Detectability and reliability analysis of the local seismic network in Pakistan

The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.

Preliminary Analysis of the Relationship between the Magnitude Deviation of the Station and Site Response in the Fujian Digital Seismic Network

Using the records of 3,069 regional earthquake events from the Fujian Digital Seismic Network from October 2008 to December 2015,in which the magnitude of each of the events was measured by at least six stations,statistics are taken on the deviation between the magnitude of a single station and the average magnitude of the network. It is found that the magnitudes average deviation of each station is-0. 31-0. 68. Statistics are also taken for the period corresponding to the maximum amplitude of the record measured in each station for calculating the magnitude,and the dominant period gained is 0. 06s-0. 38s; site response of each seismic station is inverted using the Moya method,and it is found that the site response of 98 stations is in the bands of 1-20 Hz,suggesting that the site has an amplifying or suppressing effect on the signals in certain frequency bands;Considering the site response corresponding to the inherent 0. 8s period of the WoodAnderson pendulum seismograph,and comparing the magnitude deviation caused by the site response corresponding to the dominant period time of each station with the average magnitude deviation, we obtain that there is a good linear relationship between the magnitude deviation from the dominant period site response and the average deviation of the magnitude of each station,indicating that the magnitude deviation of a single station has a close relationship with the site response of the period corresponding to the maximum amplitude measured for calculating the magnitude.

Seismic Network, Seismic Activity and Seismological Research of Vietnam

In this paper, a historical review of the seismic network of Vietnam and the ability of the network to monitor earthquakes is presented. The seismic activity of Vietnam as a country of high seismicity in the last century and since the beginning of the new century is described. The current state of seismological research in Vietnam and cooperation with institutions outside of Vietnam is outlined.

Long-term seismic network in South China Sea by floating MERMAIDs

Seismic data coverage in ocean regions is sparse,and it is highly challenging to build long-term continuous seismic networks in the oceans due to the restrictions related to the shortage of instruments and great costs.The lack of data coverage limits effective seismic imaging of deep mantle structures beneath the oceans,which cover 70%of the Earth’s surface.The newly developed Mobile Earthquake Recorder in Marine Areas by Independent Drivers(MERMAID)can drift with ocean currents at a specified depth while recording seismic signals.The Southern University of Science and Technology(SUSTech)launched 10 MERMAIDs in the South China Sea(SCS)in May 2021 that formed the South China Sea Floating Seismic Network(SCS-FSN).We analyzed the one-year-long records of the SCS-FSN,identifying 372 cataloged earthquakes and acquiring 1,015 high-quality travel time data.By analyzing the records of earthquakes with magnitudes above 7.0 and conducting synthetic waveform calculation,we found that,in addition to the epicentral distance and earthquake magnitude,the earthquake identification ability of the network is also affected by the focal mechanism,sea condition,seafloor relief,and MERMAID working state.Although the recognition rate of the SCS-FSN is only 16%for earthquakes with magnitudes above 5.5 and epicentral distances less than 90°,this network is expected to collect more than 5,000 high-quality travel time data during its five-year battery life.These new data will significantly improve the seismic data coverage,compensating for the lack of long-term continuous seismic network observations in the SCS.Most importantly,with this experiment,we are confident that setting up well-designed floating seismic networks in the world’s three oceans could solve the world-class problem of the lack of effective seismic data coverage beneath ocean regions.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

Earth slope reliability analysis under seismic loadings using neural network

A new method was proposed to cope with the earth slope reliability problem under seismic loadings. The algorithm integrates the concepts of artificial neural network, the first order second moment reliability method and the deterministic stability analysis method of earth slope. The performance function and its derivatives in slope stability analysis under seismic loadings were approximated by a trained multi-layer feed-forward neural network with differentiable transfer functions. The statistical moments calculated from the performance function values and the corresponding gradients using neural network were then used in the first order second moment method for the calculation of the reliability index in slope safety analysis. Two earth slope examples were presented for illustrating the applicability of the proposed approach. The new method is effective in slope reliability analysis. And it has potential application to other reliability problems of complicated engineering structure with a considerably large number of random variables.

