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A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data

Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.


Introduction
Changes in the surface temperature of the earth's crust prior to the occurrence of earthquakes have been attested to by numerous observations (Tronin et al., 2002). Thermal infrared (TIR) remote sensing has recently emerged as a promising technique for detecting seismic precursors. Anomalous 5 TIR emissions have been detected by satellite sensors prior to the occurrence of major earthquakes (Piroddi et al., 2014). Several studies have detected space-time anomalies in TIR satellite imagery, ranging from weeks to days both before and after earthquakes (Wang, 1984;Gorny et al., 1988;Qiang et al., 1991;Tronin, 1996;Tramutoli et al., 2001;Ouzounov and Freund, 2004;. The investigation of TIR signals as seismic precursors has gained traction worldwide, Special Sensor Microwave/Imager (DMSP-SSM/I) satellite data, applying the term 'transient TIR anomalies (Saraf et al., 2009).
There are few analytical techniques that can isolate residual TIR variations potentially associated with earthquake occurrence from TIR signals of normal variability attributable to other causes . However, in over 10 years (since 2001) of applying the general 20 robust satellite techniques (RST) (Tramutoli, 1998;Valerio, 2005;Tramutoli, 2007) methodology to the investigation of this issue, the potential of this approach for discriminating anomalous TIR signals potentially associated with seismic activity from normal fluctuations in Earth's thermal emissions related to other causes (e.g., meteorological), independent of seismic activity, has been verified (Eleftheriou et al., 2016). RST is based on the Robust AVHRR Technique (RAT), which 25 was developed for environmental monitoring using NOAA/AVHRR observations (Tramutoli, 1998).
Since that time, most reported applications of RAT have demonstrated the technique's reliability and exportability for different satellite sensors and geographic areas, and RAT has evolved into RST (Tramutoli, 2007). RST comprises two main steps: the first is characterization of behavior under normal conditions; and the second is establishment of the change-detection criteria that should be 30 3 specified for each class of phenomenon considered, and for the selected technology and the time and place of the observation (Tramutoli, 2007).
Several studies have used RST to extract and analyze the space-time distribution of TIR anomalies (henceforth, all TIR anomalies mentioned were extracted using RST) relating to different earthquakes. Using MODIS land surface temperature (LST) data, Pergola et al. studied the 6 April 5 2009 Abruzzo earthquake and found that spatially extended and time-persistent TIR anomalies (Robust Estimator of TIR Anomalies [RETIRA] > 3) occurred with some degree of space-time correlation with earthquakes of various magnitudes that had occurred in Italy during the period under consideration , and from 7 days prior to the main shock in Abruzzo (Pergola et al., 2010). Meanwhile, Bellaoui et al. studied the 21 May 2003 Boumerdes earthquake 10 and detected a TIR anomaly that had persisted for 1 week during the preceding month (Bellaoui et al., 2017). Several studies have also used data from other satellites: Aliano et al. used 8 years' worth of Meteosat TIR observations to analyze the 21 May 2003 Boumerdes/Thenia (Algeria) earthquake and found that the area of interest was affected by significant positive thermal anomalies (S/N > 2.5-3) around 1 month before the main shock (Aliano et al., 2007), while Lisi   . In addition to analyzing the TIR anomalies for a single earthquake, Tramutoli et al. studied the causes of TIR anomalies: a test over an area affected by variable gas emissions, to determine the correlation between TIR anomalies and seismicity, found that general gas dispersion models and spatial features lend support to the hypothesis of a robust 25 relationship between greenhouse gas emissions and TIR anomalies related to seismic activity (Tramutoli, Aliano et al., 2013).
