Micro-logging is widely employed in near-surface velocity investigations in seismic exploration,but its lateral resolution is low due to limited site conditions and operating cost.In this paper,we use an artificial neural network(ANN)to link cone pene-tration test(CPT)resistance with the P-wave velocity of near-surface layers to predict large-scale velocity distribution based on a small number of paired logging-CPT data.This method includes the following steps:①using lithology as the separation condition,depth and cone resistances as inputs,and velocity as the target output;② updating hidden layer neurons through a feedforward mechanism;③obtaining near-surface velocity profiles by inputting CPT data into the trained ANN model.A case study in northern Jiangsu proves that the precision of lithologic division and the size of the training sample set determine our model performance.The ANN method is superior to empirical formula methods in reliability,resolution,and robustness,and the accuracy of shallow P-ve-locity prediction is over 90％.Using this method,it is easier to locate the ghosting interface and weathered layer close to the surface and perform near-surface velocity investigation more accurately and efficiently.
维普
万方数据
