自适应损失权重的多任务大地电磁深度学习反演OA

Data-driven deep learning has great potential in solving the inverse problems of magnetotellurics.To enhance the generalization ability of deep learning in the inversion regression problems of magnetotellurics,a deep learning inversion method was investigated based on an adaptive loss weighting strategy.A"W"-shaped deep learning model MSEAdpNet was constructed,which combined a Transformer and convolutional neural network(CNN)for multi-task learning,guiding the model training through adaptive loss weighting feedback.The model took noisy data as input and consisted of a shared encoder and two independent decoders.One decoder estimated the model error for the inversion task,while the other estimated the response data error for the auxiliary task.By using adaptive error weighting,the model ensured the priority of the inversion task while improved overall task performance.The results demonstrate that the multi-task deep learning approach with an adaptive loss weighting strategy can achieve good inversion results,effectively reduce validation errors,and it is validated with real measurement results.