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seisfetch

Cloud-first seismic waveform access for EarthScope, SCEDC, NCEDC, GeoNet, and fallback FDSN services.

seisfetch is built around one core path:

cloud archive / HTTP  ->  raw miniSEED bytes  ->  pymseed  ->  numpy arrays
                                                      |
                                                      +-> xarray
                                                      +-> zarr
                                                      +-> ObsPy
                                                      +-> Earth2Studio adapters

The design goal is simple:

  • prefer cloud-native archive access first
  • use FDSN second, as a fallback path
  • decode miniSEED straight into numpy without requiring ObsPy in the main pipeline
  • stay compatible with sparse sensor and Earth2Studio workflows

Overall Structure

The intended acquisition order is:

  1. s3_open for the anonymous archives: SCEDC, NCEDC, GeoNet, and the EarthScope Open Data networks (AK, II, IU, N4, PB, TA, UU, UW)
  2. s3_auth for every other EarthScope network, which sits behind a credentialed access point (it still reads the Open Data networks anonymously)
  3. fdsn only when the archive-backed path is unavailable or the network is not served from those buckets

At the package level, the main interfaces are:

  • SeisfetchClient.get_raw() -> raw miniSEED bytes
  • SeisfetchClient.get_numpy() -> TraceBundle of numpy arrays
  • SeisfetchClient.get_xarray() -> xarray.Dataset
  • SeisfetchClient.get_waveforms() -> ObsPy Stream
  • SeismicDataFrameSource / SeismicDataSource -> Earth2Studio adapters over data you already fetched
  • SeisfetchLiveSource -> time-indexed Earth2Studio DataSource that fetches on demand

Workflow

1. Cloud-first archive access

seisfetch routes by network code:

Network family Preferred source Backend
CI, other SCEDC-routed networks SCEDC open S3 s3_open
BK, other NCEDC-routed networks NCEDC open S3 s3_open
NZ GeoNet open S3 s3_open
AK, II, IU, N4, TA, UU, UW (EarthScope Open Data) EarthScope open S3 s3_open
every other EarthScope-routed network (US, CC, temporary codes, ...) EarthScope restricted S3 s3_auth

Networks served by both SCEDC and NCEDC (NC, NP, PB, ...) route to NCEDC; PB is also on EarthScope Open Data and can be read there with datacenter="earthscope". is_earthscope_open(net) and earthscope_tier(net) report which EarthScope tier a network is on, and so does seisfetch info --route NET.

Archive details:

Archive Bucket Region Auth
EarthScope Open Data earthscope-geophysical-data us-east-2 none (8 networks)
EarthScope restricted earthscope-mseed-v2-…-s3alias access point us-east-2 EarthScope SDK, role s3-miniseed-v2, credentials scoped per network
SCEDC scedc-pds us-west-2 none
NCEDC ncedc-pds us-west-2 none
GeoNet geonet-open-data ap-southeast-2 none

Notes:

  • SCEDC, NCEDC and GeoNet are per-channel archives, so you should pass channel=....
  • GeoNet channels always carry a numeric location code (10, 20, ...), so location= is required there — a blank location raises rather than silently missing data.
  • EarthScope stores station-day miniSEED objects (every channel of a station in one file); get_numpy() filters to the requested channel after parse.
  • The eight EarthScope Open Data networks need no credentials at all. Every other EarthScope network needs earthscope-sdk>=1.8 and the s3-miniseed-v2 role; credentials are scoped per network, and per network-year for temporary FDSN codes (digit/X/Y/Z prefixes). The legacy s3-miniseed role is retired.

2. FDSN second

Use backend="fdsn" when:

  • the desired network is not available from EarthScope / SCEDC / NCEDC / GeoNet S3
  • the archive-backed attempt fails and you want a fallback provider
  • you need a non-US provider such as GEOFON, INGV, ETH, ORFEUS, etc.

This keeps the default workflow archive-first instead of HTTP-first.

3. Decode directly to numpy

The central decode path is:

  • fetch raw miniSEED bytes
  • decode with pymseed
  • work in numpy immediately

ObsPy is not required for this path.

