Earthquake Source Characteristics Research Articles

Earthquake source impacts on the generation and propagation of seismic infrasound to the upper atmosphere

SUMMARY Earthquakes with moment magnitude (Mw) ranging from 6.5 to 7.0 have been observed to generate sufficiently strong acoustic waves (AWs) in the upper atmosphere. These AWs are detectable in Global Navigation Satellite System satellite signals-based total electron content (TEC) observations in the ionosphere at altitudes ∼250–300 km. However, the specific earthquake source parameters that influence the detectability and characteristics of AWs are not comprehensively understood. Here, we extend our approach of coupled earthquake-atmosphere dynamics modelling by combing dynamic rupture and seismic wave propagation simulations with 2-D and 3-D atmospheric numerical models, to investigate how the characteristics of earthquakes impact the generation and propagation of AWs. We developed a set of idealized dynamic rupture models varying faulting types and fault sizes, hypocentral depths and stress drops. We focus on earthquakes of Mw 6.0–6.5, which are considered the smallest detectable with TEC, and find that the resulting AWs undergo non-linear evolution and form acoustic shock N waves reaching thermosphere at ∼90–140 km. The results reveal that the magnitude of the earthquakes is not the sole or primary factor determining the amplitudes of AWs in the upper atmosphere. Instead, various earthquake source characteristics, including the direction of rupture propagation, the polarity of seismic wave imprints on the surface, earthquake mechanism, stress drop and radiated energy, significantly influence the amplitudes and periods of AWs. The simulation results are also compared with observed TEC fluctuations from AWs generated by the 2023 Mw 6.2 Suzu (Japan) earthquake, finding preliminary agreement in terms of model-predicted signal periods and amplitudes. Understanding these nuanced relationships between earthquake source parameters and AW characteristics is essential for refining our ability to detect and interpret AW signals in the ionosphere.

Simultaneous magnitude and slip distribution characterization from high-rate GNSS using deep learning: case studies of the 2021 Mw 7.4 Maduo and 2023 Turkey doublet events

SUMMARY Rapid and accurate characterization of earthquake sources is crucial for mitigating seismic hazards. In this study, based on 18 000 scenario ruptures ranging from Mw 6.4 to Mw 8.3 and corresponding synthetic high-rate Global Navigation Satellite System (HR-GNSS) waveforms, we developed a multibranch neural network framework, the continental large earthquake agile response (CLEAR), to simultaneously determine the magnitude and slip distributions. We apply CLEAR to recent large strike-slip events, including the 2021 Mw 7.4 Maduo earthquake and the 2023 Mw 7.8 and Mw 7.6 Turkey doublet. The model generally estimates the magnitudes successfully at 32 s with errors of less than 0.15, and predicts the slip distributions acceptably at 64 s, requiring only approximately 30 ms on a single CPU (Central Processing Unit). With optimal azimuthal coverage of stations, the system is relatively robust to the number of stations and the time length of the received data.

Sensing Optical Fibers for Earthquake Source Characterization Using Raw DAS Records

AbstractDistributed Acoustic Sensing (DAS) is becoming a powerful tool for earthquake monitoring, providing continuous strain‐rate records of seismic events along fiber optic cables. However, the use of standard seismological techniques for earthquake source characterization requires the conversion of data in ground motion quantities. In this study we provide a new formulation for far‐field strain radiation emitted by a seismic rupture, which allows to directly analyze DAS data in their native physical quantity. This formulation naturally accounts for the complex directional sensitivity of the fiber to body waves and to the shallow layering beneath the cable. In this domain, we show that the spectral amplitude of the strain integral is related to the Fourier transform of the source time function, and its modeling allows to determine the source parameters. We demonstrate the validity of the technique on two case‐studies, where source parameters are consistent with estimates from standard seismic instruments in magnitude range 2.0–4.3. When analyzing events from a 1‐month DAS survey in Chile, moment‐corner frequency distribution shows scale invariant stress drop estimates, with an average of Δσ = (0.8 ± 0.6) MPa. Analysis of DAS data acquired in the Southern Apennines shows a dominance of the local attenuation that masks the effective corner frequency of the events. After estimating the local attenuation coefficient, we were able to retrieve the corner frequencies for the largest magnitude events in the catalog. Overall, this approach shows the capability of DAS technology to depict the characteristic scales of seismic sources and the released moment.

