TWI647475B - An earthquake prediction method and system - Google Patents
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Abstract
本發明揭露一種地震預測方法與系統，其中該方法包括：步驟1：收集一特定時間內以及特定區域內之與地震相關之經緯度位置、深度、規模與發生時間之資料，並以一信賴區間將該特定時間區間內以及特定區域內之經緯度位置、深度、規模與發生時間之資料進行篩選。步驟2：依據經篩選出之經緯度位置、深度、規模與發生時間之資料，經由計算而求得產生地震所需能量與維持原始地質狀態之能量比值。步驟3：依據該能量比值，經計算而預測已知位置或已知時間之地震發生的深度或規模。而該系統包括：一資料收集單元、一篩選單元以及一處理單元。 The invention discloses a seismic prediction method and system, wherein the method comprises: Step 1: collecting information on the location, depth, scale and time of occurrence of earthquake-related latitude and longitude within a specific time and in a specific region, and using a confidence interval The data of the latitude and longitude position, depth, scale and time of occurrence in the specific time interval and in the specific region are screened. Step 2: According to the selected latitude and longitude position, depth, scale and time of occurrence, the energy ratio between the energy required to generate the earthquake and the original geological state is calculated through calculation. Step 3: Based on the energy ratio, the depth or scale of the occurrence of an earthquake at a known location or a known time is calculated. The system includes: a data collection unit, a screening unit, and a processing unit.
Description
本發明是關於一種地震預測方法與系統，特別是有關以特定時間區間內與地震相關之全球經緯度位置、深度、規模與發生時間有效預測後續全球地震發生的方法與系統。 The present invention relates to a seismic prediction method and system, and more particularly to a method and system for effectively predicting the occurrence of a subsequent global earthquake with global latitude and longitude position, depth, scale and occurrence time associated with an earthquake in a specific time interval.
十多年前經過臺灣的921地震後，由於死傷慘重，民眾對對於地震普遍深感恐懼與害怕。綜觀目前地震預測的相關習知技術，於關聯性範圍方面，目前地震預測方式主要是分析前兆現象做為統計相關性分析，但沒有理論說明所有前兆現象的關聯性，因此急需要有一將所有前兆現象加以結合之分析方法與系統。 After the 921 earthquake that passed Taiwan more than a decade ago, the people were deeply fearful and afraid of the earthquake because of the heavy casualties. Looking at the current related techniques of earthquake prediction, in terms of the scope of relevance, the current earthquake prediction method mainly analyzes the precursor phenomenon as a statistical correlation analysis, but there is no theory to explain the correlation of all precursor phenomena. Therefore, it is urgent to have all the precursors. Analytical methods and systems that combine phenomena.
再，於尺度範圍方面，目前地震預測多以地球本身或太陽活動影響範圍來推論，將地震作為被影響方式來推論因果關係，但，對所有宇宙現象做為結果反推原始能量生成來源推估方面並無相關的技術。再，於時間範圍方面，目前地震預測方式為透過前兆現象來推估地震預計發生時間，或透過地震波時間差來提早預報，整體事件為同一地震事件，就像地震後所發布的海嘯警報，並無將個別單一事件進行整體預測，並將各地震事件進行規則化分析後，模擬宇宙活動運態而進行地震預測的方法與系統。再，於地震規模範圍方面，一般地震預測難以預測中度與輕度(規模6以下的地震)，此類地震為常態性地震，就如夏季常有午後雷陣雨，難以預測下午幾點幾分開始下雨一般，此部分也急需改善。 Furthermore, in terms of scale, most of the current earthquake predictions are inferred from the scope of the Earth itself or the influence of solar activity, and the earthquake is used as the way to infer the causal relationship. However, the estimation of the original energy generation source is the result of all the cosmological phenomena. There are no related technologies in terms of aspects. Furthermore, in terms of time horizon, the current earthquake prediction method is to estimate the earthquake occurrence time through the precursor phenomenon, or to predict the earthquake through the time difference of the earthquake wave. The overall event is the same earthquake event, just like the tsunami warning issued after the earthquake, there is no A method and system for predicting earthquakes by synthesizing individual events and conducting regular analysis of each earthquake event. Furthermore, in terms of the scale of earthquakes, general earthquake prediction is difficult to predict moderate and mild (earthquakes below 6). Such earthquakes are normal earthquakes, just as there are often thunderstorms in the summer, and it is difficult to predict what time starts in the afternoon. It is raining, and this part is in urgent need of improvement.
此外，目前在地震預測空間分析上，僅針對各因子指數進行相關性分析，未將資料進行空間轉換後再與異質空間系統進行數據分析。 In addition, in the spatial analysis of earthquake prediction, correlation analysis is only performed for each factor index, and the data is not spatially converted and then analyzed with the heterogeneous space system.
為了尋找地震活動的規律而著手解開地震因子相互關係，發明人在長年充實各領域專業知識如生態學、天文學、神學、景觀學、地球科學、量子力學、植物學、水利學、交通運輸等等，發現各領域可嘗試整合為單一方程式進行闡述，且各不同領域其運行軌跡與方式基本上雷同，藉由本發明之方法與系統，應用於地震預測上，進行參數與公式優化作業，藉此做為各專業領域模式開發之一貫性開發準則，及作為未來科學跳躍性發展之基礎，同時也可藉由此預測公式來挽救未來因地震而喪生之寶貴性命，並重新定義生命的意涵與價值。 In order to find the law of seismic activity and start to solve the relationship between seismic factors, the inventors have enriched various fields of expertise such as ecology, astronomy, theology, landscape science, earth science, quantum mechanics, botany, water science, transportation, etc. Etc., it is found that various fields can be attempted to be integrated into a single equation, and the trajectories and modes of the different fields are basically the same. The method and system of the present invention are applied to earthquake prediction to perform parameter and formula optimization operations. As a consistent development guideline for the development of models in various professional fields, and as the basis for the future development of science, we can also use this prediction formula to save the precious life lost in the future due to earthquakes and redefine the meaning of life. value.
因此，為克服前述問題，遂有本發明的產生。 Therefore, in order to overcome the aforementioned problems, the present invention has been produced.
本發明的主要目的是為解決目前無法進行時序預測地震發生位置與規模之障礙，透過量子力學概念進行模擬宇宙運行方式，來闡述目前宇宙能量運行方式，並依此作為計算框架，來分析歸納各項與地震預測相關數據，以求得未來災害性的地震預測，針對該區域人民進行精準時間預告，提供充裕疏散與避難防災時間，並減少因地震所造成的人員傷亡。並可透過本發明進行各地區地震風險評估，作為都市計畫規劃及保險業務評估之參考資訊。 The main purpose of the present invention is to solve the obstacles in the current position and scale of earthquake prediction, and to simulate the operation mode of the universe through the concept of quantum mechanics, and to explain the current operation mode of the universe energy, and use this as a calculation framework to analyze and summarize each Data related to earthquake prediction, in order to obtain future earthquake predictions, provide accurate time predictions for people in the region, provide sufficient evacuation and evacuation time, and reduce casualties caused by earthquakes. The seismic risk assessment of each region can be carried out through the present invention as reference information for urban planning planning and insurance business evaluation.