4. Convert only when needed

Once you have a TraceBundle, you can convert to:

  • xarray.Dataset for labeled arrays and ML/data workflows
  • zarr for chunked cloud/local persistence
  • ObsPy Stream for classical seismology tooling
  • Earth2Studio adapters for sparse sensor and digital twin/data assimilation workflows

Why This Package

Existing seismic client workflows often couple:

  • transport
  • decode
  • metadata
  • downstream object model

seisfetch separates those concerns:

  • transport: S3 or HTTP
  • decode: miniSEED -> numpy
  • output: xarray / zarr / ObsPy / Earth2Studio only when requested

That makes it useful for:

  • cloud-native waveform mining
  • sparse sensor ingestion
  • foundation-model training pipelines
  • Earth2Studio interoperability
  • data assimilation and digital twin workflows

ObsPy-Free, and What That Buys

ObsPy is an optional extra here, not a dependency of the data path. The evaluation behind that choice — including whether it changes the science — is written up in docs/noisepy-obspy-replacement-report.md, with every number traceable to a committed JSON under benchmarks/results/ or a test in tests/precision/.

seisfetch obspy stack
Parse an 11 MB Steim2 channel-day 21.4 ms 37.2 ms
Cold import 0.08 s 0.13 s
Parse peak memory 27.7 MB 52.0 MB
Installed footprint 80.4 MB 311.4 MB
arm64 Linux install wheels needs gcc

The footprint line is the cloud argument: AWS Lambda caps a layer at 250 MB, so the ObsPy stack does not fit and seisfetch does. ObsPy publishes no linux/aarch64 wheels, so on Graviton it compiles from source; seisfetch and pymseed install from wheels.

The science does not change

Identical archive bytes were pushed through (A) obspy.read plus NoisePy's own preprocess_raw and (B) seisfetch's parser plus the numpy/scipy ports in seisfetch.contrib.noisepy_adapter, then through NoisePy's own compute_fft and correlate. The pass criterion is bit-identity, not closeness:

Harness Result
Single-station CI.PASC, EN/EZ/NZ/ZZ at 40 sps max abs diff 0.0
Cross-station SCEDC x NCEDC x EarthScope at 20 sps max abs diff 0.0
dv/v stretching same grid cell on all pairs

The cross-station run matters because it exercises the Fourier-resample and sub-sample-alignment branches inside the chain. Tables, plots and the harnesses: benchmarks/RESULTS.md.

Scope note: this bit-identity result covers the preprocessing chain at rm_resp=NO. Response removal is validated separately, below.

Response removal without evalresp

Response removal was the one ObsPy capability the NoisePy migration still needed. seisfetch.contrib.response provides it in ~577 lines of numpy and stdlib xml.etree — no evalresp C library, no ObsPy, no lxml, and no scipy in that module. ObsPy has no pure-Python response evaluator (remove_response calls compiled evalresp), so every stage was re-derived and then checked against the compiled implementation:

Check Result
evaluate_response(mode="full") vs compiled evalresp — both CI.PASC epochs, VEL/ACC/DISP, 1 mHz–19.9 Hz max rel diff 1.6e-10
remove_response_np vs Trace.remove_response — real 6.9M-sample Tohoku day, water_level=60, pre_filt 6.6e-16 of peak
Same, CI.PASC demo hour in notebook 06 7.6e-16 of peak
Deconvolve that Tohoku day 1.9 s vs ObsPy 3.6 s

Two evaluation modes: mode="full" is evalresp-equivalent (all stages, analog and digital poles/zeros, FIR/Coefficients with DC normalization and the Decimation/CorrectionApplied phase advance). mode="paz" is the SACPZ shortcut — 0.7–1.3 % error below 4 Hz but ~23 % by 16 Hz, since the FIR anti-alias roll-off is unmodeled; don't use it above ~Nyquist/3.

Two deconvolution styles: remove_response_np ports ObsPy's water-level method for drop-in equivalence, and translate_resp_np follows SeisIO.jl's translation approach, taking stabilization from the target response's own roll-off instead of a water level.

Defective metadata fails loudly — zero or missing gains, degenerate normalization references, zero-sum FIR stages, polynomial and ResponseList stages all raise with the stage number named, never a silent NaN or unity gain. Not implemented, and raising rather than approximating: IIR Coefficients stages with denominators, polynomial (blockette-62) responses, ResponseList stages. Metadata is StationXML only; RESP and SACPZ files are not parsed.

Full derivation, the conditional-A0 finding about evalresp's normalization rule, and the SeisIO comparison: docs/response-removal-design.md. Tutorial: notebooks/05_response_removal.ipynb.

