Acta Geophysica最新文献

Recently, the seabed classification method based on back-scattering data of multi-beam echo-sounder (MBES) is widely used to analyze the distribution of seabed sediment. Although various analysis methods for seabed classification using multi-spectral MBES have been developed, they are limited in securing penetration depth to consider the characteristics of the shallow subsurface structure. In this study, the seabed and ultra-shallow subsurface classification was performed by comparative analysis of box corer sampling, back-scattering, and 2D/3D ultra-high-resolution (UHR) seismic data obtained from Yeongil Bay, South Korea. We proposed a process for seismic ultra-shallow subsurface classification by the segmentation of the primary seabed reflection wavelet and the amplitude analysis. The seabed-reflected amplitude and back-scattering intensity showed similar mapping trends in the relatively homogeneous and thick surface sediment. On the other hand, it was confirmed that back-scattering data and seabed-reflected amplitude show different patterns when the subsurface structure is related to the seabed surface. It is presumed that because seismic data containing relatively low-frequency components have a deeper penetration depth than MBES, they contain more characteristics of the ultra-shallow subsurface than back-scattering data. These were determined that back-scattering has advantages in representing acoustic anomaly distribution by surface sediment type, and seabed-reflected amplitude is advantageous for representing sediment type by ultra-shallow subsurface. In particular, these results were well shown when the surface sediment thinly covered the rocky bottom. Therefore, it is necessary not only to analyze the back-scattering of MBES but also the ultra-shallow subsurface features through seismic data for valid seabed classification.

The reliable prediction of pore pressure is essential for petroleum engineering in its different stages, with the Eaton and Bowers' methods being the most used for this purpose. However, their application in carbonate rocks still needs to be improved because carbonates do not compact uniformly with depth, as shale does. This research calculated the pore pressure using the Eaton, Bowers, and Weakley methods and well logs of a carbonate formation and found that the Weakley's approach predicts pressure more accurately. The method presented uses an acoustic impedance equation derived from the Bowers' method, whose parameters were calibrated with the Weakley's pore pressure profile. The pore pressure estimated near the borehole, via the acoustic impedance provided by the pre-stack inversion, is very close to that observed during drilling, which indicates a reliable prediction. The method was applied to a seismic line and well logs in the Middle Magdalena Valley Basin—Colombia, where the overpressured well Lizama 158 caused a significant environmental disaster in 2018. The obtained subsurface pore pressure distribution is reliable, matches overpressure in calcareous rocks near the well, and estimates anomalous pressure in zones distant from the well.

The identification of fault-karst reservoir is crucial for the exploration and development of fault-controlled oil and gas reservoirs. Traditional methods primarily rely on well logging and seismic attribute analysis for karst cave identification. However, these methods often lack the resolution needed to meet practical demands. Deep learning methods offer promising solutions by effectively overcoming the complex response characteristics of seismic wave fields, owing to their high learning capabilities. Therefore, this research proposes a method for fault-karst reservoir identification. Initially, a comparative analysis between the improved U-Net++ network and traditional deep convolutional networks is conducted to select appropriate training parameters for separate training of karst caves and faults. Subsequently, the trained models are applied to actual seismic data to predict karst caves and faults within the research area, followed by attribute fusion to acquire data on fault-karst reservoirs. The results indicate that: (1) The proposed method effectively identifies karst caves and faults, outperforming traditional seismic attribute and coherence methods in terms of identification accuracy, and slightly surpassing U-Net and FCN; (2) The fusion of predicted karst caves and faults yields clear delineation of the relationship between top karst caves and bottom fractures within the research area. In summary, the proposed method for fault-karst reservoirs identification and characterization provides valuable insights for the exploration and development of fault-controlled oil and gas reservoirs in the region.

The accurate interpretation of well-logging data is a crucial stage in the exploration of gas- and oil-bearing reservoirs. Geological formations, such as the Miocene deposits, present many challenges related to thin layers, whose thickness is often less than the measurement resolution. This research emphasizes the potential of utilizing electrofacies in such challenging environments. The application of electrofacies not only allows for the grouping of intervals with similar physical characteristics but can also be useful for estimating porosity and permeability parameters. For this purpose, various clustering methods were tested, including the 2D indexed and probabilized self-organizing map (IPSOM) method with and without supervision. Subsequently, the usefulness of the obtained results to improve the estimation of porosity and permeability parameters with the help of artificial neural networks was verified. As a result of the conducted analyses, significantly better results were obtained compared to classical petrophysical interpretation. The calculated porosity and permeability parameters were characterized by much greater variability and alignment with laboratory measurements on porosity and permeability. The best results were obtained for the IPSOM method, but the other methods did not differ significantly. In conclusion, the studies have shown a positive result of applying clustering methods, including the IPSOM method, to improve the estimation of permeability and porosity parameters in complicated, thinly-layered formations.

