Volcanic rock reservoir evaluation is a critical foundation for oil and gas reservoir development, requiring the selection of parameters based on reservoir characteristics and data features to develop targeted evaluation methods. The Mesozoic volcanic rocks in the southern slope zone of the Laizhouwan sag in the Bohai Bay basin have yielded abundant core samples, special logging data, and conventional logging data from existing wells, providing a data foundation for conducting multi-parameter quantitative reservoir evaluation. This study uses this example to investigate quantitative evaluation methods for the Mesozoic volcanic rock reservoirs. The results indicate that the volcanic rock reservoirs in the study area are primarily pore-fracture type reservoirs, with fractures being a key factor influencing reservoir quality. Based on this, parameters such as logging porosity, nuclear magnetic resonance (NMR) T2 spectra, fracture density, and fracture opening were selected for correlation analysis to identify optimal evaluation parameters. Principal component analysis was used to assign weights to each parameter, and K-means clustering was employed for reservoir evaluation. When the volcanic rock reservoirs in the study area were classified into three categories, the clustering quality was high. Reservoir types are closely related to lithology; Type I reservoirs primarily consist of volcanic breccia, characterized by high porosity and large fracture opening. 

The A’nan depression is the main hydrocarbon generating depression in the Erlian basin, and conducting structural-lithological exploration around the rich hydrocarbon generating trough is one of the important strategies for the future of this area. Based on seismic, core, drilling, and analytical data from the research area, this article systematically summarizes the formation conditions of structural-lithological oil and gas reservoirs, and summarizes oil and gas enrichment models in the Tengger Formation of the A’nan depression. The results indicate that the black mudstones in the A 4 Member and Teng 1 Member are high-quality source rocks in oil window with high organic matter abundance, and are the main hydrocarbon supply layers in the study area. The physical properties of the reservoir in the central zone of the underwater distributary channel facies in Teng 2 Member are good. The reservoir was covered by a large set of thick mudstone, forming a good reservoir-seal combination. The vertical faults and lateral sandbodies and regional unconformities served as good transport and accumulation channels. A “flower shaped” enrichment model controlled by source faults and a “stepped” enrichment model controlled by source edge faults and surfaces were established for the Ha’nan buried hill belt and the Menggulin anticline belt, respectively. Both oil and gas enrichment modes are jointly controlled by structure and lithology. The dominant migration and accumulation of oil and gas point to the central zone of the underwater distributary channel facies, where the reservoir has excellent physical properties and a large thickness, making it a sweet spot for oil and gas in structural-lithological traps.

The pronounced heterogeneity of source–reservoir architectures in continental shale systems has not yet been adequately captured by existing classification schemes, which constrains a systematic understanding of shale oil enrichment mechanisms. Although various source–reservoir classification frameworks have been proposed, their applicability to the strongly heterogeneous continental shales of China remains limited, and consequently the controlling role of source–reservoir relationships on shale oil enrichment has not been comprehensively elucidated. This paper takes the interbedded shale oil reservoirs of the Chang 71-2 Submember in Longdong area of the Ordos basin as the research object. Based on the superimposition relationship and thickness of sandstone and mudstone, combined with X-ray diffraction tests, three types of source-reservoir assemblages are classified. Furthermore, relying on high-pressure mercury intrusion tests, nitrogen adsorption tests, ordinary thin sections, casting thin sections, scanning electron microscopy images, and two-dimensional NMR T1-T2 spectra, the differences in shale oil reservoirs, pore structures, and occurrence states are comprehensively explored. The results show that: type Ⅰ source-reservoir assemblage is dominated by mesopores and macropores, with a small amount of micropores, featuring good pore connectivity and uniform pore distribution; It is mainly composed of intergranular pores, with a small number of intragranular dissolution pores developed, and microfractures are well-developed; Movable oil is widely distributed in mesopores and macropores, making it a favorable assemblage for shale oil enrichment. Type Ⅱ source-reservoir assemblage has relatively developed micropores and mesopores; It develops a small number of intergranular pores, a small number of intragranular dissolution pores, and a small number of microfractures; Heavy-component residual oil is widely distributed in micropores, with moderate mobility, resulting in moderate shale oil enrichment capacity. Type Ⅲ source-reservoir assemblage is dominated by micropores; It is mainly composed of clay mineral cementation pores and carbonate cementation pores, with dense pores and poor pore connectivity; Capillary-bound water is widely distributed in the pores, restricting the flow of movable oil and being unfavorable for shale oil enrichment. Felsic minerals significantly promote the occurrence and migration of movable oil in mesopores and macropores, whereas clay minerals favor the retention of oil in adsorbed or residual states; Together, these mineralogical components constitute the primary controlling factors for shale oil enrichment. In contrast, average pore radius, maximum mercury saturation, porosity, permeability, and specific surface area act as secondary controlling factors for shale oil enrichment. 