Quick Start

Open S3: SCEDC / NCEDC

from seisfetch import SeisfetchClient

client = SeisfetchClient(backend="s3_open")

bundle = client.get_numpy(
    "CI",
    "ABL",
    channel="BHZ",
    starttime="2024-01-15T00:00:00",
    endtime="2024-01-15T00:10:00",
)

print(bundle.ids)
arrays = bundle.to_dict()

Open S3: GeoNet (New Zealand)

NZ auto-routes to the GeoNet open-data bucket. GeoNet channels carry a numeric location code, so pass location=:

bundle = SeisfetchClient(backend="s3_open").get_numpy(
    "NZ",
    "WEL",
    location="10",
    channel="HHZ",
    starttime="2022-01-02T00:00:00",
    endtime="2022-01-02T00:10:00",
)

Open S3: EarthScope Open Data

AK, II, IU, N4, PB, TA, UU and UW are served anonymously from earthscope-geophysical-data. One object holds every channel of a station-day, so ask for the channel you want and the rest is dropped after parse:

bundle = SeisfetchClient(backend="s3_open").get_numpy(
    "IU",
    "ANMO",
    location="00",
    channel="BHZ",
    starttime="2024-01-15T00:00:00",
    endtime="2024-01-15T00:01:00",
)

EarthScope S3 (restricted networks, authenticated)

Every other EarthScope network needs earthscope-sdk>=1.8 and an account with the s3-miniseed-v2 role. See EarthScope Credentials. s3_auth still reads the Open Data networks anonymously, so one client covers both tiers:

client = SeisfetchClient(backend="s3_auth")

bundle = client.get_numpy(
    "US",
    "NEW",
    location="00",
    channel="BHZ",
    starttime="2024-01-15T00:00:00",
    endtime="2024-01-15T00:01:00",
)

EarthScope via FDSN (no S3 role required)

Works for any logged-in EarthScope account, including those without direct S3 access. Good for TA, IU, US, UW, _GSN, etc.:

client = SeisfetchClient(backend="fdsn", providers="EARTHSCOPE")

bundle = client.get_numpy(
    "TA",
    "034A",
    channel="BHZ",
    starttime="2010-06-01T00:00:00",
    endtime="2010-06-01T00:05:00",
)
print(bundle.ids)            # ['TA.034A..BHZ']
print(bundle.to_dict()[bundle.ids[0]].shape)

Station discovery

get_stations() queries fdsnws-station and auto-routes to SCEDC, NCEDC, or EarthScope based on the network code. No ObsPy required.

rows = client.get_stations(
    "TA", channel="BHZ",
    starttime="2010-06-01", endtime="2010-06-02",
)
for r in rows[:3]:
    print(r["Network"], r["Station"], r["Channel"], r["Latitude"], r["Longitude"])

FDSN fallback

client = SeisfetchClient(backend="fdsn", providers="GEOFON")

bundle = client.get_numpy(
    "GE",
    "BKB",
    channel="BHZ",
    starttime="2011-03-11T06:00:00",
    endtime="2011-03-11T06:05:00",
)

xarray output

ds = client.get_xarray(
    "CI",
    "ABL",
    channel="BHZ",
    starttime="2024-01-15T00:00:00",
    endtime="2024-01-15T00:10:00",
)

Earth2Studio-compatible sparse sensor output

from datetime import datetime
from seisfetch import SeismicDataFrameSource

df_source = SeismicDataFrameSource(bundle)
df = df_source(datetime(2024, 1, 15), list(ds.data_vars))

Metadata table + zarr sidecar

from seisfetch import bundle_to_metadata_table, to_zarr, write_metadata_csv

metadata_table = bundle_to_metadata_table(bundle)
to_zarr(bundle, "quickstart.zarr", metadata=metadata_table)
write_metadata_csv(metadata_table, "quickstart.zarr")

Dependency Tuning

You do not need every dependency for every workflow.

