A Robust Algorithm for AutomaticP‐wave Arrival‐Time Picking Based on the Local Extrema Scalogram

Abstrak Telah dilakukan pemetaan daerah rawan tsunami berdasarkan estimasi waktu tiba gelombang dan tutupan lahan di Kabupaten Kupang Provinsi Nusa Tenggara Timur menggunakan aplikasi Penginderaan Jauh dan Sistem Informasi Geografi. Penelitian ini bertujuan untuk mengidentifikasi, memetakan daerah rawan tsunami dan tingkat kerawanannya menurut estimasi waktu tiba gelombang dan tutupan lahan sebagai upaya mitigasi dampak bencana tsunami terhadap kepadatan penduduk. Metode penelitian secara umum dibagi dalam empat tahap utama yaitu pembangunan basis data berupa pembuatan peta tutupan lahan, peta gempa dan peta batimetri. Analisis data kerawanan dari peta tutupan lahan dan etimasi waktu tiba gelombang, penyajian hasil data dalam bentuk tingkat kerawanan masing-masing peta dan analisis hasil penelitian berupa tingkat kerawanan secara kualitatif masing-masing daerah titik pantau menurut peta tutupan lahan maupun estimasi waktu tiba gelombang. Selain itu, dampak kerawanan tsunami diklasifikasikan menurut tingkat kepadatan penduduk untuk kebutuhan mitigasi sebagai berikut Kecamatan Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut dan Fatuleu Barat. Kata kunci : Peta rawan tsunami, Penginderaan Jauh, Sistem Informasi Geografi, Estimasi Waktu Tiba Gelombang Abstract Mapping of hazard tsunami areas based on estimation of arrival time of wave and land cover in Kupang Regency of East Nusa Tenggara Province using remote sensing application and geographic information system has been done. The aims of this research are to mapping the hazard tsunami area and tsunami vulnerability level in Kupang Regency East Nusa Tenggara according to the estimated arrival time of the wave and land cover as an effort to mitigate the impact of the tsunami disaster on population density. These generally devided into four main phase namely development of database in the form of land cover map , seismic maps and bathymetry maps, data analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. Presentation of data results in the form of vulnerability level of each map and analysis and results analysis of research the form of vulnerability level of each map and analysis of research results in the form of qualitative vulnerability of each monitoring area according to land cover map and estimated wave arrival time. And then, the impact of tsunami vulnerability is classified according to population density levels for mitigation needs as follows Kupang Timur, Kupang Barat, Sulamu, Amfoang Timur, Semau, Semau Selatan, Amfoang Utara, Amfoang Barat Daya, Amfoang Barat Laut and Fatuleu Barat. Keywords: Tsunami Hazard Map, Remote Sensing, Geographic Information System, Estimated Time of arrival Wave

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Arrival-time picking of microseismic events based on MSNet

Geophysics ◽

10.1190/geo2020-0469.1 ◽

2021 ◽

pp. 1-66

Author(s):

Guanqun Sheng ◽

Shuangyu Yang ◽

Xiaolong Guo ◽

Xingong Tang

Keyword(s):

Arrival Time ◽

Spatial Information ◽

Synthetic Data ◽

Microseismic Monitoring ◽

Monitoring Data ◽

Convolutional Network ◽

Low Snr ◽

Microseismic Data ◽

Microseismic Events ◽

Arrival Time Picking

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

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Traveling wave arrival time detection for two-terminal non-contact measurement based on short time matrix pencil scheme

Electric Power Systems Research ◽

10.1016/j.epsr.2021.107747 ◽

2022 ◽

Vol 205 ◽

pp. 107747

Author(s):

Paul Oswald Kwasi Anane ◽

Dongsheng Cai ◽

Shaddrack Yaw Nusenu ◽

Jian Li ◽

Qi Huang ◽

...

Keyword(s):

Traveling Wave ◽

Arrival Time ◽

Matrix Pencil ◽

Contact Measurement ◽

Short Time ◽

Wave Arrival

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An Automatic Arrival Time Picking Method Based on RANSAC Curve Fitting

78th EAGE Conference and Exhibition 2016 ◽

10.3997/2214-4609.201601481 ◽

2016 ◽

Cited By ~ 1

Author(s):

L. Zhu ◽

E. Liu ◽

J.H. McClellan

Keyword(s):

Curve Fitting ◽

Arrival Time ◽

Arrival Time Picking

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