Several researchers have conducted long-term statistical analyses to determine the correlation between TIR anomalies and earthquakes.  investigate earthquakes with M > 4 that occurred in a wide area surrounding Taiwan, during the 30 4 month of September from 1995 to 2002; the false-positive rate (FPR) remained at zero when earthquakes with M > 4 or 4.5 were considered, and the FPR remained under 6% when a threshold of M > 5 was applied . Tramutoli et al. studied earthquakes with M > 4 in the southern Apennines in Italy's Po plain from July 2012 to June 2013 and found that the FPR was less than 33%, while the missing rate was as high as 67% (Tramutoli, Corrado et al., 2015). 5 Eleftheriou et al. studied earthquakes that occurred in Greece between 2004 and 2013 using TIR images acquired with MSG/SEVIRI, and found that more than 93% of all identified TIR anomalies occurred in the prefixed space-time window around the time and location of earthquakes with M > 4, with an overall FPR < 7% (Eleftheriou, Filizzola et al., 2016). It seems that RST is an effective means of extracting TIR anomalies that occur as precursors to earthquakes, but no such study has 10 hitherto been conducted on the Chinese mainland.
Several studies, however, have proven that some individual earthquake results are unreliable.
Some so-called TIR anomalies are caused by meteorological anomalies that are unrelated to earthquakes. For example, Matthew et al. studied the Gujarat (India) earthquake of 2001, and found that previous studies, which had indicated the presence of TIR anomalies prior to the earthquake, 15 were unreliable. They concluded that there was no robust evidence for the existence of LST anomalies prior to the 2001 Gujarat earthquake, and that cloud cover was a possible cause of the anomalies (Blackett et al., 2011). As such, rigorous statistical analyses of TIR anomalies over long periods are necessary.
In this paper, RST is applied to a mountainous area in China. Long-term analysis (from Sept. 20 2002to Mar. 2018) is used to verify the correlation between TIR anomalies and earthquakes. Based on the statistical results, the earthquake prediction potential of RST will be evaluated.

Study area
5 Fig.1 The distribution of faults in East-Southern Gansu province and its neighboring regions.
The southeastern Gansu province and its neighboring regions were selected as the study area, to assess the correlation between TIR anomalies and earthquakes from September 2002 to March 5 2018. As shown in Fig.1, the range of the study area is 27° 37° , 97° 107° . The study region is located at the intersection of Gansu, Qinghai, and Sichuan provinces; it also includes the intersection of the northern section of the north-south seismic belt and the Kuma seismic belt.
Structures in this area are complex and strong earthquakes are frequent (Yang, Zhang et al., 2002).
The area is on the eastern edge of the Tibetan Plateau, belonging to the upper part of the rhombic 10 block in the southeast Gansu province. The Xian River fault, the Longmen Shan Fault, and the Anning River fault intersect here, and the structure is Y-shaped. This type of geomorphology is widely encountered in plate tectonics, and the Longmen Shan fault in the north-northeast direction becomes a steep slope in the southeast Sichuan Basin and an erosion plateau northwest of the study area. 3. Data and method 3.1 Data introduction MODIS data are used to calculate the TIR anomalies, and the earthquake information used for 6 statistical analysis was provided by China Earthquake Datacenter (http://data.earthquake.cn).
The MODIS instrument is used on both the Terra and Aqua spacecrafts. It has a swath width of 2,330 km and views the Earth's entire surface every 1 to 2 days. Its detectors measure 36 spectral bands between 0.405 and 14.385 μm, and it can acquire data at three spatial resolutions: 250, 500, and 1,000 m. In this study, nighttime MODIS LST daily data (MYD11C1) are used to extract TIR 5 anomalies. Because LST data are susceptible to solar radiation during the daytime, nighttime data are selected for use. The LST data were retrieved at 5,600 m using the generalized split-window algorithm. In the day/night algorithm, daytime and nighttime LSTs are retrieved from pairs of day and night MODIS observations in seven TIR bands. Moreover, the daily nighttime cloud mask data (MYD35L2) are used to exclude the LST data covered by the cloud. The resolutions of the cloud 10 mask data are 250 and 1,000 m, so the resolution must be downscaled to correspond with the LST data.
Earthquakes caused by block movement and crust compression represent an extreme type of geological movement; earthquakes are instantaneous bursts of accumulated energy, and they may result in the presence of TIR anomalies across a large area. Earthquake occurrences within the study 15 area will also cause TIR anomalies close to its boundaries; therefore, for the earthquakes that occurred within the area 25° 40°, 95° 110° will also be analyzed to examine the TIR anomalies at 27° 37°, 97° 107°. However, earthquakes attributed to ground subsidence and anthropogenic factors are not associated with TIR anomalies, and therefore earthquakes where depth = 0 are excluded from analysis. Tronin et al. observed that anomalies were 20 sensitive to crustal earthquakes with a magnitude of more than 4.7 and over a distance of up to 1,000 km (Tronin et al., 2002). Therefore, we selected earthquakes of M ≥ 3.5 and depth > 0 that occurred within the area of 25° 40°, 95° 110° for analysis, and after screening, a total of 3,615 earthquakes satisfied these conditions.

