Earthquake Research Research Articles

Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction

Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation.

Research on seismic characteristics of supercharged boiler based on measured boundary

Supercharged boilers are susceptible to seismic impact loads, which can cause severe structural responses and exceed their ultimate bearing capacity. Compared with the traditional simplified structural model for earthquake resistance research in cold state, this paper establishes a real and complete three-dimensional physical model of the supercharged boiler base, and uses the reverse calculation method to obtain the boundary conditions of the boiler based on the temperature of the experimental measurement points. The temperature field and pressure field of the boiler are reconstructed, combined with the coupling method of thermal mechanical solid multiple physical fields. Using time-domain data of peak acceleration in various directions under typical earthquakes as external input loads, this study explores the seismic characteristics of boilers in the X, Y, and Z directions using time-domain analysis methods, and clarifies the vibration response mechanism in the stress concentration area of the tube sheet. The results show that the measurement and simulation error of the temperature of the inner and outer walls of the drum is 0.10 %; The individual effects of thermal stress and mechanical stress account for 107.56 % and 66.46 % of the total stress, respectively. Thermal stress plays a dominant role, while mechanical stress produces impedance effects; Under the action of an earthquake, the tube sheet undergoes reciprocating oscillation motion, and the impedance effect generated by the flexibility of the tube bundle gradually attenuates. The local stress in the X and Y directions increases by 7.70–8.27 MPa, while the local stress concentration phenomenon occurs in the Z direction due to low damping and stiffness.

Kernel Density Estimation for the Interpretation of Seismic Big Data in Tectonics Using QGIS: The Türkiye–Syria Earthquakes (2023)

Numerous studies have utilized remote sensing techniques to analyze seismic data in active areas. Point density techniques, widely used in remote sensing, examine the spatial distribution of point clouds related to specific variables. Applying these techniques to complex tectonic settings, such as the East Anatolian Fault Zone, helps identify major active fractures using both surface and deep information. This study employed kernel density estimation (KDE) to compare two distinct point-cloud populations from the seismic event along the Türkiye–Syria border on 6 February 2023, providing insights into the main active orientations supporting the Global Tectonics framework. This study considered two populations of seismic foci point clouds containing over 40,000 events, recorded by the Turkish Disaster and Emergency Management Authority (AFAD) and Kandilli Observatory and Earthquake Research Institute (KOERI). These populations were divided into two datasets: crude and relocated-filtered. Kernel density analysis demonstrated that both datasets yielded similar geological interpretations. The high-density cores of both datasets perfectly matched, exhibiting identical structures consistent with geological knowledge. Areas with a minimal concentration of earthquakes at depth were also identified, separating different crustal strength levels.

New Features in the pyCSEP Toolkit for Earthquake Forecast Development and Evaluation

Abstract The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global community dedicated to advancing earthquake predictability research by rigorously testing probabilistic earthquake forecast models and prediction algorithms. At the heart of this mission is the recent introduction of pyCSEP, an open-source software tool designed to evaluate earthquake forecasts. pyCSEP integrates modules to access earthquake catalogs, visualize forecast models, and perform statistical tests. Contributions from the CSEP community have reinforced the role of pyCSEP in offering a comprehensive suite of tools to test earthquake forecast models. This article builds on Savran, Bayona, et al. (2022), in which pyCSEP was originally introduced, by describing new tests and recent updates that have significantly enhanced the functionality and user experience of pyCSEP. It showcases the integration of new features, including access to authoritative earthquake catalogs from Italy (Bolletino Seismico Italiano), New Zealand (GeoNet), and the world (Global Centroid Moment Tensor), the creation of multiresolution spatial forecast grids, the adoption of non-Poissonian testing methods, applying a global seismicity model to specific regions for benchmarking regional models and evaluating alarm-based models. We highlight the application of these recent advances in regional studies, specifically through the New Zealand case study, which showcases the ability of pyCSEP to evaluate detailed, region-specific seismic forecasts using statistical functions. The enhancements in pyCSEP also facilitate the standardization of how the CSEP forecast experiments are conducted, improving the reliability, and comparability of the earthquake forecasting models. As such, pyCSEP exemplifies collaborative research and innovation in earthquake predictability, supporting transparent scientific practices, and community-driven development approaches.

