Earthquake Early Warning Research Articles

Analysis of Strong Ground Motion Characteristics for the Jishishan MS6.2 Earthquake December 18, 2023

The MS6.2 earthquake occurred in Jishishan, Gansu Province, China, on December 18, 2023. Based on some strong seismic acceleration time histories captured by some stations of China National Strong Earthquake Observation Network and National Earthquake Early Warning Network, the spatial distribution characteristics, rupture directivity effect, hanging wall and footwall effect, response spectrum characteristics, and attenuation of the peak ground acceleration (PGA) were analyzed in this paper. It can be used as reference for the site determination of post‐earthquake reconstruction and seismic design of engineering structures. The analysis shows that the spatial distribution of strong ground motion of the Jishishan MS6.2 earthquake is mainly controlled by the distribution of causative fault. Near the fault, the effect of rupture directivity is obvious. The effect of hanging wall and footwall significantly affects the distribution of PGAs for this earthquake, causing a pronounced asymmetric pattern about PGAs relative to the fault, with slower attenuation on the hanging wall compared to the footwall. The type of site obviously affects the characteristics of response spectrum. The ground motion intensity observed at the epicenter of this earthquake significantly exceeded the levels of rare and extremely rare seismic events in the current seismic design codes. The attenuation relationship of PGA obtained by regression analysis shows that ones in the 5th generation Chinese ground motion parameter zoning map for the Qinghai‐Tibet region underestimate the horizontal PGA.

Tree Dielectric Response as an Earthquake Forecasting

Seismic precursor identification is vital in the Himalayan range and near active fault lines to offer early earthquake warnings, as India and nearby areas face a high risk of seismic activity. This laboratory-based research conducted simulations aimed at replicating electromagnetic signals generated by artificial seismic events spanning frequencies from 1 Hz to 59 Hz. High-frequency seismic signal attenuation upon interaction with the Earth's surface was observed, focusing on emulating electromagnetic waves within the ultra-low frequency (ULF) to very high frequency (VHF) range and investigating energy transfer dynamics within conductive materials. Findings included a reduction in green stem ring diameter, an inversely correlated penetration depth with frequency, and fluctuations in the dielectric constant of tree tissues, linked to water molecule polarization with frequency changes. By applying an electric field, lower dielectric constants were observed at lower frequencies, stabilizing at around 40 eV as frequency increased from 1 Hz to 59 Hz. As frequency levels escalated, especially transitioning into the radio or microwave spectrum, alterations in the dielectric constant of tree tissues were noted, often marked by an increase in dielectric loss, signifying heightened absorption of electromagnetic energy and its conversion into heat. Banyan tree roots exhibited responses well in advance of any discernible alterations in the subsurface environment, offering valuable early indicators of impending seismic activity. This discovery encourages further exploration and holds the potential to advance earthquake prediction using deep-rooted trees as biosensors, ultimately contributing significantly to the preservation of human lives and overall safety.

UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: ISOLATION FOREST ALGORITHM APPLICATION TO INDONESIAN EARTHQUAKE DATA

Indonesia's position on the seismically active Pacific "Ring of Fire" necessitates advanced methods for earthquake detection and preparedness. This paper introduces an application of the Isolation Forest algorithm—an unsupervised machine learning technique—for detecting seismic anomalies in Indonesia's complex geotectonic landscape. Unlike traditional methods that rely on predefined thresholds or patterns, the Isolation Forest algorithm isolates anomalies based on their rarity and distinctness without the need for labeled data. We applied this algorithm to a comprehensive dataset from the Indonesian Meteorology, Climatology, and Geophysical (BMKG) Agency, featuring a decade's worth of seismic events, to identify outliers that may signify potential seismic hazards. Our findings reveal that the Isolation Forest algorithm effectively identifies seismic anomalies, with 874 out of tens of thousands of events flagged as statistically significant outliers. A comparative analysis with traditional seismic anomaly detection models highlights the robustness of the Isolation Forest algorithm in handling the high-dimensional and noisy nature of seismic data, emphasizing its superiority in detecting subtle anomalies

Rapid Earthquake Rupture Characterization for New Zealand Using the FinDer Algorithm

