Depth Interval Research Articles

Carbon Stocks in Two Aquatic Marshes on the Caribbean and Pacific Coast of Panama

Wetlands are critical ecosystems globally, boasting significant ecological and economic value. They play a crucial role in the hydrological cycle by storing water and carbon, thereby helping to mitigate climate variability. But in Panama, little is known about the carbon stored in freshwater wetlands. This research presents the estimation of the carbon stocks of two freshwater wetlands in Panama, located on both sides of the Caribbean (Portobelo) and Pacific (Tonosi) coasts. The methodology consisted of transects of 125 m and 40 m wide, with six circular plots every 25 m; in each transect, the diameter of the tree trunk was measured at breast height (1.3 m) and the species was recorded, and in the same plots, soil samples were collected in triplicate by depth intervals. The average total ecosystem carbon storage (TECS) for the aquatic wetlands of Tonosí was 106.26 ± 18.3 Mg C ha−1, and for Portobelo, it was 355.09 ± 70.02 Mg C ha−1. These recorded values can contribute to the conservation of wetlands, supporting Panama’s nationally determined NDC contributions. However, despite the acceptance that wetlands are important nature-based solutions, national data on soil carbon stocks in freshwater wetlands are still scarce and their protection should be increased.

Depth-prior-based range-gated 3D imaging method: integration of optical property into visual learning

3D Range-gated Imaging (3DRGI) has great potential for long-range detection in adverse weather conditions. Recently, vision-guided 3DRGI has brought new perspectives to this area as it overcomes hardware limitations and greatly increases flexibility. However, existing vision-guided methods do not consider the optical properties of range-gated imaging, which results in low accuracy. This paper proposes a depth-prior-based 3DRGI method to combine the advantages of optical-based and vision-guided methods. In this method, depth-prior is firstly deduced from principles of range-gated imaging and provides effective depth signals. Then, adaptive depth intervals are estimated using statistical methods, and a depth-prior-guided loss function is designed. The integration of the depth-prior-guided loss function within a vision-guided model enables focused attention on pixels with depth estimations that are inconsistent with the depth prior, thereby refining the overall accuracy of the depth map. To prove the feasibility of the proposed method, comparison experiments and ablation studies have been performed. The results show that the root mean square error improved by more than 12% under adverse weather conditions.

Distribution of Subsurface Nitrogen and Phosphorus from Different Irrigation Methods in a Maize Field

With the advancement of agricultural technology, most crop cultivation adopts water-saving techniques to improve nutrient utilization efficiency. However, limited research has been carried out on the applicability of water-saving techniques for summer maize in the Shandong Province, and it is necessary to assess the risk of nutrient loss in farmland when applying these technologies. This study investigated the distribution of nitrogen and phosphorus under different irrigation methods and planting patterns through soil and water samples. It included sprinkler irrigation (SI), drip irrigation (DI), and subsurface irrigation (SUBI). Different planting patterns, i.e., monoculture (MP) and intercropping pattern (IP), were also selected in the SI zones. The results show variations in soil nitrogen distribution within the layers between 0.9 and 4.5 m, with a pronounced trend of NO3−-N accumulating in deeper layers in the SI zone. Under SI conditions, the IP effectively reduces the nutrient accumulation around the shallow root zone while controlling the accumulation of nitrogen in deep layers. The Olsen-P accumulation in each zone would increase after the accumulation ratio decreased. Compared with MP, the depth interval of the accumulation ratio mutation was shallower in the IP. The trend of NO3−-N accumulation in deep layers is consistent with that of nitrogen concentration in groundwater. Phosphorus that is accumulated in the deep layers is not easily leached into groundwater. In conclusion, these findings can provide basic information for irrigation management in existing cropping systems.