NEURAL NETWORKS PREDICTION FOR SEISMIC RESPONSE OF STRUCTURE UNDER THE LEVENBERG-MARQUARDT ALGORITHM

Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.

Rediscussion on the seismic regime network

On the basis of the past research and utilization on the windows and belts of seismic regime, the seismic regime network which has been supposed and proved in the past is set up by using the monthly frequency data of small earthquakes from 1970 to 1991 over the whole country. Through checking its function in practice, it is found that the spatial distribution of precursor information is not an isolate window or belt, but a broad precursor information field before the Ms≥7. 0 earthquakes in China and its nearby regions. According to the windows and belts in the field, synchronism and generality of initial time and place of prediction, the comprehensive prediction of activity time periods of groups of strong earthquakes and the detail method of correspondence of groups are proposed. After restrict mathematical test, 10 prediction methods for references are set forth, in which two best methods are selected as references for the whole case prediction in one to three years. Some related problems are discussed at the end of this paper.

Plastic-Flow/Seismic" Network Systems and Tectonic Units in Central-Eastern Asia

The study of the netlike earthquake distribution indicates that in the central-eastern part of Asia continent there are two network systems: the central-eastern Asia system and the southeastern China system.As interpreted by the multilayer tectonic model,they might be a manifestation of the plastic-flow network systems in the lower lithosphere,including the lower crust and the mantle lid.Each network system is enclosed by different types of boundaries,including one driving boundary and some constraining and releasing boundaries.The two plastic-flow network systems with the Himalayan and Taiwan arcs as their driving boundaries play the role of controlling the intraplate tectonic deformation,stress field,seismicity,and subdivision of tectonic units.

Seismic signal recognition using improved BP neural network and combined feature extraction method

Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network.

Fractured reservoir modeling by discrete fracture network and seismic modeling in the Tarim Basin,China

Fractured reservoirs are an important target for oil and gas exploration in the Tarim Basin and the prediction of this type of reservoir is challenging.Due to the complicated fracture system in the Tarim Basin,the conventional AVO inversion method based on HTI theory to predict fracture development will result in some errors.Thus,an integrated research concept for fractured reservoir prediction is put forward in this paper.Seismic modeling plays a bridging role in this concept,and the establishment of an anisotropic fracture model by Discrete Fracture Network （DFN） is the key part.Because the fracture system in the Tarim Basin shows complex anisotropic characteristics,it is vital to build an effective anisotropic model.Based on geological,well logging and seismic data,an effective anisotropic model of complex fracture systems can be set up with the DFN method.The effective elastic coefficients,and the input data for seismic modeling can be calculated.Then seismic modeling based on this model is performed,and the seismic response characteristics are analyzed.The modeling results can be used in the following AVO inversion for fracture detection.

Seismic Monitoring Network and Seismic Activities in Thailand

The details of seismic monitoring network and seismological work in Thailand, including some information on felt earthquakes that occurred from 2000 to August 2001, are reported here.

Effect of seismic retrofit of bridges on transportation networks

The objective of this research is to determine the effect earthquakes have on the performance of transportation network systems.To do this,bridge fragility curves,expressed as a function of peak ground acceleration(PGA)and peak ground velocity(PGV),were developed.Network damage was evaluated under the 1994 Northridge earthquake and scenario earthquakes.A probabilistic model was developed to determine the effect of repair of bridge damage on the improvement of the network performance as days passed after the event.As an example,the system performance degradation measured in terms of an index,'Drivers Delay,'is calculated for the Los Angeles area transportation system,and losses due to Drivers Delay with and without retrofit were estimated.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Seismic reliability analysis of urban water distribution network

An approach to analyze the seismic reliability of water distribution networks by combining a hydraulic analysis with a first-order reliability method （FORM）, is proposed in this paper. The hydraulic analysis method for normal conditions is modified to accommodate the special conditions necessary to perform a seismic hydraulic analysis. In order to calculate the leakage area and leaking flow of the pipelines in the hydraulic analysis method, a new leakage model established from the seismic response analysis of buried pipelines is presented. To validate the proposed approach, a network with 17 nodes and 24 pipelines is investigated in detail. The approach is also applied to an actual project consisting of 463 nodes and 767 pipelines. The results show that the proposed approach achieves satisfactory results in analyzing the seismic reliability of large-scale water distribution networks.

