The deep-seated information is reflected in the late-stage data of the second field. By introducing the deep learning algorithm integrated with the characteristics of the secondary field data, we can map the contaminated data in late track data to a high-probability position. By comparing several filtering algorithms, the SFSDSA method has better performance and the denoising signal is conducive to further improving the effective detection depth.

The sandpile-based model is the paradigm model of self-organized criticality (SOC), a mechanism believed to be responsible for the occurrence of scale-free (power-law) distributions in nature. One particular SOC system that is rife with power-law distributions is that of earthquakes, the most widely known of which is the Gutenberg–Richter (GR) law of earthquake energies. Here, we modify the sandpile to be of use in capturing the energy, space, and time statistics of earthquakes simultaneously.

The monitoring of statistical network properties could be useful for short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the earthquake catalog of INGV for the case of the L’Aquila (Italy) mainshock (M_w = 6.3) of 6 April 2009, we provide evidence that network measures, both global (average clustering coefficient, small-world index) and local (betweenness centrality) ones, could potentially be used.

Based on the methods of critical fluctuations and natural time, we have shown that the fracture-induced MHz electromagnetic emissions recorded by two stations in our network prior to two recent significant earthquakes that occurred in Cephalonia present criticality characteristics, implying that they emerge from a system in critical state.

Induced seismicity is a concern for the industries relying on fluid injection in the deep parts of the Earth’s crust. At the same time, fluid injection sites provide natural laboratories to study the impact of increased fluid pressure on earthquake generation. In this study, I show that simple geometric operations on a static stress field produced by volume change at depth explains two empirical laws of induced seismicity without having recourse to complex models derived from rock mechanics.

We introduced a new modified Markov chain model to generate a time series of the earthquake sequences from a global catalogue with an optimum time sampling of 9 days. Here, we subject the time series to a known analysis method namely an ensemble empirical mode decomposition to study the state-to-state fluctuations in each of the intrinsic mode functions. Also, we establish the power-law behaviour of the time series with the Fano factor and Allan factor used in time-correlative behaviour studies

Earthquake sequencing is an intriguing research topic and the dynamics involved are complex. For directed graphs that represent earthquake sequencing, the Kuramoto model yields synchronization. Inclusion of non-local effects evokes the occurrence of chimera states or the co-existence of synchronous and asynchronous behavior among earthquake zones. It is the chaotic dynamics of them resulting in certain patterns that we begin to see in the sequence of seismic events with a very simple model.

A 531-day wobble (531 dW) signal is clearly detected with a mean amplitude of about 7 mas after applying the ensemble empirical mode decomposition (EEMD) to the 1962-2013 polar motion (PM) time series. This signal is also detected in the two longest available superconducting gravimeter (SG) records. Synthetic tests are carried out to explain why the 531 dW signal can only be observed in recent 30-year PM time series after using EEMD.