Bulletin of the Seismological Society of America · 1982 · 668 citations · 7 references
Natural SciencesComputer ScienceGeophysicsInstrumentationSeismologyTime Series AnalysisDigital Signal ProcessingGeophysical Signal ProcessingSeismic Data ProcessingSeismic InstrumentationSeismic MonitoringSignal ProcessingCharacteristic Function FormEarthquake Signal ProcessingPhase PickingSeismic Event DetectionSeismic Event PickingSeismic Signal ProcessingThreshold SelectionTriggering Algorithms
Automatic phase‑picking algorithms detect seismic signals on a single trace and time arrivals precisely, yet they are less sensitive than detection‑only algorithms and are currently limited to one‑pass time‑domain processing due to computational constraints. The paper investigates how rapidly advancing semiconductor technology will enable more powerful multi‑pass phase‑picking approaches that incorporate frequency‑domain detection and pseudo‑offline timing. The algorithm filters the data, constructs a characteristic function (simple or complex), and triggers when the short‑term average exceeds a threshold based on a long‑term average of that function. Performance metrics—sensitivity, timing error, false‑trigger and false‑report rates—are interrelated and depend on the chosen characteristic function and operating parameters.
abstract Automatic phase-picking algorithms are designed to detect a seismic signal on a single trace and to time the arrival of the signal precisely. Because of the requirement for precise timing, a phase-picking algorithm is inherently less sensitive than one designed only to detect the presence of a signal, but still can approach the performance of a skilled analyst. A typical algorithm filters the input data and then generates a function characterizing the seismic time series. This function may be as simple as the absolute value of the series, or it may be quite complex. Event detection is accomplished by comparing the function or its short-term average (STA) with a threshold value (THR), which is commonly some multiple of a long-term average (LTA) of a characteristic function. If the STA exceeds THR, a trigger is declared. If the event passes simple criteria, it is reported. Sensitivity, expected timing error, false-trigger rate, and false-report rate are interrelated measures of performance controlled by choice of the characteristic function and several operating parameters. At present, computational power limits most systems to one-pass, time-domain algorithms. Rapidly advancing semi-conductor technology, however, will make possible much more powerful multi-pass approaches incorporating frequency-domain detection and pseudo-offline timing.
7