ABSTRACT Immediately after a significant earthquake, rapid scientific information is critical for response decision-making and estimating secondary hazards, and is a key component of advisories and public communication. Characterization of the fault rupture extent is especially valuable because it strongly controls ground-motion estimates, or tsunami forecasts in offshore settings. The Finite-fault rupture Detector (FinDer) is designed to rapidly estimate location, extent, and orientation of earthquake fault rupture by matching spatial distributions of high-frequency seismic amplitudes with precomputed templates. Under a large public initiative to better prepare for and respond to natural disasters, FinDer is being implemented in New Zealand for rapid source characterization. Here, we report on implementation and performance, including offline and real-time testing using configurations modified for the New Zealand setting. Systematic testing is used to inform guidelines for real-time usage and interpretation. Analysis of rupture parameter recovery when using national network GeoNet stations demonstrates that for moderate (M 6+) onshore earthquakes FinDer can resolve magnitude and location well, and the rupture strike is also well determined for large (M 7+) onshore earthquakes. For near-offshore earthquakes (within 100 km), FinDer can provide reasonable magnitude estimates but cannot determine the location or strike. Real-time testing shows reliable detection for onshore earthquakes of M 4.5+, with reasonable location and magnitude accuracy. First detection times range between 7 and 65 s of earthquake origin, and stable solutions even for large (M 7+) magnitude events are delivered within 2 min. Although the GeoNet seismic network is not optimized for earthquake early warning, this provides a first exploration of network-based capability for New Zealand. Offline testing of significant M 7+ historic earthquakes demonstrates that FinDer’s rupture solutions can be used to improve rapid shaking predictions, and may be used to infer additional directivity and tsunami hazard even for complex events like the 2016 M 7.8 Kaikōura earthquake.

Near Real-Time Detection and Moment Tensor Inversion of the 11 May 2022, Dharchula Earthquake

On 11 May 2022, an earthquake of Mw 5.2 occurred in the Dharchula region of Uttarakhand Himalayas, India. The Uttarakhand State Earthquake Early Warning System (UEEWS) detected and transmitted the warning within 11.61 s from the origin time, taking only 4.26 s for processing, location, and magnitude estimation and warning dissemination. The complete analysis was performed using three seconds of waveforms. Using the initial earthquake parameters provided by the UEEWS, moment tensor inversion was performed using the broadband seismometers network installed in northern India. The moment tensor (MT) inversion was performed for the event using both the body waves and the surface waves. The first motion polarity was used along with waveform data to enhance the solution’s stability. This paper discusses the importance of real-time event detection and efforts towards real-time MT source inversion of earthquakes in the Uttarakhand Himalayas. Relocation of two past earthquakes near Dharchula is also undertaken in this study. The outcome of this study provides insights into mitigating seismic hazards, understanding earthquake source mechanisms, and enhancing knowledge of local fault structures.

GST-based polarization approach to picking P-and S-waves arrival time for high-speed rail early warning system

The first arrival time of seismic P- and S- waves affects the determination of the beginning of an earthquake by the Earthquake Early Warning System for High-speed Rail (HSREEWs). Accordingly, we propose an approach to picking P- and S-waves arrival time for HSREEWs. This approach can treat the continuous seismic signals in real-time without prior knowledge of whether an earthquake occurs. Firstly, we replace the fixed time window of the S transform (ST) with the dynamic window of the generalized S transform (GST) to make the necessary time of signal processing more flexible. Meanwhile, the GST extends the sequential signal to the time-frequency domain (TF), and combines the TF polarization to construct four three-component polarization filters. Accordingly, the P-wave and S-wave can be separated from the seismic waveform by those filters, which effectively avoids the phenomenon that the S-wave phase is difficult to determine because of the interference of S-wave overlapping in the coda region. Secondly, based on the instantaneous frequency of seismic signal, we construct a new STA/LTA (GSPA-STA/LTA) with the characteristic function (CF) of constrained TF spectrum to pick the first arrivals of the P- and S-waves, and provide the corresponding automatic picking algorithm. Finally, we analyze 504 seismic events larger than Ms40 near the Japanese Tohoku Shinkansen to comprehensively verify the effectiveness of our approach. Since the P-wave and S-wave are first separated, the subsequent process just has to identify the first arrival times of their respective components. The approach has a good practical value in maintaining the safe operation of high-speed rail under earthquake, which meets the industry demands for speed, scale and accuracy in HSREEWs.

Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

Customization of a deep neural network using local data for seismic phase picking

Deep-learning (DL) pickers have demonstrated superior performance in seismic phase picking compared to traditional pickers. DL pickers are extremely effective in processing large amounts of seismic data. Nevertheless, they encounter challenges when handling seismograms from different tectonic environments or source types, and even a slight change in the input waveform can considerably affect their consistency. Here, we fine-tuned a self-trained deep neural network picker using a small amount of local seismic data (26,875 three-component seismograms) recorded by regional seismic networks in South Korea. The self-trained model was developed using publicly available waveform datasets, comprising over two million three-component seismograms. The results revealed that the Korean-fine-tuned phase picker (KFpicker) effectively enhanced picking quality, even when applied to data that were not used during the fine-tuning process. When compared to the performance of the pre-trained model, this improvement was consistently observed regardless of variations in the positions of seismic phases in the input waveform, Furthermore, when the KFpicker predicted the phases for overlapping input windows and used the median value of probabilities as a threshold for phase detection, a considerable decrease was observed in the number of false picks. These findings indicate that fine-tuning a deep neural network using a small amount of local data can improve earthquake detection in the region of interest, while careful data augmentation can enhance the robustness of DL pickers against variations in the input window. The application of KFpicker to the 2016 Gyeongju earthquake sequence yielded approximately twice as many earthquakes compared to previous studies. Consequently, detailed and instantaneous statistical parameters of seismicity can be evaluated, making it possible to assess seismic hazard during an earthquake sequence.

BPMF: A Backprojection and Matched-Filtering Workflow for Automated Earthquake Detection and Location

Abstract We introduce BPMF (backprojection and matched filtering)—a complete and fully automated workflow designed for earthquake detection and location, and distributed in a Python package. This workflow enables the creation of comprehensive earthquake catalogs with low magnitudes of completeness using no or little prior knowledge of the study region. BPMF uses the seismic wavefield backprojection method to construct an initial earthquake catalog that is then densified with matched filtering. BPMF integrates recent machine learning tools to complement physics-based techniques, and improve the detection and location of earthquakes. In particular, BPMF offers a flexible framework in which machine learning detectors and backprojection can be harmoniously combined, effectively transforming single-station detectors into multistation detectors. The modularity of BPMF grants users the ability to control the contribution of machine learning tools within the workflow. The computation-intensive tasks (backprojection and matched filtering) are executed with C and CUDA-C routines wrapped in Python code. This leveraging of low-level, fast programming languages and graphic processing unit acceleration enables BPMF to efficiently handle large datasets. Here, we first summarize the methodology and describe the application programming interface. We then illustrate BPMF’s capabilities to characterize microseismicity with a 10 yr long application in the Ridgecrest, California area. Finally, we discuss the workflow’s runtime scaling with numerical resources and its versatility across various tectonic environments and different problems.

P-Alert earthquake early warning system: case study of the 2022 Chishang earthquake at Taitung, Taiwan

A series of earthquakes that struck Taiwan's southern Longitudinal Valley on September 17 and 18, 2022 severely damaged several buildings in Taitung and Hualien. The Chishang earthquake, which had a magnitude of ML 6.8 and a large foreshock with a magnitude of ML 6.6 the day before, was the mainshock in this sequence. The strongest intensity reported in the epicentral region during this earthquake sequence, which was 6 + , is the highest ever recorded since the Central Weather Bureau (CWB, renamed as the Central Weather Administration since September 15, 2023) revised its seismic intensity scale. National Taiwan University (NTU) has operated a low-cost earthquake early warning (EEW) system known as the P-Alert for a decade. In this study, we demonstrate the performance of the P-Alert network during the 2022 Chishang earthquake and the largest foreshock. The P-Alert network plotted shake maps during these earthquakes that displayed various values within 5 min. The high shaking areas on these maps were in good agreement with observed damages during this earthquake, providing valuable insights into rupture directivity, a crucial component of earthquake engineering. Individual P-Alert stations acted as on-site EEW systems and provided a lead time of 3–10 s within the blind zone of CWB. For the ML 6.8 mainshock, there was a lead time of at least 5 s, even up to 10 s, demonstrating their effectiveness in the blind zone. The P-Alert regional EEW system provided the first report about 9 s and 7 s after the mainshock and the largest foreshock occurrence, respectively, with estimated magnitudes of 5.74 and 5.67. The CWB system estimated magnitudes of 6.72 and 6.16 in the first report, respectively, about 7 s and 9 s after the earthquake occurrence. The timeliness of the two systems were not significantly different. Despite the effectiveness of the P-Alert network, data loss due to connection interruptions prompted us to develop a new compact data logger for improved data availability.

DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data

SUMMARY This paper presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e. pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a data set from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g. Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) in <1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring.

First feasibility demonstration of GNSS-seismology for anthropogenic earthquakes detection.