 Continental shale oil in the Sichuan basin is currently an important new frontier for unconventional oil and gas exploration. The Lianggaoshan Formation in the Fuling area is the high-yield interval of continental shale oil in this region, and clarifying its sweet spot characteristics can provide insights for shale oil exploration and development in southeastern Sichuan. To investigate the sweet spot characteristics of continental shale oil in Lianggaoshan Formation of the Fuling area, Sichuan basin, this study conducted organic and inorganic geochemical tests and microscopic observations, and established a sweet spot evaluation method for continental shale in Lianggaoshan Formation by integrating sedimentation, reservoir formation and hydrocarbon accumulation. The results show that the sweet spots in Lianggaoshan Formation of the Fuling area exhibit the “four-high” characteristics: High total organic carbon (TOC) abundance, high porosity, high permeability, and high hydrocarbon content. The sweet spot intervals mainly develop organic-rich mixed mudstones and organic-rich argillaceous mudstones; The pore types are dominated by inorganic pores, followed by organic matter pores, and pores are mainly developed in solid bitumen, clay minerals and solid bitumen-clay mineral complexes. The sedimentary environment of sweet spot sublayer 3 is weakly oxidizing to weakly reducing, while sweet spot sublayer 4 experienced a reducing environment in the early stage and a weakly oxidizing to weakly reducing environment in the late stage. The formation and enrichment of shale sweet spots in Lianggaoshan Formation are controlled by three major factors: Sedimentary environment, lithofacies-mineral composition, and lamination development. In summary, a genetic model for continental shale development in Lianggaoshan Formation of Fuling area is established, revealing that the sweet spot intervals of Lianggaoshan Formation in the study area are mainly developed in semi-deep to deep lacustrine facies.

 In order to comprehensively characterize the characteristics of reservoir space and pore-throat structure of the intra-platform reservoir of the Member 4 of Dengying Formation in the Gaomo area,  this study investigated the pore-throat structure characteristics of different types of reservoirs using physical property analysis, core and cast thin section observation, centrifugal NMR(nuclear magnetic resonance), and CT scans at different scales. The results show that the main storage spaces of the intra-platform reservoir are inter-crystalline (dissolved) pores and small solution cavities, with throats and fractures as the main flow channels. The rock density is low where fractures and cavities develop, and the dissolved fractures and cavities are distributed in clusters. The existence of fractures in fracture-cave type core can reduce the seepage resistance in the near-fracture area and increase the drainage efficiency of small-sized pores in centrifugal NMR experiments. Combined with the results of cast thin section and centrifugal NMR experiments, the cavity-type core was further classified into two categories: cavity gas storage and dissolved-pore gas storage. The dissolved-pore gas storage reservoir is difficult to develop but has high development potential. If reasonable reservoir improvement and surface operation measures are adopted, cavity-type core can serve as an important support for stable production in the middle and late stages of Intra-platform reservoir development.

 Volcanic rocks of the Lower Cretaceous Huoshiling Formation are widely distributed in the southern Songliao basin. To reveal the petrogenesis of intermediateacidic volcanic rocks in the southern Songliao basin and fully understand the formation and evolution of the basin and its internal fault depressions, majorelement, traceelement, and Sr-Nd-Pb isotopic analyses were conducted on intermediateacidic volcanic rocks in the study area. Core observations and thin-section microscopic identification show that the volcanic rocks are dominated by intermediate and intermediateacidic rocks, including andesite, trachyte, rhyolite, andesitic tuff, dacitic tuff, and rhyolitic tuff, with relatively fewer intermediatebasic rocks. The volcanic rocks are silicarich and alkalirich, and belong to the calcalkaline series. Calcalkaline acidic and intermediate volcanic rocks exhibit consistent major and traceelement characteristics, similar to those of orogenic volcanic rocks. They are enriched in light rare earth elements, Rb, K, and large ion lithophile elements, with low initial (87Sr/86Sr)i ratios and εNd(t) values, as well as similar Pb isotopic compositions, suggesting a common magma source and significant fractional crystallization. The calcalkaline volcanic rocks of the Huoshiling Formation in the southern Songliao fault depression were derived from metasomatized lithospheric mantle, incorporating subductionrelated components. Calcalkaline basic magmas underwent fractional crystallization, experienced crustal contamination, and ascended to the surface, forming the calcalkaline intermediateacidic volcanic rocks in the southern Songliao area, corresponding to the initial stage of lithospheric extension.