Source characteristics of earthquakes in Delhi and its vicinity: Implications for seismogenesis in the stable continental region of India

Delhi and its surrounding region are among the important intraplate seismically active regions of India. According to the Bureau of Indian Standards, it lies in seismic zone IV and is capable of generating small to moderate earthquakes. This region has also experienced moderate to large earthquakes in the past due to its proximity to the Himalayan arc and its own tectonics. There is an urgent need to assess the source characteristics of earthquakes and their scaling relationships for a reliable and accurate estimation of seismic hazard. Source parameters and their scaling relations have been estimated for this region using well-located small to moderate earthquakes (Mw 2.3–5.1) that occurred during the period from 2000 to 2020. For this purpose, we investigated three components of earthquake waveform data using the ω−2 source model. The outcomes show the occurrence of low static stress drop (0.5–52.87 ​bars) in the Delhi region indicating approximately 57% of the events exhibit a stress drop < 10 ​bars, 27 % of events fall in the range of 10–20 ​bars, and only 16 % events exhibit stress drop > 20 ​bars. This observation suggests that the shallow subsurface of the study region may have low-strength material characteristics and a heterogeneous nature. In addition, the Zuniga parameter (ε) is estimated less than 1.0 by analyzing static and apparent stress drops, which infers the partial stress drop model fits very well in the study region. The seismic moment varies from 1.02×1012 to 1.03×1016 ​N.m. for P-wave and 6.46×1011 Nm to 7.54×1015 Nm for S-wave. The average seismic source radius lies in the range of 0.1–3.05 ​km with a ratio {r(p)/r(s)} of 1.5 ​km in the study region. The estimated values of corner frequency are comparatively lower for the S-wave (1.5–18.2 ​Hz) than for the P-wave (1.88–19.3 ​Hz) suggesting the ‘shifting properties’ of the corner frequency corroborated with the theoretical agreement. The seismic energy (E) is estimated using both P- and S-wave separately and its average value varies from 4.28×106 to 6.22×1011 ​J. The estimated stress drop and seismic moment demonstrate no correlation with each other. Therefore small to moderate-size earthquakes inherently follow the self-similarity behavior. The obtained scaling relationship between Seismic Moment and Corner Frequency is Mo=7.94∗1015fc−2.97. The derived scaling relations and source parameters are expected to provide a priori information for the assessment of seismic hazards and are useful in the simulation of strong ground motion characteristics in the region.

Near-fault ground motion characteristics and its effects on a collapsed reinforced concrete structure in Hatay during the February 6, 2023 Mw7.8 Kahramanmaraş earthquake

Ground motion in the vicinity of fault rupture produces pulse-type loading that may impose severe lateral displacement demands on structures due to forward-directivity effects. The Mw7.8 Kahramanmaraş Earthquake on February 6, 2023 created very high amplitude ground motions in the city of Hatay with distinct near-fault characteristics. In the city of 400,000 inhabitants, 33 % of the building stock was severely damaged or collapsed. Among the collapsed buildings, it was observed that there were new buildings built according to the latest seismic regulations. The seismic characteristics of the ground motion in Hatay were examined, the fault rupture, ground conditions and earthquake source characteristics affecting the engineering demand parameters were determined. Then, the seismic behavior and the reasons for the collapse of a 13-story reinforced concrete building, which is thought to be earthquake resistant with its structural system and material quality, were investigated using acceleration time-series recorded at a station very close to the building location. It has been understood that in shear wall systems, wall position and seismic detailing can impart significant damage (total destruction) on these elements under the effect of large displacement and high axial load demand by limiting the required ductility. Wall boundaries require more stringent confinement detailing than stated by the code under near-fault effects.