Full-Azimuth Quantitative Identification of Near-Fault Pulse-Like Ground Motions Based on M&P Wavelet

ABSTRACT Quantitative characterization and proper parameterization of pulse-like ground motions have been a challenge for earthquake engineering research. The azimuth of the pulse-like ground motion has a great influence on the structural damage. This study proposes a full-azimuth quantitative identification method to determine the pulse inherent in near-fault pulse-like ground motions. A support vector machine was used to classify the records into pulses or non-pulses based on a new pulse index. Records with a pulse index greater than zero can be classified as pulse-like. The proposed approach was used to identify pulse-like features in arbitrary orientations and can determine the strongest pulse azimuth.

Comparison of a linear discrimination function and artificial neural networks approach to discriminate the seismic events in Ankara (Turkey)

In this study, natural and blast-induced ground vibrations (namely earthquakes and quarry blasts) in Ankara (Turkey) were analysed and distinguished from each other. A total of 156 digitized vertical component velocity seismograms of seismic events of 2009-2014 with Md≤3.5 were obtained from the Lodumlu broadband station (LOD) controlled by Boğaziçi University, Kandilli Observatory, and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (BU-KOERIRETMC). We examined the following variables: the ratio of the highest amplitude value of the S-wave and the highest amplitude value of the P-wave (Amplitude ratio) of vertical component velocity seismograms, the power ratio (Complexity), the logarithmical value of the amplitude of the S-wave of the seismogram (Log S), and total signal duration of the seismogram. It is the first time that natural and blast-induced ground vibrations were separated from each other using the Fisher’s Linear Discriminate Analysis (FLDA) technique and Artificial Neural Networks (ANNs) approach together in Ankara (The capital of Turkey). Ninety-two (59%) of the 156 seismic events studied were identified as earthquakes and sixty-four (41%) of them were described as blast-induced ground vibrations. Then the obtained accuracy percentage results of three pairs of some variables were compared. The amplitude ratio versus complexity and the amplitude ratio versus total signal duration had a high classification accuracy determination coefficient for the LOD dataset (94% for the FLDA technique and 100% for ANNs approach). ANNs approach is more successful than the FLDA technique.

Automated, Near Real-Time Ground-Motion Processing at the U.S. Geological Survey

Abstract We describe automated ground-motion processing software named gmprocess that has been developed at the U.S. Geological Survey (USGS) in support of near-real-time earthquake hazard products. Because of the open-source development process, this software has benefitted from the involvement and contributions of a broad community and has been used for a wider range of applications than was initially envisioned. Here, we give an overview and introduction to the software, including how it has leveraged other open-source libraries. We highlight some key features that gmprocess provides, compare response spectra calculated with the automated processing approach of gmprocess to the response spectra provided by the Next Generation Attenuation projects, and summarize projects that have utilized gmprocess. These use-cases demonstrate that this software development effort has been successfully leveraged in earthquake research activities both within and outside the USGS.

Characterization of Micro-seismic Activity in Northern Cyprus Using Complexity and Corner Frequency Methods