RST Methodology
The RST approach is based on multi-temporal analyses of historical satellite observational datasets acquired under similar observational conditions (Eleftheriou et al., 2016). Since the surface environment is relatively constant, high-and low-temperature locations are also relatively consistent.
Over time, the infrared brightness temperature will change, albeit very gradually and in small 7 increments with obvious seasonal characteristics. Aside from the influences of meteorological factors and earthquake TIR anomalies, the brightness temperature within the same area and during the same time period exhibits robust stability and regularity. Therefore, the basic principle that guides RST is that the background field is constructed to extract the thermal anomalies, and the mean and variance of the LST are used to evaluate the degree of TIR anomaly. 5 This method consists of three main steps, as follows:  Pre-processing RST is used to construct a reference, which is considered to be in a normal state under no influence from other factors, and to measure and extract the anomalies at the corresponding time.
( , ) are LST data in location r at time t. Therefore, the first step is to eliminate the data affected 10 by clouds, and to remove outliers.
 To eliminate the effect of day-to-day climatological changes or seasonal time drifts, preprocessing is applied to the daily LST data: Where ∆ ( , ) is the difference between the value of LST acquired at time t in location r and 15 its spatial average, ( ), computed in the investigated area considering only those pixels belonging to the same class; in this study area, all pixels belong to the land class.
 The cloud mask is constructed using cloud mask data (MYD35L2). To ensure that only cloud-free radiances contribute to the computation of the reference fields (RFs), not only those pixels but also the 24 pixels in the surrounding 5× 5 area (frequently belonging to 20 cloud edges) are excluded from the following RFs computations (Eleftheriou et al., 2016).
 An outlier-mask is constructed.
This step is to determine the outliers, and these values should be excluded from the construction of the background field and the extraction of TIR anomalies. 25 A 2 (r, t, τ) = δ 1 (r, t, τ) * δ 2 (r, t, τ) * δ 3 (r, t, τ) (3) As it is shown in eq. (3), 1 , 2 , 3 are three kinds of data that should be excluded from the construction of backfields. As demonstrated by Aliano et al. and Genzano et al., the spatial distribution of clouds over a thermal heterogeneous scene can significantly change the value 8 of ∆ in the cloud-free pixels (Aliano et al., 2008;Genzano et al., 2009). The large cloud cover area will introduce a cold spatial average effect to the computation of the RFs, so that when ( , ) < − 2 * (here, is the temporal average and the is its standard, these pixels' values will be excluded, Eleftheriou et al., 2016).
Moreover, even where no cold spatial average effect is produced, extended cloud coverage can determine the V(t) values and the values of the considered signal ∆ ( , ) , scarcely representative of the actual conditions of cloud-free pixels, so that when the cloudy fraction of the land portion of the scene is > 80%, all pixels must be excluded from the computation of the RFs (Eleftheriou et al., 2016). 10 3 is used to remove the outliers (where k ≥ 2), and its expression is as follows: ( , , ) = 1 ( , , ) * 2 ( , , ) * 3 ( , , ) computed using an iterative k -clipping technique, which begins by computing ( , , ) based on the first determination of 15 ( , , ) and 2 ( , , ) , and continues by updating their values using only space-time locations with ( , , ) = 1, as follows: The process should be iterated until no further exclusions are determined, using the latest 20 determination of (Tramutoli, 1998). And the final result of is the 2 what we want.