High-rate GNSS has been proven effective in characterising waveforms and co-seismic displacements due to medium-to-strong natural earthquakes. No application focused on small magnitude events like shallow anthropogenic earthquakes, where displacements and noise have the same order of magnitude. We propose a procedure based on proper signal detection and filtering of the position and velocity time series obtained from high-rate (10Hz) GNSS data processing with two intrinsically different approaches (Precise Point Positioning and variometry). We tested it on five mining tremors with magnitudes of 3.4-4.0, looking both at event detection and its kinematic characterisation. Here we show a high agreement, at the level of 1s, between GNSS and seismic solutions for the earthquake first epoch detection. Also, we show that high-rate multi-constellation (GPS + Galileo) GNSS can reliably characterise low-magnitude shallow earthquakes in terms of induced displacements and velocities, and, including their peak values, respectively, at the level of very few millimetres and 1-2cm/s, paving the way to the routine use of GNSS-seismology for monitoring human activities prone to cause small earthquakes and related potential damages.

Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter

Machine learning versus manual earthquake location workflow: testing LOC-FLOW on an unusually productive microseismic sequence in northeastern Italy

It is an open question whether machine-learning (ML) methods can be trusted in areas where dense and localized seismic networks are in operation, and prompt and accurate detection and location of earthquakes are essential to guide decision-making processes that contribute to seismic-risk-mitigation-strategies, even for very low-magnitude events. To address these concerns, we compare the performance of a widely-used ML phase picker, PhaseNet, integrated with several popular earthquake location methods (included in LOC-FLOW), with the results obtained by the workflow adopted since 2012 by the Collalto Seismic Network, installed to monitor natural and potentially induced microearthquakes nearby an underground gas storage. The tested dataset concerns the most populated microseismic sequence observed so far (374 events, ML⩽2.5, August 2021, Refrontolo, NE-Italy), as its unusual productivity raised some criticalities in the combination of automatic routines, and time-consuming manual revision of phase picks adopted by the standard workflow. LOC-FLOW is able to detect the majority of the events listed in the manually revised catalog, demonstrating its ability to efficiently and accurately build earthquake catalogs from continuous seismic data. We highlight both the advantages and limitations of the ML-picker and recommend the use of template-matching-techniques in the final stage of processing to increase the number of events.

Performance analysis of P-wave detection algorithms for a community-engaged earthquake early warning system – a case study of the 2022 M5.8 Cook Strait earthquake

ABSTRACT Can a P-wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community-engaged networks of low-cost ground motion sensors susceptible to noise? If so, what P-wave detection algorithm would perform the best? This study analyses the performance of four different P-wave detection algorithms using a community-engaged Earthquake Early Warning (EEW) network. The ground motion data from a 48-hour time window around a M5.8 earthquake on 22 September 2022 were used as the basis for this case study, where false and missed detections were analysed for each P-wave detection algorithm. The results indicate that a wavelet transformation-based P-wave picker is the most suitable algorithm for detecting an earthquake with minimal missed and false detections for a community-engaged EEWS. Our results show that a citizen seismology-based EEWS is capable of detecting events of interest to EEW when selecting an appropriate earthquake detection algorithm. The study also suggests future research areas for community-engaged EEWSs, including dynamically changing P-wave detection thresholds and improving citizen seismologists’ user experience and involvement.

A situation-aware emergency evacuation (SAEE) model using multi-agent-based simulation for crisis management after earthquake warning

ABSTRACT Earthquake is a disastrous natural hazard that threatens numerous cities worldwide. The interval between the foreshock and the main event can sometimes last several minutes. Meanwhile, crowd emergency evacuation and finding shelter are vital for search and rescue managers. At the same time, many unpredicted challenges, such as the sudden increase in travel demand, shifts in public behavior, and the change in the regular transport supply, may arise due to evacuation conditions, which lead to different situations. This paper aims to introduce an approach for quick decision-making and timely evacuation response required by establishing a situation-aware system to minimize these risks and ensure the success of the evacuation plans, to support and predict current and future actions within the dynamic space of the crisis. The main contribution is innovating a Situation-Aware Emergency Evacuation (SAEE) model to enable crisis managers and evacuees to make the right decisions by providing timely and reliable information about the situation. This method is utilized in two situations: designing the emergency evacuation plan and finding the shortest/safest routes to reduce travel time for evacuees. Therefore, a hybrid approach is introduced, which involves a Fuzzy Inference System (FIS) and Deep Long Short-Term Memory (DLSTM) algorithm to identify, infer, and extract the existing situation at different levels (e.g. people, vehicles, and surroundings) after a foreshock using multi-agent-based simulation. The method proposed was simulated in the traffic network of District 6 of Tehran, the capital of Iran. The model results show that the evacuees’ spatial knowledge and perception, as well as awareness of the situation of other agents and their surroundings, led to a significant (40%) reduction in the complete evacuation time. This time is considered the most pivotal factor in saving human lives and their arrival in safer areas. The role of situation awareness systems and increasing human cognition and perception can significantly help in this matter.

Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks.

Effective response strategies to earthquake disasters are crucial for disaster management in smart cities. However, in regions where earthquakes do not occur frequently, model construction may be difficult due to a lack of training data. To address this issue, there is a need for technology that can generate earthquake scenarios for response training at any location. We proposed a model for generating earthquake scenarios using an auxiliary classifier Generative Adversarial Network (AC-GAN)-based data synthesis. The proposed ACGAN model generates various earthquake scenarios by incorporating an auxiliary classifier learning process into the discriminator of GAN. Our results at borehole sensors showed that the seismic data generated by the proposed model had similar characteristics to actual data. To further validate our results, we compared the generated IM (such as PGA, PGV, and SA) with Ground Motion Prediction Equations (GMPE). Furthermore, we evaluated the potential of using the generated scenarios for earthquake early warning training. The proposed model and algorithm have significant potential in advancing seismic analysis and detection management systems, and also contribute to disaster management.

Study on VLF Electric Field Anomalies Caused by Seismic Activity on the Western Coast of the Pacific Rim

In order to explore the correlation between earthquakes and ionospheric very low-frequency (VLF) electric field disturbances, this article uses VLF data observed by the China Earthquake Electromagnetic Satellite (CSES) to analyze very low-frequency signals before and after earthquakes from January 2019 to March 2023 in terms of the amplitude and signal-to-noise ratio of electric field power spectrum disturbances. Taking 73 earthquakes with a magnitude of 6.0 or higher occurring in the Circum-Pacific seismic belt as an example, comprehensive research on the VLF electric field disturbance phenomenon caused by strong earthquakes is conducted, considering both the earthquake location and source mechanism. The research results indicate the following: (1) there is a strong correlation between earthquakes with a magnitude of 6.0 or above and abnormal disturbances in the VLF electric field, which often occur within 20 days before the earthquake and within 800 km from the epicenter. (2) From the perspective of earthquake-prone areas, the VLF electric field anomalies observed before earthquakes in the Ryukyu Islands of the Taiwan region exhibit small and concentrated field fluctuations, while the Taiwan Philippines region exhibits larger field fluctuations and more dispersed fluctuations. The discovery of this correlation between seismic ionospheric phenomena and seismic activity provides a new and effective approach to earthquake monitoring, which can be used for earthquake prediction, early warning, and disaster prevention and reduction work.

Novel prediction models of earthquake early warning for ground motion power in Taiwan

AbstractStudies have indicated that the power of seismic waves, rather than the acceleration and velocity, strongly correlates with the level of structural damage and the shutdown of high‐tech equipment during high‐precision process. However, current earthquake early warning (EEW) systems in Taiwan issue a warning only for a seismic wave with high acceleration or velocity but not for that with high power. To extend the application of the EEW system to high‐tech fields, conventional and novel hybrid prediction models were developed in this study to predict the ground motion power and to issue EEWs for high‐power ground motions. A conventional prediction model that relies on earthquake source parameters was first developed to determine ground motion characteristics, and a hybrid prediction model that relies not only on earthquake source parameters but also on the on‐site ground motion parameter of P‐waves was developed to improve the prediction accuracy. The two‐step maximum likelihood method was used to derive model coefficients for considering the mixed effect of the ground motion records. The source, path, and site effects on the short‐period power and the long‐period power of ground motions were illustrated by the proposed models. A method for determining a broadcast scenario of EEW systems for high‐power ground motions with a target success rate and damage threshold was also developed. These novel prediction models can be used to develop a customized EEW system to provide an early warning for high‐power seismic waves in Taiwan.

Disaster prevention and mitigation in railway engineering

With the rapid development of railway engineering, the scope of railway construction has been extended to mountainous areas, basins and other geological environments. In these areas, railway construction needs to pay attention to landslides, earthquakes and other disasters, otherwise it will threaten unnecessary economic losses and even personal safety. The purpose of this paper is to introduce the disaster threats faced by railways and the measures to prevent these disasters, especially earthquakes and landslides, and to look at the future development trend of prevention and control. Earthquake early warning is one of the effective means to improve the safety of high-speed railway. As for the railway crossing the high earthquake wind area, it can be considered to establish the earthquake early warning and monitoring system for effective protection. For landslide disaster, interference should be reduced in the construction process, and special evaluation and targeted engineering treatment.