Cyprus is an island country located in the eastern Mediterranean, to the south of Türkiye and the western of Syria and Lebanon, and is a popular tourist destination. Due to being surrounded by seas on all four sides, meticulous planning of rescue, assistance, and evacuation plans is necessary in the face of disasters such as earthquakes and tsunamis. Tectonically, the southern part of the island is controlled by the Cyprus Arc, while the northern part is dominated by the Kyrenia Range. The demand for raw materials for construction and industry is met through controlled quarry blasting operations carried out by open-pit quarry companies in the districts of Kyrenia and Nicosia. As a result, both natural and artificial seismic events occur in the region, and these quakes are documented in seismic catalogs by seismology centers. However, due to the low energy content of micro-seismic events and the inadequacy of seismic stations on the island, the source types of these seismic events can be misidentified in the catalogs. In this context, the study focuses on 122 seismic events with magnitudes between 0.9≤Ml≤2.7 that occurred in Northern Cyprus during the January 2018 - December 2021 period (4 years). The seismic events recorded by the station LFK, operated by Boğaziçi University Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC), were classified using Linear and Quadratic Discriminant Functions based on complexity and corner frequency methods. According to the results obtained, 10 of the 122 seismic events were identified as natural, and 96 were determined to be artificial, resulting in a general success rate of 86.89%. However, classification results for 16 seismic events were inconclusive with the methods used. As a result, more detailed secondary analyses should be conducted to accurately determine the source types of micro-seismic events, and the seismic catalogs should be updated accordingly.

A local magnitude scale (ML) for Northern Algeria

This study presents a local magnitude scale (ML) based on the original Richter definition and designed for use within the Algerian Digital Seismic Network (ADSN). The magnitude scale is derived from the analysis of 17,377 zero-peak maximum amplitude traces extracted from the vertical component, simulated as Wood-Anderson seismograms. These traces are taken from a dataset of 1901 earthquakes recorded between January 1, 2010, and June 1, 2022, at a minimum of five stations in the ADSN network. To better account for the attenuation of direct and refracted waves in northern Algeria, amplitude decay analysis reveals the presence of two transition distances at 90 and 190 km, resulting in three segments. A distance correction term, −log10(A0), is introduced and described by the following trilinear function:−logA0=0.6747∗log10R+0.0002∗R+1.6306R≤901.7736∗log10R+0.0002∗R−0.516990<R≤1902.4580∗log10R+0.0002∗R−2.0765R>190R represents the hypocentral distance in kilometers. The derived distance correction formula provides a well-constrained ML relationship for northern Algeria that is valid over a distance range of 5 to 600 km. Compared to other local magnitude relationships, the methodology proposed in this study consistently gives ML values slightly higher than those calculated by the Southern California relationship over all distances, with an average difference of 0.2 units. We computed corrections for 72 stations by minimizing the ML residuals. These corrections range from −0.50 to 0.54, highlighting the influence of local site effects on the amplitude of the seismic signal. The magnitude residuals using our magnitude relationship and incorporating the station corrections, show that the standard deviation has improved significantly, from 0.34 to 0.24. An ML relationship specific to the northern Algerian region provides a valuable tool for seismic monitoring, hazard assessment, and earthquake research in the region.

Geomagnetic Disturbances and Pulse Amplitude Anomalies Preceding M > 6 Earthquakes from 2021 to 2022 in Sichuan-Yunnan, China.

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances.

Massively parallel Bayesian estimation with Sequential Monte Carlo sampling for simultaneous estimation of earthquake fault geometry and slip distribution

In inverse analysis, Bayesian estimation is useful for understanding the reliability of the estimation result or prediction based on it because it can estimate not only optimal parameters but also their uncertainties. The estimation of an earthquake source fault based on observed crustal deformation is a typical inverse problem in the field of earthquake research. In this study, a method for simultaneous Bayesian estimation of earthquake fault plane geometry and spatially variable slip distribution on the plane has been developed. The developed method can be applied to stochastic models with arbitrary probability distribution settings, and it enables to incorporate appropriate constraints for slip distribution in the estimation process, which can lead to enhanced robustness and stability of estimation. Since this method is computationally more expensive than conventional methods, large-scale parallel computing was introduced to cope with the increased computational cost and a supercomputer was used for the analysis. To validate the proposed method, simultaneous Bayesian estimation of fault geometry and slip distribution with slip constraints was performed using the crustal deformation observed in the 2018 Hokkaido Eastern Iburi earthquake. Hierarchical parameterization and massively parallelized Bayesian inference used in this study have broad applicability not only in earthquake research but also in other scientific and engineering fields.

