Acta Geophysica最新文献

This study analyzed lightning activity along Sri Lanka using lightning imaging sensor for a 17-year period (1998–2014). To understand the influence of various parameters on the lightning activity, we investigated various meteorological parameters such as convective precipitation, relative humidity, cloud top temperature, cloud base height, convective available potential energy, total precipitable water, rain dynamic index, humidity index, convection inhibition, lifted index, K-index, total totals index, show alter index, vertical velocity and dew point depression (Dpd). North-western, Western, Southern and Sabaragamuwa regions of Sri Lanka showed high lightning activity. The analysis revealed different seasonal variations in lightning activity. The pre-monsoon season showed the maximum frequency, while winter witnessed the least. In addition, wind patterns embedded with moisture seem to influence regional variations over Srilanka. The westerly winds might influence lightning activity over Srilanka. We investigated the variations of different meteorological parameters for 40 lightning and no lightning days during the study period. During lightning days, the VV values show negative values with strong lightning and convection potential and strong atmospheric updrafts. Higher atmospheric levels have been found to contain dry air, and lower atmospheric levels have been found to contain moist air on lightning days. Extremely unstable atmospheric conditions that favour intense lightning activity were indicated by LI values less than −4.

This research addresses 2D models of the Nazca-South America subduction zone off the coast of Ecuador using the European Improved Gravity Model of the Earth by New Techniques (EIGEN-6C4), the Earth Magnetic Anomaly Grid with a 2-arc-minute resolution model (EMAG2 V3), and the seismic catalog from the Ecuadorian Geophysical Institute of the National Polytechnic School (IGEPN). Since the Upper Cretaceous, the region has experienced several geomorphological and structural alterations within the oceanic plate (Carnegie Ridge and Grijalva Fault Zone) and continental plate (North Andean Block and South American Plate). These changes result from a complex geodynamic history involving ancient subduction zones, accretions, and roll-back stages of allochthonous oceanic terrains and the autochthonous continental coast. To ensure the accuracy of the selected gravimetric model, the EIGEN-6C4 model was statistically compared with the pure-satellite gravimetric model, GO-CONS-TIM-R6e. Several geophysical analyses, such as terrain correction, geostatistical analysis, clustering, and power spectrum, were performed to gain valuable insights into gravity and magnetic sources. Then, these results were incorporated as constraints into the forward modeling procedure, which was adjusted to hypocenters, generating four subduction profiles that were then refined by an inversion method. The 0°S profile shows a subduction angle of 15° and escalates to 35° beyond 55 km depth, with the oceanic crust thickening at the Carnegie Ridge up to 18 km. The 1°S profile displayed two inflection points implying changes in the dip angle: a transition from 14° to 7° dip angle, supporting a change to low-angle subduction, and a transition from 7° to 40°, suggesting normal subduction beneath the Andean Cordillera. In the 2°S profile, the subduction initiates with a 15° angle beneath the continental plate extending to 60 km beneath the Manabi Basin, then potentially increases to 30° beyond 120 km depth. The 3°S profile beneath the Andes revealed a stable subduction zone with a constant dip angle of 14° and flat-slab subduction. Creating a three-dimensional interpolated model between the modeled profiles as a sketch of the angle variation of the subducted slab in the Equatorian region. The small root-mean-square error indicates that the models adequately represent the data. These models provide valuable observations of the geometric variations of the subducting plate, highlighting the impact of heterogeneous physiographic elements within the oceanic crust and the presence of an ancient subduction slab derived from the Farallon Plate.

Seawater intrusion is a global environmental issue, and seawater intrusion monitoring requires a multidisciplinary approach to improve accuracy. Simplified seawater/freshwater interface models for coastal aquifers are generally divided into two types: abrupt interface models and wedge-shaped interface models. Electrical resistivity tomography (ERT) is the visualization of subsurface resistivity distributions in 2D or 3D and has been widely used in seawater intrusion monitoring. This paper presents a geoelectrical recognition model for classifying simplified seawater/freshwater interface types based on a convolutional neural network (CNN). The CNN structure is composed of three convolutional layers, three max pooling layers, two fully connected layers, and one Softmax layer. A total of 686 samples were combined for model training, and obtained 0.9581 for the average accuracy (ACU) and 1.3500 for the average cross-entropy loss (CEL). Sand tank experiments were carried out to simulate the process of seawater intrusion caused by a rise in the water level of sea water rise or a decrease in the water level of fresh water, the ERT method was used to monitor the resistivity of the aquifer during the experiments, and the fully trained CNN model was used to classify the interface types. According to the output data, the probability of observing the wedge-shaped interfaces during the experiments at 300 and 345 min were 98.85% and 99.89%, while the probability of observing the abrupt interfaces were 1.15% and 0.11%. The results showed that the ERT method offers a fast and nondestructive approach for monitoring seawater intrusion, and accurate recognition results of interface types were obtained using a well-trained recognition model in the laboratory experiments.

A high temperature and lack of rainfall in the South Sumatra Region during the dry season of 2019 led to an increase in intense land fires that were attributed to biomass burning and the pyrogenic combustion process. This study tried to analyze the spatiotemporal distributions of atmospheric BC (black carbon) over the South Sumatra Region during 2016–2019 land fire events using the MERRA-2 satellite images. The spatial analysis was applied to estimate the increment in black carbon concentrations during land fire episodes. Some meteorological conditions that affect black carbon diffusion and transport over the study area are explained using a backward trajectory analysis. The results exhibited that the black carbon masses mostly came from local and long-range transports (from eastern to western) over the study area. A significant percentage increment of black carbon concentration during 2016–2019 was observed at around 139%. The highest black carbon concentration recorded in October 2019 was 3.96 × 10⁻⁶ kg/m², as hotspots were still abundant, especially on the eastern side of the study area. The black carbon trend was strongly related to total hotspots and burned areas. As a whole, this finding could be beneficial for mitigating black carbon pollution due to land fires by implementing geospatial technology for rapid monitoring of air pollution in vast areas.

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.