Rapid Source Characterization of the 2023 Mw 6.8 Al Haouz, Morocco, Earthquake

Abstract The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) estimates source characteristics of significant damaging earthquakes, aiming to place events within their seismotectonic framework. Contextualizing the 8 September 2023, Mw 6.8 Al Haouz, Morocco, earthquake is challenging, because it occurred in an enigmatic region of active surface faulting, and low seismicity yet produced significant damage and loss of life. Here, we present the rapid earthquake source products produced by the USGS NEIC, describing how the source model was derived using both seismic and geodetic observations. Our analysis indicates that the earthquake was the result of oblique-reverse faulting in the lower crust on either a steeply north-dipping fault or a moderately south-dipping fault. Finite-slip models using seismic and geodetic data reveal a compact source, with slip occurring at depths of 15–35 km. The causative fault is not apparent, because the rupture did not break the surface, and it is not possible to definitively attribute the earthquake to a known structure. The earthquake centroid depth of 25 km is noteworthy, because it shows slip extending beyond common estimates of seismogenic depth. This earthquake highlights that the seismogenic processes associated with mountain building in this wide plate boundary region are poorly understood.

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterization, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fiber-optic cables into seismic arrays with a spatial sampling density of the order of meters and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows. In order to tackle the challenges posed by noise, new denoising techniques need to be explored that are tailored to DAS. In this work, we propose a Deep Learning approach that leverages the spatial density of DAS measurements to remove spatially incoherent noise with unknown characteristics. This approach is entirely self-supervised, so no noise-free ground truth is required, and it makes no assumptions regarding the noise characteristics other than that it is spatio-temporally incoherent. We apply our approach to both synthetic and real-world DAS data to demonstrate its excellent performance, even when the signals of interest are well below the noise level. Our proposed methods can be readily incorporated into conventional data processing workflows to facilitate subsequent seismological analyses.

Earthquake Magnitude With DAS: A Transferable Data‐Based Scaling Relation

AbstractDistributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning.

Time-domain source parameter estimation ofMw 3–7 earthquakes in Japan from a large database of moment-rate functions

SUMMARYTime-domain analyses of seismic waveforms have revealed diverse source complexity in large earthquakes (Mw &amp;gt; 7). However, source characteristics of small earthquakes have been studied by assuming a simple rupture pattern on the frequency domain. This study utilizes high-quality seismic network data from Japan to systematically address the source complexities and radiated energies of Mw 3–7 earthquakes on the time domain. We first determine the apparent moment-rate functions (AMRFs) of the earthquakes using the empirical Green's functions. Some of the AMRFs show multiple peaks, suggesting complex ruptures at multiple patches. We then estimate the radiated energies (ER) of 1736 events having more than ten reliable AMRFs. The scaled energy (eR = ER/M0) does not strongly depend on the seismic moment (M0), focal mechanisms, or depth. The median value of eR is 3.7 × 10−5, which is comparable to those of previous studies; however, eR varies by approximately one order of magnitude among earthquakes. We measure the source complexity based on the radiated energy enhancement factor (REEF). The values of REEF differ among earthquakes, implying diverse source complexity. The values of REEF do not show strong scale dependence for Mw 3–7 earthquakes, suggesting that the source diversity of smaller earthquakes is similar to that of larger earthquakes at their representative spatial scales. Applying a simple spectral model (e.g. the ω2-source model) to complex ruptures may produce substantial estimation errors in source parameters.