A sensitive system based on radon amplification at soil-air interface: Aiming to advance earthquake precursor research

Radon, a natural radioactive gas, serves as a valuable tracer in geophysical research and atmospheric science such as detecting stress induced signal in bedrock. However, the conventional radon monitoring methods often lack the sensitivity required to accurately capture such signals. This limitation, coupled with interference from meteorological effects, poses challenges in distinguishing genuine stress-induced signals. In this study, we propose a novel approach utilizing radon concentration gradients at the soil-air interface to enhance sensitivity and detect stress induced radon signals more effectively. Drawing from pressure diffusion models, we demonstrate how seismic stress accumulation in bedrock alters radon profiles in the sub-soil, providing insights into the mechanisms underlying stress-induced radon variations. Building upon this theoretical framework, we introduce the “Bhabha Radon Observatory for Seismic Application (BhaROSA)," a remote sensing, solar-powered radon observatory designed for widespread deployment and continuous unattended monitoring for big database generation. Field experiments comparing BhaROSA's performance to conventional soil probe techniques validate and confirm the superior sensitivity in line with theoretical predictions. This innovative approach holds promise for improving our understanding of stress dynamics in bedrock and has potential applications in various geophysical and atmospheric science such as earthquake precursory research, geo-genic radon potential and risk assessment. To progress, we propose international alliance and application of deep learning to a big database of precursor signals, which may lead to more informed conclusions on earthquake predictability-an enduring and unsolved challenge for humanity.

Did Short‐Term Preseismic Crustal Deformation Precede the 2011 Great Tohoku‐Oki Earthquake? An Examination of Stacked Tilt Records

AbstractThe detection of preslip, occurring hours to days before a large earthquake, using geodetic measurements has been a major focus in earthquake prediction research. A recent study claims to have detected a preseismic signal interpreted as accelerating slip near the hypocenter of the 2011 great Tohoku‐oki earthquake, starting approximately 2 hr before the mainshock. This claim is based on a stacking procedure using GNSS (Global Navigation Satellite System) data. However, a follow‐up study demonstrated that the signal disappeared when specific GNSS noise was corrected. Here we utilize tiltmeter records, independent on GNSS, to check whether the claimed preseismic signal is detected using a similar stacking procedure. Our results show no acceleration‐like deformation from 2 hr before the mainshock. This indicates that no precursory slip exceeded the noise level of the tilt data, and if any preslip occurred, it was less than 5.0 × 1018 Nm in seismic moment.

Analysis of High-Rise Building Using Opensees

Numerical simulation has increasingly become an effective method and powerful tool for performance-based earthquake engineering research. Most existing numerical analyses use general-purpose commercial software, which can limit in-depth investigations on specific, complex topics. Consequently, this research focuses on the dynamic analysis of high-rise reinforced concrete (RC) frame-core tube structures using the open-source finite element (FE) code OpenSees to perform nonlinear seismic analyses. Various shear walls, a frame-core tube structure, and a tall building are simulated. The rationality and reliability of the proposed analysis method are validated through comparison with available experimental data and the analytical results of a well-validated commercial FE code. The research outcomes will provide a useful reference and an effective tool for further numerical analysis of the seismic behavior of tall buildings.

Multiple Fold Earthquakes Recorded by the Paleoseismic Surface Ruptures of Bending-Moment Faults in the Qiulitage Anticline, South Tianshan, China