因此，本發明提供一種地震預測方法，包括：步驟1：收集 一特定時間內以及特定區域內之與地震相關之經緯度位置、深度、規模與發生時間之資料，並以一信賴區間將該特定時間區間內以及特定區域內之經緯度位置、深度、規模與發生時間之資料進行篩選而產生一地震預測參數；步驟2：依據該地震預測參數，經由計算而產生一產生地震所需能量與維持原始地質狀態之能量比值；以及步驟3：依據該能量比值，經計算而產生已知位置或已知時間之地震發生的深度或規模。 Therefore, the present invention provides a seismic prediction method, comprising: Step 1: collecting data of a seismic-related latitude and longitude position, depth, scale, and occurrence time in a specific time and in a specific region, and using a confidence interval for the specific time The data of the latitude and longitude position, depth, scale and occurrence time in the interval and in the specific region are screened to generate a seismic prediction parameter; Step 2: According to the earthquake prediction parameter, the energy required for generating the earthquake and the maintenance of the original geology are generated through calculation The energy ratio of the state; and step 3: based on the energy ratio, the depth or scale at which the known location or known time of the earthquake occurs is calculated.
實施時，本發明之該地震預測方法於該步驟2更包括：步驟2-1：將經篩選之經緯度位置、深度、規模與發生時間帶入下列方程式1，而求得K值：E=K*A*T*C*G(方程式1)，其中E為維持地球存在所需能量(單位：J)；A為該經緯度位置與該深度(單位：公尺3)；T為發生時間(單位：日)；C是該規模，其是為單位時間內生成地殼斷裂單位面積內質量變化(單位：質量/秒*公尺2)；G是在單位時間內空間中維持所存在的引力之加速度(單位：公尺/秒2)；K是產生地震所需能量與維持原始地質狀態之能量之比值。 In the implementation, the earthquake prediction method of the present invention further includes: Step 2-1: Bringing the selected latitude and longitude position, depth, scale and occurrence time into the following Equation 1 to obtain a K value: E=K *A*T*C*G (Equation 1), where E is the energy required to maintain the Earth's presence (unit: J); A is the latitude and longitude position and the depth (unit: metric 3 ); T is the time of occurrence (unit : day); C is the scale, which is the mass change in the unit area of the crustal fracture generated per unit time (unit: mass / sec * ft 2 ); G is the acceleration of the gravitational force maintained in the space per unit time (Unit: metric/second 2 ); K is the ratio of the energy required to generate an earthquake to the energy that maintains the original geological state.
實施時，本發明之該地震預測方法於該步驟3中更包括：步驟3-1：將經篩選之經緯度位置、深度、規模與時間帶入一方程式2中，其中，該方程式2為E=K*[X,Y,Z]*T*c*g+e，其是將該方程式1中之該A (該經緯度位置)視為三維座標系統而以矩陣[X,Y,Z]表示，並將c視為地震規模強度且將g視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為該發生時間(單位：日)；而e是為誤差修正值；K為由該方程式1所求得之值；藉此，求得已知位置或已知時間之地震發生的深度或規模。 In the implementation, the earthquake prediction method of the present invention further includes: step 3-1: bringing the selected latitude and longitude position, depth, scale and time into a program 2, wherein the equation 2 is E= K*[X,Y,Z]*T*c*g+e, which is the matrix (X, Y, Z) expressed by considering the A (the latitude and longitude position) in Equation 1 as a three-dimensional coordinate system, Consider c as the magnitude of the earthquake and consider g as the gravitational acceleration in the matrix [X, Y, Z]; T is still considered the time of occurrence (unit: day); and e is the error correction value; K is The value obtained by the equation 1; thereby, the depth or scale of the occurrence of an earthquake of a known position or a known time is obtained.
實施時，本發明之該地震預測方法於該步驟1更包括：步驟1-]：以一變異數分析法分析該規模、該經緯度位置、該深度三者與該發生時間的相關性，若所分析結果達到一預設值，則進行該步驟2；若小於該預設值，則繼續進行以下步驟；步驟1-2：將該經緯度位置、該深度進行一第一座標轉換步驟，而使該經緯度位置、該深度成為三維之資料型態，並將該時間之格式轉換為以天為單位；步驟1-3：以該變異數分析法分別分析前述經該第一座標轉換步驟的該經緯度位置、該深度與經轉換之該時間的相關性，若所分析結果達到一預設值，則進行該步驟2；若小於該預設值，則繼續進行以下步驟；步驟1-4：將該該規模、該該經緯度位置、該深度進行一第二座標轉換步驟，藉以去除地球自轉對該等資訊的角動量之影響；步驟1-5：以該變異數分析法分析前述經該第二座標轉換步驟的該規模、該經緯度位置、該深度三者與該時間的相關性，若所分析結果達到一預設值，則進行該步驟2；若小於該預設值，則繼續進行以下步驟； 步驟1-6：將該時序資訊與該空間資訊進行一第三座標轉換步驟，藉以去除地球公轉對該經排列的該時序資訊與該空間資訊的角動量之影響；以及步驟1-7：以該變異數分析法分析前述經該第三座標轉換步驟的該規模、該位置、該深度三者與該時間的相關性，若分析結果達到一預設值，則進行該步驟2。 In the implementation, the earthquake prediction method of the present invention further includes: step 1-]: analyzing, by a variance analysis method, the correlation between the scale, the latitude and longitude position, and the depth, and the occurrence time; If the analysis result reaches a preset value, the step 2 is performed; if the preset value is less than the preset value, the following steps are continued; Step 1-2: the latitude and longitude position and the depth are subjected to a first coordinate conversion step, so that the The latitude and longitude position, the depth becomes a three-dimensional data type, and the format of the time is converted into days; step 1-3: analyzing the latitude and longitude position of the first coordinate conversion step by the variance analysis method The correlation between the depth and the converted time is performed. If the analyzed result reaches a preset value, the step 2 is performed; if the preset value is less than the preset value, the following steps are continued; Step 1-4: The scale, the latitude and longitude position, and the depth are subjected to a second coordinate conversion step to remove the influence of the angular momentum of the earth rotation on the information; Step 1-5: analyzing the second through the variation analysis method The correlation between the scale of the target conversion step, the latitude and longitude position, and the depth and the time, if the analyzed result reaches a preset value, the step 2 is performed; if the preset value is less than the preset value, the following steps are continued. Step 1-6: performing a third coordinate conversion step on the timing information and the spatial information, so as to remove the influence of the earth revolution on the aligned timing information and the angular momentum of the spatial information; and steps 1-7: The variance analysis method is used to analyze the correlation between the scale, the position, and the depth of the third coordinate conversion step and the time. If the analysis result reaches a preset value, the step 2 is performed.
實施時，本發明之該地震預測方法於該步驟1-7中，若分析結果未達該預設值，則進行以下步驟：步驟1-8：將從該資料庫存取於該特定時間區間內之的一太陽黑子座標震幅資訊，以該變異數分析法分析該太陽黑子座標震幅資訊與該時間的相關性，若所分析結果達到一預設值，則進行下一步驟；步驟1-9：將一太陽黑子座標震幅資訊與一太陽黑子空間分佈資訊帶入一方程式3，其中，該方程式3為E=K*[x,y,z]*T*d*h+f，其是將該方程式2中之該矩陣[X,Y,Z]三維座標系統轉為太陽座標系統，並將該d視為太陽黑子數及該h視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為時間(單位：日)；而f是為一誤差修正值；藉此，求得方程式3而求得已知位置或已知時間之地震發生的深度或規模；以及步驟1-10：將該方程式3中之該矩陣[X,Y,Z]與d分別對應以下任一項，包括：地球火山爆發之發生位置與該火山爆發之發生位置之火山 高度；太陽系中子雲濃度分布中心點與中子雲濃度；大氣電離層濃度分布中心點與電離層濃度；地下水氡氣濃度分布中心點與氡氣濃度；或月球座標系統之三維座標中心點與該三維座標中心點距地球之距離；藉此，分別求得複數個該方程式3之該誤差修正值f，而求得已知位置或已知時間之地震發生的深度或規模。 In the implementation, the earthquake prediction method of the present invention, in the step 1-7, if the analysis result does not reach the preset value, the following steps are performed: Step 1-8: taking the data inventory from the specific time interval The sunspot coordinate amplitude information is used to analyze the correlation between the sunspot coordinate amplitude information and the time by the variance analysis method. If the analysis result reaches a preset value, proceed to the next step; Step 1 9: Bringing a sunspot coordinate amplitude information and a sunspot spatial distribution information into a program 3, wherein the equation 3 is E=K*[x, y, z]*T*d*h+f, The matrix [X, Y, Z] three-dimensional coordinate system in Equation 2 is converted into a solar coordinate system, and the d is regarded as the sunspot number and the h is regarded as a matrix [X, Y, Z] Gravity acceleration; T is still regarded as time (unit: day); and f is an error correction value; thereby, Equation 3 is obtained to obtain a depth or scale of occurrence of an earthquake at a known position or a known time; 1-10: The matrix [X, Y, Z] and d in the equation 3 respectively correspond to any one of the following, including: the volcanic eruption of the earth The volcanic height of the location and location of the volcanic eruption; the central point and neutron cloud concentration of the neutron cloud concentration distribution in the solar system; the central point and ionospheric concentration of the atmospheric ionospheric concentration distribution; the central point of the groundwater radon concentration distribution and the concentration of helium; or the moon The distance between the three-dimensional coordinate center point of the coordinate system and the center point of the three-dimensional coordinate from the earth; thereby obtaining the error correction value f of the plurality of equations 3 respectively, and obtaining an earthquake of a known position or a known time Depth or scale.
實施時，本發明之該地震預測方法於該步驟1-1之前更包括：a1：將該經緯度位置、該深度、該規模按時間順序排列；a2：以常態分布檢定法檢測經按時間順序排列的該時序資訊與該空間資訊是否符合常態分佈，若符合，則繼續進行該步驟1-1；若不符合，則將經按時間順序排列的該規模、該位置、該深度三者進行一資料轉換步驟而符合常態分佈後，再進行該步驟1-1。 In the implementation, the earthquake prediction method of the present invention further comprises: a1: arranging the latitude and longitude position, the depth, and the scale in chronological order; a2: detecting the chronological order by the normal distribution test method; Whether the time series information and the spatial information conform to the normal distribution, if yes, proceed to step 1-1; if not, the data in the chronological order, the position, and the depth are performed. After the conversion step is in accordance with the normal distribution, the step 1-1 is performed.
實施時，本發明之該地震預測方法之該信賴區間設定為介於0.6至0.98之間。 In implementation, the confidence interval of the seismic prediction method of the present invention is set to be between 0.6 and 0.98.
本發明另提供一種地震預測系統，包括：一資料收集單元，其供收集一特定時間區間內以及特定區域內之與地震相關之經緯度位置、深度、規模與發生時間之資料；一篩選單元，其供將該特定時間區間之經緯度位置、深度、規模與發生時間之資料根據一信賴區間進行篩選而產生一地震預測參數； 一處理單元，其供將該地震預測參數加以計算而產生一產生地震所需能量與維持原始地質狀態之能量比值：以及，以該能量比值而求得已知位置或已知時間之地震發生的深度或規模。 The present invention further provides a seismic prediction system, comprising: a data collection unit for collecting data of a seismic-related latitude and longitude position, depth, scale, and occurrence time in a specific time interval; and a screening unit. Generating a data of the latitude and longitude position, depth, scale and occurrence time of the specific time interval according to a confidence interval to generate a seismic prediction parameter; a processing unit for calculating the seismic prediction parameter to generate an earthquake The ratio of the energy required to maintain the original geological state: and the depth or scale at which the known location or known time of the earthquake occurs.
在一實施例中，本發明之地震預測系統之該處理單元更供將經篩選出之經緯度位置、深度、規模與發生時間帶入下列方程式1，而求得K值：E=K*A*T*C*G(方程式1)，其中E為維持地球存在所需能量(單位：J)；A為該經緯度位置與該深度(單位：公尺3)；T為發生時間(單位：日)；C是該規模，其是為單位時間內生成地殼斷裂單位面積內質量變化(單位：質量/秒*公尺2)；G是在單位時間內空間中維持所存在的引力之加速度(單位：公尺/秒2)；K是產生地震所需能量與維持原始地質狀態之能量比值；以及該處理單元更供將經篩選之經緯度位置、深度、規模與時間帶入一方程式2中，其中，該方程式2為E=K*[X,Y,Z]*T*c*g+e，其是將該方程式1中之該A(該經緯度位置)視為三維座標系統而以矩陣[X,Y,Z]表示，並將c視為地震規模強度且將g視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為該發生時間(單位：日)；而e是為誤差修正值；K為由該方程式1所求得之能量比值；藉此，求得已知位置或已知時間之地震 發生的深度或規模。 In an embodiment, the processing unit of the earthquake prediction system of the present invention further provides the selected latitude and longitude position, depth, scale, and occurrence time into the following Equation 1, and obtains a K value: E=K*A* T*C*G (Equation 1), where E is the energy required to maintain the Earth's presence (unit: J); A is the latitude and longitude position and the depth (unit: metric 3 ); T is the time of occurrence (unit: day) ; C is the scale, which is the mass change in the unit area of the crustal fracture generated per unit time (unit: mass / sec * ft 2 ); G is the acceleration of the gravitational force maintained in the space per unit time (unit: Meter / sec 2 ); K is the energy ratio of the energy required to generate the earthquake and maintain the original geological state; and the processing unit is further used to bring the selected latitude and longitude position, depth, scale and time into a program 2, wherein The equation 2 is E=K*[X,Y,Z]*T*c*g+e, which is to treat the A (the latitude and longitude position) in the equation 1 as a three-dimensional coordinate system and a matrix [X, Y, Z] means, and consider c as the magnitude of the earthquake and consider g as the gravitational acceleration in the range of the matrix [X, Y, Z]; T is still considered to be the Time (unit: day); e is the error correction value; energy ratio obtained by the equation of 1 K by; whereby, determined known position or known depth or time scale earthquakes occurred.
1‧‧‧資料收集單元 1‧‧‧ data collection unit
2‧‧‧篩選單元 2‧‧‧ screening unit
3‧‧‧處理單元 3‧‧‧Processing unit
1、2、3‧‧‧步驟 1, 2, 3 ‧ ‧ steps
第1A圖是為本發明地震預測之系統實施例之架構方塊示意圖。 Figure 1A is a block diagram showing the architecture of an embodiment of the earthquake prediction system of the present invention.
第1B圖是為本發明地震預測之方法之流程圖。 Figure 1B is a flow chart of the method of earthquake prediction of the present invention.
第2圖是為全球地震規模7以上之地震分布圖，方框為台灣地區所在位置。 Figure 2 shows the distribution of earthquakes with a magnitude of more than 7 in the global earthquake. The box is the location of the Taiwan region.
第3A圖是依據規模資料進行資料分群，再針對時間序列進行時間分群，而比較緯度之地震事件數統計長條圖。 Figure 3A is a data bar graph based on the scale data, and then time-grouping for the time series, and comparing the latitude and longitude seismic event statistics bar graph.
第3B圖是依據規模資料進行資料分群，再針對時間序列進行時間分群，而比較經度之地震事件數統計長條圖。 Figure 3B is a statistical bar graph of the number of earthquake events compared to the long-term grouping of time series based on the scale data.
第4A圖是依據規模資料進行資料分群，再針對時間序列進行時間分群，而比較深度之地震事件數統計長條圖。 Figure 4A is a statistical bar graph of the number of seismic events compared to the time series by time grouping according to the scale data.
第4B圖是依據緯度資料進行資料分群，再針對時間序列進行時間分群，而比較規模之地震事件數統計長條圖。 Figure 4B shows the data grouping based on the latitude data, and then time-grouping the time series, and comparing the statistical bar graphs of the number of earthquake events.
第5A圖是依據經度資料進行資料分群，再針對時間序列進行時間分群，而比較規模之地震事件數統計長條圖。 Figure 5A is a bar graph of the number of earthquake events compared to the time series by time grouping according to the longitude data.
第5B圖是依據深度資料進行資料分群，再針對時間序列進行時間分群，而比較規模之地震事件數統計長條圖。 Figure 5B shows the data grouping based on the depth data, and then time-grouping the time series, and comparing the statistical bar graphs of the number of earthquake events.
第6圖是將前述之統計長條圖轉換為3D立體資料分佈圖。 Figure 6 is a diagram for converting the aforementioned statistical bar graph into a 3D stereo data distribution map.
第7圖是本發明之一實施例中時間、緯度、經度、深度轉三 維關係圖。 Fig. 7 is a three-dimensional relationship diagram of time, latitude, longitude and depth in an embodiment of the present invention.
第8圖為本發明之一實施例中台灣地區3D視角地震發生位置時序圖。 Figure 8 is a timing chart showing the location of an earthquake in a 3D viewing angle in Taiwan in an embodiment of the present invention.
為對於本發明之特點與作用能有更深入之瞭解，茲藉實施例配合圖式詳述於後，各圖中相同之符號是表示相同或等同的元件。 In the following, the same reference numerals are used to refer to the same or equivalent elements in the drawings.
本發明揭示一種地震預測系統，請參考第1A圖，包括一資料收集單元1、一篩選單元2以及一處理單元3，其中該資料收集單元1是與該篩選單元2連接；該篩選單元2是與該處理單元3連接。該資料收集單元1供收集一特定時間區間內與一特定區域中與地震相關之全球經緯度位置；該篩選單元2供將該特定時間區間之全球經緯度位置、深度、規模與發生時間並根據一信賴區間進行篩選，而該信賴區間設定為介於0.6至0.98之間，亦可由使用者自行調整為0.6至0.98之間的任何區間範圍；該處理單元3供以該地震預測參數而求得產生地震所需能量與維持原始地質狀態之能量比值：以及，以該能量比值而預測特定經緯度位置與時間之下次地震發生的深度與規模。該資料收集單元1、該篩選單元2以及該處理單元3是可分別或個別設於一行動裝置、電腦或伺服器中之單一處理器或多個處理器中，而該裝置是可為為個人裝置如電腦、平板電腦、智慧型手機、物聯網之主機、智慧型手錶等等等。另一方面，該資料收集單元1、該篩選單元2以及該處理單元3亦可縮小為一虛擬機器(Virtual machine)，藉以因應未來科技發展的各種需求。此外，該處理單元3更供將經篩選出之經緯度位置、深度、規模與發生時間帶入下列方程式1，而求得K值：E=K*A*T*C*G(方程式1)，其中 E為維持地球存在所需能量(單位：J)；A為該經緯度位置與該深度(單位：公尺3)；T為發生時間(單位：日)；C是該規模，其是為單位時間內生成地殼斷裂單位面積內質量變化(單位：質量/秒*公尺2)；G是在單位時間內空間中維持所存在的引力之加速度(單位：公尺/秒2)；K是產生地震所需能量與維持原始地質狀態之能量比值；以及，該處理單元3更供將經篩選之經緯度位置、深度、規模與時間帶入一方程式2中，其中，該方程式2為E=K*[X,Y,Z]*T*c*g+e，其是將該方程式1中之該A(該經緯度位置)視為三維座標系統而以矩陣[X,Y,Z]表示，並將c視為地震規模強度且將g視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為該發生時間(單位：日)；而e是為誤差修正值；K為由該方程式1所求得之能量比值；藉此，求得已知位置或已知時間之地震發生的深度或規模。 The present invention discloses a seismic prediction system. Referring to FIG. 1A, a data collection unit 1, a screening unit 2, and a processing unit 3 are included. The data collection unit 1 is connected to the screening unit 2; the screening unit 2 is It is connected to the processing unit 3. The data collection unit 1 is configured to collect global latitude and longitude positions related to earthquakes in a specific time zone in a specific time interval; the screening unit 2 provides global latitude and longitude position, depth, scale and occurrence time of the specific time interval according to a trust The interval is selected, and the confidence interval is set to be between 0.6 and 0.98, and can also be adjusted by the user to any interval range between 0.6 and 0.98; the processing unit 3 obtains the earthquake by using the earthquake prediction parameter. The ratio of the energy required to maintain the original geological state: and, by this energy ratio, the depth and scale of the next earthquake at a particular latitude and longitude position and time. The data collection unit 1, the screening unit 2, and the processing unit 3 are respectively singly or individually disposed in a single processor or a plurality of processors in a mobile device, a computer or a server, and the device can be an individual Devices such as computers, tablets, smart phones, IoT consoles, smart watches, etc. On the other hand, the data collection unit 1, the screening unit 2, and the processing unit 3 can also be reduced to a virtual machine, in response to various needs of future technological development. In addition, the processing unit 3 is further configured to bring the selected latitude and longitude position, depth, scale and occurrence time into the following Equation 1, and obtain a K value: E=K*A*T*C*G (Equation 1), Where E is the energy required to maintain the Earth's presence (unit: J); A is the latitude and longitude position and the depth (unit: metric 3 ); T is the time of occurrence (unit: day); C is the scale, which is the unit The mass change in the unit area of the crustal fracture is generated in time (unit: mass / sec * ft 2 ); G is the acceleration of the gravitational force existing in the space per unit time (unit: metric / sec 2 ); K is generated The energy ratio between the energy required for the earthquake and the original geological state is maintained; and the processing unit 3 is further configured to bring the selected latitude and longitude position, depth, scale and time into a program 2, wherein the equation 2 is E=K* [X, Y, Z] * T * c * g + e, which is to represent the A (the latitude and longitude position) in Equation 1 as a three-dimensional coordinate system and in a matrix [X, Y, Z], and c is regarded as the magnitude of the earthquake scale and g is regarded as the gravitational acceleration in the range of the matrix [X, Y, Z]; T is still regarded as the occurrence time (unit: day); and e is the error Correction value; energy ratio obtained by the equation of 1 K by; whereby, determined known position or known depth or time scale earthquakes occurred.
本發明另揭示一種地震預測方法，請參考第1B圖，包括：步驟1：收集一特定時間區間內與地震相關之全球經緯度位置、深度、規模與發生時間並將該特定時間區間之全球經緯度位置、深度、規模與發生時間並根據一信賴區間進行篩選而產生一地震預測參數；步驟2：依據該地震預測參數，經由計算而產生一產生地震所需能量與維持原始地質狀態之能量比值；以及步驟3：依據該能量比值，經計算而產生已知位置或已知時間之地震發生的深度或規模。 The present invention further discloses a seismic prediction method. Please refer to FIG. 1B, including: Step 1: Collecting the global latitude and longitude position, depth, scale, and occurrence time associated with the earthquake in a specific time interval and the global latitude and longitude position of the specific time interval. , depth, scale and time of occurrence and screening according to a confidence interval to generate a seismic prediction parameter; Step 2: calculating, according to the seismic prediction parameter, a ratio of energy required to generate the earthquake and maintaining the original geological state; Step 3: Based on the energy ratio, the calculated depth or scale of occurrence of an earthquake at a known location or known time.
以下將詳述本發明之方法與系統。首先，收集一特定時間區間內與一特定區域內之與地震相關之全球經緯度位置、深度、規模與發生時間，請參考表1，透過如：美國地質調查局(USGS)或臺灣中央氣象局 等地理資訊收集相關機構所下載的資料格式，經由一信賴區間而篩選出時間(X1)、緯度(X2)、經度(X3)、深度(X4)及規模(X5)，而該信賴區間設定為介於0.6至0.98之間而彙整為地震分布資料，而在另一實施例中，該信賴區間也可自行調整。在一實施例中，請參考第2圖，為了預測特定地震規模，而將全球地理資訊系統中之地震規模7以上之地震分布以3D空間圖的方式呈現，上方的小方框為台灣所在位置，剛好位於兩大主震帶交叉點，以能量波來解釋，即為兩波的交集，能量累積為其他區域兩倍，與台灣造山活動頻繁現象符合，先將前述資料進行微分與分類，再將前述資料進行積分而歸納能量波之趨勢曲線。 The method and system of the present invention will be described in detail below. First, collect the global latitude and longitude position, depth, scale and time of occurrence of earthquakes in a specific time zone within a specific time interval. Please refer to Table 1, such as the US Geological Survey (USGS) or the Central Meteorological Bureau of Taiwan. The data format downloaded by the relevant information collection agency selects time (X1), latitude (X2), longitude (X3), depth (X4), and scale (X5) through a confidence interval, and the confidence interval is set to The data is distributed between 0.6 and 0.98 as seismic distribution data, and in another embodiment, the confidence interval can also be adjusted by itself. In an embodiment, please refer to FIG. 2, in order to predict a specific earthquake scale, the earthquake distribution of the earthquake scale of 7 or more in the global geographic information system is presented in a 3D space map, and the upper small box is the location of Taiwan. It is located at the intersection of the two major main shocks, explained by the energy wave, which is the intersection of two waves. The energy accumulation is twice that of other regions. It is consistent with the frequent phenomenon of Taiwan's orogenic activities. The above data is first differentiated and classified. The above data are integrated to summarize the trend curve of the energy wave.
在另一實施例中，其中於該步驟1更包括：步驟1-1：以一變異數分析法(ANOVA)分析該規模、該經緯度位置、該深度三者與該發生時間的相關性，若所分析結果達到一預設值，則進行該步驟2；若小於該預設值，則繼續進行以下步驟。請參考第3A圖，以規模(X5)X軸資料進行資料分群，先以規模4.7與規模5將地震資料分為三類，再針對時間(X1)作為Y軸分類，以參數32500(晚)與38200(早)將地震資料分為三類，分析經度地震分布狀態，可發現地震主要集中在低緯度區域(東經80度至南緯180度)，由本地震資料可看出，地震左側規模小的地震分類群資料統計量，由早至晚規模小的地震數越來越少，相較於右側規模大的地震分類群，恰好相反，由早至晚規模大的地震越來越多。 In another embodiment, wherein the step 1 further comprises: Step 1-1: analyzing, by a variation analysis method (ANOVA), the correlation between the scale, the latitude and longitude position, and the depth, and the occurrence time, if If the analysis result reaches a preset value, the step 2 is performed; if it is less than the preset value, the following steps are continued. Please refer to Figure 3A for data clustering on the scale (X5) X-axis data. The seismic data is first classified into three categories by size 4.7 and scale 5, and then classified as the Y-axis for time (X1) with the parameter 32500 (late). Seismic data is divided into three categories with 38200 (early), and the distribution of longitude seismic distribution is analyzed. It can be found that the earthquake is mainly concentrated in the low latitude area (80 degrees east longitude to 180 degrees south latitude). It can be seen from the seismic data that the scale on the left side of the earthquake is small. The statistics of earthquake taxonomic group data are smaller and smaller from early to late. Compared with the large-scale earthquake taxonomic group on the right side, on the contrary, there are more and more earthquakes from early to late.
再，請參考第3B圖，透過地震事件數統計長條圖比對第2圖的地震集中範圍，在緯度、經度以肉眼看近似常態分佈，由本發明之方法與系統所分析之地震資料可看出，地震左側規模小的地震分類群資料統計量，由早至晚規模小的地震數越來越少，相較於右側規模大的地震分類群，恰好相反，由早至晚規模大的地震越來越多，且地震深度具有單尾偏峰現象，因此，進一步分析緯度與地震規模關係。再，請參考第4A圖，以規模(X5)作為X軸分類、以時間(X1)作為Y軸分類，分析地震深度(X4)之分布狀態，可觀察到地震主要集中在淺源地震且在深度0至40公里的範圍，深源地震主要集中在深度200公里至250公里之範圍，由本發明之方法與系統所分析之地震資料可看出，地震左側規模小的地震分類群資料統計量，由早至晚規模小的地震數越來越少，相較於右側規模大的地震分類群，恰好相反，由早至晚規模大的地震越來越多。由第4A圖可明顯看出在地震規模隨 著時間增長而加深，並比較左上角長條圖與右下角長條圖，其長條分布幾乎雷同，說明地震發生位置與規模發生轉換，故此可藉由歷史資料進行未來的地震模擬，並可作為地震規模強度預測模型參數修正依據。 Again, please refer to Figure 3B. The seismic concentration range of the seismic event is compared with the seismic concentration range of Figure 2, and the normal distribution is visually observed in latitude and longitude. The seismic data analyzed by the method and system of the present invention can be seen. The statistics of the seismic classification group with small scale on the left side of the earthquake are less and less from the early to late scale. Compared with the large-scale earthquake taxonomic group on the right side, the earthquake is large in scale from early to late. More and more, and the depth of the earthquake has a single tail peak phenomenon, therefore, further analysis of the relationship between latitude and earthquake size. Then, please refer to Figure 4A. The scale (X5) is used as the X-axis classification, and the time (X1) is used as the Y-axis classification. The distribution of the seismic depth (X4) is analyzed. It can be observed that the earthquake is mainly concentrated in the shallow earthquake and In the range of 0 to 40 km in depth, the deep-seismic earthquakes are mainly concentrated in the range of 200 km to 250 km in depth. The seismic data analyzed by the method and system of the present invention can be seen that the statistics of the seismic classification group with small scale on the left side of the earthquake, The number of earthquakes with small scales from morning to night is decreasing. Compared with the large-scale earthquake taxonomic group on the right side, on the contrary, there are more and more earthquakes from early to late. It can be clearly seen from Fig. 4A that the scale of the earthquake is deepened with time, and the bar graph in the upper left corner and the bar graph in the lower right corner are compared. The strip distribution is almost the same, indicating that the location and scale of the earthquake are changed, so Future seismic simulations can be carried out by historical data, and can be used as a basis for correcting seismic intensity prediction model parameters.
請參考第4B圖，以緯度(X2)作為X軸分類、以時間(X1)作為Y軸分類，分析規模(X5)前、中、後期個時期規模次數分布狀態。依據分析結果可發現，可發現前期地震可達7.4規模，在中期時間僅達到6.7規模，但在後期可達到9以上規模，分別詳述如下。首先，南緯8.5度以南地震規模由前期小規模之具有4.5-5.2規模之地震為主；在中期4.5-5.2規模地震次數降低而轉變為5.2-6規模；在後期轉變至大規模6.7-9地震分布；地震規模比例由小規模地震為主，局部轉為大規模地震。再，南緯8.5度至北緯17.5度地震，前期規模分布主要在規模4.5-5.2；中期規模4.5-5.2比例提升，但，在後期4.5-5.2比例提升下降七成，轉移至6-9規模。再，北緯17.5以北地震，前期主要分布在規模4.5-5.2，中期在規模5.2-6比例略微提升，後期在4.5-9規模次數皆有所提升。請繼續參考第4B圖，顯然可以肉眼看出在緯度方面具有和經度相似分布狀態，且隨著時間推移，地震規模越趨強烈。 Please refer to Figure 4B, with latitude (X2) as the X-axis classification, time (X1) as the Y-axis classification, and analysis of the scale (X5) before, during, and after the period. According to the analysis results, it can be found that the pre-earthquake can reach the scale of 7.4, and only reaches the scale of 6.7 in the medium-term time, but can reach the scale of 9 or more in the later period, as detailed below. First, the earthquake scale south of 8.5 degrees south latitude is dominated by small-scale earthquakes with a magnitude of 4.5-5.2 in the early stage; the number of earthquakes in the medium-term 4.5-5.2 scale is reduced to 5.2-6 scale; in the later stage, the scale is changed to large scale 6.7-9. Seismic distribution; the scale of earthquake scale is dominated by small-scale earthquakes, and localized to large-scale earthquakes. Then, from 8.5 degrees south latitude to 17.5 degrees north latitude earthquake, the scale of the previous period was mainly in the scale of 4.5-5.2; the medium-scale scale increased by 4.5-5.2, but the proportion of 4.5-5.2 in the later period decreased by 70%, and shifted to the scale of 6-9. Furthermore, the earthquake north of 17.5 north latitude was mainly distributed in the scale of 4.5-5.2 in the early stage, slightly increased in the medium-term scale of 5.2-6, and increased in the number of times in the later period of 4.5-9. Please continue to refer to Figure 4B. Obviously, it can be seen by the naked eye that there is a similar distribution with longitude in terms of latitude, and the scale of the earthquake becomes stronger with the passage of time.
接著，請參考第5A圖，將地震深度來切割，很明顯淺源地震規模由前期4.5至5.2為主的地震規模，在後期轉移至6-9地震規模，且後期地震規模在各深度接轉變為6-9地震規模。請參考第5A、5B與6圖，為綜合判斷緯度深度及時間地震次數，次數最高發生範圍為緯度北緯29度至南緯4度，深度為0至72公里，時間尺度為近3年，配合上述圖說，在此區域的地震具有大規模地震機會最高，且隨著時間增加而增強增多。 Next, please refer to Figure 5A to cut the depth of the earthquake. It is obvious that the scale of the shallow source earthquake is from the previous 4.5 to 5.2 earthquake scale, and later transferred to the scale of 6-9 earthquake, and the later earthquake scale is transformed at each depth. For the scale of the 6-9 earthquake. Please refer to pictures 5A, 5B and 6 for comprehensive judgment of latitude depth and time seismic times. The highest frequency range is from 29 degrees north latitude to 4 degrees south latitude, depth is 0 to 72 kilometers, and the time scale is nearly 3 years. The map shows that earthquakes in this region have the highest chance of large-scale earthquakes and increase with time.
再，於步驟1-2中，將該經緯度位置、該深度進行一第一座 標轉換步驟，而使該經緯度位置、該深度成為三維之資料型態，並將該時間之格式轉換為以天為單位。請參考表2，本發明將前述資料庫之前述資料之時間格式轉換為以天為單位的描述如下：透過地球橢圓率而換算各地震之位置三維座標，同時，將時間標準化(以天為單位)而進行時間序列分析。 Then, in step 1-2, the latitude and longitude position and the depth are subjected to a first coordinate conversion step, so that the latitude and longitude position and the depth become a three-dimensional data type, and the format of the time is converted into a day unit. Referring to Table 2, the present invention converts the time format of the aforementioned data in the aforementioned database into a description in days as follows: the three-dimensional coordinates of the positions of the earthquakes are converted by the ellipticity of the earth, and the time is normalized (in days) And perform time series analysis.
再，於步驟1-3中，以該變異數分析法分別分析前述經該第一座標轉換步驟的該經緯度位置、該深度與經轉換之該時間的相關性，若所分析結果達到一預設值，則進行該步驟2；若小於該預設值，則繼續進行以下步驟。請參考第7圖，在一實施例中，於此步驟中之台灣地區之地震發 生時間與其餘變數之三維關係圖，由第7圖中之亮黃色線為台灣地區的四維曲線，線條密集程度可以再配合將時間順序加以改變而求得每次地震間彼此關聯的程度。 Then, in step 1-3, the latitude and longitude position of the first coordinate conversion step, the correlation between the depth and the converted time are respectively analyzed by the variance analysis method, and if the analysis result reaches a preset If the value is less than the preset value, continue with the following steps. Please refer to FIG. 7. In an embodiment, the three-dimensional relationship between the earthquake occurrence time and the remaining variables in the Taiwan region in this step is the four-dimensional curve of the Taiwan region from the bright yellow line in FIG. The degree of correlation between each earthquake can be determined by changing the chronological order.
再，於步驟1-4中，將該該規模、該該經緯度位置、該深度進行一第二座標轉換步驟，藉以去除地球自轉對該等資訊的角動量之影響。在一實施例中，於此步驟中，請參考第8圖，其為資料庫中原始經緯度座標轉換為三維空間表示，顏色說明為地震發生時序動態位置，透過時間再現空間比對分析，橘色點位地震位置為7.2至11.5年週期歷史地震、紅色大圈為近105年度地震發生點位。本發明經交叉比對後剩餘橘色點位為將來可能發生地震區域，依據點位密集程度，可推估至少未來5年地震規模與可能發位置。以台灣地區3D視角地震時序圖為例，本發明之系統與方法技術效果為：所求得之預測時間(106年2月10日)發生的地震位置與2007年1月20日規模4.8之地震位置相近；以及，亦與於2006年06月17日規模4.6地震位置接近。因此，本發明透過每次地震發生之個案而分析空間分類間距與時間間隔，做為資料分群依據進行全球化，地震方程式預測模擬。 Then, in step 1-4, the scale, the latitude and longitude position, and the depth are subjected to a second coordinate conversion step to remove the influence of the angular momentum of the earth rotation on the information. In an embodiment, in this step, please refer to FIG. 8 , which converts the original latitude and longitude coordinates in the database into a three-dimensional representation, and the color description is the dynamic position of the earthquake occurrence time, and the space reproduction time comparison analysis, orange The point seismic location is 7.2 to 11.5 years of historical earthquakes, and the red circle is the point of occurrence of the nearly 105-year earthquake. After the cross comparison, the residual orange color point is a possible earthquake region in the future, and the magnitude and possible location of the earthquake in at least the next five years can be estimated according to the intensity of the point. Taking the 3D perspective seismic sequence diagram in Taiwan as an example, the technical effects of the system and method of the present invention are: the predicted earthquake location (February 10, 2006) and the earthquake of magnitude 4.8 on January 20, 2007. The location is similar; and, also close to the size of the 4.6 earthquake on June 17, 2006. Therefore, the present invention analyzes spatial classification intervals and time intervals by each occurrence of an earthquake, and performs globalization, seismic equation prediction simulation as a basis for data grouping.
再，於步驟1-5中，以該變異數分析法分析前述經該第二座標轉換步驟的該規模、該經緯度位置、該深度三者與該時間的相關性，若所分析結果達到一預設值，則進行該步驟2：若小於該預設值，則繼續進行以下步驟。再，於步驟1-6中，將該時序資訊與該空間資訊進行一第三座標轉換步驟，藉以去除地球公轉對該經排列的該時序資訊與該空間資訊的角動量之影響。再，於步驟1-7中，以該變異數分析法分析前述經該第三座標轉換步驟的該規模、該位置、該深度三者與該時間的相關性，若分析結果 達到一預設值，則進行該步驟2。 Then, in step 1-5, the correlation between the scale, the latitude and longitude position, and the depth of the second coordinate conversion step and the time is analyzed by the variance analysis method, and if the analysis result reaches a pre-determination If the value is set, proceed to step 2: If it is less than the preset value, continue with the following steps. Then, in step 1-6, the timing information and the spatial information are subjected to a third coordinate conversion step, so as to remove the influence of the earth revolution on the aligned timing information and the angular momentum of the spatial information. Then, in step 1-7, the correlation between the scale, the position, and the depth of the third coordinate conversion step is analyzed by the variance analysis method, and the analysis result reaches a preset value. Then proceed to step 2.
再，於步驟2中，以經篩選出之全球經緯度位置、深度、規模與發生時間(即該地震預測參數)而求得產生地震所需能量與維持原始地質狀態之能量比值。該步驟2更包括步驟2-1：將該地震預測參數帶入下列方程式1，而求得K值，。 Then, in step 2, the ratio of the energy required for generating the earthquake to maintaining the original geological state is obtained by the selected global latitude and longitude position, depth, scale, and occurrence time (ie, the earthquake prediction parameter). The step 2 further includes step 2-1: taking the earthquake prediction parameter into the following Equation 1, and obtaining the K value.
方程式1：E=K*A*T*C*G(方程式1)，其中E為維持地球存在所需能量(單位：J)；A為該經緯度位置與該深度(單位：公尺3)；T為發生時間(單位：日)；C是該規模，其是為單位時間內生成地殼斷裂單位面積內質量變化(單位：質量/秒*公尺2)；G是在單位時間內空間中維持所存在的引力之加速度(單位：公尺/秒2)；K是產生地震所需能量與維持原始地質狀態之能量之比值。 Equation 1: E = K * A * T * C * G (Equation 1), where E is the energy required to maintain the Earth's presence (unit: J); A is the latitude and longitude position and the depth (unit: metric 3 ); T is the occurrence time (unit: day); C is the scale, which is the mass change in the unit area of the crustal fracture generated per unit time (unit: mass / sec * ft 2 ); G is maintained in space per unit time The acceleration of the gravitational force (unit: metric/second 2 ); K is the ratio of the energy required to generate the earthquake to the energy that maintains the original geological state.
