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If you sent out a Twitter or email message after Tuesday's Southern California earthquake, you had a lot of company. As is typical in technology-heavy California, the moderate earthquake generated a following seismic of people checking in with each other.

In fact, indicates that tracking Internet earthquake reports is nearly as accurate as seismographs in detecting earthquakes.

Scientists at the European-Mediterranean Seismological Centre's (EMSC) have tested using software to map visitors to its site after an earthquake by their IP addresses. The technique accurately located an earthquake in February 2007 near the Azores in the Atlantic Ocean within 15 minutes, according to .

The technique could help record small quakes that are nonetheless worrying for residents, scientists said.

Earthquakes that are particularly severe could also be tracked via the software, by noting areas with no Internet reports -- due to, presumably, losing Internet access from the quake.

Currently, reports are on the order of within 10km (6 miles) accuracy, and the organization hopes to improve that this year, the 2007 report said.

Meanwhile, ubiquitous Internet access meant quickly became available, as well as Twitter reports that came in even before official stories -- including one woman who reportedly was in the gynecologist's office. Using services such as Twitter to make reports also takes a load off the cellular telephone system, providers said.

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As observed, Internet reports and social posts are a fast, human layer on top of instrument networks — useful for mapping felt intensity, spotting local damage, and (when dense enough) helping detection systems triage events. The U.S. Geological Survey’s “Did You Feel It?” program is a mature example of turning citizen reports into rapid intensity maps and research-ready datasets. (usgs.gov)

Smartphones add a different data stream: built‑in accelerometers can supply waveform-like measurements rather than just text reports. University of California–Berkeley’s MyShake project shows how crowdsourced phone motion data can complement traditional seismic stations and, with enough sensors, support rapid detection and even early warning. (news.berkeley.edu)

Social networks (Twitter in particular) have been shown to act as “social sensors” — classifiers plus spatio‑temporal models can detect quakes from sudden keyword-and-rate spikes — but the signal is noisy and must be filtered. IP-based location is convenient but imprecise (and privacy-sensitive): commercial GeoIP products report sizable variability by country, ISP and connection type, so prefer explicit opt‑in location or device GPS when accuracy matters. (ir.webis.de)

Practical checklist for anyone building or evaluating a crowdsourced detection pipeline:

  • require timestamp + explicit location where possible; prefer GPS/accelerometer opt‑in over raw IP;
  • deduplicate and rate‑limit to reduce bot/spam influence;
  • cluster reports by time and distance, then apply a minimum‑member threshold before flagging;
  • always cross‑validate with seismic stations before issuing alerts; log minimally and respect opt‑out/privacy.

Example clustering sketch (replace with production libraries and rigorous testing):

# reports = [{'lat':..., 'lon':..., 'ts': unix_seconds, 'source':...}, ...]
def cluster_reports(reports, window_s=60, radius_km=50):
    clusters=[]
    for r in sorted(reports, key=lambda x: x['ts']):
        placed=False
        for c in clusters:
            if abs(r['ts']-c['ts'])<=window_s and haversine(r['lat'],r['lon'],c['lat'],c['lon'])<=radius_km:
                c['members'].append(r); update_center(c); placed=True; break
        if not placed:
            clusters.append({'lat':r['lat'],'lon':r['lon'],'ts':r['ts'],'members':[r]})
    return clusters

Treat social and smartphone reports as complementary to, not replacements for, instrument data; design thresholds conservatively and document how false positives will be handled.

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