Abstract Bending-moment faults (BMFs), as a fundamental type of secondary faulting, are intrinsically linked to the primary causative faults within active thrust-fold belts. When these faults thrust through the ground surface, the resulting geomorphic scarps offer the characteristics of local earthquake recurrence. This information helps to fill a gap left by main faults, which often lack coseismic surface ruptures. The Qiulitage anticline where the 1949 M 7¼ Kuqa earthquake occurred is an active thrust-and-fold belt predominantly governed by blind faults. In addition, several typical BMFs extensively crop out as surface scarps in the front of the mountain. Our research concentrates on the well-developed BMF scarps in this region and seeks to explore the recurrence characteristics of paleoearthquakes, which remain inadequately comprehended. Our study reveals that (1) secondary BMF with high enough magnitude can directly generate coseismic ground ruptures, and (2) the seismic behavior of BMFs exhibits a degree of repeatability, potentially linked to the concurrent movement of various BMFs or the solitary action of a single fault. However, the case study presented in this article also highlights the limitation of fold earthquake research because of the swift attenuation of coseismic fault slip as it approaches the ground surface.

Seismic activities in Ghana: A systematic review

Seismic activities pose significant challenges to societies globally. Therefore, it is crucial to understand their occurrence, patterns, and impacts. By studying seismic activities, including earthquakes, researchers can investigate their occurrence, distribution, and characteristics which can provide effective management and risk reduction strategies. The southern part of Ghana is prone to earthquakes and this study aims to shed more light into the nature of seismic events in the area and country at large. A systematic review was conducted using the PRISMA technique across three electronic databases (SCOPUS, Dimensions and Google Scholar) to identify relevant studies published between 2000 and 2023. Extraction of data and quality assessment were performed in order to ensure reliability and validity of included studies. Results identified only 17 papers from published records to meet the inclusion criteria. Despite the grave threat earthquakes pose to vital infrastructure and human life in Ghana, research in this area remains remarkably deficient. Our findings underscore the urgent need for further study given the catastrophic potential of seismic disasters in the region. Moreover, upon scrutinizing the methodologies deployed in extant literature concerning seismic activity in Ghana, a recurring constraint that emerged was the scarce availability of data. In essence, this study offers an indispensable panorama of earthquake research in Ghana, bridging the existing knowledge chasm on seismic phenomena in the region. The insights gleaned from this review promise to fortify our comprehension of Ghana's seismic activities, thereby bolstering the country's capabilities for more effective preparedness and response strategies.

A Bibliometric Review of Earthquake and Machine Learning Research

This article presents a bibliometric review of earthquake research and its integration with machine learning techniques. Over the past two decades, there has been a growing interest in using machine learning to enhance earthquake prediction and research. The review collected 1172 scholarly articles from the Web of Science database, focusing on the keywords "earthquake" and "machine learning." Machine learning has shown promise in improving earthquake forecasting models and aiding decision-making in disaster management, infrastructure design, and emergency response. However, it is noted that the application of machine learning in earthquake engineering is still in its early stages and requires further exploration. Key findings of this review include the increasing importance of certain keywords in earthquake and machine learning research, such as "prediction," "neural network," "classification," "logistic regression," and "performance." These keywords highlight the central areas of research focus within this field. The review also identifies research trends and gaps, including the need for more exploration of large-scale, high-dimensional, nonlinear, non-stationary, and heterogeneous spatiotemporal data in earthquake engineering. It emphasizes the necessity for novel machine learning algorithms tailored specifically for earthquake prediction and analysis. Furthermore, it highlights the need for addressing uncertainty in earthquake research and improving forecasting models. The review underscores the growth in interest and collaboration in earthquake research and machine learning, evident in the increasing number of scholarly contributions over the years. In summary, this bibliometric review highlights the importance of accurate forecasting and the potential of machine learning techniques in advancing this field.

February 6th, Kahramanmaraş earthquakes and the disaster management algorithm of adult emergency medicine in Turkey: An experience review.