Soil fertility status under mixed pastures in irrigated Tsitsikamma dairy farms: case studies

Animal manures are increasingly receiving renewed interest as an alternative to synthetic fertilizers. While they may improve ecosystem functions, there is limited information on short-term effects of organic amendments on soil reaction and nutrient dynamics in irrigated mixed dairy pastures, especially in the context of the Tsitsikamma region, South Africa. This study examined the soil fertility status of minimum tilled 6-year-old pasture-mixed dairy farms (F1, F2, F3, F4 and F5) in the Upper (UT) and Lower (LT) Tsitsikamma regions treated with different rates of NPK (nitrogen, phosphorus, potassium) fertilizer alone or in combination with poultry manure (PM) and/or dairy effluent (DE). Soil samples were collected at 0–15, 15–30, 30–45 and 45–60 cm depth intervals and analysed for soil pH, P, K, Ca and Mg. Results of this study revealed variable trends on soil pH and nutrient changes between farms, suggesting an influence of some inherent soil properties in addition to the 6-year applied management practices. When averaged over sampled farms, surface placement of soil amendments and limited soil disturbance resulted in surface stratification of soil pH, P, K, Ca and Mg. On the other hand, integration of organic and inorganic fertilizers induced significant changes in nutrient contents and stocks to a depth of 60 cm, especially in the LT region. A combination of NPK fertilizer, PM and/or DE applied in pasture mixtures generally showed potential to improve soil fertility in both regions. As such, adoption of this combination by farmers could cut down reliance on expensive synthetic fertilizers and costs of dairy production. However, studies are still necessary in this region to validate the observed results.

Direct measurements of sediment geoacoustic properties in the New England Mud Patch and shelf breaka).

This paper reports on an original set of direct sound speed measurements collected with the acoustic coring system in the New England Mud Patch (NEMP) and shelf break area to the south. Cores collected within the NEMP show range-dependence of the mud with slower sound speed and lower attenuation on the west side. In the shelf break region, the highest sound speeds are observed between the 200- and 350-m isobaths. The depth-dependence of the mud layer in the NEMP includes a surficial layer with a negative sound speed gradient of 28 s-1. The remainder of the mud column has a weak positive sound speed gradient of 6.2 s-1 over an isovelocity layer. Comparison between in situ and ex situ sound speed measurements provides an assessment of the effects of sediment disturbance from gravity coring operations. Small differences in the upper 2.5 m were attributed to the changes in the geoacoustic properties caused by disturbance from the coring process. Below 2.5 m, the average difference is close to zero, suggesting that these sediments were minimally disturbed. Finally, an in situ measurement of shear speed was obtained near the depth of maximum penetration. The shear speed was well correlated with sound speed from approximately the same depth interval.

Unearthing the impact of land use on deep soil carbon−an example from the southwestern Australian wheatbelt

AbstractClimate change resulting from anthropogenic greenhouse gas (GHG) emissions is both a topic that is gaining greater societal attention and an area where greater efforts are being made for their mitigation. Increasing soil organic carbon (SOC) has been proposed as one of the mechanisms through which atmospheric carbon can be sequestered and has the potential co‐benefit of also improving soil productivity. A wide range of factors have been found to influence SOC contents, especially when depth is considered, and as such regionally specific sampling is useful to understand SOC dynamics. In this study, soil samples from an agricultural property in the West Australian Wheatbelt were analysed. The samples came from four different land uses: salinity revegetation, annual grazing, forage shrub, and perennial grazing. Samples were taken at four depth intervals up to 1 m and their general characteristics as well as each of their total organic carbon, dissolved organic carbon and microbial biomass carbon values analysed. A microbial incubation experiment was conducted over 28 days to assess the stability of the soil carbon and the extent to which this depends on the soil's clay content. This study found that depth and land use were statistically significant in both SOC concentrations and SOC stability. Soil clay content was a significant factor in the stability of SOC, explaining 38% of the variation in SOC stability. Furthermore, the study reveals that excluding soil below 30 cm depth in SOC analysis can result in a global underestimation of SOC values. An understanding of this is important for wheatbelt producers to be better equipped to understand and respond to, the market pressures exerted by societies and industries to move closer to their claimed net‐zero emissions targets.

