Development of a Technique for Automatic Lineament Allocation Based on a Neural Network Approach

Abstract

The purpose of the scientific work is to study the potential of neural network technologies in the field of extracting linear structures from digital terrain models SRTM. Linear structures, also known as lineaments, play an important role in the verification of known faults, the identification of fault-fracture structures, the detailing of the framework of discontinuous faults, as well as in the exploration of minerals. Their accurate and effective extraction in solving the designated tasks is of fundamental importance. The use of neural network technologies provides a number of advantages over sequential algorithms, including the ability to search for universal criteria for selecting lineaments based on a training sample. The paper considers a comprehensive innovative methodology that includes several key stages. The first stage is the author’s method of data preparation, which helps to ensure the quality of the training sample and minimize the impact of noise. The second stage is to develop an algorithm for vectorizing the results of the neural network, which allows you to easily export the results (lineaments) to a geographic information system (GIS). The third stage provides a method for minimizing the noise component of the training sample and optimizing the selection of synaptic weighting coefficients by retraining the neural network using simulated data reflecting various localization conditions of the lineaments. To verify the results obtained, a spatial comparison of linear structures extracted by a neural network and lineaments isolated by the operator was carried out. The results of this comparison demonstrate the high potential of the proposed approach and show that the use of neural network technologies is an actual and promising approach to solving the problem of extracting linear structures from digital terrain models. Positive conclusions are made about the expediency of using the results obtained for their practical application in the field of Earth sciences.