Nine hours apart, two catastrophic earthquakes (Mw 7.8 and 7.6) occurred on 6 February 2023, in eastern Türkiye. The mainshocks destroyed several cities and villages in 11 provinces, including Kahramanmaraş, Hatay, Adıyaman, Malatya, Adana and Gaziantep. Over about 50,000 aftershocks were detected in the nine months. Because the resolution of hypocentral locations in the Turkish national earthquake catalogs is limited, about 31,000 events are relocated using the double-difference method in this study. The aftershock distribution and its relation to energy release on the faults and Coulomb stress change areas are investigated. The improved hypocenters give a clearer image to understand the activity on the faults. The event distribution indicates that the rupture of the first mainshock in Pazarcık (Mw 7.8) propagates unilaterally on the northernmost segment of the Dead Sea Fault Zone and then transfers to the East Anatolian Fault Zone at the Maraş Triple Junction. The bilaterally extending rupture causes dense aftershock activity on the fault zone from Samandağ to Pütürge. The second mainshock in Elbistan (Mw 7.6) was triggered because of the positive Coulomb stress change on the Çardak fault, which generates dense clusters at both ends of the fault. It is observed that a high number of aftershocks occur on the low slip patches, and the high-energy release areas have low activity because of the stress equilibrium on the fault surfaces.

The soil moisture drought is an intermediate event between meteorological and agricultural droughts. The information on soil moisture droughts provides an indication about the resilience of the agricultural systems. In the present study, a comparative assessment of the monthly soil moisture gridded data products of Modern Era Retrospective analysis for Research and Applications, i.e., MERRA-2 (0.5° × 0.5°), Climate Prediction Center, i.e., CPC (0.5° × 0.5°), Global Land Data Assimilation System, i.e., GLDAS (0.25° × 0.25°), and European Space Agency Climate Change Initiative, i.e., ESA CCI (0.25° × 0.25°) during 2000 to 2019 was carried out in terms of drought occurrence and severity. The long-term soil moisture information was transformed into standardized soil moisture index (SSMI) using nonparametric distribution, followed by computation of drought duration and magnitude using thresholding approach. The long-term trends of drought parameters, i.e., duration and magnitude, were extracted using Mann–Kendall test and Sen’s Slope method, respectively. It was interesting to note that irrespective of zones, the SSMI derived from MERRA-2 and CPC have maximum coherence in terms of both pattern and intensity, followed by GLDAS. The trends of drought duration and magnitude differ based on the data products; however, frequent droughts were observed over parts of peninsular India and Indo-Gangetic plains irrespective of data products. The increased drought duration and magnitude were found over major parts of central and peninsular India, western parts of north-eastern India and eastern parts of north-western India.

Throughout history, several large-magnitude earthquakes have caused damage to the Himalayan region and humanity. To understand the present-day strain rate distribution and associated seismic moment budget, a high-resolution velocity field is an essential component. The present study estimates the contemporary seismic moment budget along three spatial sections over the Nepal Himalaya using the state-of-the-art high-resolution velocity field. For this, (1) we integrate 5 years of InSAR data with 77 available GPS observations over the Nepal Himalaya; (2) we then calculate strain rate distribution (dilatational and maximum shear strain rates) from this integrated velocity field, and (3) at last, we compare the geodetic moment accumulation rate estimated from strain rate tensors with the seismic moment release rate based on an earthquake database of 500 years. The results reveal that: (1) the geodetic strain rate is not homogeneous over the Nepal Himalaya, rather along the main central thrust, a relatively higher strain rate is observed; (2) the geodetic moment rate from west to east across three sections ranges from (23.39times 10^{18}) to (16.59times 10^{18}) Nm/yr, with the minimum of (8.05times 10^{18}) Nm/yr in central Nepal, whereas the seismic moment rate varies between (5.02times 10^{18}) and (11.41times 10^{18}) Nm/yr, with the minimum of (3.69times 10^{18}) Nm/yr in central Nepal; (3) the difference between geodetic and seismic moment rates from west to east provides a moment deficit rate of (18.37times 10^{18}) to (5.18times 10^{18}) Nm/yr, with the minimum of (4.36times 10^{18}) Nm/yr in central Nepal, and more importantly, (4) the inferred moment deficit rate suggests that the western and eastern Nepal have an earthquake potential of magnitude (M_w) 8.5 and (M_w) 8.1, respectively, whereas the central Nepal has energy budget equivalent to an (M_w) 7.9 event. In summary, the present study provides spatial distribution of earthquake potential in Nepal Himalaya using the most updated high-resolution InSAR and GPS velocity field, and the findings inevitably contribute to the time-dependent earthquake hazard analysis of the study region.

Different types of rocks have different content and distribution characteristics of radioactive uranium, potassium, and thorium elements. Even for rocks of the same lithology, the content of radioactive elements varies due to different ages of formation, different origins, and different geological evolution processes. However, in various rocks and formations, the distribution of radioactive elements follows the general rule that the content of radioactive elements in igneous rocks is higher than that in sedimentary rocks, and increases with the acidity of the rocks. The younger the age of the rocks formed by the same type, the higher the content of radioactive elements. Based on this characteristic of radioactive elements in rocks, the author takes a region in the Qinling Mountains of China as an example. By using high-precision airborne gamma-ray spectrometry measurement data and existing geological information, the total content, K, eU, and eTh content, variation coefficient, ternary colour image, and other parameters of airborne gamma-ray spectrometry measurement are statistically analysed. The distribution characteristics of radioactive elements in various lithologies and formations in the area are studied, and the distribution rules are summarized. The rocks and formations in the research area are reclassified, and the typical fault structures in the area are analysed. The well-developed structural zone is mainly oriented in the northeast–southwest or north–northeast direction in the northern part and in the northwest–southeast direction in the southern part, which controls the favourable uranium mineralization zones in the research area.