Event latitude (degrees)
event_longitude float Yes Event longitude (degrees)
event_depth_km float | null Yes Event depth in km; null if not recorded
mccc_rmse float | null Yes Global MCCC RMSE (seconds); null if MCCC not run
seismograms array Yes Per-seismogram entries (see below)

Per-seismogram fields

Field Type Always present Description
seismogram_id UUID string Yes Seismogram record identifier
name string Yes Station name in NETWORK.NAME format
channel string Yes Channel code (e.g. BHZ)
select bool Yes Selection state at snapshot time
flip bool Yes Whether polarity was flipped at snapshot time
t1 ISO 8601 Yes Frozen absolute arrival-time pick
iccs_cc float | null Yes Correlation coefficient with ICCS stack
mccc_cc_mean float | null Yes Mean pairwise MCCC correlation coefficient
mccc_cc_std float | null Yes Std of pairwise MCCC correlation coefficients
mccc_error float | null Yes Formal timing standard error from MCCC (seconds)

iccs_cc is null for snapshots taken before the event was first opened in AIMBAT. All MCCC fields are null for snapshots taken before MCCC was run.

Working with the output

Filtering with jq

Extract only selected seismograms:

aimbat snapshot results <SNAPSHOT_ID> | \
  jq '[.seismograms[] | select(.select == true)]'

Extract stations where MCCC timing error is below 0.05 s:

aimbat snapshot results <SNAPSHOT_ID> | \
  jq '[.seismograms[] | select(.mccc_error != null and .mccc_error < 0.05)]'

Export station names and pick times as CSV:

aimbat snapshot results <SNAPSHOT_ID> | \
  jq -r '.seismograms[] | [.name, .t1] | @csv'

Python

import json

with open("results.json") as f:
    data = json.load(f)

print(f"Event: {data['event_time']}  ({data['event_latitude']}, {data['event_longitude']})")
print(f"MCCC RMSE: {data['mccc_rmse']} s")

for seis in data["seismograms"]:
    if seis["select"]:
        print(f"  {seis['name']:12s}  t1={seis['t1']}  err={seis['mccc_error']}")

ICCS vs MCCC picks

Picks exported from an ICCS-only snapshot have t1 values refined by the iterative stack alignment. Picks from a post-MCCC snapshot have t1 values replaced by the least-squares pairwise solution — slightly different in value, with the addition of formal standard errors in mccc_error.

The MCCC-derived t1 values are generally preferred for applications that require formal uncertainties. For applications where only relative picks are needed and uncertainties are not, an ICCS-only snapshot is sufficient.

See Aligning with ICCS and MCCC Alignment for a full description of what each algorithm produces and when to use each one.