Dynamic parameters of weak earthquakes on the southeastern slope of the Baltic Shield

The dynamic parameters of earthquake sources characterize the features of the process of destruction of the seismogenic medium. These parameters are defined in the world for earthquakes of different magnitudes and different genesis. For the seismically weakly active region of Fennoscandia, the source characteristics of earthquakes were characterized in the 1990s from analog records. In this paper, we obtained a summary of the indicated valuesfor weak earthquakes with ML=1-2 that took place on the southeastern slope of the Fennoscandian shield in 2009-2019 for two earthquake swarms of different origins - tectonic Kouvola and technogenic Erkilia. The work was carried out according to the data of the St. Petersburg digital seismic network. In the process of research, using seismograms of the network, the spectra of direct waves Sg were constructed, the values of the seismic moment, corner frequency, source radius and stress drop for 15 earthquakes of a tectonic and technogenic nature were calculated. The results obtained, although they have a spread in values, do not differ much from the world averages. However, it turned out that the seismic moment and stress drop significantly depend on the genesis of events.

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization.

Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph-structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

Mapping faults in the laboratory with seismic scattering 1: the laboratory perspective

SUMMARY Seismic waves produced by stressed and deforming rocks lose coherence when they cross regions of high heterogeneity. The delay in the arrival of maximum seismic energy amplitude (peak delay), an essential attribute to model earthquake source characteristics, is increasingly used to map complex crustal geology, heterogeneous reservoirs and fault networks. However, no laboratory calibration for the sensitivity of this parameter to fractures is currently available due to both experimental challenges and the difficulty in modelling wavefields in the near field. In this study, peak delays have been measured and mapped in space in the frequency range 50 kHz to 1 MHz using acoustic emission data recorded during a triaxial deformation experiment of Darley Dale Sandstone. Peak delays can increase dramatically throughout the experiment, but their behaviour depends on frequency and, especially, anomalous azimuth-dependent scattering. The changes in frequency depend on strain. At low frequencies, peak delays are sensitive to surface waves generated at the sample boundaries, but they also mark the zones of shadow and intense/intermediate strains expected for an heterogeneous sample. At high frequencies, peak delays detect the zone of intense strain corresponding to the post-deformation shear zone. Temporal variations of peak delays show a frequency-dependent sensitivity to fracture nucleation, fault coalescence and sample failure. Scattering from these heterogeneities produces waves reverberating through seismic coda if the source–station path is close to an acoustic boundary, such as the fault zone or the sample boundaries. Our results confirm that peak delay has notable sensitivity to heterogeneity and can map and monitor structural- and deformation-induced changes in the near-field. The companion modelling paper tests this sensitivity and the corresponding imaging potential.

Numerical modeling of seismically-induced infrasound impacts on ionospheric plasma densities and mesospheric airglow emissions

Seismically-induced infrasonic waves (IWs) are known sources of disturbances in the upper layers of the atmosphere. These waves experience growth and potential for nonlinear evolution with height and may ultimately exhibit amplitudes of ten(s) of % of local Mach number following events of sufficient strength and scale. Along with a routine detection of their impacts on the ionospheric plasma, e.g., in GPS signal-based measurements of integrated total electron content (TEC), studies demonstrate opportunities to exploit these observations for the investigation of earthquake sources.We present the results of specific case and parametric studies of the feasibility to infer earthquake source characteristics based on observations of IW impacts on mesospheric airglow emissions and ionospheric densities. Studies include numerical simulations performed with three-dimensional coupled seismic wave propagation, and neutral atmospheric and ionospheric models, covering the chain of processes from earthquake sources to observable signatures. The results suggest that upper-atmospheric/ionospheric measurements of IWs may supplement classical seismic and geodetic observations over complex ruptures or undersea earthquakes, by providing additional independent information. They also reveal key dependencies on model specifications – from physical processes to propagation environments in multiple media – and thus require comprehensive validation to establish their quantitative utility.

