As one of the most serious geological disasters, earthquake is of sudden and destructive characteristic. Therefore, it is of great significance to earthquake monitoring and early warning. The phenomenon of surface thermal infrared radiation enhancement is a common precursor of moderate and strong earthquakes and has been used as an important reference information for early warning and short term prediction. A variety of explanations have been given to understand internal mechanism of the above phenomenon, in which the stress-induced heating hypothesis is widely accepted and has been confirmed in the laboratory rock mechanical loading experiments, that is, under ideal conditions in the laboratory, the rock heats up when it is pressed and cools down when it is stretched. Under field conditions in practice, however, weak seismic precursors of thermal anomalies are often interfered by various environmental factors(solar radiation, atmospheric movement and human activities, etc.), and it has not been investigated whether the corresponding relationship between the above crustal compression-extension motions and thermal radiation anomalies can be observed under field conditions. The earth's gravity field, as one of the basic physical fields of the earth, contains the density distribution of crustal structure, which can be served to study the migration of the earth's material, the deformation of the crust and the change of the stress field. In this paper, we use GRACE gravity and MODIS thermal infrared remote sensing data to verify the stress-induced heat hypothesis in the field with Wenchuan earthquake as the time node. Firstly, the crustal mass density obtained by GRACE satellite was compared with thermal infrared radiation. Then, the gravity anomalies extraction method based on maximum shear strain and in-situ temperature method were used to obtain the gravity anomalies and thermal anomalies respectively. Furthermore, the correlation between the two anomalies before the earthquake was detected from the time scale and space scale respectively, and the consistency analysis between the above anomalies and the spatial distribution of the tectonic fault zone was carried out. For this purpose, two important indicators i.e., anomaly intensity and anomaly distribution, were established in time domain and space domain, respectively. The following conclusions could be drawn: 1)The stress-induced heating hypothesis can be verified by remote sensing in field conditions. The warming zone of the crust(positive thermal offset index)corresponds to the compression zone, and the cooling zone(negative thermal offset index)corresponds to the stretching zone. The consistency of positive and negative variation between the crustal mass density and thermal offset index is 88.9%, which provides field observation evidence for the stress-induced heating hypothesis. 2)The spatio-temporal variation of gravity anomalies and thermal anomalies before earthquake has strong correlation. In the time domain, there is a strong correlation between the gravity anomalies and the thermal anomalies, which shows that the intensity of the two anomalies suddenly increases synchronously and reaches the maximum simultaneously three months before the earthquake. In the spatial domain, gravity anomalies mostly occur at the junction of positive and negative values of thermal offset index, which indicates that the spatial distribution of gravity anomalies and thermal anomalies also has a certain correlation. In addition, the two anomalies appear to be distributed along the fault zone for many times, which shows that they are closely related to tectonic activities.
The occurrence of earthquakes is closely related to the crustal tectonic movement and the migration of earth mass, which consequently cause the changes of the earth‘s gravitational field. Global time-varying gravity field data obtained by GRACE gravity satellite can be used to detect pre-seismic gravity anomalies. For example, gravity signals caused by several large earthquakes, such as the 2005 MW8.6 Indonesia earthquake, the 2010 MW8.8 Chile earthquake and the 2011 MW9.0 Japan earthquake, have been successfully extracted using GRACE data. However, previous studies on GRACE satellite-based seismic gravity changes focused more on the dynamics of the co-seismic gravity field than on the pre-seismic gravity anomalies which are of great significance for the early warning of earthquakes. Moreover, the commonly adopted difference disposal of the gravity field with the gravity field of adjacent months or the average gravity field of many years when obtaining gravity anomalies cannot effectively remove the inherent north-south stripe noise in GRACE data. On the contrary, it is more likely to cause the annihilation of the medium-high order information in GRACE gravity field model, which results in the loss of some gravity information related to tectonic activities. To explore the pre-seismic gravity anomalies in a more refined way, this study proposes a method of characterizing gravity variation based on the maximum shear strain of gravity, inspired by the concept of crustal strain. In other words, the gravity strain tensor is obtained by further calculating the second-order gradient of the increment of disturbance potential after the removal of hydrological disturbance, and then the maximum shear strain of gravity is ultimately generated to characterize the pre-earthquake tectonic activities. Then, to better understand the seismogenic process of the fault zone by further extracting the pre-earthquake anomalous changes, the data of the maximum shear strain time series are analyzed in this study by means of the offset index K to describe the gravity anomaly. Because the maximum shear strain is calculated by the second-order gradient of GRACE gravity field, this method can suppress the stripe noise better than the difference disposal, thus effectively improving the sensitivity of gravity anomaly detection. The exploratory experiments are carried out in the Tibetan plateau and its surrounding area, which locates among the Pacific Ocean, the Indian Ocean and Eurasia, with the highest altitude, most complex topography and frequent strong earthquakes. Ultimately, the Wenchuan earthquake and Nepal earthquake were used as an example to complete the extraction of pre-earthquake gravity anomaly information by the above method, and the pre-earthquake tectonic activity of the fault zones was analyzed. The results show that a large area of gravity anomalies consistent with the spatial distribution of the fault zone appeared on the Longmenshan fault zone during the half a year before the earthquake, and the maximum anomalous value appeared within 50km from the epicenter, while no anomalies appeared during the non-earthquake period. In addition, compared with the traditional methods, the proposed method has a better ability to extract anomaly information of gravity field, which provides a new idea for understanding the dynamic mechanism of large earthquakes using GRACE data.