The Shanghuofang area is located at the central crater of the Guangxing-Zhirui basin in the Guyuan-Hongshanzi uranium and polymetallic metallogenic belt. Many uranium-molybdenum industrial-grade drill holes and mineralization occurrences have been identified. The molybdenum resource potential has reached the level of a medium-sized deposit, and the metallogenic conditions are favorable. The main ore-hosting country rock for uranium-molybdenum mineralization is rhyolite porphyry. In order to further investigate the geochemical characteristics and the tectonic setting of the rhyolite porphyry, this paper employed zircon U-Pb dating and whole-rock major and trace element analyses. The results show that the zircon U-Pb age of the rhyolite porphyry is 157 Ma, indicating that it formed in the Late Jurassic. Geochemical studies show that it has high SiO2 (w(SiO2)＞70%), high total alkali content (w(K2O+Na2O) >8%), low MgO (w(MgO)<0.5%), and is characterized by enrichment in large-ion lithophile elements (Rb, K, Ba, Th) and Zr, relative depletion in high-field-strength elements (P, Ta, Ti). The trace element characteristics indicate that the rhyolite porphyry in the Shanghuofang area is a typical post-orogenic A-type granite. Based on the regional tectonic background, we suggest that its formation is related to the post-collision extensional tectonic action following the closure of the Mongolia-Okhotsk Ocean.

The Baiyinshana basin is an important Mesozoic uranium-producing volcanic basin in the southern Great Xing’an Range, where several volcanic-type uranium occurrences have been discovered; However, there is a lack of research on the petrogenesis of the volcanic rocks and the tectonic background of their formation. In this paper, zircon U-Pb geochronology, geochemistry, and Hf isotope analyses were conducted on the Mesozoic volcanic rocks of the Manketouebo and Baiyingaolao Formations in the Baiyinshana basin, as well as on the granitic porphyries that intrude them. The dating results show that the ages of rhyolites from the Manketouebo Formation, the Baiyingaolao Formation, and the granite porphyry are 154.3-152.3, 137.7-129.8 and 129.6-129.3 Ma, respectively. Those rocks are characterized by high SiO2, moderate alkali, low CaO and MgO, and are strongly peraluminous. They are enriched in rare earth elements (REEs) and large-ion lithophile elements (LILEs), but depletion in heavy rare earth elements (HREEs) and high-field-strength elements (HFSEs). combined with theIR low P2O5 contents (0.01%-0.04%), 10 000Ga/Al ratios (1.90-2.79), and low zircon saturation temperatures (averaging 785.2 ℃), these features suggest that they belong to I-type granites. The εHf (t) values of the rocks range from 6.1 to 7.9, 5.5 to 9.8 and 6.4 to 9.5, and their TDM2 ages range from 812 to 700, 834 to 562 and 774 to 579 Ma, respectively. These data indicate that the magma was derived from crustal material that originated from the Neoproterozoic depleted mantle. Based on regional tectonic evolution and tectonic discrimination diagrams, it is determined that the study area was in an extensional tectonic environment during the Late Juraassic to Early Cretaceous. This extension may be related to lithospheric extension following the closure of the Mongol-Okhotsk Ocean in the Late Jurassic. Furthermore, the Early Cretaceous was influenced by the superimposition of the Mongol-Okhotsk and the Paleo-Pacific tectonic domains.

 The northward subduction of the Proto-Tethys Ocean, the subsequent ocean closure, and the continent-continent collision represent major tectonic events in the Paleozoic evolution of the East Kunlun region. To investigate the petrogenesis and tectonic setting of granitic rocks in the Gouli area, and to constrain the Late Paleozoic geological evolution of the eastern segment of the South East Kunlun orogenic belt, we conducted detailed petrographic, whole-rock geochemical, zircon U-Pb geochronological, and isotopic geochemical analyses on granodiorite dikes from the Walega South ore district. The results indicate that the granodiorites are characterized by high contents of SiO2 (74.90%-79.07%), Al2O3 (13.66%-16.21%), and total alkalis (K2O+Na2O=5.83%-7.08%). They exhibit relatively low total rare earth element (REE) concentrations (ΣREE = 32.05×10-6-54.18×10-6), with an average (La/Yb)N ratio of 15.99, indicating moderate fractionation between light and heavy REEs. Both Eu and Ce display weakly positive to weakly negative anomalies. The samples are enriched in large-ion lithophile elements (LILEs; e.g., Rb, Ba, Pb) and depleted in high-field-strength elements (HFSEs; e.g., Nb, Ce), consistent with the geochemical affinities of medium-K calc-alkaline, weakly peraluminous I-type granites. LA-ICP-MS zircon U-Pb dating yields a crystallization age of (403±6) Ma, corresponding to the Early Devonian. The granodiorites are crust-derived granites that formed predominantly by partial melting of juvenile basaltic lower crust, with input from ancient felsic crustal materials. Trace element characteristics suggest that the granodiorite pluton was emplaced in an intraplate tectonic setting.