 Computing Reference Fields  The ∆ ( , , ∆ ) is the mean of location r for time series T. The variance ∆ 2 ( , , ) is applied at time using homogeneous historical records collected under the temporal constraint ∈ (t < τ) and the ( , , ∆ ) is the background field. 25  Change-detection  The RETIRA (Robust Estimator of TIR Anomalies, Filizzola, 2004) must be computed, and the bigger the absolute value is, the more evident the anomaly is.
the RETIRA of location r at time , which belongs to the time series T. 5 is affected by cloud should be determined. From the results, it may easily be concluded that some areas will lack data at certain times, and for these scenarios a special value must be implemented to indicate that these data are affected by clouds and should be excluded from the ensuing analyses. 10

Identification of TIR anomalies
After the calculation of ( , , ) , the next step is to identify the TIR anomalies and correlate them with earthquake occurrences. In this paper, a ( , , ) V rT    that exceeds the threshold indicates the presence of a TIR anomaly; further conditions will be applied to confirm the 15 and ( , ), only in those cases where the following conditions are satisfied can it be concluded that the TIR anomaly is related to ( , ): 1) The RETIRA ( , , ) V rT    > 2. In Eleftheriou's study, the threshold was set at 4 (Eleftheriou et al., 2016) ; however, from a statistical perspective, when the value is greater than two times the standard deviation, it already falls within the abnormal category. In this 20 study, therefore, the threshold is set at 2.
2) The ( , ) is not blocked by clouds or affected by other factors.
3) Spatial persistence: The TIR anomalies cluster together and are not isolated, being part of a group covering at least 150 2 within an area of 1° * 1° (400 pixels in the images).
4) Temporal persistence: At least one more TIR anomaly appears within 7 days after the first 25 TIR anomaly. 10 5) The TIR anomalies appear 30 days before or 15 days after the ( , ) (Eleftheriou et al., 2016) .
6) The shortest distance from a given point in the TIR anomalies group to the epicenter of ( , ) is less than = 10 0.43 .
Where the TIR anomalies satisfy conditions 1), 2), 3) and 4), but do not satisfy at least either 5) 5 or 6), TIR anomalies are present with no corresponding earthquake. There are also cases wherein no TIR anomalies occur.

Results and analysis
A comprehensive statistical analysis and the TIR extraction results are detailed in this chapter. 10 In chapter 4.1, a statistical analysis is conducted to ascertain the basic seismological conditions in the study area, while the statistical results for the correlation between earthquakes and TIR anomalies are presented and analyzed in chapter 4.2. Finally, an analysis of the earthquake prediction potential of RST is presented in chapter 4.3. been during period A. The temporal distribution indicates that seismic activity prior to 2008 had been relatively weak, but in 2008, the seismic activity was extremely intense and reached its peak.
After 2008, seismicity in this area continued to maintain this intensity.   Fig. 3 The spatial distribution of earthquakes in the study area. The orange rectangle represents the study area (27° 37°,97° 107°). Earthquakes beyond the parameters of the study area are shown because earthquakes close to the study area may also cause TIR anomalies within the study area. 5 Figure 3 shows the spatial distribution of earthquakes within the study area. The results indicate that seismic events are clustered primarily in the west and center of the study area (25°N to 40°N , 95°E to 110°E ) which are mountainous regions. The earthquakes are mainly aggregated along faults, with a much sparser spatial distribution in the east and in the Sichuan Basin. 10 There is a clustering phenomenon centered on earthquakes of M ≥ 6, since earthquakes usually occur along the fault lines of active geological movements.
The purpose of investigating the temporal and spatial characteristics of earthquakes is to acquire a general understanding of the seismic activities within the study area. There is another important reason, however, which is to avoid significant accumulation of earthquakes within a short timeframe, 15 and concentrated within a small area, with the result that the same TIR anomaly corresponds to numerous earthquakes; this phenomenon excessively distorts the statistical results presented above.
Around 233 earthquakes were observed to occur after the May 12 2008 Ms 8.0 Wenchuan earthquake, in locations close to the epicenter of Wenchuan event. In chapter 4.2, the statistical 14 analysis will be divided into two sections: one dealing with the earthquakes that occurred during period C (from 2008.04 to 2008.07), and the other dealing with those that occurred outside of period C.

