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Fault damage zones can influence various aspects of the earthquake cycle, such as the recurrence intervals and magnitudes of large earthquakes. The properties and structure of fault damage zones are often characterized using dense arrays of seismic stations located directly above the faults. However, such arrays may not always be available. Hence, our research aims to develop a novel method to image fault damage zones using broadband stations at relatively larger distances. Previous kinematic simulations and a case study of the 2003 Big Bear earthquake sequence demonstrated that fault damage zones can act as effective waveguides, amplifying high-frequency waves along directions close to fault strike via multiple reflections within the fault damage zone. The amplified high-frequency energy can be observed by stacking P-wave spectra of earthquake clusters with highly-similar waveforms (Huang et al., 2016), and the frequency band which is amplified may be used to estimate the width and velocity contrast of the fault damage zone. We attempt to identify the high-frequency peak associated with fault zone waves in stacked spectra by conducting a large-scale study of small earthquakes (M1.5–3). We use high quality broadband data from seismic stations at hypocentral distances of 20-80 km in the 2019 Ridgecrest earthquake regions. First, we group the Ridgecrest earthquakes in clusters by their locations and their waveform similarity, and then stack their velocity spectra to average the source effects of individual earthquakes. Our results show that the stations close to the fault strike record more high-frequency energies around the characteristic frequency of fault zone reflections. We find that the increase in the amount of high-frequencies is consistent across clusters with average magnitudes ranging from 1.6-2.4, which suggests that the azimuthal variation in spectra is caused by fault zone amplification rather than rupture directivity. We will apply our method to other fault zones in California, in order to search for fault damage zone structures and estimate their material properties.
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Insights on earthquake source processes from the 2019 Ridgecrest earthquake source spectra and its azimuthal variation
The 2019 Ridgecrest, CA earthquake sequence has provided a unique opportunity and a rich dataset to understand earthquake source properties and near-fault structure. Using the high-quality seismic data provided by the SCEC Stress Drop Validation group, we first estimate the corner frequency of M2.0-4.5 earthquakes by applying the spectral ratio method based on empirical Green’s function (Liu et al., 2020). We relate corner frequency estimates to stress drops assuming the Brune source model and circular cracks. Our preliminary results show increasing median stress drops with magnitude for both P and S waves, from 1 MPa for M2.0 events to 10 MPa for M4.0 events, though the limited frequency bandwidth may cause underestimation for small events. The estimated moment magnitude is proportional to the catalog magnitude by a factor of 0.72, which is close to 0.74 estimated by Trugman (2020) for the Ridgecrest earthquake sequence. In the second part of the study, we examine the impact of fault zone structure on the azimuthal variation of the source spectra. Using kinematic simulations and observations of the 2003 Big Bear earthquake sequence, Huang et al. (2016) showed that fault damage zones can act as an effective wave guide and cause high-frequency wave amplification along directions close to fault strike. We use clusters of M1.5-3 earthquakes in the Ridgecrest region to further examine the azimuthal variation of the stacked source spectra and investigate if the near-source structure can affect our corner frequency estimates. We aim to develop robust methods that utilize high-quality seismic data to illuminate earthquake source processes and fault zone properties.