 To determine the formation age, source characteristics and tectonic setting of the two types of Early Jurassic I-type granites in the southern Zhangguangcai Range, this paper presents zircon U-Pb geochronology, whole-rock geochemistry and zircon Hf isotope data for five biotite monzogranite and granodiorite plutons in the southern Zhangguangcai Range. The results show that the zircons from the four granite samples all exhibit narrow and clear oscillatory zoning, characteristic of typical magmatic zircons, with concordant ages ranging from 196 to 181 Ma, indicating that the granites formed in the Early Jurassic. Geochemical analysis indicates that this suite of granites belongs to the high-K calc-alkaline series, characterized by high silica (Si) and total alkali contents, enriched in large-ion lithophile elements (LILEs) such as K and Rb, and strongly depleted in elements such as Ba, Sr, and Nb, with A/CNK values ranging from 1.01 to 1.05. These granites display the geochemical characteristics of I-type granites. They can be further divided into weakly differentiated typical I-type granites (CJM, SYB, and SHC intrusions) and highly differentiated I-type granites (TG and NSJ intrusions). The latter are distinguished by extremely high SiO2 content (73.52%-76.07%), high alkali content (Na2O+K2O>8%), relatively high Ga/Al ratios (2.56-2.80), and strong negative anomalies of Ba, Sr, Eu, P, and Ti. Both types of I-type granites have consistent zircon Hf isotope compositions, with εHf(t) values ranging from 6.2 to 9.5, indicating that they are derived from the partial melting of juvenile lower crustal materials. Combined with the regional geological background, we propose that the formation of these Early Jurassic I-type granites was controlled by the subduction of the Paleo-Pacific plate beneath the Eurasian plate. 

 The Nuomuhong culture is an important ancient human culture on the Qinghai-Xizang Plateau. Its formation and decline have attracted wide attention from researchers. In this study, the Nuomuhong culture was explored from the perspective of the geological background and evolution of the Talitaliha site in the Qaidam basin by means of a field geological survey and optically stimulated luminescence dating. The results show that the Talitaliha site is located in the aeolian sedimentary belt of the Nuomuhong alluvial fan. The aeolian sedimentary facies in this area have high soil fertility and are easier to cultivate than the ice-water deposits and modern river deposits in the main part of the Nuomuhong alluvial fan, which made them the preferred location for ancient human settlements during the Bronze Age in this area, showing the significant influence of the geological environment on the selection of ancient human settlements. The age of the latest aeolian deposits on the Nuomuhong alluvial fan is between 2.30-1.79 ka, which is consistent with the end of the Nuomuhong culture. The latest aeolian deposition may represent an aridification event in the Qaidam basin, which may have had an important impact on the decline of the Nuomuhong culture.

To investigate the effects of varied operational parameters on hydraulic fracturing in deep shale reservoirs, a three-dimensional numerical model for multi-cluster hydraulic fracturing in deep shale formations was established, anchored in logging data collected from a 3 500 m deep shale reservoir site within the Longmaxi Formation of the Sichuan basin. The micro-statistical damage finite element method was integrated into this model, which was tailored to the specific conditions and geological features of hydrocarbon-bearing reservoirs. Simulations were carried out separately to examine the effects of differing perforation cluster spacing, fracturing fluid viscosity, and number of perforation clusters. The variations in fracture initiation pressure, acoustic emission energy, and fracture propagation patterns under different operational parameters were analyzed. Specific results showed: When the number of perforation clusters was 7 and the viscosity of the fracturing fluid was 1 mPa·s, the cluster spacing increased from 6 m to 12 m, the fracture initiation pressure linearly increased from 16.47 MPa to 22.14 MPa, the acoustic emission energy increased from 10.27 kJ to 13.98 kJ, and the fracture propagation areas on the horizontal and vertical planes significantly increased. When the cluster spacing was 10 m and the viscosity was 1 mPa·s, the number of perforation clusters increased from 3 to 9, the fracture initiation pressure increased from 10.65 MPa to 25.36 MPa, the acoustic emission energy increased from 6.43 kJ to 16.09 kJ, and the fracture propagation area increased. When the cluster spacing was 10 m and the number of clusters was 7, the viscosity of the fracturing fluid increased from 1 mPa·s to 6 mPa·s, the fracture initiation pressure decreased from 20.08 MPa to 12.14 MPa, the acoustic emission energy decreased from 13.06 kJ to 7.18 kJ, the fracture propagation area decreased and the shape tended to be circular in the horizontal and vertical planes.

