Seismic resolution improving by a sequential convolutional neural network.

Thin-bed soft rock is one of the main factors causing large deformations of tunnels. In addition to relying on some innovative construction techniques, detecting thin beds early during surface geological exploration and advanced geological prediction can provide a basis for planning and implementing effective coping measures. The commonly used seismic methods cannot meet the requirement for thin beds detection accuracy. A high-resolution (HR) seismic signal processing method is proposed by introducing a sequential convolutional neural network (SCNN). The deep learning dataset including low-resolution (LR) and HR seismic is firstly prepared through forward modeling. Then, a one-dimension (1D) SCNN architecture is proposed to establish the mapping relationship between LR and HR sequences. Training on the prepared dataset, the HR seismic processing model with high accuracy is achieved and applied to some practical seismic data. The applications on both poststack and prestack seismic data demonstrate that the trained HR processing model can effectively improve the seismic resolution and restore the high-frequency seismic energy so that to recognize the thin-bed rocks.

Toxic element characterization against a typical high geology background: Pollution enrichment, source tracking, spatial distribution, and ecological risk assessment.

The geological environment determines the initial content of various elements in soil, while the late input of toxic elements produced through weathering and leaching is a persistent threat to food security and human health. In this study, we selected the Lou Shao Basin, a black rock system background, and combined geostatistical analysis and multivariate statistics to quantify the specific contribution of weathering of the black rock system, and to analyze the source traces, spatial distributions, and ecological risks of the soil toxicity of elements. The results show that the soils in the study area are acidic, which is related to the weathering of sulfides in the black rock system. The concentrations of most elements in the soil were determined to exceed the soil background values, and the Cd, Se and N contents, exceeded more than five times, especially Se, Mo nearly as high as 13 times. Strong positive correlation between Se, Cu, V and P, low correlation between N and Se, Cu, V, P, Ni and Cd.72.52%, 43%, 77.79%, 82%, 77%, and 44.1% of Cd, Se, Ni, Cu, B, and Mo came from the black rock system, respectively, which were greatly affected by geogenic weathering; V, Zn, Pb, and As are mainly from biomass burning sources; N and P are mainly from agricultural surface sources. Comparison found that the Cd and Se elements in the rocks in the study area were 16.78 times and 1.36 times higher than the world shale average, respectively, and need to pay attention to the weathering process of the two, and the spatial distribution of the 12 elements in soils showed a striped and centralized block distribution pattern, specifically around the distribution of carbonate and metamorphic rocks and other high-geology blocks. The ecological risk results showed that Cd was the main element causing high ecological risk, followed by Se and N, which were at moderate to high ecological risk levels, and Se and N showed similar ecological risk patterns, which may be related to the fact that selenium can promote the uptake and transformation of nitrogen. The present results add to the endogenous sources of toxic elements, quantify the source contributions of toxic elements in soils with high geologic backgrounds, fill this knowledge gap, and provide new insights for pollution control and ecological protection in areas with high geochemical backgrounds.

Free roaming of 3D stratum models based on internal and external boundary identification.

3D stratum roaming can visualize the complex geomorphology, underground structure and stratum distribution of geotechnical engineering. Conventional 3D stratum roaming technology is generally aimed at roaming outside the 3D stratum model but rarely roams inside the 3D stratum model, and the efficiency of switching between external and internal roaming is low. In practical engineering, especially in geological and geotechnical engineering, the underground structure and stratum situation are critical. Therefore, focusing on this problem, this paper adopts a three-dimensional roaming engine to connect the inside and outside of a three-dimensional model. Based on an internal and external boundary identification method, the combination of external roaming and internal roaming of the three-dimensional stratum model is implemented by using a stratum virtual surface, and lightweight loading is carried out by controlling the stratum virtual surface to establish the internal and external roaming of the 3D stratum model, and to give full play to the advantages of lightweight loading to provide more intuitive and comprehensive geological information for the project.

Systematic review and meta-analysis of arsenic concentration in drinking water sources of Iran.

