Earthquake Prediction Research Articles

Enhancing the Statistical Evaluation of Earthquake Forecasts—An Application to Italy

Abstract Testing earthquake forecasts is essential to obtain scientific information on forecasting models and sufficient credibility for societal usage. We aim to enhance the testing phase proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP, Schorlemmer et al., 2018) with new statistical methods supported by mathematical theory. To demonstrate their applicability, we evaluate three short-term forecasting models that were submitted to the CSEP-Italy experiment, and two ensemble models thereof. The models produce weekly overlapping forecasts for the expected number of M4+ earthquakes in a collection of grid cells. We compare the models’ forecasts using consistent scoring functions for means or expectations, which are widely used and theoretically principled tools for forecast evaluation. We further discuss and demonstrate their connection to CSEP-style earthquake likelihood model testing, and specifically suggest an improvement of the T-test. Then, using tools from isotonic regression, we investigate forecast reliability and apply score decompositions in terms of calibration and discrimination. Our results show where and how models outperform their competitors and reveal a substantial lack of calibration for various models. The proposed methods also apply to full-distribution (e.g., catalog-based) forecasts, without requiring Poisson distributions or making any other type of parametric assumption.

Cloud radon data for earthquake magnitude prediction using machine learning

<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>

Modification of IPI Method for Extraction of Short-Term and Imminent OLR Anomalies and Case Study of Two Large Earthquakes

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies.

An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake

AbstractThe exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes.

Rapid earthquake impact prediction using non-ignorable missing data from non-expert observers

A rapid method for predicting earthquake impacts is proposed that uses spatial data received from non-expert individuals immediately after an earthquake. The approach addresses the issue of non-ignorable missing data caused by spatial latent random fields, which seriously affect the accuracy of predictions. A spatial model was employed to jointly model missingness and earthquake impact measurement by considering the latent spatial random fields as shared parameters. The proposed method demonstrated better prediction compared with that of existing models in both simulated and actual datasets. When applied to the real-world 2017 Sarpol-e Zahab earthquake in Iran, the model revealed concentrated impacts south and southeast of the epicenter.

Investigation on the Impact of the 2022 Luding M6.8 Earthquake on Regional Low-Frequency Time Code Signals in Northern China

Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction.

Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES

The China Seismo-Electromagnetic Satellite (CSES-01) is the first satellite of the space-based observational platform for the earthquake (EQ) monitoring system in China. It aims to monitor the ionospheric disturbances related to EQ activities by acquiring global electromagnetic fields, ionospheric plasma, energy particles, etc., opening a new path for innovative explorations of EQ prediction. This study analyzed 47 shallow strong EQ cases (Ms ≥ 7 and depth ≤ 100 km) recorded by CSES-01 from its launch in February 2018 to February 2023. The results show that: (1) For the majority (90%) of shallow strong EQs, at least one payload onboard CSES-01 recorded discernible abnormal signals before the mainshocks, and for over 65% of EQs, two or three payloads simultaneously recorded ionospheric disturbances; (2) the majority of anomalies recorded by different payloads onboard CSES-01 predominantly manifest within one week before or on the mainshock day, or occasionally about 11–15 days or 20–25 days before the mainshock; (3) typically, the abnormal signal detected by CSES-01 does not directly appear overhead the epicenter, but rather hundreds of kilometers away from the epicenter, and more preferably toward the equatorward direction; (4) the anomaly recognition rate of each payload differs, with the highest rate reaching more than 70% for the Electric Field Detector (EFD), Search-Coil Magnetometer (SCM), and Langmuir Probe (LAP); (5) for the different parameters analyzed in this study, the plasma density from LAP, and electromagnetic field in the ULF band recorded by EFD and SCM, and energetic electrons from the High-Energy Particle Package (HEPP) show a relatively high occurrence of abnormal phenomena during the EQ time. Although CSES-01 has recorded prominent ionospheric anomalies for a significant portion of EQ cases, it is still challenging to accurately extract and confirm the real seismic precursor signals by relying solely on a single satellite. The combination of seismology, electromagnetism, geodesy, geochemistry, and other multidisciplinary means is needed in the future’s exploration to get infinitely closer to addressing the global challenge of EQ prediction.