Standard BP (back propagation) neural network suffers from inherent drawbacks such as easy trapping in local optimal solutions and high sensitivity to initial weights. To address these limitations, the crayfish optimization algorithm (COA) was introduced to optimize the BP neural network, and a COA-BP landslide susceptibility evaluation model was constructed. Taking Wuyi County in Zhejiang Province as the study area, the landslide susceptibility evaluation was carried out. Firstly, four unimodal test functions and one multimodal test function were adopted to test the performance of COA, and comparative analysis was conducted with the multi-verse optimizer (MVO), sparrow search algorithm (SSA), and marine predators algorithm (MPA). Subsequently, 13 environmental conditioning factors including elevation, slope, aspect, and precipitation were extracted from the study area. The proposed COA-BP model was applied to landslide susceptibility evaluation, with BP, MVO-BP, MPA-BP and SSA-BP models set as comparison models. The classification accuracy was verified via the confusion matrix and two-level index radar chart. The results demonstrate that COA outperforms the other three optimization algorithms. The COA-BP model presents superior predictive capability with clearer zoning of landslide susceptibility levels and high recognition accuracy for both landslide and non-landslide samples. The area under curve (AUC) value of the model exceeds 0.9, which meets the accuracy requirements for landslide susceptibility evaluation in the study area.

 This study examines the mechanical properties of metakaolin-based geopolymer-modified soil under freezing construction conditions. Using silty clay from Fuzhou, the effects of freezing temperature, activator modulus, the water glass to metakaolin mass ratio (S-MK ratio), geopolymer content, and curing age on the uniaxial compressive strength were analyzed. The relationship between strength development and these factors was explored through soil stress-strain curves and failure modes. Scanning electron microscopy (SEM) was used to analyze the microstructure of geopolymer and cement-modified soils with varying activator moduli, providing insights into the micro-level strength development mechanism. The results indicate that the compressive strength of metakaolin-modified soil is significantly higher under freezing conditions compared to normal temperatures. The unconfined compressive strength declines with increasing alkali activator modulus, accompanied by brittle failure at low modulus levels. With the rise of the S-MK ratio, the strength exhibits an initial increase followed by a subsequent decrease. Additionally, the strength improves as geopolymer content increases, yet the enhancement trend gradually levels off once the dosage exceeds 25%. The optimal mix was found to be an activator modulus of 1.3, an S-MK ratio of 1.4, and 25% geopolymer content. 

  Pore water pressure in frozen soil is a key physical quantity for revealing the mechanism of frost heave and thaw settlement in cold regions. To systematically analyze the development pattern and influencing mechanism of pore pressure in frozen soil under dynamic loading, pore pressure in warm saturated frozen soil under dynamic loading was measured based on indoor dynamic triaxial tests. The effects of temperature, dynamic stress amplitude, and initial dry density on pore pressure were investigated. Based on this, a three-parameter pore pressure-vibration order empirical model considering the above factors was established, and the applicability of the model was verified. The results show that the development pattern of pore pressure in warm saturated frozen soil under dynamic loading is similar to that of thawed soil, and can be divided into three main stages: rapid increase, slow increase, and dynamic stabilization. Temperature has the most significant impact on pore pressure, especially at the test temperature of -0.5 ℃, where the rate of pore pressure change is the fastest, reaching a peak value of 265.24 kPa. The peak value of frozen soil pore pressure increases with increasing temperature and dynamic stress amplitude, and decreases with increasing dry density. In the early stage of loading, the pore pressure and strain of frozen soil show an approximately linear relationship. The established model has a good fitting effect (R2≥0.98) and can accurately predict the variation pattern of pore pressure in frozen soil under different test conditions.