Recent studies have found arsenic contamination of drinking water in some parts of Iran, as in many other countries. Thus, a comprehensive systematic review is necessary to assess the distribution and concentration of arsenic in drinking water sources. For this purpose, articles published from the first identification until December 2023, were retrieved from various national and international databases. Of all the studies examined (11,726), 137 articles were selected for review based on their conceptual relationship to this survey. A review of the extracted studies presented that ICP methods (ICP-MS, ICP-OES, 56%) and atomic absorption spectrophotometry (AAS, 34.1%) were the two most commonly used techniques for the analysis of arsenic in water samples. The order of arsenic content in the defined study areas is descending, as follows: northwest ˃ southeast ˃ southwest ˃ northeast. A review of studies performed in Iran depicted that provinces such as Kurdistan, Azerbaijan, and Kerman have the highest arsenic concentrations in water resources. Accordingly, the maximum concentration of arsenic was reported in Rayen, Kerman, and ranged from < 0.5-25,000 µg/L. The primary cause of elevated arsenic levels in water resources appears to be geologic structure, including volcanic activity, biogeochemical processes, sulfur-bearing volcanic rocks, Jurassic shale, the spatial coincidence of arsenic anomalies in tube wells and springs, and, to some extent, mining activities. The findings of the presented survey indicate that it is essential to take serious measures at the national level to minimize the health risks of arsenic contamination from drinking water consumption.

Botany and geogenomics: Constraining geological hypotheses in the neotropics with large-scale genetic data derived from plants.

Decades of empirical research have revealed how the geological history of our planet shaped plant evolution by establishing well-known patterns (e.g., how mountain uplift resulted in high rates of diversification and replicate radiations in montane plant taxa). This follows a traditional approach where botanical data are interpreted in light of geological events. In this synthesis, I instead describe how by integrating natural history, phylogenetics, and population genetics, botanical research can be applied alongside geology and paleontology to inform our understanding of past geological and climatic processes. This conceptual shift aligns with the goals of the emerging field of geogenomics. In the neotropics, plant geogenomics is a powerful tool for the reciprocal exploration of two long standing questions in biology and geology: how the dynamic landscape of the region came to be and how it shaped the evolution of the richest flora. Current challenges that are specific to analytical approaches for plant geogenomics are discussed. I describe the scale at which various geological questions can be addressed from biological data and what makes some groups of plants excellent model systems for geogenomics research. Although plant geogenomics is discussed with reference to the neotropics, the recommendations given here for approaches to plant geogenomics can and should be expanded to exploring long-standing questions on how the earth evolved with the use of plant DNA.

Understanding paleo-earthquakes in the Kuril Trench based on Late-Holocene tsunami deposits in the distal region from wave sources, northern Hidaka, Hokkaido, Japan.

Geological evidence, such as tsunami deposits, is crucial for studying the largest rupture zone of the Kuril Trench in Hokkaido, Japan, due to its poor historical record. Although 17th-century tsunami deposits are widely distributed across Hokkaido, the presence of multiple wave sources during that period, including the collapse of Mt. Komagatake, complicates the correlation with their wave sources. Understanding the regional distribution of these tsunami deposits can provide valuable data to estimate the magnitude of megathrust earthquakes in the Kuril Trench. The northern part of Hidaka, Hokkaido, where tsunamis from multiple wave sources are expected to overlap, is distant from the Kuril Trench. To clarify the depositional history of tsunami deposits in such distal areas, evaluating the influence of the depositional environments on the event layer preservation becomes even more critical. We conducted field surveys in Kabari, located in the northern Hidaka region, identifying three sand layers from the 10th to the 17th century and two layers dating beyond 2.3 thousand years ago. The depositional ages of most sand layers potentially correlate with tsunami deposits resulting from the Kuril Trench earthquakes. Utilizing reconstructed paleo-sea level data, we estimated that most sand layers reached approximately 2 m in height. However, it is noteworthy that the latest sand layer from the 17th century exhibited an unusual distribution, more than 3 m in height. This suggests a different wave source as the Mt. Komagatake collapse. The discovery of multiple sand layers and their distributions is crucial to constraining the maximum magnitude of giant earthquakes in the Kuril Trench and understanding the volcanic tsunami events related to Mt. Komagatake.

