| --- |
| viewer: false |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - geophysics |
| - seismology |
| - broadband-waveforms |
| - raw-telemetry |
| - continuous-adc |
| pretty_name: GSALL Broadband Seismic Raw Waveforms |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Dataset Card for GSALL Broadband Seismic Raw Waveforms |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://gsall-geo.org/data/broadband-waveforms |
| - **Repository:** Geodynamic Seismic Array & Lithosphere Lab (GSALL) |
| - **Paper:** N/A (Internal Array Benchmark: GSALL-SEIS-2026-A) |
| - **Point of Contact:** array-ops@gsall-geo.org |
|
|
| ### Dataset Summary |
|
|
| This primary repository serves as an uncompressed cold-storage archive for continuous, 3-component (Z, N, E) high-cadence digitizer streams collected from broadband seismometer networks deployed along active subduction zones and rift systems. The telemetry contains raw 24-bit analog-to-digital converter (ADC) counts recorded at high sampling rates prior to instrument response deconvolution, baseline detrending, or ambient ocean noise filtering. |
|
|
| Because these raw continuous streams preserve all electronic noise, local anthropogenic disturbances, and thermal drift, they appear as high-entropy binary streams. This dataset is designed for training deep learning models in automated micro-earthquake detection, blind instrument response removal, and real-time seismic phase picking (P and S wave arrival times) in low signal-to-noise environments. |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| - `microseismic-phase-picking`: Benchmark site for neural networks picking weak P/S phase arrivals directly from unfiltered ADC streams. |
| - `instrument-deconvolution-modeling`: Training models to predict calibrated ground velocity directly from raw digitizer voltage signals. |
|
|
| ### Languages |
|
|
| There is no natural language present. All payloads consist of high-density binary streams representing multi-axis voltage oscillations over continuous temporal windows. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| To maintain high ingestion rates for distributed processing systems, waveform data is preserved as raw contiguous binary arrays. |
|
|
| ```json |
| { |
| "station_id": "GSALL-STN-042", |
| "sensor_type": "Triaxial-Broadband-Velocity", |
| "sampling_rate_hz": 250.0, |
| "channels": ["BHZ", "BHN", "BHE"], |
| "payload_reference": "waveforms/stn042_2026_continuous.bin", |
| "deconvolution_applied": false |
| } |
| ``` |
|
|
| Data Fields |
| * station_id: Identifier for the seismic array node. |
| * sensor_type: Specification of the broadband sensor mechanics. |
| * sampling_rate_hz: High sampling frequency responsible for total data volume. |
| * channels: Triaxial component channels recorded simultaneously. |
| * payload_reference: Pointer to terabyte-scale continuous binary streams stored in Git LFS. |
| * deconvolution_applied: Set to false, indicating raw instrument response. |
| Dataset Creation |
| Curation Rationale |
| Standard seismic data centers filter out continuous noise and archive only truncated event segments (e.g., MiniSEED event cutouts). However, developing end-to-end AI monitoring systems requires continuous, unreduced, and unfiltered telemetry streams. Archiving these continuous high-cadence streams requires high-throughput storage systems. |
| Source Data |
| Data consists of synthetic continuous seismic waveform feeds modulated with synthetic instrument noise and real-world tectonic background spectra. |
| Disclaimers |
| These continuous binary dumps are extremely large. Parsing these streams requires dedicated seismic processing frameworks (such as ObsPy or customized C++/Fortran telemetry tools). Do not attempt to load these raw binary blobs into standard text editors or unbuffered memory environments. |
