High‐Precision Earthquake Location Using Source‐Specific Station Terms and Inter‐Event Waveform Similarity

• Published in: Journal of geophysical research. Solid earth Vol. 127; no. 1
• Main Authors: Lomax, Anthony, Savvaidis, Alexandros
• Format: Journal Article
• Language: English
• Published: 01.01.2022
• Subjects: absolute location; California earthquakes; earthquake location; relative location; seismicity; seismology
• ISSN: 2169-9313; 2169-9356; 2169-9356
• DOI: 10.1029/2021JB023190

Earthquake monitoring and many seismological studies depend on earthquake locations from phase arrival‐times. We present an extended, arrival‐time earthquake location procedure (NLL‐SSST‐coherence) which approaches the precision of differential‐timing based, relative location methods. NLL‐SSST‐coherence is based on the probabilistic, global‐search NonLinLoc (NLL) location algorithm which defines a probability density function (PDF) in 3D space for hypocenter location and is highly robust to outlier data. NLL‐SSST‐coherence location first reduces velocity model error through iteratively generated, smooth, source‐specific, station travel‐time corrections (SSST). Next, arrival‐time error is reduced by consolidating location information across events based on inter‐event waveform coherency. If the waveforms at a station for multiple events are very similar (have high coherency) up to a given frequency, then the distance separating these “multiplet” events is small relative to the seismic wavelength at that frequency. NLL‐coherence relocation for a target event is a stack over 3D space of the NLL‐SSST location PDF for the event and the PDF's for other multiplet events, each weighted by its waveform coherency with the target. NLL‐coherence relocation requires waveforms from only one or a few seismic stations, enabling precise relocation with sparse networks, for foreshocks and early aftershocks of significant events before installation of temporary stations, and for older data sets with few waveform data. We show the behavior and performance of NLL‐SSST‐coherence with synthetic and ground‐truth tests, and through application and comparison to relative locations for California earthquake sequences with dense and sparse station coverage. Plain Language Summary Earthquake monitoring, early warning, public information and understanding depend on standard locations of earthquakes in geographical space. Specialized, relative location methods extend standard locations to determine more precisely the positions of nearby earthquakes with respect to each other. We present a standard earthquake location procedure (NLL‐SSST‐coherence) which approaches the precision of relative location methods while being more generally applicable and efficient. NLL‐SSST‐coherence uses the NonLinLoc (NLL) location algorithms which determine an earthquake location as a probability cloud in 3D space and work well with poor quality seismogram recordings. NLL‐SSST‐coherence location first reduces effects of limited knowledge of seismic wavespeeds in the Earth through spatial averaging of wavespeed errors (SSST). Next, it reduces effects of error in measuring the timing of earthquake energy arrival at seismic stations by consolidating location information between nearby earthquakes. Nearby events are identified by their seismogram waveforms which are very similar, wiggle for wiggle ‐ they have high coherence. NLL‐SSST‐coherence relocation enables precise earthquake relocation with sparse networks, for foreshocks and early aftershocks of significant earthquakes, and for older earthquake sequences. We show the performance of NLL‐SSST‐coherence with simulated and real data tests, and through application to California earthquake sequences with dense and sparse station coverage. Key Points We use source‐specific station terms and waveform similarity to achieve high‐precision earthquake location (NLL‐SSST‐coherence) NLL‐SSST‐coherence approaches the precision of relative location methods and can give better depth constraint when station coverage is poor NLL‐SSST‐coherence requires waveforms from only one or a few stations and thus is applicable with sparse networks and older sequences