Local Earthquakes Research Articles

Examining the Connections Between Earthquake Swarms, Crustal Fluids, and Large Earthquakes in the Context of the 2020–2024 Noto Peninsula, Japan, Earthquake Sequence

AbstractEarthquake swarms are most commonly composed of small‐magnitude earthquakes. However, a recent study by Yoshida, Uchida, et al. (2023, https://doi.org/10.1029/2023GL106023) analyzed a swarm beneath the Noto Peninsula in Japan that, after more than two years of moderate‐magnitude seismicity, triggered the moment magnitude (Mw) 6.2 Suzu mainshock in May 2023. Based on high‐precision earthquake locations and a slip inversion of the mainshock, these authors found that the Mw 6.2 Suzu earthquake occurred on the updip extension of a fault that was active during the swarm, likely driven by increased fluid pressure. After publication of that paper, a much larger and more destructive Mw 7.5 event occurred nearby on 1 January 2024. These events underscore the potential for swarms to be precursors to large, damaging earthquakes. Forecasting the eventual evolution of swarms is currently very challenging but could be aided in the future by new observations and models.

A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.

Probabilistic location of early instrumental earthquakes based on macroseismic and instrumental data

There are regions of moderate seismicity that have long lacked a dense network of seismic stations due to their sparse population, inaccessibility, and low economic levels. Characterization of earthquakes in such regions is typically limited to the small amount of macroseismic information available and data from only one or two seismic stations. Most often, the location of such earthquakes was based on the use of macroseismic information alone. We propose a method for probabilistic estimation of the location of such earthquakes based on a joint analysis of their macroseismic and instrumental data. The technique is implemented in the ProLoM program (Probabilistic Locator by Macroseismics). In this study, we test the performance of the method on a test earthquake of May 20, 1967 and present the results of the analysis of the earthquakes that occurred in the north of the East European Platform on June 30, 1911 and January 13, 1939.

Monitoring the Workflow of Earthquake by Using Machine Learning

This study explores the innovative application of machine learning techniques in monitoring and analyzing the workflow associated with earthquakes. The primary objective is to enhance the accuracy and efficiency of earthquake detection, prediction, and response mechanisms the development of machine learning technology enables more robust real-time earthquake monitoring through automated implementations. However, the application of machine learning to earthquake location problems faces challenges in regions with limited available training data. This paper proposes a machine learning-based approach to monitor seismic workflows efficiently. We apply various Machine Learning algorithms, including deep learning neural networks and decision trees, to process and interpret vast amounts of seismic data. These algorithms were trained on historical earthquake records to identify patterns and anomalies that precede seismic events. Our approach not only focused on the detection of seismic activities but also on the prediction of potential aftershock locations and magnitudes. Earthquake monitoring and response systems play a crucial role in mitigating potential damages and ensuring rapid response to seismic events. Traditional methods rely on sensor networks and manual analysis, often leading to delays in detection and response. The system leverages real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. By employing deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), the system can detect and classify seismic activities accurately. Additionally, anomaly detection techniques enable the identification of unusual seismic patterns that might indicate impending earthquakes or aftershocks. The system integrates geographical information systems (GIS) to map the seismic activities spatially, aiding in the visualization and understanding of seismic patterns across regions. The machine learning models are trained on historical seismic data to enhance their predictive capabilities and adaptability to varying seismic behaviors. This proposed system offers a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. Its ability to continuously learn from incoming data ensures adaptability to changing seismic behaviors, thereby improving overall efficiency and reliability in earthquake monitoring workflows.

Unraveling the Complex Interplay: Exploring the Relationships between Seismic and Volcanic Activities in the Colca River Area Using the Coulomb Stress Transfer

Abstract The Colca River area is affected by shallow small-to-moderate earthquakes due to the activity of normal and strike-slip crustal faults on the overriding South American plate. In addition, volcanic activity from the Sabancaya volcano has been recorded. However, the complex relationship between seismic and volcanic activities and the factors that trigger them are poorly understood. To better understand the factors that influence seismic and volcanic activity and their potential connection, it is crucial to characterize the interactions between subsequent earthquakes and assess the impact of magmatic inflation on seismic events. In this study, we analyzed the static Coulomb stress transfer caused by the selected largest earthquakes from 1991 to 2022. We focused on both the assumed source faults and the receiver faults. Furthermore, we examined the Coulomb stress change due to magmatic inflation in 2013–2022 on nodal planes of the selected earthquakes. The results confirm the tectonic source for most earthquakes in the Colca region. Commonly, the magmatic source enhanced the stress change induced by the tectonic source. Although the Coulomb stress change caused by the significant earthquakes had a greater impact than the one resulting from the magmatic inflation, the Coulomb stress transfer seems not to be a dominant factor determining the occurrence and location of earthquakes in this area. The results indicate that most source faults of the analyzed earthquakes were not brought closer to failure due to a positive Coulomb stress transfer caused by seismic activity or magmatic inflation.