Minimal core

Use this when you only want miniSEED -> numpy from S3:

pip install seisfetch

Includes:

  • numpy
  • boto3
  • pymseed

Add FDSN fallback

Use this if you want direct HTTP fallback providers:

pip install "seisfetch[fdsn]"

Adds:

  • httpx

Add authenticated EarthScope S3

Use this for EarthScope networks outside the Open Data set:

pip install "seisfetch[auth]"
pip install earthscope-cli

Adds:

  • earthscope-sdk
  • EarthScope CLI login flow

Add xarray

Use this for labeled arrays and ML pipelines:

pip install "seisfetch[xarray]"

Add metadata tables

Use this if you want canonical metadata tables, CSV sidecars, or metadata-aware zarr output:

pip install "seisfetch[pandas]"

Add zarr

Use this for chunked persistent stores:

pip install "seisfetch[zarr]"

Add ObsPy

Use this only if you need ObsPy interop or ObsPy-backed FDSN behavior:

pip install "seisfetch[obspy]"

Suggested dependency bundles

Need Install
S3 open data -> numpy only pip install seisfetch
Metadata table / metadata.csv export pip install "seisfetch[pandas]"
Archive-first + FDSN fallback pip install "seisfetch[fdsn]"
EarthScope networks outside Open Data pip install "seisfetch[auth]" and pip install earthscope-cli
xarray / ML / Earth2Studio-style workflows pip install "seisfetch[xarray]"
zarr persistence pip install "seisfetch[zarr]"
ObsPy interop pip install "seisfetch[obspy]"
Most common research stack pip install "seisfetch[fdsn,auth,xarray,zarr,obspy]"

Installation Modes

pip

For scripts and lightweight pipelines:

pip install seisfetch

From source:

git clone https://github.com/Denolle-Lab/seisfetch
cd seisfetch
pip install .

pixi

For notebook work and development:

git clone https://github.com/Denolle-Lab/seisfetch
cd seisfetch
pixi install
pixi install -e notebooks
pixi run -e notebooks kernel-install

The notebook environment is the intended Jupyter environment for this repo.

EarthScope Credentials

EarthScope has three access tiers:

  1. Open Data S3 (backend="s3_open") — anonymous, no account. Only the networks AK, II, IU, N4, PB, TA, UU, UW (seisfetch.EARTHSCOPE_OPEN_NETWORKS, verified against the live bucket listing on 2026-09-09).
  2. FDSN web service (backend="fdsn", providers="EARTHSCOPE") — any logged-in account, any network. No S3 role required.
  3. Restricted S3 (backend="s3_auth") — every network outside the Open Data set, from the earthscope-mseed-v2 access point. Needs earthscope-sdk>=1.8 and the s3-miniseed-v2 role. Credentials are scoped per network, and per network-year for temporary FDSN codes; seisfetch exchanges one credential per scope and remembers EarthScope's refusals, so a denied network is asked about once rather than once per day. Fastest and cheapest from us-east-2.

Setup

pip install "seisfetch[auth]"
pip install earthscope-cli
es login

The [auth] extra installs earthscope-sdk>=1.8. Earlier SDKs cannot scope a credential to a network, and S3AuthClient refuses to start on them.

Verify (CLI)

es user get-profile                # prints your name, email, institution

Verify (Python)

from earthscope_sdk import EarthScopeClient

with EarthScopeClient() as client:
    print(client.user.get_profile())
    try:
        creds = client.user.get_aws_credentials(
            role="s3-miniseed-v2", network="FDSN:US"
        )
        print("S3 access to US granted:", creds.aws_access_key_id[:8])
    except Exception as exc:
        print("S3 access NOT granted:", type(exc).__name__, exc)

What the errors mean:

  • "You are not allowed to assume role 's3-miniseed'" — the legacy v1 role, now retired. Upgrade earthscope-sdk and seisfetch; only v2 is used here.
  • UnauthorizedError (HTTP 403) on a scope — your account may not read that network (or that network-year). Not retried. Use backend="fdsn", providers="EARTHSCOPE" in the meantime and email data-help@earthscope.org.
  • UnauthenticatedError (HTTP 401) — the login token was rejected; run es login.
  • HTTP 400 on a temporary code — the scope needs a year. seisfetch always sends one, so this points at a code EarthScope does not recognise.
  • HTTP 404 — the archive has no such network-year; treated as no data.

seisfetch raises these as seisfetch.CredentialError (a FetchError) with .scope and .status set.

Headless / CI

es user get-refresh-token
export ES_OAUTH2__REFRESH_TOKEN="<your-refresh-token>"

Override the access point or the role with EARTHSCOPE_S3_ACCESS_POINT and EARTHSCOPE_ROLE if EarthScope issues new ones.