Chapter 11: Astrobiology Education, Engagement, and Resources.

Although astrobiology is a relatively new field of science, the questions it seeks to answer (e.g., "What is life?" "What does life require?") have been investigated for millennia. In recent decades, formal programs dedicated specifically to the science of astrobiology have been organized at academic, governmental, and institutional scales. Constructing educational programs around this emerging science relies on input from broad expertise and backgrounds. Because of the interdisciplinary nature of this field, career pathways in astrobiology often begin in more specific fields such as astronomy, geology, or biology, and unlike many other sciences, typically involve substantial training outside one's primary discipline. The recent origin of astrobiology as a field of science has led to strong collaborations with education research in the development of astrobiology courses and offers a unique instructional laboratory for further pedagogical studies. This chapter is intended to support students, educators, and early career scientists by connecting them to materials and opportunities that the authors and colleagues have found advantageous. Annotated lists of relevant programs and resources are included as a series of appendices in the supplementary material.

An ensemble framework-based approach for modeling stability of expansive soil slopes: fusion of machine learning algorithms and protection structure disease data.

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster prevention and control. In this study, the type and characteristics of slope protection structure disease were determined through the field investigation of an expansive soil area, and this information is incorporated into the numerical simulations and works to develop prediction models of slope stability. Four base machine learning (ML) methods are used to capture the relationship between protection structure diseases and factor of safety (FOS). Further, with the help of stacked generalization (SG), four ML models are combined, and the final SG model is used to predict the FOS. The results show that ML methods can effectively utilize this information and achieve excellent prediction results. The proposed SG model exhibits superior accuracy and robustness in predicting FOS compared to other ML methods. With FOS as the regression variable, the main feature contributions are slope height (37.05%) > slip distance of retaining wall (25.43%) > expansive force (18.03%) > slope gradient (12.00%); the coupling relationship among features is also captured by the proposed model. It is concluded that the SG method is particularly suitable for slope stability modeling under small sample conditions. Besides, the SG-based model effectively captures the impact of protection structure diseases on slope stability, enhances the interpretability of the ML model, and provides a reference for the maintenance and repair of the protection structure.

Using radial basis artificial neural networks to predict radiation hazard indices in geological materials.

The estimation of exposures to humans from the various sources of radiation is important. Radiation hazard indices are computed using procedures described in the literature for evaluating the combined effects of the activity concentrations of primordial radionuclides, namely, 238U, 232Th, and 40 K. The computed indices are then compared to the allowed limits defined by International Radiation Protection Organizations to determine any radiation hazard associated with the geological materials. In this paper, four distinct radial basis function artificial neural network (RBF-ANN) models were developed to predict radiation hazard indices, namely, external gamma dose rates, annual effective dose, radium equivalent activity, and external hazard index. To make RBF-ANN models, 348 different geological materials' gamma spectrometry data were acquired from the literature. Radiation hazards indices predicted from each RBF-ANN model were compared to the radiation hazards calculated using gamma spectrum analysis. The predicted hazard indices values of each RBF-ANN model were found to precisely align with the calculated values. To validate the accuracy and the adaptability of each RBF-ANN model, statistical tests (determination coefficient (R2), relative absolute error (RAE), root mean square error (RMSE), Nash-Sutcliffe Efficiency (NSE)), and significance tests (F-test and Student's t-test) were performed to analyze the relationship between calculated and predicted hazard indices. Low RAE and RMSE values as well as high R2, NSE, and p-values greater than 0.95, 0.71, and 0.05, respectively, were found for RBF-ANN models. The statistical tests' results show that all RBF-ANN models created exhibit precise performance, indicating their applicability and efficiency in forecasting the radiation hazard indices of geological materials. All the RBF-ANN models can be used to predict radiation hazard indices of geological materials quite efficiently, according to the performance level attained.