Development and evaluations of the broadband ocean bottom seismometer (Yardbird-BB OBS) in Taiwan

This study focuses on developing and evaluating the broadband ocean bottom seismometer (Yardbird-BB OBS) in Taiwan. The Yardbird-BB OBS is a crucial instrument for recording seismic signals in deep-sea environments. Rigorous testing ensures optimal performance and data recording capabilities. Following assembly, the Yardbird-BB OBS undergoes a 3–6 month deployment test in the deep sea, capturing seismic signals worldwide. Data from 2016 and 2017 deployments in the Okinawa Trough analyze significant seismic events, including a magnitude 7.8 earthquake in New Zealand and a magnitude 6.3 earthquake from a North Korean nuclear test. Waveform analysis, focusing on tele-seismic events and waveform quality, assesses the OBS’s performance, highlighting successful automatic leveling adjustment. These high-quality recordings benefit research, aiding the study of plate tectonics, crustal age estimation, seafloor ambient noise determination, and earthquake location accuracy improvement. The study also details methods for verifying instrumental self-noise, dynamic range, digitization sensitivity, linearity error, clock drift, and data logger power consumption. Calibration procedures and evaluation methods provide insights into Yardbird-BB OBS performance characteristics, contributing to its understanding and enhancement for effective long-term underwater data recording and valuable scientific research.

Monitoring volcanic activity with distributed acoustic sensing using the Tongan seafloor telecommunications cable

AbstractThe devastation caused by the January 2022 eruption of Hunga Tonga-Hunga Ha’apai volcano (HTHH) in the Tongan archipelago reminded us of the importance of monitoring shallow-sea volcanic activity. Seismic observations are essential for such monitoring, but there were no operational seismic stations in Tonga at the time of the eruption. There are only a few islands near Tongan volcanoes, and installation and maintenance of seismic stations on remote islands are expensive. Seismic observations based on distributed acoustic sensing (DAS) using a seafloor cable may provide a more practical and economical solution. To investigate the potential of this approach, we made preliminary DAS observations for 1 week using the seafloor domestic broadband telecommunications cable in Tonga. DAS equipment was installed at the landing station of the seafloor cable at Nuku’alofa on Tongatapu, the main island of Tonga. To provide reference data, we installed several seismometers on Tongatapu. The DAS data we obtained showed high noise levels in areas of shallow coral reef, but noise levels decreased greatly in deeper water areas, indicating that DAS is suitable for seismic observations of the deep seafloor. We detected many local and regional earthquakes during our week of observation and determined 17 earthquake hypocenters by picking P- and S-wave arrival times from the DAS and onshore seismic data. Although most of these were tectonic events related to the subduction of the Pacific plate along the Tonga trench, several events were detected around the volcanic chain of the Tongan archipelago including one event beneath the HTHH crater, implying that activity at HTHH has continued since the 2022 eruption. The much lower cost of installation of DAS equipment compared to that for pop-up type ocean-bottom seismometers and the ability of DAS systems to monitor seismic activity in real-time make it an attractive option for monitoring the activity of HTHH and other volcanoes near seafloor cables in the Tongan archipelago. Graphical Abstract

Probabilistic Location of Early Instrumental Earthquakes Based on Macroseismic and Instrumental Data

Probabilistic Location of Early Instrumental Earthquakes Based on Macroseismic and Instrumental Data

Intrinsic and scattering attenuations of the Sichuan-Yunnan region in China from S coda waves

