论文标题
支持向量机在地震图分析和分化中的应用
Application of Support Vector Machines for Seismogram Analysis and Differentiation
论文作者
论文摘要
支持向量机（SVM）是一种计算技术，在各个科学领域都使用了具有K级分类能力的分类器，K为2,3,4等。火山震颤的地震图通常包含噪音，这些声音通常会证明有害于正确的解释。 PCAB站（位于意大利Panarea岛北部地区）一直在记录附近安装的泵的地震信号，从而破坏了Strombolli Volcano的有用信号。通过网格搜索进行优化后具有K = 2分类技术的SVM在泵中的地震信号的识别和分类中发挥了作用，达到99.7149％的分数，这些模式符合类的实际成员（通过交叉验证确定）。 SVM的预测标签已用于估计泵的活性持续时间，从而导致相应的地震图冗余（不适合处理和解释）。但是，当使用同一训练的SVM来确定Pino等人2011年在2003年4月4日在同一PCAB站记录的地震图是否包含泵的信号时，SVM显示泵信号缺乏100％的信号，从而在后面进行了研究工作。
Support Vector Machines (SVM) is a computational technique which has been used in various fields of sciences as a classifier with k-class classification capability, k being 2,3,4, etc. Seismograms of volcanic tremors often contain noises which can prove harmful for correct interpretation. The PCAB station (located in the northern region of Panarea island, Italy) has been recording seismic signals from a pump installed nearby, corrupting the useful signals from Strombolli volcano. SVM with k=2 classification technique after optimization through grid search has been instrumental in identification and classification of the seismic signals coming from pump, reaching a score of 99.7149% of patterns which match the actual membership of class (determined through cross-validation). The predicted labels of SVM has been used to estimate the pump's duration of activity leading to the declaration of corresponding seismograms redundant (not fit for processing and interpretation). However, when the same trained SVM was used to determine whether the seismogram used by Pino et al., 2011 recorded at the same PCAB station on 4th April, 2003 contained pump's signals or not, SVM showed 100% absence of pump's signals thereby authenticating the research work done in the latter.