Hilbert–Huang Transform and Energy Rate Functions for Earthquake Source Characterization—A Study from the Japan Trench

ABSTRACT The Hilbert–Huang Transform (HHT) has been sparsely applied to problems in seismology, although previous studies have pointed to its broad scope. In this maiden attempt, we use the HHT to represent earthquake energy release duration and frequency content and compare the results with two conventional inversion methods. By selecting examples from interplate, intraplate, and intraslab settings, we demonstrate that the HHT has the power to discriminate energy release of earthquakes with different tectonic affiliations. We observe that the dominant frequencies for energy release are higher for intraslab earthquakes than for interplate and intraplate events. We use the empirical mode decomposition-based HHT and introduce a new parameter, which we name the energy rate function (ERF), to quantify the energy release. By employing empirical Green’s functions to remove the path and site effects and using a linear combination of a select set of intrinsic mode functions, we generate the station-specific relative measure of energy that we refer to as relative ERFs (RERFs). Averaged over RERFs from multiple stations, the ERF represents a measure of the total relative energy release, comparable to the moment rate functions (MRFs) and SCARDEC source time functions (STFs). Results for six of the seven earthquakes we analyzed show high cross correlation with the STFs (0.84 ± 0.03) and MRFs (0.79 ± 0.06), but there are mismatches between ERFs and MRFs or STFs when the energy release is complex and involves multisegment or bilateral ruptures. The proposed method is computationally efficient, requiring only 3.46 ± 2.62 s on average, compared to ~20 min (~1200 s) for the teleseismic inversion method we employ. With its ability to represent the seismic source in terms of energy release, the ERF method has the potential to evolve not as an alternative to waveform inversion but as a rapid time–frequency analysis tool, useful for earthquake hazard assessment.

Construction of a Ground-Motion Logic Tree through Host-to-Target Region Adjustments Applied to an Adaptable Ground-Motion Prediction Model

ABSTRACT The purpose of a median ground-motion logic tree is to capture the center, body, and range of possible ground-motion amplitudes for each earthquake scenario considered in a seismic hazard analysis. For site-specific hazard analyses, the traditional approach of populating the logic tree branches with ground-motion prediction models (GMPMs) selected and weighted on the basis of vaguely defined applicability to the target region is rapidly being abandoned in favor of the backbone GMPM approach. In this approach, the selected backbone model is first adjusted to match the earthquake source and path characteristics of the target region, and then it is separately adjusted to account for the site-specific geotechnical profile. For a GMPM to be amenable to such host-to-target adjustments, the magnitude scaling of response spectral ordinates should be consistent with the theoretical scaling of Fourier amplitude spectra. In addition, the influence of individual source and path parameters should be clearly distinguished in the model to allow the adjustments to be applied individually, and reliable estimates of the source and path parameters from the host region of the GMPM should be available, as should a reference rock profile for the model. The NGA-West2 project GMPM of Chiou and Youngs (2014; hereafter, CY14) has been identified as a very suitable backbone model. Moreover, rather than adopting generic source and path parameters and a rock site profile from the host region for CY14, which is not easily defined because the data from which it was derived came from several geographical locations, recent studies have inverted the model to obtain a CY14-consistent reference rock profile and CY14-compatible source and path parameters. Using these host-region characteristics, this study illustrates the process of building a ground-motion logic tree through the sequential application of multiple host-to-target-region adjustments, each represented by a node on the logic tree to achieve a tractable model for the total epistemic uncertainty.

Source Characteristics and Exacerbated Tsunami Hazard of the 2020 Mw 6.9 Samos Earthquake in Eastern Aegean Sea

AbstractOn 30 October 2020, an Mw 6.9 normal faulting earthquake occurred off the northern coasts of Samos Island, Greece. The earthquake with moderate magnitude generated the largest tsunami in the eastern Mediterranean since the 1956 Amorgos event. Here, we investigate the earthquake source characteristics and examine the factors contributing to the exacerbated tsunami using an integrated approach, including the source inversion from InSAR and GPS data, tsunami simulation, and spectral analysis of tsunami waveforms. The results show that a set of conjugate faults, determined in this study, can explain the geodetic surface deformation equally well. Our tsunami modeling results indicate that the shallow north‐dipping fault was most likely responsible for the mainshock. With the key features of our preferred source models, we infer that the tsunami was intensified by several conditions: (a) the spatial distribution of major coseismic slips coincided with the deepest portion of the Samos Basin which served as an energy reservoir for the tsunami generation; (b) the tsunami was generated in a semi‐closed bay which trapped most of the tsunami energy; (c) the east‐west orientation of the mainshock fault and the location of the concentrated coseismic slips directed the majority of the tsunami energy toward Sığacık Bay, which formed the extremely high tsunami of 3.8 m; (d) the primary tsunami wave period, close to natural oscillation frequency of the harbor, resulted in the observed significant oscillation. We conclude that the tsunami of the 2020 Samos earthquake was exacerbated by both the source characteristics and tsunami resonance property.