Characterization and Classifications of Saline/Sodic Soils of Ambo Area of Irrigated Farm Land in Golina Watershed in Raya Valley, Amhara Region, Ethiopia

All soils are known to contain a certain amount of soluble salts and exchangeable sodium, magnesium, potassium, and calcium. However, excessive enrichment of any one of them can interfere with many soil processes, including plant growth, and the effects depend on the degree of enrichment and the type of plant. Therefore, the objective of this study was to characterize the salt-affected soils of the Ambo area for irrigated farmlands in the Golina Watershed in Raya Kobo Woreda, Amhara region. One profile was excavated from an irrigated field to carry out this activity. One profile was excavated from the irrigated field to conduct this activity. Ten soil samples were taken from the soil profile at 2 m depth at 20 cm depth intervals. The samples were analyzed for several soil physical properties, including soil pH, soluble cations and anions, soluble salts (electrical conductivity, EC), exchangeable cations (Ca, Mg, Na, K), total nitrogen, organic carbon, soluble phosphorus, percent exchangeable sodium, sodium absorption, and soil physical properties (soil color, texture, bulk density, and soil porosity). Chemical properties were also analyzed. The analysis showed that the irrigated farmland soils had a pH of 7.6 to 8.8, electrical conductivity of 1.3 to 14 dsm-1, organic carbon of 0.3 to 1.2%, total nitrogen of 0.11 to 0.35%, soluble phosphorus of 24 to 41.4 mg kg-1, and cation exchange capacity of 50.3 to 65.5 cmol(+) kg-1. The distribution of exchangeable calcium was not consistent across the soil profiles of the irrigated farmlands. In general, the top layer of soil at both sites (because of its agricultural use) was considered for EC, ESP, and pH values. The irrigated farmland soils were classified as saline soils.

Prediction of soil texture using remote sensing data. A systematic review

Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water infiltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be time-consuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artificial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction.

Performance Evaluation of Woybo Irrigation Scheme: Wolaita Zone, Southern Ethiopia

Performance evaluation of irrigation scheme is vital for realizing the present status and identifying the area for improvement. The study was conducted in Woybo irrigation scheme which is located in Boloso Sore district, Wolaita zone, Southern Ethiopia. This study was aimed to evaluate the performance of Woybo irrigation scheme with internal performance indicators. A total of nine demonstrating farmers' fields were selected from three canal reaches (head, middle, and tail) through purposive sampling. Primary and secondary data were collected, recorded, and analyzed. To measure the water applied to each experimental field, Parshall flume was installed, and soil samples were taken to determine soil texture, field capacity, permanent wilting point, and soil moisture content before and after each irrigation event at regular soil depth intervals. The results revealed that the mean conveyance efficiency was 60.85% and the application efficiency was 48.53%. Water distribution uniformity, deep percolation, and storage efficiencies were 84.56%, 51.47%, and 85.22%, respectively. However, the overall irrigation efficiency was found to be 29.53%, which was poorly performed due to inadequate water application, ineffective field water management, and low efficiency in water delivery systems. The main factors contributing to the poor water delivery performance included failures in water regulating gates, leakages, and siltation of canals. To address these problems, recommendations are made such as improvements in water application techniques should be implemented, through proper irrigation scheduling, water conservation practices, surge irrigation techniques, and providing extension support, training, and experience sharing. Additionally, maintenance and lining of earthen canals are necessary, along with the replacement and repair of leakages and broken water regulating and controlling structures. These measures can help to upgrade water delivery efficiency and improve the overall performance of the irrigation scheme.

