There has been a recent rise in the uses and applications of passive seismic data, such as tomographic imaging, volcanic monitoring, and hydrocarbon exploration. Consequently, the sharp increase in passive seismic applications requires real-time event detection capabilities with high accuracy. Proper analysis of such events depends largely on the signal-to-noise ratio improvement through noise suppression techniques. Recent advances in the fields of signal processing and deep learning coupled with the available computational resources provide a great opportunity to address this challenge. In this work, a workflow is proposed where a residual deep neural network is customized and employed to detect passive seismic events. The automated detection is followed by a denoising step to extract the signal of interest from background noise using an IIR Wiener filter. The proposed method does not require any prior knowledge of the signal/noise, and therefore, it can work with various types of signals/noises. Another benefit of the proposed detection method is that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Nevertheless, it exhibits high accuracy in detecting and denoising events from real passive seismic data sets. In particular, field seismic data is recorded in northern Saudi Arabia and used to test the complete detection and denoising method. The detection method proved its capability of detecting events automatically in large data sets and in real time (due to off-line training).

We present a dispersion‐inversion method which inverts for the P‐velocity model from guided waves propagating in wave guides of arbitrary geometry. Its misfit function is the squared summation of differences between the predicted and observed dispersion curves of guided P waves, and the inverted result is a high‐resolution estimate of the near‐surface P‐velocity model. We denote this procedure as wave‐equation dispersion inversion of guided P waves (WDG), which is valid for near‐surface waveguides with irregular layers and does not require a high‐frequency approximation. It is more robust than full waveform inversion and can sometimes provide velocity models with higher resolution than wave‐equation traveltime tomography. Both the synthetic‐data and field data results demonstrate that WDG for guided P waves can accurately invert for complex P‐velocity models at the near surface of the Earth.

Many ground-penetrating radar (GPR) studies incorporate tomographic methods that use straight raypaths for direct model reconstruction, which is unrealistic for media with gradually changing petrophysics. Ray-bending algorithms can sometimes lead to unreliable resolution, especially at interfaces of abrupt dielectric changes. We present an improved GPR tomography technique based on a combination of seismic tomographic methods and a finite-difference solution of the eikonal equation. Our inversion algorithm uses velocity gradient zones and bending rays that represent realistic geology in the subsurface. We tested the technique on theoretical and experimental models with anomalous bodies of varying saturations and velocity and applied it to data from a GPR field experiment thatanalyzed the root zones of trees. Synthetic results showed that the resolution of our technique is better than that of published methods, especially for local anomalies with sharp velocity contacts. Our laboratory experiments consisted of four objects buried in sand with various water saturations. The GPR tomogram could map the objects and determine their degree of saturation. The velocities are compatible with those of the complex refraction index method; their relationship to the water content fits a previously published empirical equation. Our original field experiment around a poplar tree could map the heterogeneous subsurface and distinguish a central low velocity beneath the tree from the peripheral negative anomaly of a refill. This zone reflects the whole root zone and is caused by its bulk water content of both the organic root network and its surrounding soils.

In refraction tomography, the low signal-to-noise ratio (S/N) can be a major obstacle in picking the first-break arrivals at the far-offset receivers. To increase the S/N, we evaluated iterative supervirtual refraction interferometry (ISVI), which is an extension of the supervirtual refraction interferometry method. In this method, supervirtual traces are computed and then iteratively reused to generate supervirtual traces with a higher S/N. Our empirical results with both synthetic and field data revealed that ISVI can significantly boost up the S/N of far-offset traces. The drawback is that using refraction events from more than one refractor can introduce unacceptable artifacts into the final traveltime versus offset curve. This problem can be avoided by careful windowing of refraction events.

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.