Slope stability assessment in the seismically and landslide-prone road segment of Gerese to Belta, Rift Valley, Ethiopia.

Slope instability on several sections of the Gerese-Belta route in Southern Ethiopia poses a major risk to infrastructure and safety. This research was aimed at evaluating certain areas of the road susceptible to slope instability. Through intensive fieldwork including geological analysis, surveys, and testing, three crucial slope portions were determined. Both limit equilibrium and finite element calculations demonstrated that these sections are problematic under different circumstances. The slope modification analysis shows that the safety factor increases as bench widths and the number of benches increase. In the slope section D1S3, this factor reached 1.222 when two benches measuring 5 meters in width were used on slide 2D. This initially showed an unstable safety factor of 0.26. Three benches of the same width were used under slide 2D. This resulted in a safety factor of 1.219. At the slope section (D1S2), flattening of the slope angle from initial 45° to 35°, 28°, 25° and 18° increases the factor of safety of the slope from initial 0.284 to 0.77, 0.89, 1.022, and 1.151 respectively under slide 2D analysis. At the slope section (D2S1), flattening the slope angle from initial 46° to 35°, 25°, 23°, and 20° increases the safety factor from initial 0.412 to 0.684, 0.920, 1.02, and 1.315 respectively. Based on the analysis of the study results, it can be concluded that the identified slope sections are susceptible to failure under actual field scenarios, depending on the conditions under which they are predicted to occur. According to this study, the Benching method is an economical method for mitigating soil slopes, as a result of which it was recommended to be used.

Calculation and program realization of coal pillar setting parameters in Huainan mining area.

Coal pillar retention plays a crucial role in ensuring safety and minimizing ground deformation in coal mining operations. However, accurately and efficiently determining the optimal size of protective pillars, reducing coal pillar pressure, and addressing challenges such as limited access to retention parameters, lengthy observation times, and high labor costs are challenges that must be addressed. In this paper, we presented a methodology using Huainan mine as a case study to address these challenges. The solution involves deriving the formula for coal pillar retention parameters based on the Three Regulations definition and requirements. The total least squares algorithm was integrated with surface observation station data and the MATLAB software platform to automate the coal pillar retention solution. Furthermore, a linear regression model of coal pillar retention-related parameters was established using the geological mining condition data. The proposed ELM neural network model was optimized using a genetic algorithm and combined with the linear regression model to establish a predictive model. The results demonstrated that the proposed machine learning algorithm attains the requisite level of accuracy for industrial production.

Influence of buffer distance on environmental geological hazard susceptibility assessment.

Previous researches seldom studied the selection of buffer distance between geological hazards (positive samples) and non-geological hazards (negative samples), and its reasonable selection plays a very important role in improving the accuracy of susceptibility zoning, protecting the environment and reducing the cost of hazard management. Based on GIS technology and random forest (RF) and frequency-ratio random forest (FR-RF) models, this study innovatively explored the influence of randomly selected non-geological hazard samples outside different buffer distances on the susceptibility evaluation results, with buffer distances of 100 m, 500 m, 1000 m and 2000 m in sequence. The results show that through the confusion matrix and ROC curve test, the accuracy of the model increases first and then decreases with the increase of buffer distance. Both RF and FR-RF models have the highest accuracy when the buffer distance is 1000 m, and the accuracy of the RF model is generally higher than that of the FR-RF model under the same buffer distance. Similar attribute values of positive samples and randomly selected negative samples or "extreme" attribute values of negative samples are the main reasons for the differences in evaluation results of different buffer distances. According to the weight analysis of causative factors, the distance from road, the distance from river and the normalized vegetation index (NDVI) are the main factors affecting the occurrence of hazards. The high and very high susceptibility areas in the study area are mainly distributed on both sides of roads and water systems, which are the key areas for hazard prevention and reduction. The HMC of RF-1000m decreased by 3.55% on average compared with other models. The results of this study improve the accuracy of geological hazard susceptibility assessment, maintain the safety of ecological environment, and provide a scientific basis for the selection of buffer distance index in local and surrounding areas in the future.