Full-waveform centroid moment tensor inversion of passive seismic data acquired at the reservoir scale

SUMMARY Passive seismic inversion at the reservoir scale offers the advantages of low cost, negligible environmental impact and the ability to probe a target area with low-frequency energy not afforded by even the most modern active-source seismic technology. In order to build starting models suitable for full-waveform wave speed tomography, characterization of earthquake sources is an indispensable first step. We present a workflow for the centroid moment tensor (CMT) inversion of seismic events identified in a passive seismic data set acquired by a large and dense array of three-component broad-band seismic sensors in a mountainous setting in the Himalayan foothills. The data set comprised 256 instruments operating for 2×4 months over an area of 8000 km2. An initial 3-D wave speed model was determined for the region via the analysis of first-arriving traveltime picks. Of the 2607 identified seismic events that were well recorded at frequencies between 0.2–50 Hz, 86 with magnitudes 1.3 ≤ M ≤ 3.0 initially had their CMT focal mechanisms determined by a waveform fitting procedure built on a Green’s function approach in a 1-D layered average wave speed model, for stations within an offset of 10 km, in the frequency range 0.2–1.4 Hz. Here, we obtain updated CMT mechanisms for the 86 events in that catalogue via multicomponent full-waveform inversion in the 3-D wave speed model. Our workflow includes automated data- and model-driven data selection using a combination of different metrics derived from signal-to-noise considerations and waveform-fitting criteria, and relies upon spectral-element simulations of elastic wave propagation in the 3-D wave speed model, honouring topography. Starting from the initial CMT solutions, we seek improvement to the data fit within the frequency band 0.5–2.5 Hz by minimizing the waveform difference between observed and synthetic data, while accommodating wave speed-model errors by allowing for small time-shifts. We balance uneven data coverage and tune their contributions via data-space weighting functions. We quantify the improvements to the data fit in terms of different metrics. We summarize the changes to the CMT solutions, and present and analyse the resulting catalogue for the region, including their breakdown into double-couple and non-double couple components, and their relation to mapped faults.

The Potential of Using Fiber Optic Distributed Acoustic Sensing (DAS) in Earthquake Early Warning Applications

ABSTRACT As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground-motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain-derived ground-motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground-motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground-motion metrics from DAS data. To obtain high-quality ground-motion observations, EEW-compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

Earthquake source characterization by machine learning algorithms applied to acoustic signals

Underwater seismic events generate acoustic radiation (such as acoustic-gravity waves), that carries information about the source and can travel long distances before dissipating. Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require a rapid characterisation of the fault properties: geometry and dynamics. In this work, we analysed hydrophone recordings of 201 earthquakes, located in the Pacific and the Indian Ocean, by employing acoustic signal processing and classification methods. The analysis allows identifying the type of earthquake (i.e. slip type, magnitude) and provides near real-time estimation of the effective properties of the fault dynamics and geometry. The results were compared against values reported by the Harvard Global Centroid Moment Tensor catalog (gCMT), revealing statistical significance between the extracted acoustic properties used to feed machine learning algorithms and the predicted slip and magnitude values.

Implications of Site Effects and Attenuation Properties for Estimation of Earthquake Source Characteristics in Kinnaur Himalaya, India

Implications of Site Effects and Attenuation Properties for Estimation of Earthquake Source Characteristics in Kinnaur Himalaya, India