Seismic attributes, which are extracted from seismic information, are physical indexes used specificallyfor the measurement of geometric, dynamic, or statistical characteristics of seismic data. Current methods for seismic multi-attribute inversion include linear and nonlinear methods. By adopting the wireless module of NFC24l01, combined with the seismic data acquisition sensor, constitutes an intelligent network sensor, and then it sends the collected data to the topmost machine for analysis. Methods for the nonlinear inversion of seismic multi-attributes usually employ tools such as neural networks and support vector machines (SVMs)for mapping. Hence, inversion results obtained via nonlinear methodsare more accurate than those obtained via linear methods. In this work, with spontaneous-potential (SP) curves as the objective of nonlinear inversion, an optimized seismic attribute combination for the inversion of SP curves was identified, and the nonlinear inversion of seismic multi-attributes was achieved via the use of a deep neural network (DNN) to obtain 3D SP data. Finally, the foresetting process of a sand body of the intermediate section in Member 3 of the Shahejie Formation in the Dongying Delta was illustrated via the horizon slice of the SP data.