The time-reversal imaging method has become a standard technique for seismic source location using both acoustic and elastic wave equations. Although there are many studies on the determination of the relevant parameter for visualization of the time-reversal method, little has been done so far to investigate the accuracy of seismic source location depending on parameters such as the geometry of the seismic network or underestimation of the velocity model. This paper investigates the importance of the accuracy of seismic source location using the time-reversal imaging method of input variables such as seismic network geometry and the assumed geological model. For efficient visualization of seismic wave propagation and interference, peak-to-average power ratio was used. Identification of the importance of variables used in seismic source location was obtained using the Morris elementary effect method, which is a global sensitivity analysis method.

Subsidence, especially in populated areas, is becoming a threat to human life and property. Monitoring and analyzing the effects of subsidence over large areas using in situ measurements is difficult and depends on the size of the subsidence area and its location. It is also time-consuming and costly. A far better solution that has been used in recent years is Differential Interferometry Synthetic Aperture Radar (DInSAR) monitoring. It allows the monitoring of land deformations in large areas with high accuracy and very good spatial and temporal resolution. However, the analysis of SAR images is time-consuming and involves an expert who can easily overlook certain details. Therefore, it is essential, especially in the case of early warning systems, to prepare tools capable of identifying and monitoring subsidence in interferograms. This article presents a study on automated detection and monitoring of subsidence troughs using deep-transfer learning. The area studied is the Upper Silesian Coal Basin (southern Poland). Marked by intensive coal mining, it is particularly prone to subsidence of various types. Additionally, the results of trough detection obtained with the use of convolutional neural networks were compared with the results obtained with the Hough transform and the circlet transform.

This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.

The time reversal method has become a standard technique for the location of seismic sources. It has been used both for acoustic and elastic numerical modelling and for 2D and 3D propagation models. Although there are many studies concerning its application to point sources, little so far has been done to generalise the time reversal method to the study of sequences of seismic events. The need to describe such processes better motivates the analysis presented in this paper. The synthetic time reversal imaging experiments presented in this work were conducted for sources with the same origin time as well as for the sources with a slight delay in origin time. For efficient visualisation of the seismic wave propagation and interference, a new coefficient—peak average power ratio—was introduced. The paper also presents a comparison of visualisation based on the proposed coefficient against a commonly used visualisation based on a maximum value.