Architecture

SeisfetchClient
|
+- get_raw()        -> raw miniSEED bytes
+- get_numpy()      -> TraceBundle (numpy)
+- get_xarray()     -> xarray.Dataset
+- get_waveforms()  -> ObsPy Stream
|
+- backend="s3_open"
|  +- SCEDC open bucket
|  +- NCEDC open bucket
|  +- GeoNet open bucket
|  +- EarthScope Open Data bucket (AK II IU N4 PB TA UU UW)
|  +- auto-routing by network code
|
+- backend="s3_auth"
|  +- EarthScope restricted access point via earthscope-sdk
|  |  (s3-miniseed-v2, credentials scoped per network / network-year)
|  +- Open Data networks read anonymously
|
+- backend="fdsn"
|  +- single-provider HTTP client
|  +- multi-provider fan-out client
|
+- backend="obspy_fdsn"
   +- ObsPy-backed fallback for harder FDSN cases

Earth2Studio Compatibility

The package includes adapters in seisfetch.earth2 for Earth2Studio-style usage:

  • SeismicDataSource — wraps a bundle you already fetched
  • SeismicDataFrameSource — sparse sensor table; auto_coords=True fills station lat/lon from the FDSN station service
  • SeisfetchLiveSource — time-indexed DataSource that fetches on demand
  • bundle_to_earth2

These are intended for:

  • sparse sensor tables
  • observation pipelines
  • foundation-model data preparation
  • Earth2Studio / digital twin workflows

Typical path:

miniSEED -> numpy -> xarray / sparse dataframe -> Earth2Studio adapter

Live source

SeisfetchLiveSource has the shape every other Earth2Studio source (GFS, ERA5, ...) has: you call it with timestamps and it fetches, auto-routing per network across all four archives and caching day bundles in memory. It reads anonymously, so EarthScope networks outside the Open Data set are not reachable from it.

from datetime import datetime
from seisfetch.earth2 import SeisfetchLiveSource

source = SeisfetchLiveSource(
    channels=["CI.PASC..BHZ", "BK.PKD.00.BHZ", "II.PFO.00.BHZ"],
    window_s=3600,
    calibrate="gain",
)
da = source(datetime(2022, 1, 2, 6))   # -> (time, variable, sample) DataArray

Channels are NET.STA.LOC.CHA strings; the returned variable coordinate spells them with underscores (CI_PASC__BHZ), which is also what the optional variable= argument accepts. All channels in one call must share a sampling rate — request mixed rates (a 40 sps BHZ alongside a 100 sps HHZ) in separate calls.

Physical units are required — this source never returns raw counts:

  • calibrate="gain" (default): divide by the channel's total sensitivity from the FDSN station service. Exact at the reference frequency, one metadata request per channel, no extra dependencies.
  • calibrate="response": full spectral deconvolution through seisfetch.contrib.response (StationXML fetch plus an evalresp-equivalent evaluator). Agrees with ObsPy to 7.6e-16 of peak amplitude on real data.

fetch() is genuinely async (asyncio.to_thread), so pipelines can overlap this source with others.

Recipes

A few common end-to-end tasks the package is designed for.

Mine a single station-day to numpy

from seisfetch import SeisfetchClient

bundle = SeisfetchClient(backend="s3_open").get_numpy(
    "CI", "ABL", channel="BHZ",
    starttime="2024-01-15T00:00:00",
    endtime="2024-01-16T00:00:00",
)
data = bundle.to_dict()["CI.ABL..BHZ"]   # int32 numpy array, full day

Bulk fetch many station-channels in parallel

requests = [
    {"network": "CI", "station": s, "channel": c,
     "starttime": "2025-04-14T17:07:30", "endtime": "2025-04-14T17:10:30"}
    for s in ["ABL", "SDD", "PASC"]
    for c in ["BHZ", "BHN", "BHE"]
]
client = SeisfetchClient(backend="s3_open")
summary = client.get_numpy_bulk(requests, max_workers=8)
print(summary.succeeded, "/", summary.total)

Missing station-channel combinations are reported in summary.failed and do not abort the run.