Seismic attenuation is a fundamental property of the Earth's media. Attenuation structure for the complicated geological structures with strong seismicity in the Sichuan-Yunnan region is poorly studied. In this study, we collected 108,399 waveforms of 11,517 local small earthquakes with magnitudes between 1.5 and 3.5 from January 2014 to September 2021 in the Sichuan-Yunnan region and its adjacent areas. We employed an envelope inversion technique for separating the intrinsic and scattering attenuations of the S coda wave, and obtained the intrinsic and scattering attenuation structures for frequencies between 0.25 and 8.00 Hz. The attenuation structures correlate well with the geological units, and some major faults mark the attenuation variations where historic large earthquakes have occurred. The regional average attenuation shows a negative frequency dependence. The average scattering attenuation has a faster descending rate than the average intrinsic attenuation, and is dominant at low frequencies, while at high frequencies the average intrinsic attenuation is stronger. The lateral variation in the intrinsic attenuation is consistent with the variation in heat flow, the scattering attenuation may be related to the scatter distribution and size. The total attenuation is consistent with the previous studies in this region, and the separate intrinsic and scattering attenuation may be useful in understanding regional tectonics and important in earthquake prevention and disaster reduction.

Incorporating Intensity Distance Attenuation Into PLUM Ground‐Motion‐Based Earthquake Early Warning in the United States: The APPLES Configuration

AbstractWe develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground‐motion prediction procedures. Under APPLES, instead of using a fixed radius to forward‐predict observed peak ground shaking to the area surrounding a seismic station, the forward‐predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real‐time tests to determine warning time performance. Finally, we compare real‐time alert behavior during the 2022 M6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over‐alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source‐based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.

OVERVIEW OF UNDERSTANDING AND PREPAREDNESS FOR EARTHQUAKES IN THE PERSPECTIVE OF VOCATIONAL HIGH SCHOOL STUDENTS

Knowledge of disaster mitigation is the most essential component and a key factor for preparedness. The possessed knowledge typically influences attitudes and concerns, fostering readiness to anticipate disasters. Vocational High School students should be informed about the various disasters that frequently occur in their areas, as well as the procedures to face sudden disasters. The significant loss of lives and various damages to buildings and facilities in the vicinity underscore the need for a deeper examination of mitigation efforts. The objective of this study is to assess the level of understanding and preparedness of Vocational High School students regarding earthquake natural disasters. The research method employed is quantitative, with descriptive statistics as the analytical technique. The instruments used include test instruments and non-test instruments utilizing Google Forms to collect data from several Vocational High Schools in various cities in DKI Jakarta, Sukabumi, and Tangerang. The research findings reveal that 42% of students are unaware of earthquake disaster mitigation measures. Additionally, 69% of students lack knowledge about minimizing damage from earthquakes in terms of building construction. Furthermore, 65% of students are unfamiliar with images related to earthquake-resistant construction. Moreover, 52% of students still do not know the local earthquake resilience features of earthquake-resistant houses. Lastly, 52% exhibit a panicky attitude, hastily evacuating buildings during an earthquake. The conclusion drawn from this research is that the knowledge of Vocational High School students regarding earthquake disasters and their mitigation measures is still low.

Monitoring the workflow of Earthquake by using Machine Learning

The main goal is to improve the accuracy and efficiency of earthquake detection, prediction and response through automation and, over time, to improve machine learning techniques for earthquake monitoring. However, the application of machine learning to earthquake location problems faces challenges in areas with limited data. This article presents an engine-based approach to monitor earthquake performance. We use a variety of Machine Learning algorithms, including deep learning neural networks and decision trees, for processing and interpret large amounts of seismic data. These algorithms are trained on historical earthquake records to identify patterns and anomalies prior to seismic events. Our approach focuses not only on detecting seismic events, but also on predicting the location and magnitude of earthquakes. Earthquake monitoring and response systems play an important role in reducing potential losses and providing rapid response to seismic events. Conventional methods rely on sensor networks and manual analysis, often with delays in detection and response. The system receives real-time data from seismic sensors, incorporating advanced machine learning algorithms for data analysis and pattern recognition. The system can detect and describe seismic events using deep learning models such as convolutional neural networks (CNN) and random neural networks (RNN).In addition, anomaly detection techniques allow the identification of unusual seismic patterns that may indicate impending earthquakes or earthquakes. The system integrates geographic information systems (GIS) to comprehensively map seismic activity, helping to visualize and understand seismic patterns in the region. Machine learning models are trained on historical seismic data to improve their predictive capabilities and adapt to different seismic behaviors. This proposed system provides a real-time monitoring solution that can assist in early earthquake detection, accurate event classification, and timely response coordination. The ability to continuously learn from incoming data enables adaptation to change in seismic activity, thereby increasing the efficiency and reliability of earthquake monitoring operations.