 To fully utilize the revealed hydrogeological information for the generation of deep hydrogeological profiles, a generation method of deep hydrogeological profiles based on conditional generative adversarial network (CGAN) was proposed. Firstly, the shallow geological profiles were segmented, the revealed geological profile data were extracted as training samples, and the corresponding virtual boreholes were generated as conditional data to construct the sample library for the conditional generative adversarial network. Subsequently, the conditional generative adversarial network was constructed and trained. The U-Net architecture was adopted by the generator, a multi-scale convolutional network was employed by the discriminator, and a multi-scale discriminative loss function was designed. Then, the deep borehole data were input into the well-trained generator to effectively generate the deep hydrogeological profile of the target area. Finally, a comparative experiment was conducted with the XGBoost model based on iterative convolution.The results show that the CGAN method presents obvious advantages in the detail and structural representation of profiles, with more complete structure and higher accuracy of generated profiles. When inputting the same six boreholes, compared with the XGBoost method, the peak signal-to-noise ratio (PSNR) is averagely increased by 1.165 2 dB, the structural similarity index measure (SSIM) is averagely improved by 0.051 0, and the learned perceptual image patch similarity (LPIPS) is averagely reduced by 0.023 8. When six deep boreholes are input, the profile generated by the proposed model is highly consistent with the hydrogeological profile of the mining area compiled by integrating multiple types of data.

 The Xining basin is rich in geothermal resources, with geothermal water exhibiting high salinity characteristics. To reveal the formation mechanism and circulation evolution of the high-TDS geothermal water in this area,this study selects a typical geothermal field in Huzhu County, located in the northeastern part of the Xining basin, as the research subject. Through collaborative analysis of multiple methods, including geothermal water sampling and testing, isotope techniques, traditional hydrogeochemical methods, and hydrogeochemical modeling, the hydrochemical characteristics and genetic mechanisms of the geothermal water in the area are systematically investigated. On the basis of determining the hydrochemical characteristics of the Huzhu County geothermal field, the study explores the recharge sources and circulation depth of the geothermal water, quantitatively evaluates the mineral reaction mechanisms responsible for the high salinity during the water cycle, and constructs a genetic model of the geothermal field. The research results indicate: The main hydrochemical components of the geothermal water in the area are Na+, Cl-, and SO42-, with a hydrochemical type of Cl·SO4-Na. The geothermal water in the area is weakly alkaline, with high salinity (TDS) and high fluoride content, where TDS concentrations range from 9 530 to 43 056 mg/L, and fluoride ion concentrations range from 1.26 to 2.31 mg/L. The geothermal water in the area is recharged by atmospheric precipitation infiltration, with a circulation depth of 2 274.9 to 2 635.7 m. During its flow path, it passes through limestone, gneiss, sandstone, and sandy conglomerate strata. The geothermal system in the area is a medium-low temperature conductive type, with the heat source primarily derived from the terrestrial heat flow of the lower crust and upper mantle. The caprock consists of Paleogene mudstone, gypsum rock, and sandy mudstone, while the reservoir is composed of Cretaceous sandstone-sandy conglomerate, with a reservoir temperature of 63.50-72.70  ℃. The high salinity (TDS) of the geothermal water in the area is mainly attributed to the dissolution of gypsum and halite, as well as positive cation exchange. Specifically, the dissolution of halite and positive ion exchange increase the concentrations of Na+ and Cl-, while the dissolution of gypsum from evaporites increases the concentrations of Ca2+ and SO42-. H2SiO3 and F- are derived from the dissolution of quartz and fluorite, respectively. 

 Caffeine (CAF), as an emerging contaminant, has been widely detected in various environmental media. Its environmental persistence and potential ecological risks pose a threat to aquatic environmental safety, making the development of an efficient CAF removal technology crucial for ensuring water security. This study developed a novel environmentally friendly solvent-free method to synthesize three-dimensional porous CuFe2O4-P catalyst. Experiments on CAF degradation were conducted using the CuFe2O4-P catalyzed peroxymonosulfate (PMS) system. Electron spin resonance (ESR) analysis, radical quenching experiments, X-ray photoelectron spectroscopy (XPS) characterization, and water quality factor impact experiments were comprehensively utilized to systematically investigate the degradation efficiency, underlying mechanism, and the influence of water quality factors on CAF degradation in this system. The results Showed that  the CuFe2O4-P/PMS system exhibited excellent catalytic activity, it could  completely degrade CAF (100% removal within 180 s) under optimized conditions (pH=7, PMS concentration=18 mg/L, catalyst dosage=0.3 g/L). In the CuFe2O4-P/PMS process, hydroxyl radicals (·OH) and sulfate radicals (SO4·-) contributed 66% and 34%, respectively, to the degradation of CAF. The presence of Fe(Ⅲ)/Fe(Ⅱ) and Cu(Ⅱ)/Cu(Ⅰ) redox cycles on the catalyst surface played a crucial role in enhancing SO4·- generation, thereby significantly improving CAF degradation efficiency. Carbonate (CO32-) and humic acid (HA) significantly inhibited the degradation process, reducing CAF removal to 8% and 20%, respectively, while nitrate (NO3-) had negligible effects. Notably, the system still achieved a 40% removal efficiency for CAF in real wastewater treatment plant effluent, highlighting its potential for practical applications.

