Showing papers in "International Journal of Geophysics in 2021"
TL;DR: In this article, spectral analysis was applied to the combined Bouguer anomaly map to evaluate the thickness of sediments in some localities, and the Euler deconvolution method was also applied to determine the position, orientation, and depth of the different superficial faults of the study area.
Abstract: The southwestern coastal region of Cameroon is an area of interest because of its hydrocarbon potential (gas and oil). Terrestrial and satellite gravity data were combined and analyzed to provide a better precision in determining the structure of the study area. Firstly, the two gravity databases (in situ and satellite) have been coupled and validated using the least square collocation technique. Then, spectral analysis was applied to the combined Bouguer anomaly map to evaluate the thickness of sediments in some localities. We found that the sedimentary cover of the southwestern coastal region of Cameroon has a thickness that varies laterally from to , especially in the western part. This result confirms that our target area is a potential site for hydrocarbon exploration. The horizontal gradient method coupled with the upward continuation at variable heights has been used to highlight several lineaments and their directions (N-S, E-W, SW-NE, and SSW-NNE). Lineaments trending in an N-S orientation are predominant. The Euler deconvolution method was also applied to the Bouguer anomaly map to determine the position, orientation, and depth of the different superficial faults of the study area. It appears that the majority of superficial faults have an N-S and SSW-NNE orientation. These directions are correlated with those previously highlighted by the maxima of horizontal gradient. The structural map could be used for a better identification of the direction of fluid flow within the subsurface or to update the geological map of our study area.
7 citations
TL;DR: The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.
Abstract: In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.
2 citations
TL;DR: In this article, the authors apply a recently developed approach for inferring in situ fluid pressure changes from induced seismicity observations to datasets from geothermal reservoirs at St. Gallen (Switzerland), Paralana ( Australia), and Cooper Basin (Australia), respectively.
Abstract: We apply a recently developed approach for inferring in situ fluid pressure changes from induced seismicity observations to datasets from geothermal reservoirs at St. Gallen (Switzerland), Paralana (Australia), and Cooper Basin (Australia), respectively. The approach, referred to as seismohydraulic pressure mapping (SHPM), is based on mapping the seismic moment of induced earthquakes. Relative fluid pressure changes are inferred from the stress deficit of fracture patches slipping repeatedly. The SHPM approach was developed for the specific scenario, where induced earthquakes occur on a single, larger-scale plane with slip being driven by the regional stress field. We demonstrate that this scenario applies to the three datasets under investigation, indicating that geothermal systems in crystalline rock could typically be fault-dominated. For all datasets, individual earthquake source geometry could not be determined from source spectra due to the attenuation of the high signal frequencies. Instead, SHPM was applied assuming a constant stress drop in a circular crack model. Absolute values of inferred pressure change scale with the assumed stress drop while the spatiotemporal pattern of pressure changes remains similar even when varying stress drop by one order of magnitude. We demonstrate how the associated mismapping of seismic moment tends to average out when hypocentres are densely spaced. Our results indicate that SHPM could provide important information for calibrating numerical reservoir models.