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- Award ID(s):
- 1943742
- PAR ID:
- 10316433
- Date Published:
- Journal Name:
- SSA Annual Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Fault damage zones can influence various aspects of the earthquake cycle, such as the recurrence intervals and magnitudes of large earthquakes. The properties and structure of fault damage zones are often characterized using dense arrays of seismic stations located directly above the faults. However, such arrays may not always be available. Hence, our research aims to develop a novel method to image fault damage zones using broadband stations at relatively larger distances. Previous kinematic simulations and a case study of the 2003 Big Bear earthquake sequence demonstrated that fault damage zones can act as effective waveguides, amplifying high-frequency waves along directions close to fault strike via multiple reflections within the fault damage zone. The amplified high-frequency energy can be observed using the stacked P-wave spectra of earthquake clusters with highly-similar waveforms (Huang et al., 2016). We attempt to identify the high-frequency peak associated with fault zone waves in stacked spectra by conducting a large-scale study of small earthquakes (M1.5–3). We use high quality broadband data from seismic stations at hypocentral distances of 20-100km in the 2004 Parkfield and 2019 Ridgecrest earthquake regions. First, we group earthquakes in clusters by their locations and their waveform similarity, and then stack their velocity spectra to average the source effects of individual earthquakes. We applied our method to the 2019 Ridgecrest earthquake sequence, and our preliminary results show that stations close to the fault strike tend to record more high-frequency energies around the characteristic frequency of fault zone reflections. The frequency bands in which amplified high-frequency energies are observed may be used to estimate the width and velocity contrast of the fault damage zone. We aim to develop a robust and versatile method that can be used to search for fault damage zone structures and estimate their material properties, in order to shed light on earthquake source processes.more » « less
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ABSTRACT We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.more » « less
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Abstract The spectra of earthquake waveforms can provide important insight into rupture processes, but the analysis and interpretation of these spectra is rarely straightforward. Here we develop a Bayesian framework that embraces the inherent data and modeling uncertainties of spectral analysis to infer key source properties. The method uses a spectral ratio approach to correct the observedS‐wave spectra of nearby earthquakes for path and site attenuation. The objective then is to solve for a joint posterior probability distribution of three source parameters—seismic moment, corner frequency, and high‐frequency falloff rate—for each earthquake in the sequence, as well as a measure of rupture directivity for select target events with good azimuthal station coverage. While computationally intensive, this technique provides a quantitative understanding of parameter tradeoffs and uncertainties and allows one to impose physical constraints through prior distributions on all source parameters, which guide the inversion when data is limited. We demonstrate the method by analyzing in detail the source properties of 14 different target events of magnitude M5 in southern California that span a wide range of tectonic regimes and fault systems. These prominent earthquakes, while comparable in size, exhibit marked diversity in their source properties and directivity, with clear spatial patterns, depth‐dependent trends, and a preference for unilateral directivity. These coherent spatial variations source properties suggest that regional differences in tectonic setting, hypocentral depth or fault zone characteristics may drive variability in rupture processes, with important implications for our understanding of earthquake physics and its relation to hazard.more » « less
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Fault damage zones can influence various aspects of the earthquake cycle, such as the recurrence intervals and magnitudes of large earthquakes. Hence, our research aims to develop a novel method to image fault damage zones using high-frequency P-waves reflected within them. Previous studies have demonstrated that fault damage zones can amplify high-frequency waves along directions close to fault strike. The associated frequency band of the amplified secondary peak may be used to estimate the width and velocity contrast of the fault damage zone. Here we use the stacked P-wave velocity spectra of M1.5–3 earthquakes in the Parkfield region to identify the azimuthal variation in high-frequency energy. Our preliminary results show that for 62% of the Parkfield clusters, stations close to the fault strike record more high-frequency energies around 10–20 Hz. The frequency band is lower than what we observed for the 2019 Ridgecrest earthquakes region, and corresponds to a fault zone velocity reduction of ~50% assuming a fault zone width of 200m. We also observe along-strike differences in our results, where clusters along some fault sections show greater azimuthal variation than clusters in other sections. Moreover, to account for the possible effects of site conditions underneath the stations, we will quantify their effects using the spectra of regional earthquakes. We will compute the root-mean-square spectra at different frequency bands for each event, and calculate the average deviation in spectra at each station. We can then generate an empirical correction term for each station as a function of frequency. By applying these corrections to the stacked P-wave velocity spectra of our earthquake clusters, we can separate the contribution of site effects from fault zone structures. Our results demonstrate that the new method can be applied to search for fault damage zone structures in different tectonic regions with broadband stations in order to enhance our understanding of the co-evolution of fault zones and earthquake cycle.more » « less
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