 To enhance the accuracy of high-density electrical resistivity methods in the investigation of reservoir embankments and eliminate the influence of their trapezoidal geometry, a three-dimensional forward modeling approach based on a hybrid finite element-infinite element method with unstructured meshes was employed. Numerical simulations were conducted on a representative embankment model, incorporating variations in geometric structure, electrode array configurations, and survey line positions. The results indicate that the trapezoidal structure of reservoir embankments exerts a significant influence on high-density electrical methods, particularly on the apparent resistivity at the deeper sections, often causing overestimation of apparent resistivity at depth. All tested array configurations exhibits this effect, and the closer the survey line is to the crest of the embankment, the stronger the impact of the trapezoidal structure. To address this issue, a correction method based on apparent resistivity ratio was proposed. Both theoretical model numerical simulation results and field data experimental results confirm that this correction effectively eliminates the artificial deep high-resistivity anomalies induced by the trapezoidal structure. The corrected inversion results no longer exhibit pseudo-layering effects associated with embankment geometry, thereby improving the reliability of both forward and inverse modeling outcomes.

The precise positioning of underground pipelines is crucial for preventing construction accidents and ensuring urban development. As an efficient and non-destructive detection method, ground penetrating radar (GPR) can rapidly acquire subsurface structural information. However, existing GPR data processing largely relies on manual interpretation of B-Scan images, which suffers from complexity, subjectivity, and limited accuracy. To address these issues, this paper proposes a convolutional neural network (CNN)-based inversion method capable of automatically identifying pipeline features from GPR data to achieve precise positioning. First, underground pipeline models with varying materials, dimensions, and burial depths were constructed to simulate the diversity and complexity of real pipelines. Subsequently, the gprMax module was utilized to perform forward modeling on these models to generate a training dataset. An end-to-end deep learning network was then established, taking normalized GPR forward responses as input and outputting target permittivity models to learn the mapping between them. Finally, the trained network was applied to invert observed GPR data to derive the permittivity models of underground pipelines. Quantitative experimental results demonstrate the robustness of the proposed inversion scheme. Even in challenging scenarios where the signal-to-noise ratio ranges from 0 dB down to -15 dB, the method maintains a structural similarity index above 0.86 and a mean squared error below 0.1. These metrics confirm that the algorithm is capable of high-precision inversion regarding pipeline location and dimensions while exhibiting excellent noise immunity. Moreover, validation using measured field data further substantiates the reliability and practical effectiveness of the method for actual GPR applications.

 In marine controlled-source electromagnetic method (MCSEM) exploration, factors such as seawater motion can cause dipole receiver arms to vibrate, generating dipole vibration noise that reduces the accuracy of subsequent inversion and interpretation. Dipole vibration noise is characterized by its large amplitude, wide frequency band, and short duration. It often manifests as pulse-like distortions, which degrade the waveform, phase, and magnitude versus offset (MVO) curve of MCSEM data. Conventional noise suppression methods are often insufficient for effectively processing this type of noise. This paper introduces the local time-frequency transform (LTFT) based on local smoothness constraint to perform time-frequency analysis on frequency-domain MCSEM data. Leveraging LTFT’s capability for finite discrete frequency point signal decomposition and reconstruction, this method provides low-storage, high-precision time-frequency information representation for MCSEM data with strong time variability. By exploiting the differences in time-frequency domain sparse characteristics between signals and dipole vibration noise, the method separates the noise from the data through time-frequency domain soft-thresholding. This process enables the accurate reconstruction of valid signals. The processing results of synthetic data and field data show that this method can effectively suppress dipole vibration noise, restore the amplitude and phase information of MCSEM signals, make the MVO curve attenuation characteristics more reasonable. Consequently, the proposed approach significantly improves the accuracy of inversion results. 

