The DOI matches its Crossref record.
An automatic phase picker for local and teleseismic events
Abstract
An automatic detection algorithm has been developed which is capable of time P-phase arrivals of both local and teleseismic earthquakes, but rejects noise bursts and transient events. For each signal trace, the envelope function is calculated and passed through a nonlinear amplifier. The resulting signal is then subjected to a statistical analysis to yield arrival time, first motion, and a measure of reliability to be placed on the P-arrival pick. An incorporated dynamic threshold lets the algorithm become very sensitive; thus, even weak signals are timed precisely. During an extended performance evaluation on a data set comprising 789 P phases of local events and 1857 P phases of teleseismic events picked by an analyst, the automatic picker selected 66 per cent of the local phases and 90 per cent of the teleseismic phases. The accuracy of the automatic picks was “ideal” (i.e., could not be improved by the analyst) for 60 per cent of the local events and 63 per cent of the teleseismic events.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
Zhu, W., & Beroza, G. C. (2018) · Geophysical Journal International · doi:10.1093/gji/ggy423 · Cited by 1,120
Advanced structural health monitoring of concrete structures with the aid of acoustic emission
Behnia, A., Chai, H. K., & Shiotani, T. (2014) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2014.04.103 · Cited by 447
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
Kim, S., Yoon, B., Lim, J.-T., & Kim, M. (2021) · Energies · doi:10.3390/en14051499 · Cited by 19
Metadata
- Authors
- Baer, M., & Kradolfer, U.
- Venue
- Bulletin of the Seismological Society of America
- Published
- 1987-08-01
- Volume
- 77
- Issue
- 4
- Pages
- 1437-1445
- ISSN
- 0037-1106 (ISSN-L)
- 1943-3573
- OpenAlex
- W2278399961
- Cited by
- 460
- References
- 0
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An automatic phase picker for local and teleseismic events. https://app.cochat.ai/papers/10.1785/bssa0770041437
- The paper
- Baer, M., & Kradolfer, U. (1987). An automatic phase picker for local and teleseismic events. Bulletin of the Seismological Society of America. https://doi.org/10.1785/bssa0770041437
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.