Potential Locations of Strong Earthquakes in Bulgaria and the Neighbouring Regions

Information about potential earthquake sources is a key issue for seismic hazard assessment. This study presents the application of a phenomenological approach based on pattern recognition to determine the possible locations of strong earthquakes in the Bulgarian region. The technique assumes the origin of strong earthquakes in morphostructural nodes formed around the intersections of morphostructural lineaments identified by morphostructural zoning. For the territory of the Bulgaria and neighbouring regions, 178 nodes were defined in this work. The CORA-3 pattern recognition algorithm identified 59 seismogenic nodes analysing a set of geophysical and geological node’s characteristics. The identified seismogenic nodes are capable to generate earthquakes with magnitude equal to or greater than 6 and are located at the boundaries between the largest tectonic domains: Rila, Pirin, and Rhodope orogens; the Serbian-Macedonian massif; and in the Stara Planina belt. The set of characteristic features of seismogenic nodes indicates that the vicinity of potential nodes is characterized by a high contrast of neotectonic movements of the Earth’s crust and the presence of deep heterogeneities in the Earth’s crust. About 40% of the recognized nodes are not associated with any earthquakes, while the rest of the recognized seismogenic nodes are characterized by an area with a radius of 25 km where earthquakes are known to occur. Part of these “non active” seismogenic nodes are close to the historical events with magnitudes higher than 5.5 since the magnitude and location of historical events have large uncertainties. Another part of the seismogenic nodes may slightly change the location due to the uncertainties in morphostructural zonation. Other nodes may indicate unknown historical seismicity or paleoearthquakes. Defined M6+ seismogenic nodes can fill the potential gaps in the recorded seismicity on the territory of Bulgaria, thus to improve the seismic hazard assessment of the studied region.

Deciphering the low-frequency seismic signals in the Weiyuan Shale gas field: implications for reservoir and structural heterogeneity

SUMMARY Hydraulic fracturing (HF) often stimulates the local earthquake productivity which provides a unique opportunity to characterize the crustal heterogeneities, reservoir properties and fluid injection effects. However, the velocity models acquired solely based on the arrival time records are often undermined due to the seismic network coverage and interpolation techniques. Instead, we adopt the waveform-based approach to apprehend; (1) structural heterogeneities, (2) reservoir distribution and (3) signatures of the injected fluid in the Weiyuan shale gas field. We categorize the waveforms into dominant high and low frequencies based on the qualitative inspection and frequency index analysis of the seismic waveforms. We first inspect the waveform to access the potential controlling mechanisms (source, site and path effects) at both single and multiple stations in different azimuthal orientations. As a result, we find the path effect as a dominant factor to influence the waveform characteristics, for example S-wave amplitude, and frequency. Subsequently, to localize the path effect, we conduct an in-depth examination of events within 10 km of each seismic station and classify the waveform records using their frequency indices. Notably, certain stations record a significant proportion of low-frequency waveforms (LFWs, up to 20 per cent), while others have limited occurrences (∼1 per cent) indicating suspected anomalous zones. Afterward, we identify two suspected anomalous zones based on LFWs intensity and ray tracing map. Both zones are in close proximity to fault zones and preserved reservoirs with no HF activities, where fault damage zones or the fluid-rich reservoir may contribute to our observed LFWs.

