高階類神經網路與差分進化法於井測資料反推

Abstract

我們採用多層感知器的類神經網路於井測資料的反推，網路的訓練方式分別採以梯度坡降法為基礎的倒傳遞學習法以及先利用差分進化法與再使用梯度坡降法的兩階段訓練方式。類神經網路的輸入是井測資料的視導電率 (apparent conductivity, Ca)而輸出是井測資料的地層真實導電率 (true formation conductivity, Ct)。訓練網路時，網路的輸入是由原始的特徵值與其高階的特徵值所組成。 實驗顯示，採用此種擴展型特徵值的倒傳遞學習法網路可以加速網路的學習效率以及降低實際輸出值與期望輸出值之間的絕對值誤差。雖然先利用差分進化法再使用梯度坡降法去訓練高階類神經網路的訓練時間較長，但是卻可以得到一個更精確的結果。 當網路先利用差分進化法再利用梯度坡降法來訓練時，在輸入為十個特徵值且擴展到三階，隱藏節點為八個，以及輸出節點為十個的時候，可以得到與模擬資料間的最小絕對值誤差。接著再把這個網路利用在真實的井測資料上，以測試網路的反推結果。

Multilayer perceptron is adopted for well log data inversion. The gradient descent method is used in the back propagation learning rule and a hybrid method, which combines differential evolution (DE) and gradient descent method, are used in training process respectively. The input of the neural network is the apparent conductivity (Ca) of the well log and the desired output is the true formation conductivity (Ct). The higher order of the input features and the original features are the network input for training. From our experimental results, we find the expanding input features with back propagation learning rule can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The hybrid method will provide more precise results, despite it takes a longer training time. The multilayer perceptron network, which is trained by the hybrid method, with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data.