The acoustic remote detection technology can effectively detect geological structures within tens of meters around the well, which holds significant importance for predicting oil distribution and evaluating reservoir productivity. Currently, fault identification in acoustic remote detection imaging largely relies on manual operation. This paper proposes an automatic fault identification method that integrates the greedy algorithm with the ant colony algorithm. The method uses the capping method to correct outliers, improving the clarity of the imaging. It employs an edge detection method based on pheromone tagging to highlight fault features, utilizes a greedy algorithm for preliminary fault identification, and optimizes the greedy identification results through correlation analysis and horizontal stretching. Based on the results of the greedy identification, the ant colony algorithm is used for secondary identification, addressing the issue of the greedy algorithm getting stuck in local optima when tracking complex faults, while also avoiding the accuracy loss caused by the random search of the ant colony algorithm. Practical applications show that the fault identification method, which utilizes the complementary advantages of greedy algorithm and ant colony algorithm, can accurately identify faults in acoustic remote detection.