Dynamics of soil water, temperature, and salt and their coupled effects in Haloxylon ammodendron forests of different ages in an arid desert oasis ecotone

Study regionThe study region is a typical desert oasis ecotone of the Hexi Corridor, Northwest China. Study focusBased on four years of in situ observational data of soil water, temperature, and electrical conductivity obtained from three Haloxylon ammodendron (H. ammodendron) forests (20, 30 and 50 years) in a typical desert oasis ecotone (DOE) of the Hexi Corridor, in this study, the mechanisms of change and coupled effects of water, heat, and salt in frozen desert soils along a long-term shrub plantation in a typical desert oasis ecotone are presented. New hydrological insights for the regionThis study revealed that changes in soil water, heat, and salt and their coupling effects were completely different between shallow (0–80 cm) and deep (160–200 cm) depths. At 0–80 cm, soil water gradually decreased in the 50-year-old H. ammodendron plot, but soil salt first increased in the 30-year plot and then decreased in the 50-year plot. When the freezing process occurred in the 0–80 cm depth interval, the soil water and salt contents decreased nonlinearly with the absolute value of the soil temperature (|T|), following a power function and logarithmic function, respectively. For the thawing process in the 0–80 cm depth interval, soil water and salt increased with increasing temperature, and there was a significant linear relationship between soil water and salt (P<0.01). In the 160–200 cm layer, soil water and salt significantly increased after 30–50 years during the growing season.

Evaluating the importance of vertical environmental variables for albacore fishing grounds in tropical Atlantic Ocean using machine learning and Shapley additive explanations (SHAP) approach

AbstractThis study aims to find reliable vertical environmental variables for modeling the fishing grounds of albacore (Thunnus alalunga) in the tropical waters of the Atlantic Ocean. Logbook data of 13 Chinese longliners operating in the high seas of the Atlantic Ocean from 2016 to 2019 were collected and matched with vertical environmental variables including dissolved oxygen, temperature, and salinity from 0 to 500 m at 50‐m depth intervals. Then four machine learning (ML) models: decision tree (DT), random forest (RF), light gradient boosting (LGB) and categorical boosting (CGB) were constructed and compared with generalized additive models (GAMs) within spatial resolutions of .5° × .5°, 1° × 1°, and 2° × 2° grids to find the significant features. The importance of each variable was ranked and compared based on Shapley additive explanations (SHAP) approach across five ML models at three resolutions. Results showed that (1) the vertical environmental variables—temperature at the depth of 100 m and dissolved oxygen concentration at the depth of 100 and 150 m—were the significant features that contributed most to all the ML models at three spatial resolutions; (2) the models with a spatial resolution of 2° × 2° grid exhibited higher accuracy compared to the models with .5° × .5° and 1° × 1° grids; (3) the RF model had the best prediction performance among all the models tested. Our results suggested that significant vertical environmental variables showed similar importance across different ML models at different resolutions, and these specific variables can be relied upon for accurately predicting the fishing grounds of albacore in the tropical waters of the Atlantic Ocean.

Soil Carbon and Nitrogen Stocks Across Hillslopes Underlain by Continuous Permafrost in the Northern Arctic Foothills, Alaska, United States

ABSTRACTConstraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost‐affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1‐m depth interval were high (52.0 ± 15.1 kg C m−2 and 2.74 ± 0.82 kg N m−2) and linearly related (R2 = 0.74, p &lt; 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m−2 at 2 m; 117.1 ± 10.4 kg C m−2 at 3 m). The unique small‐scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

Recognition of Artificial Gases Formed during Drill-Bit Metamorphism Using Advanced Mud Gas

Drill-bit metamorphism (DBM) is the process of thermal degradation of drilling fluid at the interface of the bit and rock due to the overheating of the bit. The heat generated by the drill when drilling into a rock formation promotes the generation of artificial hydrocarbon and non-hydrocarbon gas, changing the composition of the gas. The objective of this work is to recognize and evaluate artificial gases originating from DBM in wells targeting oil accumulations in pre-salt carbonates in the Santos Basin, Brazil. For the evaluation, chromatographic data from advanced mud gas equipment, drilling parameters, drill type, and lithology were used. The molar concentrations of gases and gas ratios (especially ethene/ethene+ethane and dryness) were analyzed, which identified the occurrence of DBM. DBM is most severe when wells penetrate igneous and carbonate rocks with diamond-impregnated drill bits. The rate of penetration, weight on bit, and rotation per minute were evaluated together with gas data but did not present good correlations to assist in identifying DBM. The depth intervals over which artificial gases formed during DBM are recognized should not be used to infer pay zones or predict the composition and properties of reservoir fluids because the gas composition is completely changed.