Ocean Bottom Seismometer Clock Correction using Ambient Seismic Noise

Ocean-bottom seismometers (OBSs) are equipped with seismic sensors that record acoustic and seismic events at the seafloor, which makes them suitable for investigating tectonic structures capable of generating earthquakes offshore. One critical parameter to obtain accurate earthquake locations is the absolute time of the incoming seismic signals recorded by the OBSs. It is, however, not possible to synchronize the internal clocks of the OBSs with a known reference time, given that GNSS signals are unable to reach the instrument at the sea bottom. To address this issue, here we introduce a new method to synchronize the clocks of large-scale OBS deployments. Our approach relies on the theoretical time-symmetry of time-lapse (averaged) crosscorrelations of ambient seismic noise. Deviations from symmetry are attributed to clock errors. This implies that the recovered clock errors will be obscured by lapse crosscorrelations' deviations from symmetry that are not due to clock errors. Non-uniform surface wave illumination patterns are arguably the most notable source which breaks the time symmetry. Using field data, we demonstrate that the adverse effects of non-uniform illumination patterns on the recovered clock errors can be mitigated by means of a weighted least-squares inversion that is based on station-station distances. In addition, our methodology permits the recovery of timing errors at the time of deployment of the OBSs. This error can be attributed to either: i) a wrong initial time synchronization of the OBS or ii) a timing error induced by changing temperature and pressure conditions while the OBS is sunk to the ocean floor. The methodology is implemented in an open-source Python package named OCloC, and we applied it to the OBS recordings acquired in the context of the IMAGE project in and around Reykjanes, Iceland. As expected, most OBSs suffered from clock drift. Surprisingly, we found incurred timing errors at the time of deployment for most of the OBSs.

High-Resolution 3D QP and QS Models of the Middle Eastern Boundary of the Sichuan–Yunnan Rhombic Block: New Insight into Implication for Seismogenesis

Abstract The eastern boundary of the Sichuan–Yunnan rhombic block (EB-SYRB) has complex structures and strong seismicity. Although multiple 3D high-resolution velocity models have been constructed for this region, its seismogenic environment has been controversial. Seismic wave attenuation (inversely proportional to Q) describes the anelastic properties of the Earth’s medium, and is more sensitive to changes in subsurface fluid and temperature than seismic wave velocity. Based on the data of a long-term dense array in downstream of the Jinsha River, this article uses local earthquake tomography to obtain 3D QP and QS models of the middle EB-SYRB with the highest resolution to date, improving the lateral resolution of the Q model from 100 km to 5–10 km and the depth resolution from 10 to 2 km. Combined with the existing high-resolution velocity and resistivity models and geochemical observation results, we can comprehensively understand the medium structure and the seismogenesis in the study area. The results show that the high-attenuation characteristics in the shallow layer of the Xiaojiang fault zone and the Zemuhe fault zone (within a depth of ∼5 km) are consistent with the topographic relief and the distribution of hot springs, which reveals the Quaternary sedimentary characteristics of the basins and the presence of shallow fluids in the fault zone. The columnar high-attenuation anomaly beneath Huize reveals the fluid channel created by deep melting. The Ludian earthquake sequence occurred in a prominent low-attenuation area, which is favorable for stress accumulation and has a seismogenic environment for strong earthquakes. The high attenuation near the southwest end of the Huize fault is closely related to the Huize earthquake cluster, which is driven by fluids in the upper crust.

Seismic anisotropy and stress-field variations along the Dead Sea Fault zone in northern Israel

Geological observations based mainly on stress (strain)-indicating meso-scale markers suggest that the local maximum horizontal compressive stress (SHmax) direction along the Dead Sea Fault (DSF) system in northern Israel changes considerably over short distances from a ∼ N-S, fault-parallel orientation in the south to an ∼E-W, fault-perpendicular orientation in the north. However, the association of the meso-scale markers to the present-day stress field is not well constrained as many of them could have formed at any stage during the DSF evolution. In this study, we use a large dataset of Shear-Wave Splitting (SWS) measurements, taken from local DSF-related earthquakes, to characterize the azimuthal seismic anisotropy of the upper-crust at this critical turning point of the DSF system, near the Lebanese Restraining Bend. The SWS directions commonly align parallel to the local SHmax as revealed by the meso-scale markers, supporting their association with the current stress field. In several stations, SWS fast directions indicate azimuthal anisotropy at high angles to the trace of the master fault, indicating that stress-induced anisotropy represents a more plausible mechanism for SWS than structure-controlled anisotropy. The SWS measurements suggest that a northward counterclockwise rotation of the crustal SHmax is present along this segment of the DSF, supporting previous notation on the transition from divergent to convergent strike-slip faulting in this area. Plain langauge summaryGeological observations based mainly on stress (strain)-indicating meso-scale markers suggest that the local maximum horizontal compressive stress direction along the plate-boundary Dead Sea Fault (DSF) system in northern Israel changes considerably over short distances. However, the association of the meso-scale markers to the present-day local stress field is not well constrained since many of them could have formed at any stage of the DSF evolution. In this study, we use a common method of measuring local seismic anisotropy called “shear wave splitting” (SWS) to characterize the directional-dependent seismic wave speeds in the upper crust. The SWS measurements are obtained from local seismic and micro-seismic DSF-related events, and characterize the upper-crust seismic anisotropy at this critical turning point of the DSF system. The SWS directions commonly align parallel to the maximum compression as revealed by the meso-scale markers, supporting their association with the current stress field. The SWS measurements suggest that a northward counterclockwise rotation of the crustal maximum compression is present along this segment of the DSF.