Statistical analysis of the correlation between TIR anomalies and earthquakes 5
In this section, TIR anomalies are extracted and a statistical analysis of the correlation between TIR anomalies and earthquakes of M ≥ 4 is conducted. Evaluation of the TIR anomalies conforms strictly to the guidelines detailed in chapter 3.3. Fig .4 Two examples of the correlation between TIR anomalies and earthquakes: on the left is the 10 TIR anomaly recorded on 2006.12.29 that corresponded to two earthquakes, and on the right is the TIR anomaly recorded on 2010.10.22 that did not correspond to earthquakes.
As shown in Fig. 4, the TIR anomalies are extracted using RST and the identification rules are applied to determine the correlation between TIR anomalies and earthquakes. After  1, 2, 3 , 4, 7, 8, 9, 13, 15, 16, 18, 19, 20, 21, 22, 31, 34, 36, 38, 41, 42, 43, 5 44, 45, 49, 50, 51 and 54) do not. The correlation rate is 51.7%. It may be seen from Fig. 6 that most TIR anomalies appear as precursors to earthquakes. However, as mentioned in section 4.1, period C may be associated with a significant increase in the total number of TIR anomalies and the correlation rate. As such, the experiment was also performed without period C, and the number of TIR anomalies is still 58, while the correlation rate is 51.7%, both of which are the same as the former result. Theoretically, the high earthquake 15 frequency and magnitudes of period C should generate numerous TIR anomalies and correlate strongly with earthquakes. However, only a single TIR anomaly corresponding to five earthquakes was observed. Figure 7 may indicate the reason for this: earthquakes cluster along several faults, but the spatial locations of these faults are always blocked by cloud cover with a percentage in excess of 90%. With the lengthy persistence of cloud coverage over a large area, the TIR anomalies 20 caused by earthquakes during period C cannot be extracted using RST. correspond to earthquakes and the correlation rate reaches 63.9%, which is significantly higher than 51.7%. Figures 2 and 8 illustrate this phenomenon: in period A, the earthquake intensity magnitudes are small, the frequency is low, and almost half of all earthquakes occur in the cloudy region, or 5 adjacent to it; therefore, it is difficult to determine any correspondence between the earthquakes and the extracted anomalies, and some anomalies may have not been extracted owing to the cloud cover.
Regarding the results from period B, the frequency and magnitudes of earthquakes in sparsely clouded areas are significantly increased, so that TIR anomalies are more likely to be extracted and more likely to correspond to earthquakes. 10 Fig. 7 The distribution of earthquakes and the frequency of each pixel blocked by cloud cover in period C; higher values indicate that the pixels are more frequently blocked by clouds.
18 Fig.8 The distribution of earthquakes in periods A and B and the frequency of each pixel being blocked by cloud cover in period C; higher values indicate that the pixels are more frequently blocked by clouds.

Evaluation of the earthquake prediction potential of RST using MODIS LST data in Sichuan area
With the aim of evaluating the earthquake prediction potential of RST using MODIS LST data for the Sichuan area, the true-positive rate (TPR) of correspondence between TIR anomalies and earthquakes with M ≥ 4.0 alone is insufficient. Therefore, four types of data are incorporated, with 10 four types of ratio calculated as follows: TP1: True-positive 1, the total number of TIR anomalies that correspond to earthquakes.
FP: False-positive, the total number of TIR anomalies that do not correspond to any earthquakes. TP2: True-positive 2, the total number of earthquakes that correspond to TIR anomalies. FN: False-negative, the total number of earthquakes that do not correspond to any TIR 15 anomalies.
Positive predictive value (PPV): The ratio of TIR anomalies that correspond to earthquakes to the total number of TIR anomalies.
False discovery rate (FDR): The ratio of TIR anomalies that do not correspond to any earthquakes to the total number of TIR anomalies. 20 TPR: The ratio of earthquakes that correspond to TIR anomalies to the total number of 19 earthquakes.