Seismic Behavior Improvement of Rigid Steel Frame Braced with Cable and Optimal Rotational Friction Damper

The research focused on enhancing the seismic performance of steel moment frames using cable braces and a central friction damper. By optimizing the design and pretensioning force of the cable braces, this study aimed to improve the energy absorption and overall behavior of the frames under cyclic earthquake loads. A quasi-cyclic loading test was developed through FE simulations using ABAQUS software, version 2023. To verify the modeling, an experimental test was compared with the numerical modeling, and the numerical results confirmed the accuracy of the experimental data. Results made by modeling in ABAQUS software (version 2023) include the impact of pretensioning force on stiffness and energy absorption, the relationship between pretensioning force and force required to move the target, the increase in absorbed energy with pretension force up to 25%, and the superior seismic performance of frames with rotational friction dampers. This study also highlighted the benefits of using cable braces with a friction damper regarding the symmetry of hysteresis diagrams, cyclic performance, and energy absorption capacity. The amount of pretensioning of the cables affects the energy dissipation capacity. As the pretensioning of the cables increases, the energy dissipation capacity initially increases. However, further increases in pretensioning lead to decreased energy dissipation capacity beyond a certain point. When the percentage of cable brace pretension increases from 2% to 25%, the energy dissipation capacity is enhanced by 2%, and when in the 25–30% range, it stabilizes at around 35%. Energy dissipation capacity decreases for pretensions of more than 30%.

Detecting infrastructure hazard potential change by SAR techniques on postseismic surface deformation: A case study of 2016 Meinong earthquake in southwestern Taiwan

Over the past two decades, multi-temporal InSAR techniques have been successfully utilized to monitor surface deformation throughout earthquake cycles. As sensor resolution and observation frequency of radar satellites have significantly improved, the spatial monitoring capability has expanded from tectonic to regional and local scales. Although regions with higher deformation rates are generally considered to have greater hazard potential, from an engineering perspective, locations with abrupt displacement rate changes may pose greater risks. This paper presents a robust method to transform tectonic-scale geodetic data into engineering-scale deformation tolerance ratios for assessing infrastructure safety during the postseismic period. Using the persistent scatterer InSAR (PSI) method, we measured line-of-sight (LOS) displacements from Sentinel-1 satellite images. By correcting LOS velocities with GNSS data and constraining the velocity in the N-S direction, we inverted the 2D (E-W and UD) postseismic deformation rates for the three years following the 2016 Meinong earthquake. The 2D rates were then converted into annual deformation tolerance ratios (ADTR), with a statistically established threshold to identify infrastructure segments with high hazard potential. Additionally, we conducted time-series variation analysis on the inherent pixel unit to evaluate the state of hazard potential and characterize the spatiotemporal behavior of fault systems during the postseismic period. Our results indicated that the postseismic deformation rates in the E-W and UD directions are 1.5 and 2–3 times higher, respectively, than those in the interseismic period. The horizontal ADTR index of high-speed railway infrastructures identified a high-hazard potential segment located on the Chegualin fault in Kaohsiung, while the vertical ADTR index highlighted a high-hazard potential segment at a junction between multiple fault extensions in Tainan. Moreover, statistical time-series variation analysis revealed a gradual decrease in hazard potential after 2017, which is related to a slow earthquake that occurred in early to mid-2018, modulating regional stress during the postseismic period. Our novel method not only highlights the impact of postseismic deformation on infrastructure but also provides a crucial basis for future earthquake hazard prevention and mitigation.

Application of Neural Network Technologies for Tectonic Earthquake Forecast

Successful earthquake prediction includes statistical, tectonic and physical forecasting. The main requirements for this are the establishment of the laws of earthquake mechanics and control of the geodynamic state in the region at the right times. However, resolving this issue faces difficulties of both theoretical and practical nature. Despite the fact that specialists all over the World have collected the fairly complete database on earthquakes, tectonic, electromagnetic, hydrological, etc. signs of earthquakes, the very nature of predicting the future source remains uncertain. The results obtained in the world on statistical forecasting using artificial intelligence give hope for the possibility of predicting earthquakes if we combine tectonic forecasting with the destruction of materials under experimental conditions and numerical modeling under the roof of deep learning neural network technologies. The paper provides the first results of predicting medium-term tectonic earthquakes using artificial intelligence for the Fergana depression in Uzbekistan.

