The exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes.

The exploration of multi-layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio-temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Building upon previous research on seismic ionospheric signal characteristics and data from the China Seismo-Electromagnetic Satellite (CSES), we have enhanced the Pattern Informatics(PI) Method and proposed an Improved Pattern Informatics(IPI) Method. The IPI method enables the calculation of the spatio-temporal dynamics of electronic density anomalies detected by the CSES satellite. Taking the 2021 Maduo Mw7.3 earthquake as a case study, we analyzed the seismic signals potentially contained in the electronic density anomaly disturbances. The results show that: 1) Compared to original electronic density images, the IPI method-derived models extracted distinct electronic density anomaly signals, regardless of the data collected whether during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. 2) The electronic density anomalies appeared about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies followed a pattern of appearance, persistence, disappearance, re-emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images calculated from descending and ascending orbit data were similar. These results suggest that the IPI method can capture the spatio-temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes.

Statistical earthquake forecasting models, such as the Epidemic‐type Aftershock Sequence (ETAS), embed decades of empirical knowledge and assumptions about earthquake triggering, clustering, and catalog completeness. In contrast, neural spatio‐temporal point process (STPP) models often treat seismicity as generic spatiotemporal data without such domain‐specific considerations. Using an earthquake catalog from the China Earthquake Networks Center, we investigate how seismology‐motivated inputs and training strategies affect the forecasting performance of the Deep Spatio‐Temporal Point Process (DeepSTPP). These domain‐driven designs include introducing magnitude as an input feature, incorporating an auxiliary training period, extending visible event history, and varying magnitude thresholds. We benchmark DeepSTPP against ETAS and a homogeneous Poisson process to assess relative strengths and limitations. Our results show that certain domain‐motivated configurations, such as including an auxiliary period, improve performance, particularly in forecasting immediate aftershocks. However, limitations in the model architecture, including short memory and attention dilution, restrict the benefits of event magnitude and long‐range history. The results also show that DeepSTPP remains inferior to ETAS in terms of overall spatiotemporal log‐likelihood, although it outperforms ETAS in temporal forecasting at lower magnitude thresholds, specifically for . This indicates that DeepSTPP does not yet perform as well as ETAS in the magnitude range most relevant to operational earthquake forecasting . The gains observed at lower thresholds may primarily stem from DeepSTPP's flexibility in handling catalog incompleteness and artifacts. These findings highlight the value of integrating seismological practice into neural model design and point toward future architectures that can fully exploit earthquake catalogs.

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies.