物探化探计算技术
物探化探計算技術
물탐화탐계산기술
COMPUTING TECHNIQUES FOR GEOPHYSICAL AND GEOCHEMICAL EXPLORATION
2015年
1期
16-21
,共6页
李超%江玉乐%胡明科%蒋亚东%郑成
李超%江玉樂%鬍明科%蔣亞東%鄭成
리초%강옥악%호명과%장아동%정성
重力勘探%异常分异%细胞神经网络%权值
重力勘探%異常分異%細胞神經網絡%權值
중력감탐%이상분이%세포신경망락%권치
gravity exploration%abnormal differentiation%CNN%weights
介绍采用细胞神经网络CNN(cellular neural network)方法,对铬铁矿区内的矿体和围岩的重力异常进行分异。首先阐述了CNN方法的原理和算法,采用拟BP学习算法训练网络的权值,用全局误差函数求导方法推导权值的修正公式,讨论了如何根据目标异常训练适合该地质条件的网络的连接权值;其次将重力异常数据预处理,以达到适合 CNN 方法处理的数据格式和要求;最后由于该矿区内没有已知的重力数据作为网络训练的目标输出,根据相关地质图设置相应的地下构造模型。利用“点元”法分别正演出叠加异常和矿体异常,进而训练出适合全区的网络连接权值,实现了对全区重力异常的分异。应用结果表明,细胞神经网络方法较好地突出该矿区高异常和矿体的边界,只要选择了合适的网络连接权值,就能将横向叠加异常区分开,故 CNN方法可以实现矿体和围岩的重力异常分异。
介紹採用細胞神經網絡CNN(cellular neural network)方法,對鉻鐵礦區內的礦體和圍巖的重力異常進行分異。首先闡述瞭CNN方法的原理和算法,採用擬BP學習算法訓練網絡的權值,用全跼誤差函數求導方法推導權值的脩正公式,討論瞭如何根據目標異常訓練適閤該地質條件的網絡的連接權值;其次將重力異常數據預處理,以達到適閤 CNN 方法處理的數據格式和要求;最後由于該礦區內沒有已知的重力數據作為網絡訓練的目標輸齣,根據相關地質圖設置相應的地下構造模型。利用“點元”法分彆正縯齣疊加異常和礦體異常,進而訓練齣適閤全區的網絡連接權值,實現瞭對全區重力異常的分異。應用結果錶明,細胞神經網絡方法較好地突齣該礦區高異常和礦體的邊界,隻要選擇瞭閤適的網絡連接權值,就能將橫嚮疊加異常區分開,故 CNN方法可以實現礦體和圍巖的重力異常分異。
개소채용세포신경망락CNN(cellular neural network)방법,대락철광구내적광체화위암적중력이상진행분이。수선천술료CNN방법적원리화산법,채용의BP학습산법훈련망락적권치,용전국오차함수구도방법추도권치적수정공식,토론료여하근거목표이상훈련괄합해지질조건적망락적련접권치;기차장중력이상수거예처리,이체도괄합 CNN 방법처리적수거격식화요구;최후유우해광구내몰유이지적중력수거작위망락훈련적목표수출,근거상관지질도설치상응적지하구조모형。이용“점원”법분별정연출첩가이상화광체이상,진이훈련출괄합전구적망락련접권치,실현료대전구중력이상적분이。응용결과표명,세포신경망락방법교호지돌출해광구고이상화광체적변계,지요선택료합괄적망락련접권치,취능장횡향첩가이상구분개,고 CNN방법가이실현광체화위암적중력이상분이。
This article describes the separation of the mining of ore bodies and rock gravity anomalies by the cellular neural network method.First the principle and algorithm of CNN method were elaborated,a pseudo-BP algorithm was used to train the weights of neural network,using the global error function derivative method to derive corrected formula of weights,and how to train the network connection weight based on target gravity anomalies in the particular geological conditions were dis-cussed.Secondly,in order to achieve data format and requirements of the CNN processing,do some pretreatment about the gravity anomaly data.Furthermore,since there is no known gravity data in the region as target output in the network training, so set the corresponding subsurface structure model according to the relevant geological map.The superimposed anomaly and the ore anomaly was forward modeled by the “point element”method,and obtained the targeted connection weights by training the network,to fractionate the region gravity anomaly.The results of application show that the neural network well highlight the high anomaly of the mining area and ore body boundary.As long as the appropriate network connection weights are chosen, lateral stacking anomalies can be separated.So the CNN method can serve to separate out the mining of ore bodies and rock gravity anomalies.