Artificial Intelligence for Earthquake Prediction: A Preliminary System Based on Periodically Trained Neural Networks Using Ionospheric Anomalies

There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in time, to one or a few events. To date, no solution has been presented that consistently yields the location and magnitude of future earthquakes and thus can be used to develop a warning service. The purpose of this research is to improve on the possible use of Global Ionospheric Maps for earthquake prediction. The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. This network was trained on all large earthquakes occurring from the beginning of the year 2011 to the beginning of October 2024 but it is proposed that it be periodically retrained with new data. This network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes. Nevertheless, this percentage increases considerably if the classification into neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system. The author believes that success in this endeavor has to come from a collaborative effort. For this reason, the training and validation data with three-dimensional matrices (latitude/longitude/time) of total electron content values along with the subsequent earthquake magnitudes are provided in this paper along with the trained network. Researchers are strongly encouraged to improve on the current neural network with or without the inclusion of additional information.

Strong motion recording baseline drift recognition based on CNN-LSTM

Strong motion records, as one of the important means to obtain earthquake information and understand the nature of earthquakes, provide a scientific basis for earthquake prediction and disaster prevention and mitigation. However, records that contain baseline drift can degrade the quality of the data and affect subsequent studies. In this paper, a baseline drift identification model based on convolutional neural networks and long short-term memory algorithms is proposed for identifying records containing baseline drift from strong motion records. To improve the accuracy of the model, Bayesian optimization is used to optimize the hyperparameters of the model. Using the strong motion records from the 1999 Taiwan Chi-Chi earthquake, we constructed a dataset and divided the data into two categories: high-quality records and low-quality records. The experimental results show that the proposed baseline drift recognition model can effectively identify baseline drift records, with an accuracy of 83 % and an AUC value of 0.847. It also demonstrates good generalization performance on cross-domain test sets composed of data from the Japan KiK-net and European ESM databases. Compared to other models, the recognition performance of the model in this paper is superior.

DEEP LEARNING-BASED CLASSIFICATION OF SEISMIC EVENTS USING WAVEFORM DATA

Artificial Intelligence (AI)-enhanced seismology has evolved to its tremendous capability in analyzing real-time seismic data. This supports various tasks such as earthquake detection, phase detection, epicentral location prediction, time prediction of future earthquakes, phase picking, first motion polarity, early warning systems etc. This is crucial for the management of emergency hazard response spectra and post-disaster activities.

Automatically Detected CSES Ionospheric Precursors Before Part of the Strong Aftershocks of the 23 January 2024 Wushi MS 7.1 Earthquake in Northwest China

Earthquake prediction is still a large challenge worldwide so far. In this paper, an automatic detection method was put into service immediately after the Wushi MS 7.1 earthquake on 23 January 2024 to weekly detect possible CSES (China Seismo-Electromagnetic Satellite) precursory information before impending aftershocks. An electron perturbation with an enhanced magnitude of 38.3% was first detected on 24 January 2024 at night orbit 33175 and the corresponding variations in different plasma parameters measured at this orbit presented a typical feature of electron depletion or plasma bubble with an abrupt decrease and then an increase after one minute. The Kp index was also checked during this period and the values reached 3.7 once on 23 and 24 January, which indicates that these ionospheric variations probably originated from solar activities instead of three strong aftershocks with a magnitude more than five in the following three days. However, uncertainties still exist. Then, an electron perturbation with amplitude of 24.6%, as well as an O+ one of 27.3%, was successfully searched automatically at the same revisiting orbit 33251 on 3 February 2024 in a magnetically quiet period. These two plasma variations, as well as ones of other ionospheric parameters, were characterized by highly synchronous properties, which increase the availability as seismic precursors. However, no obvious variations were observed at other revisiting orbits or other orbits near the aftershock areas during this period. An aftershock with magnitude of MS 5.3 and the strongest one of MS 5.8 took place on 24 and 25 February, respectively, 20 days after and 1000 km away.

