Abstract

This article is devoted to solving the inverse problems of exploration seismology of fractures and their uniformly oriented systems using convolutional neural networks. The use of convolutional neural networks is optimal due to the multidimensionality of the studied data object. A training sample was formed using mathematical modeling. In the numerical solution of direct problems, a grid-characteristic method with interpolation on unstructured triangular meshes was used to form a training sample. The grid-characteristic method most accurately describes the dynamic processes in exploration seismology problems, since it takes into consideration the nature of wave phenomena. The approach used makes it possible to construct correct computational algorithms at the boundaries and contact boundaries of the integrational domain. Fractures were set discretely in the integration domain in the form of boundaries and contact boundaries. The article presents the results of solving inverse problems for single fracture length, placement and orientation detection and for system of fractures with variations in the angle of inclination of fractures, height of fractures, density of fractures in the system.

中文翻译：

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非均质地质层勘探地震学反问题机器学习方法求解

摘要

本文致力于使用卷积神经网络解决裂缝及其均匀定向系统的勘探地震学的逆问题。由于所研究数据对象的多维性，卷积神经网络的使用是最佳的。使用数学建模形成训练样本。在直接问题的数值求解中，采用在非结构三角形网格上插值的网格特征法形成训练样本。网格特征方法最准确地描述了勘探地震学问题中的动态过程，因为它考虑了波动现象的性质。所使用的方法可以在积分域的边界和接触边界处构建正确的计算算法。裂缝以边界和接触边界的形式在集成域中离散设置。本文介绍了解决单个裂缝长度、位置和方向检测以及裂缝倾角、裂缝高度、系统中裂缝密度变化的裂缝系统的反问题的结果。