Archive-first with FDSN fallback

from seisfetch import SeisfetchClient, earthscope_tier, route_network

def get_archive_first(net, sta, *, starttime, endtime, location="*", channel="*",
                       fallback="GEOFON"):
    dc = route_network(net)
    restricted = dc == "earthscope" and earthscope_tier(net) == "restricted"
    primary = "s3_auth" if restricted else "s3_open"
    try:
        return SeisfetchClient(backend=primary).get_numpy(
            net, sta, location=location, channel=channel,
            starttime=starttime, endtime=endtime,
        )
    except Exception:
        return SeisfetchClient(backend="fdsn", providers=fallback).get_numpy(
            net, sta, location=location, channel=channel,
            starttime=starttime, endtime=endtime,
        )

Persist a multi-channel package to zarr with metadata sidecar

from seisfetch import bundle_to_metadata_table, to_zarr, write_metadata_csv

metadata = bundle_to_metadata_table(bundle)
to_zarr(bundle, "package.zarr", metadata=metadata)
write_metadata_csv(metadata, "package.zarr")

The resulting package.zarr/ contains channel groups plus a metadata/channel_table group readable with xarray.open_zarr(..., group="metadata/channel_table").

Earth2Studio sparse-sensor handoff

from datetime import datetime
from seisfetch import SeismicDataFrameSource, bundle_to_xarray

ds = bundle_to_xarray(bundle)
df = SeismicDataFrameSource(bundle)(datetime(2024, 1, 15), list(ds.data_vars))
df[["time", "variable", "network", "station", "channel",
     "sampling_rate", "amplitude_rms", "num_samples"]].head()

Command Line

Examples:

# SCEDC open S3 (no auth)
seisfetch download CI ABL -s 2024-01-15 -e 2024-01-15T01:00:00 -c BHZ -o data.mseed
seisfetch numpy    CI SDD -s 2024-06-01 -c BHZ -o data.npz
seisfetch zarr     CI ABL -s 2024-01-15 -c BHZ -o data.zarr

# EarthScope Open Data (no auth): AK II IU N4 PB TA UU UW
seisfetch download IU ANMO -s 2024-01-15 -e 2024-01-15T01:00:00 -c BHZ -o anmo.mseed

# EarthScope restricted networks (requires `es login` and the s3-miniseed-v2 role)
seisfetch download US NEW -s 2024-01-15 -e 2024-01-15T01:00:00 -c BHZ --backend s3_auth -o new.mseed

# Routing and provider info (--route names the EarthScope tier)
seisfetch info --route CI
seisfetch info --route US
seisfetch info --providers

# Bulk
seisfetch bulk requests.csv -o output/ -f npz

Notebooks

See notebooks/ for worked examples:

Notebook setup instructions are in notebooks/README.md.

Tests

pixi run test
pixi run test-cov
pixi run test-int

Dependencies

Package Status Role
numpy core array container
boto3 core S3 transport
pymseed core miniSEED decode
httpx optional [fdsn] FDSN HTTP client
pandas optional [pandas] canonical metadata tables and CSV export
xarray optional [xarray] labeled dataset output
zarr optional [zarr] persistent chunked storage
obspy optional [obspy] ObsPy interop and alternative FDSN backend
earthscope-sdk (>=1.8) optional [auth] EarthScope restricted-tier credentials

See THIRD_PARTY_NOTICES.md for attribution and licenses.

Benchmarks with plots: benchmarks/RESULTS.md (or the self-contained RESULTS.html). Changes: CHANGELOG.md. Response-removal tutorial: notebooks/05_response_removal.ipynb.

Citation

When using data accessed through seisfetch:

  • EarthScope: cite the network operators and NSF SAGE facility
  • SCEDC: doi:10.7909/C3WD3xH1
  • NCEDC: doi:10.7932/NCEDC
  • GeoNet: GNS Science, GeoNet open data (CC BY 4.0) — cite per GeoNet's data policy
  • other FDSN providers: cite the underlying network/provider

Software references:

License

MIT AND LGPL-3.0-only. The package is MIT (LICENSE) with one exception: seisfetch/contrib/obspy_ports.py contains numerically exact translations of ObsPy routines and is LGPL-3.0-only (ObsPy is © The ObsPy Development Team, LGPL v3). Using seisfetch as a library is unaffected; redistributors of modified versions of that one file must honor LGPL terms. Details in THIRD_PARTY_NOTICES.md.

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Fast seismic miniSEED from EarthScope, SCEDC, NCEDC, and 37+ FDSN servers. No ObsPy required.

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