This compilation covers emergency medical management lessons from the February 6th Kahramanmaraş earthquakes. The objective is to review relevant literature on emergency services patient management, focusing on Koenig's 1996 Simple Triage and Rapid Treatment (START) and Secondary Assessment of Victim Endpoint (SAVE) frameworks. Establishing a comprehensive seismic and mass casualty incident (MCI) protocol chain is the goal. The prehospital phase of seismic MCIs treats hypovolemia and gets patients to the nearest hospital. START-A plans to expedite emergency patient triage and pain management. The SAVE algorithm is crucial for the emergency patient secondary assessment. It advises using Glasgow Coma Scale, Mangled Extremity Severity Score, Burn Triage Score, and Safe Quake Score for admission, surgery, transfer, discharge, and outcomes. This compilation emphasizes the importance of using diagnostic tools like bedside blood gas analyzers and ultrasound devices during the assessment process, drawing from 6 February earthquake research. The findings create a solid framework for improving emergency medical response strategies, making them applicable in similar situations.

Slip distribution of the 2024 Noto Peninsula earthquake (MJMA 7.6) estimated from tsunami waveforms and GNSS data

AbstractThe 1 January 2024 Noto-Hanto (Noto Peninsula) earthquake (MJMA 7.6) generated strong ground motion, large crustal deformation and tsunamis that caused significant damage in the region. Around Noto Peninsula, both offshore submarine and partially inland active faults have been identified by previous projects: Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and Japan Sea Earthquake and Tsunami Research Project (JSPJ). We inverted the tsunami waveforms recorded on 6 wave gauges and 12 tide gauges around Sea of Japan and the GNSS data recorded at 53 stations in Noto Peninsula to estimate the slip amount and seismic moment on each of active faults. The results show that the 2024 coseismic slips were 3.5 m, 3.2 m, and 3.2 m on subfaults NT4, NT5 and NT6 of the JSPJ model, located on the northern coast of Noto Peninsula and dipping toward southeast. A smaller slip, 1.0 m, estimated on NT8 on the southwestern end of the 2024 rupture, may be attributed to its previous rupture during the 2007 Noto earthquake. The total length of these four faults is ~ 100 km, and the seismic moment is 1.90 × 1020 Nm (Mw = 7.5). Almost no slip was estimated on the northeastern subfaults NT2 and NT3, which dip northwestward, opposite to NT4–NT5–NT6, and western subfault NT8. Aftershocks including the MJMA 6.1 event occurred in the NT2–NT3 region, but they are smaller than the potential magnitude (Mw 7.1) those faults can release in a tsunamigenic earthquake. Similar features are also found for the MLIT model; the 2024 slip was only on F43 along the northern coast of Noto Peninsula, and northeastern F42 did not rupture, leaving potential for future event. Graphical Abstract

Numerical investigation of seismic amplification characteristics in loess ridge region of Xiji, northwest China.

The seismic effects on sloped terrain, which are of paramount importance for engineering design and earthquake risk mitigation, have always been a central focus of earthquake engineering research. In this study, generalized geometric models of loess ridges at varying heights were created, and a three-dimensional nonlinear numerical model was established using FLAC3D. Seismic ground motion time histories at different frequencies and actual earthquake ground motion records were input into the model to analyze the peak acceleration amplification effects experienced by the surface of loess ridges when subjected to SV waves. The study's outcomes reveal that seismic amplification on the slopes of loess ridges is characterized by non-linearity with respect to slope height. Instead, it exhibits rhythmic variations, with the rate of change in these rhythms increasing in correspondence with the frequency of seismic motion and the height of the slope. Under low-intensity seismic motion, a linear increase in acceleration amplification is observed at the ridge's crest concerning the height of the loess ridge. However, under high-intensity seismic motion, the relationship between amplification and slope height becomes less significant. Typically, the peak acceleration at the ridge's crest is reported to be 1.5 to 2.5 times that observed at the slope's base. The amplification effect at the ridge's crest is more pronounced in the low-frequency and high-frequency segments when compared to the mid-frequency range. Conversely, significant amplification is observed in the high-frequency range in the lower sections of the slope near the base. It is further noted that the amplification effect at the ridge's crest displays distinct behavior at different frequencies, characterized by narrow frequency bands of maximum amplification, with peak amplification factors exceeding 10 in some cases. These research findings have practical significance and provide valuable references for engineering construction and seismic risk mitigation planning in loess regions.