Seismic Activity Analysis in California: Patterns, Trends, and Predictive Modeling

For decades, California has been considered one of the most seismically active areas in the United States, if not the most, due to the frequent occurrence of earthquakes from tectonic activity, notably along the San Andreas Fault. Understanding seismic activity is a matter not only of safety but also of major importance for urban planning. In the recent past, Machine learning algorithms (ML) have emerged as a promising invention for advancing earthquake prediction by facilitating the pinpointing of patterns in large datasets that may be hard to detect through traditional techniques. The primary objective of this research project is to assess historical seismic data to identify trends and patterns in California’s seismic activity. Equally important, the goals of this research focused on developing predictive models that can provide insight into the possibility of seismic events in the future. To assess seismic activity in California, data were gathered from a credible and reputable source, most notably, the United States Geological Survey (USGS) Earthquake Database, which provides detailed records of earthquakes spanning over six decades. This dataset included all recorded seismic events in California, capturing the key details about the place of occurrence in latitude and longitude, the magnitude of the seismic event, the depth at which it happened, and the time when the seismic activity took place. To devise and curate a reliable earthquake prediction algorithm for California, distinct machine learning models were considered, comprising, Random Forest, XG-Boost, and Logistic Regression. Overall, the Random Forest algorithm exemplified high accuracy. Optimizing this algorithm will lead to more reliable predictions, potentially aiding disaster preparedness and risk mitigation in California's earthquake-prone areas. The findings from seismic activity analysis have deep implications for urban planning and disaster preparedness in California. Knowledge of the pattern, place, and magnitude of earthquakes over time will help urban planners and policymakers structure communities that can remain resilient during such calamities. For instance, higher earthquake frequencies would automatically call for increased stringency in building codes, especially along fault lines, to ensure that houses situated on such lines are designed to withstand major seismic activity. This operation may even limit or reallocate urban development out of high-risk zones into less risky zones.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

Towards a method for forecasting earthquakes in Iceland using changes in groundwater chemistry

An advance has been made towards a method for forecasting earthquakes several months before they occur. The method relies on changes of groundwater chemistry as earthquake precursors. In a study published in 2014, we showed that changes of groundwater chemistry occurred prior to and were associated with two earthquakes of magnitude 5 and higher, which affected northern Iceland in 2012 and 2013. Here we test the hypothesis that similar or larger earthquakes could have been forecast in the following decade (i.e. 2014–2023) based on our published findings. We found that we could have forecast one of the three greater than magnitude 5 earthquakes that occurred. Noting that changes of groundwater chemistry were oscillatory, we infer expansion and contraction of the groundwater source region caused by coupled crustal dilation and fracture mineralisation associated with the stress build-up before earthquakes. We conclude by proposing how our approach could be implemented elsewhere.

A model of the earthquake cycle along the Gofar oceanic transform faults

The Gofar oceanic transform fault at the East Pacific Rise has one of the best seismic cycles recorded by modern instruments. The timing, location, and magnitude of major earthquakes (Mw>5.5) have been well constrained by data from global seismic networks for the past 30 years. The earthquake interval is short, about 3-5 years. Several segments have already experienced 5 cycles since 1995, when the seismic network was good enough for surface wave relocation. Two ocean bottom seismometer deployments (2008-2009, 2021-2023) also provide constraints on the seismic properties on the fault. This makes Gofar an ideal place to study earthquake cycles. Here, we developed a model for the seismic cycle along the Gofar transform fault using a semi-analytical approach for rapidly calculating 3D time-dependent deformation and stress caused by screw dislocations embedded within an elastic layer overlying a Maxwell viscoelastic half-space. The 160-km long fault is divided into three major segments with six asperities. Our model simulates the earthquake pattern on this fault for the past 30 years. Most of the time, each asperity ruptured as a large earthquake every 3-5 years. Most segments have a nearly constant Coulomb stress threshold of 2-3 MPa, providing optimal conditions for the forecasting of future earthquakes along Gofar. For three cases that deviated from this simple regular pattern, a large earthquake occurred with a centroid location between two asperities. This is likely due to concurrent rupture that involved both asperities. We also modeled surface deformation with different elastic layer thicknesses and mantle viscosities. Even though most deformation is in the horizontal direction, the difference in both horizontal and vertical directions between models can be as large as a few centimeters per year. Several seafloor geodesy methods can be used to differentiate between models, and seafloor pressure might be the most appropriate one at this remote location.