There are thermal infrared anomalies(TIA)before earthquake, and TIA has become one of the important parameters for assessing regional earthquake risk. However, not all of the surface infrared anomalies are related to tectonic activities or earthquakes. How to eliminate the influence of non-structural factors and extract the weak signals from strong disturbances is the key and difficult point for tectonic activities studies based on the thermal infrared remote sensing techniques. Land surface temperature(LST)background field is the basis for thermal infrared anomalies extraction. However, the established background fields in previous researches cannot eliminate the influence of climate changes, so the accuracy of thermal anomaly extraction is limited. Now an improved method is proposed. Combined with the periodic character of LST time series, harmonic analysis is lead into the process of LST background field establishment. Specifically, the yearly trend of LST is fitted based on Fourier Approximation method. As a new background field, the yearly trend is dynamic, includes the local and the yearly information. Then, based on the rule of "kσ", the earthquake anomalies, calculated by RST with the yearly trend of LST, can be extracted. At last, the effectiveness of the algorithm can be tested by the quantitative analysis of anomalies with anomaly area statistics, anomaly intensity statistics and distance index statistics. The Wenchuan earthquake was discussed again based on the proposed algorithm with MODIS land temperature products in 2008. The results show that, there were obvious pre-earthquake thermal anomalies along the Longmen Mountains faults with a longer time; but there were no anomalies when the earthquake happened; and the post-earthquake thermal anomalies occurred with much smaller amplitudes and scopes. Compared with the results derived from the traditional RST which is based on the spatial average of LST values, the TIA extracted by the new RST, which is based on the yearly trend of LST, is more fit with the active faults, and the process of the anomalies occurring and removing can be described in more detail. Therefore, as the background field to extract earthquake anomalies, the yearly trend of LST is more reliable.
Earthquake prediction is one of the key areas of earthquake research. Thermal infrared abnormity, which is the abnormally increased land surface temperature, is universal before earthquake and has complex nonlinear relation with the three elements of earthquake. Combining the advantage of neural network, this paper provides a method for earthquake prediction by taking thermal anomaly as information source and constructing a neural network to carry out the test. Based on the MODIS data which has synthesis of eight days with 1km resolution, taking a 10°×10° rectangle, whose center is the epicenter, as research area, and a two-month time before earthquake as the time range, we used RST algorithm to extract thermal anomaly information before earthquake. Considering the time-space relationship between thermal anomaly information and the fault zone, thinking carefully about the information of the neural network input neurons, we constructed BP neural network and used 100 earthquake cases with magnitudes larger than 5, and 70 random samples without earthquake in the research region for training and simulating. According to the statistical analysis, the prediction accuracy is 80%, missing prediction rate is 20%, and false prediction rate is 13.3%. Prediction accuracy of magnitude with error within magnitudes of 2 is 69%, prediction accuracy of earthquake origin time with error within 30 days is 87.5%, and prediction accuracy of epicenter location with error within 3°is 81.2%. The result shows that BP neural network-based earthquake prediction is feasible by using thermal infrared abnormal precursor extracted by RST method. However, in this experiment, the determination of the start time of thermal abnormity, the origin point and range of research area are based on the known epicenter location and time. In fact, the result depicts a non-linear relationship between earthquake and thermal abnormity, and the accuracy of prediction reflects the correlation degree. Therefore, the prediction accuracy of future earthquake may be not as large as our result. For future earthquake prediction, accurate selection of research area and neuron number of hidden layer in neural network has great influence on prediction result.
