###### Abstract  
Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of \(5^{\circ}\times 5^{\circ}\) at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi _Ms_6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding _Ms_6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies. time series forecasting models; infrared longwave radiation data; seismic anomaly; confidence interval; BILSTM +
Footnote †: journal: Medical  
thermal anomalies before earthquakes in and around India after studying earthquakes in the region. Although there is no consensus on the mechanism of pre-earthquake infrared anomalies, it is generally accepted that a certain range of thermal anomalies can occur at some time before an earthquake. Significantly, Chinese seismologists introduced their research results of seismic infrared remote sensing into daily earthquake forecasting practice and obtained a good correspondence in the prediction of earthquake risk areas in 2008, 2009, and 2013 [9]. The Earth's outgoing longwave radiation is the primary driver of the Earth's climate. This energy's reflection, absorption, and emission occur through a complex system of clouds, aerosols, atmospheric constituents, oceans, and land surfaces [10]. By definition, longwave radiation is the energy density of electromagnetic waves of all wavelengths radiated into outer space by the Earth's atmospheric system. It can reflect the energy radiation of the Earth's atmospheric system [11]. The outgoing longwave radiation data used were acquired by the High-Resolution Infrared Radiation Sounder (HIRS) carried by the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting weather satellites. The NOAA satellite series has been continuously collecting longwave radiation data since the 1970s. These data have been used in a variety of applications due to the long accumulation of data and open-source access. The rapid development of society has led to the generation of vast amounts of time series data in economics, meteorology, geology, and the environment. The correct application of time series data and forecasting models has been of great value to social activities. Classical time series forecasting models mainly include autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models. Machine learning has a regression function that has been adapted to the characteristics of time series data. Various machine learning-based forecasting methods have been applied to time series data, including support vector machines, Bayesian networks, and Gaussian processes.