Seismic noise properties in Southern California are considered. The initial data are continuous records of vertical oscillations with a sampling frequency of 1 Hz at 81 broadband stations for 12 years, 2008–2019. These data were converted to a time step of 1 min by calculating the average values in successive time intervals of 60 samples in length. The time series of low-frequency seismic noise with a time step of 1 min from each station was further converted to a sequence of daily values of the minimum normalized entropy of the distribution of the squared orthogonal wavelet coefficients. Since entropy estimates can be obtained from each station with a time step of 1 day, this makes it possible to construct a daily map of its changes in space. A general map of the distribution of entropy values is obtained by averaging daily maps. The main attention is paid to areas in which maximum entropy values are most often realized. These areas are identified by estimating the spatial probability density of the distribution of points at which a given number of maximum values are realized in each daily map. It has been shown that since 2012, the region of the most frequent realizations of entropy maxima is in direct contact with the epicenter of a strong earthquake on July 6, 2019, with magnitude 7.1. We consider the “secondary” entropy calculated for the probability densities of the distribution of the maximum values of the “primary” entropy of waveforms of seismic noise in a semi-annual moving time window. It is shown that the time intervals of increasing seismic activity correspond to a decrease in secondary entropy, which is interpreted as the concentration in the space of the distribution of the maxima of the primary noise entropy. Estimates of the change in the correlation coefficients between the daily values of the noise entropy in a semi-annual time window at network nodes of 12 reference points covering the region under study made it possible to study the spatiotemporal dynamics of strong correlations. The area of the future strong earthquake is characterized by high correlations of entropy values at the nearest reference points with other points.

考虑了南加州的地震噪声特性。初始数据是垂直振动的连续记录，其在81个宽带站的采样频率为1 Hz，为期12年，2008-2019年。通过计算长度为60个样本的连续时间间隔中的平均值，将这些数据转换为1分钟的时间步长。来自每个站的时间步长为1分钟的低频地震噪声的时间序列进一步转换为正交小波系数分布的最小归一化熵的日值序列。由于可以从每个站点以1天的时间步长获取熵估计，因此可以构建其空间变化的每日地图。熵值分布的一般图是通过对每日图进行平均获得的。主要关注那些最常实现最大熵值的区域。通过估计每个每日地图中实现给定数量的最大值的点的分布的空间概率密度来标识这些区域。研究表明，自2012年以来，最频繁出现的熵极大值区域与2019年7月6日发生的7.1级强烈地震的震中直接接触。我们考虑为在半年移动时间窗口中地震噪声波形的“一次”熵的最大值的分布的概率密度计算的“二次”熵。结果表明，地震活动增加的时间间隔对应于二次熵的减小，它被解释为一次噪声熵的最大值分布在空间中的集中。在覆盖研究区域的12个参考点的网络节点的半年时间窗口中，噪声熵的每日值之间的相关系数变化的估计值使得研究强相关性的时空动态成为可能。未来强震区域的特征是，最近的参考点与其他点的熵值高度相关。在覆盖研究区域的12个参考点的网络节点的半年时间窗口中，噪声熵的每日值之间的相关系数变化的估计值使得研究强相关性的时空动态成为可能。未来强震区域的特征是，最近的参考点与其他点的熵值高度相关。在覆盖研究区域的12个参考点的网络节点的半年时间窗口中，噪声熵的每日值之间的相关系数变化的估计值使得研究强相关性的时空动态成为可能。未来强震区域的特征是，最近的参考点与其他点的熵值高度相关。