Multiband array detection and location of seismic sources recorded by dense seismic networks

Abstract

We present a new methodology for detection and space-time location of seismic sources based on multiscale, frequency-selective coherence of the wave field recorded by dense large-scale seismic networks and local antennas. The method is designed to enhance coherence of the signal statistical features across the array of sensors and consists of three steps: signal processing, space-time imaging, and detection and location. The first step provides, for each station, a simplified representation of seismic signal by extracting multiscale non-stationary statistical characteristics, through multiband higher-order statistics or envelopes. This signal processing scheme is designed to account for a priori unknown transients, potentially associated with a variety of sources (e.g. earthquakes, tremors), and to prepare data for a better performance in posterior steps. Following space-time imaging is carried through 3-D spatial mapping and summation of station-pair time-delay estimate functions. This step produces time-series of 3-D spatial images representing the likelihood that each pixel makes part of a source. Detection and location is performed in the final step by extracting the local maxima from the 3-D spatial images. We demonstrate the efficiency of the method in detecting and locating seismic sources associated with low signal-to-noise ratio on an example of the aftershock earthquake records from local stations of International Maule Aftershock Deployment in Central Chile. The performance and potential of the method to detect, locate and characterize the energy release associated with possibly mixed seismic radiation from earthquakes and low-frequency tectonic tremors is further tested on continuous data from southwestern Japan.

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

“…Though advanced and automated procedures can ensure an efficient picking process (e.g., Akram & Eaton, ; Grigoli et al, ; Ross et al, ; Withers et al, ), robust phase identification and picking are still challenging for low SNR events, and missed arrival times and picking errors may directly cause location errors. Other nonnegligible limitations of most traveltime‐ or ray‐based methods consist of their incapability of utilizing multichannel coherency (Poiata et al, ; Shi, Angus, et al, ), or considering secondary features (like scattering) or finite‐frequency effects (Dahlen et al, ; H. Zhang & Thurber, ).…”

“…Analogous to double difference method (Waldhauser & Ellsworth, ), Zhang et al () proposed the station‐pair double difference method and used it to locate nonvolcanic tremors successfully. Recently, several studies implemented this idea into waveform‐based methods with cross‐correlation techniques from seismic interferometry (e.g., Dales et al, ; Droznin et al, ; Grandi & Oates, ; Poiata et al, ; Ruigrok et al, ; Shi, Angus, et al, ). By including higher‐order cross correlations, K. L. Li, Sadeghisorkhani, et al () and K. L. Li, Sgattoni, et al () extended the previous single cross‐correlation stacking to a double‐correlation method and a more generalized high‐order correlation method, which showed superior performance in lateral imaging resolution for locating tremors with sparse station networks.…”

“…Besides the imaging operator, the PWS methods also differ in preprocessing of the input waveforms and the event detection and location detection criteria. There are various CFs adopted to improve the SNR and compensate the side effects of source radiation patterns; for example, the input data are converted to envelope (Gharti et al, ; Liao et al, ; Zeng et al, ); semblance (Furumoto et al, ; W. Zhang & Zhang, ; Staněk et al, ; C. Zhang et al, ); short‐term average to long‐term average ratio (STA/LTA; Drew et al, ; Grigoli et al, ; L. Li et al, ); kurtosis (Langet et al, ; Poiata et al, ); multichannel coherency (Shi, Angus, et al, ). …”

“…The backprojection process of waveform‐based methods can also be regarded as a generalized type of beamforming with an image function. Although the idea of waveform stacking is quite the same, the PWS methods distinguish themselves from the beamforming method in at least two major aspects (e.g., Rost & Thomas, ; Liao et al, ; Poiata et al, ; L. Li et al, ): First, the nature of problems solved by beamforming is determination of the direction from which the source energy arrives, instead of determining the spatial and temporal distribution of seismic sources. Second, PWS methods stack only waveforms of selected phases (primary phases in most cases) within specific time intervals, while beamforming method sums up all observed phases through a summation over time. More specifically, the general beamforming result is equal to the sum of autocorrelations and approximately delayed cross correlations of the waveforms, and the cross‐correlation term dominates the beamforming value. …”

“…Cross‐correlation stacking shares the same principle of exploiting differential traveltimes at pair‐wise stations from common events, with master‐station method and station‐pair method mentioned above. This method has been applied to microseismic monitoring in the oil and gas industry (Eisner et al, ; Grandi & Oates, ; L. Li et al, ; Trojanowski & Eisner, ; Shi et al, ), volcanic tremor data (Droznin et al, ; K. L. Li et al, ; K. L. Li et al, ), mining‐induced seismicity (Dales, ; L. Li et al, ), and slow (tectonic tremor and low‐frequency earthquakes) and regular earthquake (Poiata et al, ; Ruigrok et al, ; Shi et al, ). In this study, we will call it interferometric‐type method.…”