Seismic attributes, which are extracted from seismic information, are physical indexes used specifically for 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 methods are 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.

从地震信息中提取的地震属性是专门用于测量地震数据的几何，动态或统计特征的物理索引。当前用于地震多属性反演的方法包括线性和非线性方法。通过采用NFC24l01的无线模块，结合地震数据采集传感器，构成一个智能网络传感器，然后将收集到的数据发送到最顶层的机器进行分析。地震多属性的非线性反演方法通常采用神经网络和支持向量机（SVM）等工具进行地图绘制。因此，通过非线性方法获得的反演结果比通过线性方法获得的反演结果更准确。在这项工作中 以自发势（SP）曲线为非线性反演的目标，确定了SP曲线反演的优化地震属性组合，并通过使用深度神经网络（DNN）实现了地震多属性的非线性反演。 ）以获取3D SP数据。最后，通过SP数据的水平切片，说明了东营三角洲沙河街组3段中段砂体的前兆过程。