信息化测震数据管理平台的设计与实现

依托公共安全信息化工程,测震台网数据管理平台在数据资源池环境下,采用分层架构和模块化开发,实现了实时流波形接入和存储、站网基础信息管理、常规数据产品产出、针对特定地震的产品产出、波形数据质量分析等测震台网核心业务的信息化。通过平台的接口和界面,事件波形处理与服务、数据质量分析、震源参数基础数据库建设等测震台网业务取得了长足进步。该平台具有业务集成性、功能实用性、资源共享性和技术先进性等特点,有效提升了测震台网信息化水平,为测震台网现代化发展发挥了积极作用。

基于LSTM网络的单台仪器地震烈度预测模型

烈度是地震预警系统的关键产出.如何实现快速预测目标场址的地震烈度是地震预警方法技术研究中的核心问题.本文提出了一种基于长短时记忆神经网络(Long Short-Term Memory,LSTM)的单台仪器地震烈度的预测模型(LSTM-Ⅰ).该模型以一个台站观测到地震动参数的时间序列特征为输入,实现动态预测该台站可能遭受的最大烈度.选取了日本K-NET台网记录的102次地震的5103条强震加速度记录训练了神经网络,利用89次地震的3781条数据检验了模型的泛化能力.利用准确率、漏报率以及误报率三个评价指标评价了LSTM-Ⅰ模型的性能.结果表明,当采用P波触发后3 s的序列进行预测时,模型出现漏报的概率为46.78%,出现误报的概率为1.25%;当采用P波触发后10 s的序列进行预测时,模型出现漏报的概率大幅降低到17.6%,出现误报的概率降低到1.14%.结果表明LSTM-Ⅰ模型很好把握住了时间序列中蕴含的特征.进一步基于LSTM-Ⅰ模型评估了Ⅵ度下台站所能提供的预警时间.本文模型能够提供的预警时间与P-S波到时差接近,说明LSTM-Ⅰ模型具有较高的时效性.

微震定位精度影响下采场裂隙表征与冲击地压预警

我国深部煤矿高强度开采和复杂地质环境造成冲击地压灾害频发,对矿井安全高效生产造成严重威胁。目前,我国冲击地压矿井配备的微震监测系统在震源定位精度上仍存在不足,冲击地压灾害依然存在“灾源找不准、灾害控不住”的难题。为准确描述采场微震活动空间演化特征,降低震源定位误差对微震预警造成的不利影响,基于冲击地压矿井现有微震监测条件,采用仿真正演试验法探究了煤矿微震台网定位误差矢量分布特征,提出了一种考虑微震定位精度影响的微震裂隙贯通表征方法,并在某矿井回采工作面进行了冲击地压预警实践。研究结果表明:采场不同区域震源定位精度的矢量性差异是造成冲击地压微震预警偏差的重要原因;提出的微震破裂贯通可能性指数F_(sum)采用与震源能级相关的近场区域半径表征震源破裂尺度,并考虑定位误差对不同距离震源间破裂贯通的影响,最大程度还原了采场煤岩体裂隙扩展贯通可能性的分布规律,与冲击地压危险具有F_(sum)良好对应关系;在冲击地压危险相关性上显著优于微震频次,同时危险区域识别效率又强于微震能量,可兼顾预警精准率和召回率,是理想的冲击地压周期性预警评价指标。研究成果将为评价微震台网监测能力、提高冲击地压微震预警能力与防治效率提供参考。