FNR: The ratio of earthquakes that do not correspond to any TIR anomalies to the total number of earthquakes. For a more accurate understanding of the eight parameters, an example is presented in Table 2.  Table 2. As may be seen from Fig 9, PPV declines as the magnitude increases, while FP is also clearly seen to increase. This phenomenon indicates that with increased magnitude, the number of TIR anomalies that correspond to the earthquakes decreases. TPR and FN can be seen to decrease steadily as the total number of earthquake samples decreases. 5 The ratios (PPV and TPR) demand closer attention, however. First, a general perceptual analysis reveals that PPV decreases steadily as M increases, while TPR increases when M≤ 6.5 and M=6.8~7.3 ≤ 6.6 and M = 7.2, 7.3; the maximum TPR is 50% when M = 7.3, and TPR decreases when M=6.5~6.8 = 6.7~7.1 . When M is 3.5 and 4.0, PPV is 63.8% and 51.7%, respectively, indicating that when a TIR anomaly is evident, there is a 63.8% (51.7%) possibility that earthquakes 10 of M ≥ 3.5 (4.0) will occur. When M is 5, 6, or 7, PPV is 25.9%, 15.5%, 1.7%, and these are much lower than the PPV of M = 3.5 and 4.0. It may be concluded from the change in the PPV curve that where a TIR anomaly is present, there will be a more than 50% possibility of an earthquake with M ≥3.5 (4.0) in the study area. It does not necessarily follow, however, that when there is a TIR anomaly, there will be strong earthquakes with M ≥ 5.0 in the study area. On the contrary, the probability that 15 earthquakes of high magnitude will occur remains low.
The TPR curve indicates the probability that an associated TIR anomaly will be present when earthquakes occur. When M =3.5 the TPR is 2.7%, and as M increases, TPR increases steadily, although it remains low when M ∈ [3. 5, 5.4] and the TPR is lower than 20%. The results show that lower-magnitude earthquakes are relatively less likely (less than 20%) to correspond to TIR 20 anomalies, while earthquakes of M ≥ 6.0, which are very destructive, have a relatively high likelihood of corresponding. High correspondence is particularly significant with regard to earthquake prediction: it indicates that destructive earthquakes are considerably more likely to be predictable in this case.
According to both sets of results, we may conclude that when a TIR anomaly is present, there 25 is a 51.7% possibility that an earthquake of M ≥ 4.0 will occur, and in the case of earthquakes of M ≥ 6.0 occurs, more than one third correspond to TIR anomalies. Most TIR anomalies correspond to earthquakes of M ≥ 4.0. However, when M ≥ 6.0, the PPV is relatively low, resulting in a higher false alarm rate for strong earthquakes. TPR increases with magnitude, and when M = 7.3, it is 50%.
It may be concluded based on the TPR curve that the greater an earthquake's magnitude, the more effective this method is likely to be in predicting it. However, the PPV and TPR are low, or the FDR 5 and FNR, which are negative with regard to the predictive potential of RST, are high. Overall, the false alarm rate for M ≥ 4.0 is 48.3%, and as M increases so too does FDR. The missing rate for M ≥ 4.0 is 96.2%, and it seems that when M < 5.5, there is no obvious correlation between TIR anomalies and earthquakes; nevertheless, TPR tends to increase when M increases, though its maximum remains at 50%, which is also an unsatisfactory value. As such, the prediction potential 10 of RST using MODIS LST data in the Sichuan area is limited. However, it doesn't indicate that the RST is not effective for earthquake prediction, on the contrary, many other cases prove that this method is very effective for extracting TIR anomalies. The low PPV and TPR may be caused by the limitation of RST, nature of MODIS LST data, special topographic and weather background of study area, or something else. 15

Discussion
To compare these results with those from previous similar studies, a summaries of four such studies are presented in Table 3. It is evident that PPV is relatively lower in this study than in the others, so it is important to verify its actual added value in comparison with a random alarm function 20 (see, for example, Eleftheriou et al., 2016). The detailed method is available in chapter 3.4 of Eleftheriou et al. (2016), and the result is presented in Fig. 10. When M ≥ 3.5, the point is at the upper extreme of the random guess, with the result that there is no obvious correlation between TIR anomalies and earthquakes with M ≥ 3.5; rather, the correlation appears to be casual. When M ≥ 4.0 and M≥ 4.5 , both of these points are still very close to the line, though at the lower part, 25 meaning that a non-casual correlation is actually present among the extracted TIR anomalies and earthquakes (M ≥ 4.0 and ≥ 4.5). However, the correlation is not strong. The result in this study is different from that achieved by Eleftheriou: in her study, the strong correlation between the TIR anomalies and earthquakes is much more evident. This may be attributable to the fact that, as shown in Fig. 8, the east and southeast corner of the study area is consistently blocked by clouds, i.e., for 30 24 over 90% of the time. Several earthquakes also occur in this area, but insufficient data prevents correlation between the earthquakes and TIR anomalies, and they are inevitably classified into FNR, which is v in Molchan error analysis, making the correlation weaker. For M ≥ 5.0, 5.5 and 6.0, the points are clear under the random guess, and as M increases, the non-causal correlation is strengthened. 