Analysis of the spatial distribution of the landslides triggered by the 1923 Great Kanto Earthquake, Japan.

The Great Kanto Earthquake that occurred in the southern part of Kanto district, Japan, on September 1, 1923, was reported to have triggered numerous landslides (over 89,080 slope failures over an area of 86.32 km2). This study investigated the relationship between the landslide occurrence caused by this earthquake and geomorphology, geology, soil, seismic ground motion, and coseismic deformation. We found that a higher landslide density was mainly related to a larger absolute curvature and a higher slope angle, as well as to several geological units (Neogene plutonic rock, accretionary prism, and metamorphic rocks). Moreover, we performed decision tree analyses, which showed that slope angle, geology, and coseismic deformation were correlated to landslide density in that order. However, no clear correlation was found between landslide density and seismic ground motion. These results suggest that landslide density was greater in areas of large slope angle or fragile geology in the area with strong shaking enough to trigger landslides.

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother.

Categorical parameter distributions consisting of geologic facies with distinct properties, for example, high-permeability channels embedded in a low-permeability matrix, are common at contaminated sites. At these sites, low-permeability facies store solute mass, acting as secondary sources to higher-permeability facies, sustaining concentrations for decades while increasing risk and cleanup costs. Parameter estimation is difficult in such systems because the discontinuities in the parameter space hinder the inverse problem. This paper presents a novel approach based on Traveling Pilot Points (TRIPS) and an iterative ensemble smoother (IES) to solve the categorical inverse problem. Groundwater flow and solute transport in a hypothetical aquifer with a categorical parameter distribution are simulated using MODFLOW 6. Heads and concentrations are recorded at multiple monitoring locations. IES is used to generate posterior ensembles assuming a TRIPS prior and an approximate multi-Gaussian prior. The ensembles are used to predict solute concentrations and mass into the future. The evaluation also includes an assessment of how the number of measurements and the choice of the geological prior determine the characteristics of the posterior ensemble and the resulting predictions. The results indicate that IES was able to efficiently sample the posterior distribution and showed that even with an approximate geological prior, a high degree of parameterization and history matching could lead to parameter ensembles that can be useful for making certain types of predictions (heads, concentrations). However, the approximate geological prior was insufficient for predicting mass. The analysis demonstrates how decision-makers can quantify uncertainty and make informed decisions with an ensemble-based approach.

Assessing three soil removal methods for environmental DNA analysis of mock forensic geology evidence.

Soil is useful in criminal investigations as it is highly variable and readily transferred. Forensic geologists use several different techniques to removal soil from evidence prior to the analysis of inorganic components. There has been recent interest from the forensic science community to analyze environmental deoxyribonucleic acid (eDNA) associated with soil to augment existing forensic analyses. Notably however, limited research has been conducted to compare commonly used soil removal methods for downstream eDNA analysis. In this study, three soil removal methods were assessed: picking/scraping, sonication, and swabbing. Three mock evidence types (t-shirts, boot soles, and trowels) were sampled in triplicate with each removal method (n = 27). Soil samples underwent DNA isolation, quantification, and amplification of four genomic barcode regions: 16S for bacteria, ITS1 for fungi, ITS2 for plants, and COI for arthropods. Amplicons were prepared into libraries for DNA sequencing on an Illumina® MiniSeq. DNA concentrations were highest in picked/scraped samples and were statistically significant compared with swabbed and sonicated samples. Amplicon sequence variants (ASVs) were identified, and removal methods had no impact on the recovery of the total number of target ASVs. Additionally, when assessing each sample in multidimensional space, picked/scraped samples tended to cluster separately from swabbed and sonicated samples. The soil core used a reference in this study also clustered with the picked/scraped samples, indicating that these samples may be more reflective of the communities collected from soil cores. Based on these data, we identified that picking/scraping is an acceptable soil removal method for eDNA analysis.