Quantitative analysis of microplastics in beach sand via low-temperature solvent extraction and thermal degradation: Effects of particle size and sample depth

Quantifying trace levels of microplastics in complex environmental media remains a challenge. In this study, an approach combining field collection of samples from different depths, sample size fractionation, and plastic quantification via pyrolysis-gas chromatography–mass spectrometry (Py-GC–MS) was employed to identify and quantify microplastics at two public beaches along the northeast coast of the U.S. (Salisbury beach, MA and Hampton beach, NH). A simple sampling tool was used to collect beach sand from depth intervals of 0–5 cm and 5–10 cm, respectively. The samples were sieved to give three size fractions: coarse (>1.2 mm), intermediate (100 μm–1.2 mm), and fine (1.2 μm–100 μm) particles. Following density separation and wet peroxide oxidation, a low-temperature solvent extraction protocol involving 2-chlorophenol was used to extract polyester (PET), polystyrene (PS), polyamide (PA), and polyvinyl chloride (PVC). The extract was analyzed using Py-GC–MS for the respective polymers, while the solid residue was pyrolyzed separately for polyethylene (PE) and polypropylene (PP). The one-step solvent extraction method significantly simplified the sample matrix and improved the sensitivity of analysis. Among the samples, PET was detected in greater quantities in the fine fraction than in the intermediate size fraction, and PET fine particles were located predominantly in the surface sand. Similar to PET, PS was detected at higher mass concentrations in the fine particles in most samples. These results underscore the importance of beach environment for plastic fragmentation, where a combination of factors including UV irradiation, mechanical abrasion, and water exposure promote plastic breakdown. Surface accumulation of fine plastic particles may also be attributed to transport of microplastics through wind and tides. The proposed sample treatment and analysis methods may allow sensitive and quantitative measurements of size or depth-related distribution patterns of microplastics in complex environmental media.

Statistical characteristics of aquitard hydraulic conductivity, specific storage and porosity

Aquitard hydraulic conductivity (K), specific storage (Ss) and porosity (ϕ) are critical to the modeling of flow and mass and heat transport in groundwater systems, yet comprehensive insights into their statistical characteristics remain limited. This study compiles 456 K values from 47 studies, 202 Ss values from 49 studies and 239 ϕ values from 18 studies, followed by rigorous statistical analysis of aquitard K, Ss and ϕ with dependence on the features of test method, lithology and burial depth. Additionally, the interrelationships among K, Ss and ϕ are analyzed. A predictive model for aquitard K and Ss is developed utilizing the random forest (RF) approach. The statistical findings unearth that laboratory experiments tend to yield lower K while higher Ss, particularly when burial depths are less than 50 m. Clay, silt clay, and glacial till exhibit comparable K values, in contrast to significantly lower K values observed in shale formations. Aquitard K, Ss, and ϕ exhibit depth-decaying characteristics. Between two adjacent depth intervals, 0 ∼ 50 m, 50 ∼ 150 m and >150 m, there is nearly an order of magnitude difference in either K or Ss. Aquitard K tends to increase with ϕ when ϕ is below 0.25 and decreases vice versa. This phenomenon is probably attributed to the significant amount of immobile-bound water adhering to clayey particles within the aquitard with a high ϕ value, which reduces the pore space available for water flow. Elastic Ss, which indicates recoverable aquitard groundwater depletion, is about one order of magnitude lower than inelastic Ss, which indicates permanent aquitard groundwater depletion, implying that the majority of groundwater depletion from aquitards under long-term groundwater exploitation is non-recoverable. The RF-based predictive model indicates that ϕ is a highly important feature for predicting K and outperforms lithology, test method and burial depth in predicting Ss. The statistical findings of this study present a valuable basis for aquitard hydraulic parameters.