Source mechanism of the 2023 Ms 5.5 earthquake in Subei, Gansu Province revealed by relocated aftershocks and InSAR: complement to the ‘shallow slip deficit’ of the eastern boundary of the Altyn Tagh fault

The Ms 5.5 earthquake struck on 24 October 2023, in Subei County, Gansu Province, China, occurring along the eastern segment of the Altyn Tagh fault. It raises the question of whether this earthquake is linked to the ongoing shortening slip rate along this segment or triggered by other seismic events. Analyzing the fault geometry of the Subei earthquake and understanding the significance of the weakening activity rate for seismic hazards in neighboring regions is crucial. The surface deformation from small- and medium-sized earthquakes (magnitudes less than Mw5.5) is often subtle, and the coseismic deformation detected by interferometric synthetic aperture radar (InSAR) is vulnerable to atmospheric disturbances, leading to significant measurement errors. Moreover, inaccuracies in the regional crustal velocity structure can cause errors in earthquake localization based on seismic data. These challenges complicate the establishment of a rupture model for seismogenic faults and hinder the inversion of fault slip models. To overcome these limitations, we employed the time-series InSAR stacking method and aftershock relocation to determine the fault geometry of the Subei earthquake. A two-step inversion method was utilized to ascertain both the fault geometry and slip distribution. Our modeling indicates that the 2023 Subei earthquake had a thrust mechanism with a component of strike-slip. The rupture did not reach the surface, with the maximum fault slip measuring 0.45 m at a depth of 2.5–3.5 km. The fault dips westward, and the moment magnitude is calculated at 5.4. This earthquake is associated with the ongoing weakening of the left-lateral strike-slip rupture along the Altyn Tagh fault in the Subei region. Furthermore, retrograde thrust tectonics significantly contribute to the absorption of accumulated stress during this process.Our findings highlight the potential of utilizing time-series InSAR images to enhance earthquake catalogs with geodetic observations, offering valuable data for further studies of the earthquake cycle and active tectonics. This approach is also applicable in other tectonically active regions, enhancing understanding of seismic hazards and risk assessment.

Exploring frictional properties of upper plate fault reactivation in subduction zones: The Atacama Fault System in northern Chile

The Maule 2010 and Tohoku-Oki 2011 earthquakes demonstrated how dormant upper plate faults can be reactivated as normal faults by plate margin relaxation following megathrust slip. However, the reactivation mechanisms of these types of faults are yet unexplored. To provide a better understanding of these mechanisms, we collected fault core samples from fault segments of the Atacama Fault System in northern Chile. The sampled fault segments have clear morphological evidence of Quaternary reactivation as normal fault. We performed laboratory experiments to measure the fault strength, stability and dynamic weakening. We consider low-velocity tests for exploring the frictional strength and velocity dependence of friction via a double-direct shear apparatus and ii) high-velocity tests for investigating the frictional properties at seismic velocities via a rotary shear apparatus. The experiments revealed that fault cores have low frictional strength, velocity-strengthening behaviour and strong dynamic weakening. Additionally, a novel experimental procedure that simulates stress relaxation by stepwise reducing of the normal stress on the sample assembly showed: 1. Accelerating creep towards dynamic weakening in chlorite-rich gouges and 2. oscillatory sliding in fault gouges enriched in illite. By extrapolating our experimental observations to natural conditions, we conclude that stable sliding is favoured during the interseismic phase of the subduction earthquake cycle, whereas unstable sliding is favoured during the coseismic and postseismic phases. The latter occurs via normal stress reduction during the shift from interseismic compression to co- and postseismic tension at the plate margin.