table1_Clustering of Experimental Seismo-Acoustic Events Using Self-Organizing Map (SOM).docx

The analogue experiments that produce seismo-acoustic events are relevant for understanding the degassing processes of a volcanic system. The aim of this work is to design an unsupervised neural network for clustering experimental seismo-acoustic events in order to investigate the possible cause-effect relationships between the obtained signals and the processes. We focused on two tasks: 1) identify an appropriate strategy for parameterizing experimental seismo-acoustic events recorded during analogue experiments devoted to the study of degassing behavior at basaltic volcanoes; 2) define the set up of the selected neural network, the Self-Organizing Map (SOM), suitable for clustering the features extracted from the experimental events. The seismo-acoustic events were generated using an ad hoc experimental setup under different physical conditions of the analogue magma (variable viscosity), injected gas flux (variable flux velocity) and conduit surface (variable surface roughness). We tested the SOMs ability to group the experimental seismo-acoustic events generated under controlled conditions and conduit geometry of the analogue volcanic system. We used 616 seismo-acoustic events characterized by different analogue magma viscosity (10, 100, 1000 Pa s), gas flux (5, 10, 30, 60, 90, 120, 150, 180 × 10−3 l/s) and conduit roughness (i.e. different fractal dimension corresponding to 2, 2.18, 2.99). We parameterized the seismo-acoustic events in the frequency domain by applying the Linear Predictive Coding to both accelerometric and acoustic signals generated by the dynamics of various degassing regimes, and in the time domain, applying a waveform function. Then we applied the SOM algorithm to cluster the feature vectors extracted from the seismo-acoustic data through the parameterization phase, and identified four main clusters. The results were consistent with the experimental findings on the role of viscosity, flux velocity and conduit roughness on the degassing regime. The neural network is capable to separate events generated under different experimental conditions. This suggests that the SOM is appropriate for clustering natural events such as the seismo-acoustic transients accompanying Strombolian explosions and that the adopted parameterization strategy may be suitable to extract the significant features of the seismo-acoustic (and/or infrasound) signals linked to the physical conditions of the volcanic system.

产生地震声学事件（seismo-acoustic events）的模拟实验，对于理解火山系统的脱气过程具有重要意义。本研究的目标是构建一种无监督神经网络，用于对实验测得的地震声学事件进行聚类分析，以探究所获信号与相关物理过程之间潜在的因果关联。本研究聚焦两项核心任务：1）确定合适的参数化策略，对专为研究玄武岩火山脱气行为开展的模拟实验中记录的实验地震声学事件进行参数化处理；2）为选定的自组织映射神经网络（Self-Organizing Map，简称SOM）确定适配的配置方案，以实现对从实验事件中提取的特征进行聚类。本次实验通过定制化实验装置，在不同物理条件下生成模拟地震声学事件：模拟岩浆的黏度可变、注入气体流量（流速）可变、导管表面粗糙度可变。我们测试了自组织映射神经网络对受控条件下及模拟火山系统导管几何结构下生成的实验地震声学事件的聚类能力。本次实验共使用616组地震声学事件，其模拟参数涵盖不同黏度的模拟岩浆（10、100、1000 Pa·s）、不同流量的注入气体（5、10、30、60、90、120、150、180 × 10⁻³ L/s）以及不同粗糙度的导管（对应分形维数分别为2、2.18、2.99）。我们针对不同脱气机制动力学过程产生的加速度计信号与声学信号，分别在频域通过线性预测编码（Linear Predictive Coding）、在时域通过波形函数对地震声学事件进行参数化处理。随后，我们将自组织映射算法应用于参数化阶段提取的地震声学数据特征向量，最终得到4个主要聚类簇。聚类结果与已有实验结论相符，即岩浆黏度、气体流速及导管粗糙度均会对脱气机制产生影响。该神经网络能够有效区分不同实验条件下生成的事件。上述结果表明，自组织映射神经网络适用于对天然地震声学瞬态事件进行聚类分析，例如伴随斯特龙博利式喷发的地震声学信号；同时本次采用的参数化策略可有效提取与火山系统物理状态相关的地震声学（及/或次声）信号的关键特征。