Unraveling mudstone compaction at microscale: A combined approach of nanoindentation mapping and machine learning data analysis

Compaction processes within mudstone samples across varying depths (723.5–3213.5 m) in the Vienna Basin were investigated, focusing on porosity changes and the resulting micromechanical response in the clay-rich, fine-grained fraction (“clay matrix”). A novel approach combining nanoindentation mapping with machine learning data analysis was developed to efficiently extract representative micromechanical parameters of the clay matrix. Emphasis was put on capturing the properties of the fine-grained, clay-dominated composite rather than individual mineral phases, enabling the analysis of compaction-induced strength changes at microscale. A significant enhancement in the mechanical strength of the clay matrix with increasing burial depth was observed. Reduced elastic modulus (Er) and hardness (H) increased from 6.8 ± 3.4 to 22.6 ± 7.5 GPa and from 0.2 ± 0.2 to 0.9 ± 0.2 GPa, respectively, over the depth interval of 2490 m. Moreover, a strong correlation between depth and porosity and consequently micromechanical properties of the clay matrix exists. This highlights the substantial influence of intra-clay porosity on mechanical properties, which follow a general compaction trend with depth. Broad ion beam-scanning electron microscopy (BIB-SEM) analysis was used for textural investigations at micro-to nanoscale and indicated that the reduction in porosity predominantly resulted from mechanical compaction rather than mineral diagenesis, which would have been noticeable by signs of significant dissolution or cementation. The correlation coefficient matrix between multiscale porosity measurements and nanoindentation affirmed that the porosity loss was closely linked to the enhanced mechanical properties of the fine-grained composite clay matrix, while other compositional variables like total clay mineral content showed weak correlations. Empirical mathematical equations were derived to describe the mechanical properties as generalized functions of depth and porosity. These may be used as geomechanical input for future basin analysis and geoenergy applications (e.g., seal rock studies in a geological storage context). This study introduces a new approach to unravel compaction processes in fine-grained rocks at microscale, emphasizing the interplay between intra-clay porosity and micromechanical changes during proceeding burial.

Application of Deep Learning for Reservoir Porosity Prediction and Self Organizing Map for Lithofacies Prediction

While there is a connection between petrophysical logs and reservoir porosity, finding analytical solutions for this relationship is still difficult. This paper presents a novel approach for forecasting porosity and lithofacies by using a convolutional neural network (CNN) model in conjunction with a bi-directional long short-term memory (BLSTM) network. The BLSTM network uses a self-organizing map (SOM) technique to form connections between input and destination data. The SOM is used to organize depth intervals with similar facies into four separate clusters, each exhibiting internal consistency in petrophysical parameters. The CNN is responsible for extracting spatial characteristics, while the BLSTM network gathers comprehensive spatiotemporal components, guaranteeing that the model accurately represents the spatiotemporal aspects of log data. The accuracy of the model was verified by analyzing simulation logging data. The findings indicate that the BLSTM network model successfully recovers significant characteristics from logging data, resulting in improved estimate accuracy. In addition, Facies-01 has lower gamma ray levels in comparison to other facies. Facies-01 is also suggestive of pristine sandstone formations, which are greatly sought as reservoir rocks. The BLSTM network model is effective in predicting physical characteristics of reservoirs, offering a new method for precise reservoir characterization parameter prediction.

