Artificial neural networks method (ANNs) is a common estimation tool used for geophysical applications. Considering borehole data, when the need arises to supplement a missing well log interval or whole logging—ANNs provide a reliable solution. Supervised training of the network on a reliable set of borehole data values with further application of this network on unknown wells allows creation of synthetic values of missing geophysical parameters, e.g., resistivity. The main assumptions for boreholes are: representation of similar geological conditions and the use of similar techniques of well data collection. In the analyzed case, a set of Multilayer Perceptrons were trained on five separate chronostratigraphic intervals of borehole, considered as training data. The task was to predict missing deep laterolog (LLD) logging in a borehole representing the same sequence of layers within the Lublin Basin area. Correlation between well logs data exceeded 0.8. Subsequently, magnetotelluric parametric soundings were modeled and inverted on both boreholes. Analysis showed that congenial Occam 1D models had better fitting of TM mode of MT data in each case. Ipso facto, synthetic LLD log could be considered as a basis for geophysical and geological interpretation. ANNs provided solution for supplementing datasets based on this analytical approach.

人工神经网络方法（ANN）是用于地球物理应用的常用估算工具。考虑到井眼数据，当需要补充缺失的测井间隔或整个测井时，ANN提供了可靠的解决方案。通过对网络进行可靠的井眼数据值的监督训练，并在未知井上进一步应用该网络，可以创建缺少地球物理参数（例如电阻率）的合成值。井眼的主要假设是：表示相似的地质条件，并使用类似的数据采集技术。在分析的情况下，一组多层感知器在钻孔的五个独立地层时间间隔上进行了训练，被视为训练数据。任务是预测代表鲁布林盆地地区内相同层序的井筒中缺少深层测井（LLD）测井。测井数据之间的相关性超过了0.8。随后，对两个大孔的大地电磁参数测深进行了建模和反演。分析表明，在每种情况下，同类Occam 1D模型均能更好地拟合MT数据的TM模式。实际上，合成的LLD测井可被视为地球物理和地质解释的基础。人工神经网络提供了基于这种分析方法补充数据集的解决方案。分析表明，在每种情况下，同类Occam 1D模型均能更好地拟合MT数据的TM模式。实际上，合成的LLD测井可被视为地球物理和地质解释的基础。人工神经网络提供了基于这种分析方法补充数据集的解决方案。分析表明，在每种情况下，同类Occam 1D模型均能更好地拟合MT数据的TM模式。实际上，合成的LLD测井可被视为地球物理和地质解释的基础。人工神经网络提供了基于这种分析方法补充数据集的解决方案。