5 Tronin indicated that the anomaly was sensitive to crustal earthquakes that were of a magnitude greater than 4.7 over a distance of up to 1,000 km (Tronin, Hayakawa et al., 2002). In this study, however, when M = 4.7, the TPR is 9.6% and the FNR is 90.4%, and at the point in Fig. 10 at which M ≥ 4.5 is very close to the random guess, the statistical result does not support the theory that the TIR anomaly is sensitive to the earthquakes of M ≥ 4.7. When M ≥ 5.9, earthquakes appear to be 10 sensitive to TIR anomalies, as may be seen from Table 3. This failure to conform to previous conclusions may be attributable to the regional structure and geological movement, cloud cover, and effectiveness of the method for extracting TIR anomalies, among other factors. Further study is required, however. We calculated the total number of TIR anomalies and numbers of FP for each month in both studies, and found that in Eleftheriou's study TIR anomalies clustered in November, September, 20 January and February, while in the present study they cluster in November, September and January.

15
in Fig. 11. In this paper, there is a significant positive proportional correlation between the number of TIR anomalies and the area not blocked by clouds. When the percentage is high, the number of TIR anomalies is also high, and when the percentage is low, the number of TIR anomalies is low. Therefore, while several TIR anomalies related to earthquakes may be present, they are blocked by 5 cloud cover and cannot be extracted. However, the question of what the true cause is, i.e., cloud clover, seasonal weather, or some other factor, remains to be answered satisfactorily. Moreover, another interesting phenomenon is that several TIR anomalies that do not correspond to any earthquakes cluster in November and September, both of which are cold months that do not tend to be cloudy. Therefore, the clustering of numerous FPs during these 2 months also remains to be fully 10 investigated. Fig. 10 Molchan error diagram analysis computed for different classes of magnitude and TIR anomalies during the study period (from 2002.09 to 2018.03); the red circles refer to earthquakes that occurred before and after the appearance of TIR anomalies. 15 Fig. 11 The monthly average percentage of the area not blocked by clouds in Zhang's study region, i.e., that of this study. The bar chart presents the numbers of various types of TIR anomaly. 'Total in Z' is the total number of TIR anomalies in this study; 'FP in Z' is the number of TIR anomalies that do not correspond to any earthquakes in this study; 'Total in E' is the total number of TIR 5 anomalies in Eleftheriou's study; 'FP in E' is the number of TIR anomalies that do not correspond to any earthquakes in Eleftheriou's study.

Conclusions
(1) Statistical analysis of 18 years' worth of data on the correlation between earthquakes and TIR 10 anomalies indicate that 51.7% of TIR anomalies correspond to earthquakes of M ≥ 4.0 in the Sichuan region, and the higher the M, the more likely it is that the earthquakes will correspond to TIR anomalies. The low PPV and TPR may be attributable to the large portions of the study region that are covered by clouds throughout the year.
(2) The low PPV and TPR suggest that the earthquake prediction potential of RST using MODIS 15 LST data with regard to the Sichuan region is limited. For stronger earthquakes, with M ≥ 6.0, although the false alarm rate is high, the missing rate is relatively low. RST was applied to the study area and was found to have significant predictive potential with regard to strong earthquakes.
(3) There is no obvious correlation between earthquakes of M < 5.0 and the TIR anomalies 20 extracted using RST and MODIS LST data in the Sichuan region. However, the underlying causes of this situation merit further investigation.

27
(4) The RST proposed in this study and in Eleftheriou's study is still considerably affected by cloud cover and seasonal influences. It is necessary to improve and optimize algorithms and statistical methods that facilitate the exclusion of cloud and seasonal influences.