Evaluation of changes in soil physical properties 20 years after skidding operations

ABSTRACT Soil degradation can negatively impact its physical and chemical properties over the long term. We investigated the natural recovery of physical properties of disturbed soils on an abandoned machine operating trail in a deciduous mountain forest in northern Iran. The machine operating trail hadn’t been used for the last 20 years. Dry bulk density and total porosity were assessed for three levels of traffic intensity (light, moderate, and heavily trafficked trails) and two levels of slope gradients (gentle, ≤20%, and steep, >20%), in four different soil depths (0–10, 10.1–20, 20.1–30, and 30.1–40 cm) as well as in undisturbed (control) areas. Over the course of the 20-year recovery period, it was observed that in the depth intervals of 0–10, 10.1–20, 20.1–30, and 30.1–40 cm, trails subjected to heavy traffic on gently sloping terrain displayed mean values that exhibited an increase of 78%, 62%, 55%, and 47% in bulk density, respectively, and a decrease of 27%, 26%, 28%, and 27% in total porosity, respectively, when compared to undisturbed soil. In the case of steep slopes, analogous assessments indicated an increase of 82%, 69%, 39%, and 31% in bulk density, respectively, and a reduction of 28%, 29%, 26%, and 24% in total porosity, respectively, within the aforementioned depth intervals. However, in the case of lightly trafficked trails situated on gentle slopes, there was a discernible amelioration in surface soil compaction. Our findings consequently suggest that the natural recovery of soil physical properties in the study area extends beyond the examined timeframe of twenty years.

Innovative imaging of iron deposits using cross‐gradient joint inversion of potential field data with petrophysical correlation

AbstractThis study demonstrates the application of the cross‐gradient joint inversion method to investigate iron mineralization zones within a volcano‐sedimentary environment. The presence of minerals with intense contrasts in density or magnetic susceptibility, such as hematite or magnetite, facilitates modelling the distribution of ore bodies with depth. Our approach involves establishing a unified interpretation of reconstructed density and susceptibility models through both independent and joint inversion with sparsity regularization in conjunction with a petrophysical model resulting from core data. This approach provides an ideal strategy to uncover the realistic geologic setting of iron ore deposits. We initially simulated a synthetic model closely resembling real‐case scenarios to assess the efficacy of the cross‐gradient joint inversion algorithm in comparison to independent inversion. Subsequently, the inversion algorithms were implemented on gravity and magnetic data, collected over an area of 500 × 600 m2 in Shavaz iron‐bearing deposits located in the central Iranian block. The primary iron oxide–apatite type mineralization in the study area is associated with the Nain–Dehshir–Baft fault as a NW–SE trending strike‐slip fault. Although both inversion methods yield satisfactory models, incorporating the cross‐gradient constraint in joint inversion resulted in a more constrained delineation of iron–oxide ore deposits in the fault system. This improvement facilitates the differentiation between hematite and a small percentage of magnetite, providing a more accurate estimation of ore depth. Inversion results suggest that the magnetite mineralization is coated with extensive hematite mineralization and both are positioned relatively within the same depth interval, covered by approximately a 15–25 m sequence of sediments.

Low resistivity anomalies in the upper crust of the Midlands of Tasmania from combined magnetotelluric datasets

Heat flow data obtained in connection with geothermal resource exploration suggests anomalous upper crustal structure and processes in parts of central east Tasmania. The regional scale crustal geology of the Midlands of Tasmania is, however, mostly obscured at the surface by the Permo-Triassic sedimentary sequences of the Tasmania Basin together with extensive exposures of Jurassic dolerite. We investigate controls on undercover crustal processes in this region by combining long period and broadband magnetotelluric (MT) datasets in 3D inversions for the geoelectric structure; followed by an interpretation that is informed by aspects of the pre-existing 3D regional geological and geophysical model. The new 3D model allows improved resolution of low resistivity anomalies together with a qualitative appraisal of spatially variable model sensitivity. The most robust features ( < 1 Ω m ) in the 2–3 km depth interval occur where N–S and E–W faults intersect with a high point in the topography of the upper surface of a deep seated granite body. Enhancement of conductivity in this zone by clay, graphite or mineralisation, or a combination thereof, is likely. Other low resistivity features suggest that conductive pathways exist where major or multiple faults are present. These interpretations provide support for continued exploration in the Midlands of Tasmania for a variety of resources related to crustal fluids and fracturing.