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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/88201

    Title: 利用深度學習辨識地震波 P 與 S 到時以及後續關聯與定位;Picking P and S phase based on deep learning and followed by associating and locating
    Authors: 張立衡;Chang, Li-Heng
    Contributors: 地球科學學系
    Keywords: 深度學習;關聯;挑波
    Date: 2022-06-27
    Issue Date: 2022-07-13 18:41:56 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 台灣位於環太平洋地震帶,每個月都會有數以千計的地震發生。近年來,儀器的廣
    本研究主要利用 python 中的 obspy 套件做為主要的 I/O 套件,並且使用 keras 作為
    深度學習的框架。在研究中,我們利用 Mousavi 等人在 2020 年提出的 Earthquake
    transformer 模型作為基礎進行修改與優化,其訓練的基礎建立在為 P 波到時與 S 波到
    出層後加上了辨識器,使其成為一個生成對抗模型,與原先的 Earthquake transformer 相
    比,有著更加穩定的輸出。在獲得地震到時後,本研究將 Chang 等人於 2019 發表的地
    本研究中使用了和平 2017 年的資料進行訓練,並且使用和平 2020 年的資料做為測
    試,並將整體流程應用在氣象局 2012 年一月的資料上。在和平 2020 年的資料中,P 波
    與 S 波的到時召回率分別為 0.95 與 0.85,而精確度則分別為 0.87 與 0.81。透過本研究
    一定的可靠度,便能投入既有的工作流程。;Thousands of earthquakes occur in Taiwan because Taiwan locates in the circum-pacific
    seismic belt. In recent years, with numerous settings of the seismic sensor and the improvement
    of storage technology, the number of earthquakes that have been recorded and stored increased
    a lot. An average of 18000 earthquakes are recorded each year. With the development of science
    and technology, artificial intelligence can handle some single task to increase the processing
    speed while reducing the manpower requirements.
    In our research, we use the model structure which is modified and optimized based on the
    model proposed from the study by Mousavi et al. in 2020. Training model based on designing
    the label for P and S onset time. In addition, the original label for the seismic signal is changed
    to the noise label, and also adds a discriminator after the final layer of the origin model. Make
    the entire model structure become a generative adversarial network. Compared with the
    earthquake transformer, the model in our study has more stable performance. After getting the
    time P and S onset, we optimize the associator algorithm proposed by Chang et al. in 2019 and
    add it to our process flow. And locate the earthquake by seisan then output the result in s-file
    We use the seismic waveform in Hoping during 2017 as training data, and use the data in
    the same region during 2020 as testing data. We also applied the workflow to the continuous
    data from CWB during January 2012. The recall of P and S onset in 2020 reaches 0.95 and 0.85,
    and the precision reaches 0.87 and 0.81.
    The workflow in our research can analyze a large amount of data very fast. In the future,
    we also can increase the training dataset by the workflow. After the performance becomes stable,
    we can add the flow to our other research, too.
    Appears in Collections:[地球物理研究所] 博碩士論文

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