The 2023 New Zealand Ground-Motion Database

ABSTRACT This article summarizes the development of the 2023 New Zealand ground-motion database (NZGMDB). A preceding version was formally used as the central ground-motion database in the ground-motion characterization modeling for the 2022 New Zealand (NZ) National Seismic Hazard Model (NSHM) revision. The database contains ground motions for events with a moment magnitude greater than ∼3.0 from the years 2000 to the end of 2022. Several challenges associated with NZ earthquake source metadata are explained, including determination of earthquake location, magnitude, tectonic classification, and finite-fault geometry, among others. The site table leverages the site database developed as a part of the 2022 NZ NSHM revision, and several definitions of source-to-site distance are computed for the propagation path table. The ground-motion quality classification was initially assessed using a neural network. Subsequent waveform quality verification was conducted and additional quality criteria were enforced to ensure a sufficiently high-quality database. Standard processing techniques were applied to the ground motions before intensity measure (IM) calculation. IMs in the database include peak ground acceleration, 5%-damped pseudoacceleration response spectra, smoothed Fourier amplitude spectra, and other cumulative and duration-related metrics. The NZGMDB is publicly available and routinely updated as new and higher quality data become available.

MACHINE LEARNING FOR FAST AND RELIABLE SOURCE-LOCATION ESTIMATION IN EARTHQUAKE EARLY WARNING

We develop a random forest (RF) model for rapid earthquake location with an aim to assist earthquake early warning (EEW) systems in fast decision making. This system exploits Pwave arrival times at the first five stations recording an earthquake and computes their respective arrival time differences relative to a reference station (i.e., the first recording station). These differential P-wave arrival times and station locations are classified in the RF model to estimate the epicentral location. We train and test the proposed algorithm with an earthquake catalog from Japan. The RF model predicts the earthquake locations with a high accuracy, achieving a Mean Absolute Error (MAE) of 2.88 km. As importantly, the proposed RF model can learn from a limited amount of data (i.e., 10% of the dataset) and much fewer (i.e., three) recording stations and still achieve satisfactory results (MAE<5 km). The algorithm is accurate, generalizable, and rapidly responding, thereby offering a powerful new tool for fast and reliable source-location prediction in EEW.

Potential of Earthquake Strong Motion Observation Utilizing a Linear Estimation Method for Phase Cycle Skipping in Distributed Acoustic Sensing

AbstractDistributed acoustic sensing (DAS) using existing optical fiber cables facilitates high‐density seismic observation. However, few studies have examined the reliability of the seismic waveform amplitude recorded by DAS. In this study, a DAS network was connected to optical fiber cables installed over a distance of 75 km along a high‐speed train (Shinkansen) railway in the Kumamoto prefecture, Japan. We successfully observed strong motions of the Mj6.6 earthquake (approximately 150 km from the fiber) on 22 January 2022, in Hyuga‐nada, in addition to several small local earthquakes. The observed strong motions from the Mj6.6 earthquake, using DAS, exhibited cycle skipping (clipping) issues due to dynamic range limitations at numerous channels. To address this, we estimated the shaking map, representing maximum strain distributions for Mj6.6, by replacing the clipped data with information from nearby unclipped channels and scaling their RMS amplitudes based on S‐coda (unclipped). Furthermore, we verified the reliability of the amplitude information obtained from DAS by estimating the distance attenuation of seismic waves while correcting for the differences in the structure type and coupling as much as possible. The distance attenuation property of local earthquakes was consistent with that of the peak ground velocities obtained from seismometers, indicating that DAS data acquired using fibers installed on infrastructure (various structures) can also be utilized to assess the spatial distribution of the relative amplitude values along the fiber. Obtaining high‐density seismic motion distributions is important for earthquake early warning and accurate damage estimation of strong motions.