再，於步驟3中，以前述步驟中所求得之比值而求得與預測已知位置或已知時間之地震發生的深度或規模。其中於該步驟3更包括：步驟3-1：將經分析之經緯度位置、深度、規模與時間帶入一方程式2中，其中，該方程式2為E=K*[X,Y,Z]*T*c*g+e，其是將該方程式1中之該A(該經緯度位置)視為三維座標系統而以矩陣[X,Y,Z]表示，並將c視為地震規模強度且將g視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為該發生時間(單位：日)；而e是為誤差修正值；K為由該方程式1所求得之值；藉此，求得所預測於下次地震發生的已知經緯度位置與已知時間之地震深度與規模。使用 該方程式2的原因是簡化該方程式1的複雜性，藉以使計算過程簡化而減少本發明之系統的前述元件之資源負擔。 Further, in step 3, the depth or scale of occurrence of an earthquake with a predicted known position or a known time is obtained by the ratio obtained in the foregoing step. The step 3 further includes: Step 3-1: Bring the analyzed latitude and longitude position, depth, scale and time into a program 2, wherein the equation 2 is E=K*[X, Y, Z]* T*c*g+e, which is the A (the latitude and longitude position) in Equation 1 is regarded as a three-dimensional coordinate system and represented by a matrix [X, Y, Z], and c is regarded as the magnitude of the earthquake and will g is regarded as the gravitational acceleration in the range of the matrix [X, Y, Z]; T is still regarded as the occurrence time (unit: day); and e is the error correction value; K is the value obtained by the equation 1; Thereby, the depth and scale of the earthquake predicted by the known latitude and longitude position and the known time of the next earthquake are obtained. The reason for using Equation 2 is to simplify the complexity of Equation 1, thereby simplifying the calculation process and reducing the resource burden of the aforementioned components of the system of the present invention.
於該步驟1-1至1-7中，若分析結果未達該預設值，則進行以下步驟：首先，於步驟1-8中，將從該資料庫存取於該特定時間區間內之的一太陽黑子座標震幅資訊，以該變異數分析法分析該太陽黑子座標震幅資訊與該時間的相關性，若所分析結果達到一預設值，則進行下一步驟。接著，於步驟1-9中，將一太陽黑子座標震幅資訊與一太陽黑子空間分佈資訊帶入一方程式3，其中，該方程式3為E=K*[x,y,z]*T*d*h+f，其是將該方程式2中之該矩陣[X,Y,Z]三維座標系統轉為太陽座標系統，並將該d視為太陽黑子數及該h視為矩陣[X,Y,Z]範圍內之重力加速度；T仍視為時間(單位：日)；而f是為一誤差修正值。藉此，求得方程式3而求得已知位置或已知時間之地震發生的深度或規模。接著，於步驟1-10中，將該方程式3中之該矩陣[X,Y,Z]與d分別對應以下至少任一項，包括：地球火山爆發之發生位置與該火山爆發之發生位置之火山高度、太陽系中子雲濃度分布中心點與中子雲濃度、大氣電離層濃度分布中心點與電離層濃度、地下水氡氣濃度分布中心點與氡氣濃度與月球座標系統之三維座標中心點與該三維座標中心點距地球之距離中至少一者，藉此，分別求得複數個該方程式3之該誤差修正值f，而預測下次地震發生的經緯度位置、深度、規模或時間。 In the steps 1-1 to 1-7, if the analysis result does not reach the preset value, the following steps are performed: First, in step 1-8, the data inventory is taken from the specific time interval. A sunspot coordinate amplitude information is used to analyze the correlation between the sunspot coordinate amplitude information and the time by the variance analysis method. If the analysis result reaches a preset value, the next step is performed. Next, in step 1-9, a sunspot coordinate amplitude information and a sunspot spatial distribution information are brought into a program 3, wherein the equation 3 is E=K*[x, y, z]*T* d*h+f, which converts the matrix [X, Y, Z] three-dimensional coordinate system in Equation 2 into a solar coordinate system, and regards the d as the sunspot number and the h as a matrix [X, The gravitational acceleration in the range of Y, Z]; T is still regarded as time (unit: day); and f is an error correction value. Thereby, Equation 3 is obtained to find the depth or scale of the occurrence of an earthquake at a known position or a known time. Next, in step 1-10, the matrix [X, Y, Z] and d in Equation 3 respectively correspond to at least one of the following, including: a location where the earth volcanic eruption occurs and a location where the volcanic eruption occurs Volcanic height, solar system neutron cloud concentration distribution center point and neutron cloud concentration, atmospheric ionospheric concentration distribution center point and ionosphere concentration, groundwater radon concentration distribution center point and radon concentration and the three-dimensional coordinate center point of the lunar coordinate system and the three-dimensional At least one of the distances between the coordinate center points and the earth, thereby obtaining the error correction value f of the plurality of equations 3, respectively, and predicting the latitude and longitude position, depth, scale or time of the next earthquake.
本發明於該步驟1-1前更包括：於步驟a1中，先將該經緯度位置、該深度、該規模按時間順序排列。再，於步驟a2中，以常態分布檢定法檢測經按時間順序排列的該時序資訊與該空間資訊是否符合常態分佈，若符合，則繼續進行該步驟1-1：若不符合，則將經按時間順序排列的 該規模、該位置、該深度三者進行一資料轉換步驟而符合常態分佈後，再重新進行該步驟1-1。 The present invention further comprises, before the step 1-1, in the step a1, first arranging the latitude and longitude position, the depth, and the scale in chronological order. Further, in step a2, the normalized distribution verification method is used to detect whether the time series information and the spatial information are in a normal distribution, and if yes, proceed to step 1-1: if not, the The chronologically arranged size, the position, and the depth are subjected to a data conversion step to conform to the normal distribution, and then the step 1-1 is performed again.
因此，本發明具有以下之優點： Therefore, the present invention has the following advantages:
1.本發明是以個別單一事件進行整體預測，並將各地震事件進行規則化分析後，模擬宇宙活動運態，來進行地震預測，固可進行多年期地震預測，有別於現行1天內的地震預警。 1. The present invention performs overall prediction with individual single events, and after regular analysis of each seismic event, simulates the state of the universe to conduct earthquake prediction, and can perform multi-year earthquake prediction, which is different from the current one day. Earthquake warning.
2.本發明是將所有前兆現象加以結合之分析，改設現有地震預測方法與系統的準確度。 2. The present invention combines all precursor phenomena to analyze the accuracy of existing earthquake prediction methods and systems.
3.本發明能有效分析地震活動目前位於哪一時間點、會有甚麼樣的地震型態，尤其在地震強度極大期(規模7以上地震)，具有顯著性預測功效。 3. The present invention can effectively analyze the time point at which the seismic activity is currently located, and what kind of earthquake type will occur, especially in the period of the earthquake intensity (the earthquake of magnitude 7 or higher), which has significant predictive power.
4.本發明透過空間因子座標系統轉換方式，搭配量子力學空間共振效應進行異質座標系統空間比對，來回歸地震發生時間與空間位置預測。 4. The present invention performs spatial comparison of heterogeneous coordinate systems by using the space factor coordinate system conversion method and the spatial resonance effect of quantum mechanics to return the earthquake occurrence time and spatial position prediction.
5.本發明透過座標系統轉換，可不斷優化模擬演算方程式，進行精準空間位置與規模推估，各空間因子可進行前後時間間距推估，作為可避難時間推估，進行避難疏導決策依據，以增加保全生命財產。 5. The invention can continuously optimize the simulation calculus equation through the coordinate system conversion, and carry out the accurate spatial position and scale estimation. The spatial factors can be estimated by the time interval before and after, as the evacuation time estimation, and the refuge guidance decision basis is Increase the preservation of life and property.
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