Application of Full Convolution Neural Network in Garbage Soil Investigation

Due to the difference in resistivity between the garbage and the original soil, the most commonly used garbage soil detection methods are high-density resistivity method and time-domain electromagnetic method. However, the low efficiency of manual interpretation of inversion results and the difficulty in ensuring accuracy still need further study. This research introduces the application of the full convolution neural network in the garbage soil investigation. Through the identification of the garbage soil detection data of the underground buildings of a demolished green space reconstruction project, the garbage soil range was determined, which shows the effectiveness, practicability and reliability of this method. It is the reference basis for waste soil investigation, earthwork calculation and improvement of land properties.