 Fractures, as major storage spaces and transport channels within underground media, play a significant role in oil and gas reservoir exploration as well as in the storage and migration of carbon dioxide. Additionally, the movement and friction between subsurface fractures are closely related to geological hazards such as earthquakes and landslides. Therefore, identifying fractures and continuously monitoring their stress and movement are of substantial research significance. Compared to the classical stress-strain relationships in rock deformation, research on nonlinear elasticity in rocks demonstrates that, even under relatively small stress, the stress-strain relationship of rocks shows nonlinear characteristics due to their complex internal structure. Studying the nonlinear elastic properties of rocks will help us understand the mechanisms of small-scale structural changes within rocks under stress, thereby developing new exploration methods to improve exploration resolution rate or long-term monitoring of underground structural movements to prevent geological disasters. Fractures have a crucial impact on the nonlinear elastic characteristics of rocks. This experiment investigates the influence of cracks on nonlinear elastic characteristics using polymethyl methacrylate (PMMA) and sandstone. The PMMA material contains almost no internal microstructures and has a uniform material density distribution, giving it characteristics of approximate linear elasticity.  This  eliminates the interference of complex internal structures of  rocks on the experimental results. Sandstone, a common rock type in oil and gas reservoirs, is of significant research importance in the fields of oil and gas exploration and well logging. It contains numerous pores and fractures  internally, exhibiting obvious nonlinear elastic characteristics. Experimental results from two different materials with varying numbers of cracks confirm that cracks enhance the nonlinear elastic characteristics of solid materials. This study improves the model for cracks under normal stress by reflecting the increase and decrease of crack space under tensile and compressive strains, successfully explaining the phenomenon of periodic strain leading to the enhancement and weakening of material nonlinear elasticity.

 Total organic carbon (TOC) content is an important parameter for evaluating the geochemical characteristics of shale reservoirs, serving as a key indicator of organic matter content and hydrocarbon generation potential. Logging data, with its high vertical resolution, can compensate for the limitations of insufficient sampling and high testing costs. As a result, using logging data to predict TOC content has become a crucial technology in shale oil exploration.This paper proposes a combined model integrating the Gaussian mixture model (GMM) and the light gradient boosting machine (LightGBM) to achieve well-logging-based TOC content  prediction. Taking the first section of  Qingshankou Formation in  Changling depression, southern Songliao basin, as the study area, we collected acoustic time difference, neutron porosity, natural gamma, resistivity, and density logging data. Outlier detection and processing were conducted using the boxplot method. Cross-validation and grid search methods were employed to optimize the parameters and establish the TOC content prediction model. The proposed method was compared with five models: The improved Δlg R, K-nearest neighbors, decision tree, extreme gradient boosting, and LightGBM. The results indicate that the GMM-LightGBM model achieved the best prediction performance, with evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) being 0.254, 0.211, and 0.766, respectively. Furthermore, the model was applied to another core well, demonstrating satisfactory performance with RMSE, MAE, and R2 values of 0.547, 0.462, and 0.647, respectively.

 Aiming at the problems that deep learning change detection methods are sensitive to illumination and registration errors and lack of samples, this paper proposes and implements a set of intelligent extraction methods and technical processes for surface remote sensing image change patches in coal mining areas based on Cesium WebGIS and deep learning theory from theoretical and practical perspectives. Firstly, a multi-task spatio-temporal attention change detection network (MSTACDN) is constructed by improved fusion of spatio-temporal feature matching consistency detection and large convolutional kernel spatio-temporal attention, to realize intelligent extraction of remote sensing image change patches. Then, the constructed WebGIS platform is used for visual display, management, and multi-user human-computer collaborative editing of the extracted change detection patches, so as to improve the correctness and accuracy of detection results. Finally, the manually edited results are stored in the sample library as new samples and fed back to the deep learning model for autonomous learning to further improve the model accuracy. The practical application results of change detection for three typical features (buildings, water bodies, and roads) show that compared with FC-SD (fully convolutional-siamese difference), SNUNet (siamese nested UNet), BITNet (bi-temporal image transformer network), and ChangeFormer algorithms, the proposed deep learning algorithm achieves the optimal detection accuracy. The two accuracy indicators of IoU (intersection over union) and F1 scores for buildings, water bodies, and roads are 82.26% and 91.79%, 81.68% and 91.50%, and 72.13% and 87.59%, respectively, which are significantly better than other models. Autonomous learning with the enhanced sample library effectively compensates for the shortage of samples. After one round of sample enhancement, the IoU and F1 scores of buildings, water bodies, and roads are increased to 89.53% and 91.86%, 85.74% and 91.78%, and 82.32% and 89.77% accordingly.

 Sea ice plays a critical role in influencing various aspects of  atmosphere, ecology, oceans, and even human activities, making the task of sea ice retrieval (SIR) immensely important. At present,  machine learning methods based on global navigation satellite system  reflectometry (GNSS-R) technology have benn applied  in the field of sea ice remote sensing, achieving good results. However, the existing algorithms are hindered by over-reliance on labels, structural redundancies, and limited effectiveness in dealing with sample imbalances. To overcome these challenges, the anomaly detection driven semi-supervised multi-task retrieval algorithm for sea ice based on GNSS-R is proposed in this paper. The approach of semi-supervised anomaly detection is applied to the field of remote sensing SIR, which solves the retrieval problem in the case of over-reliance on labels and sample imbalance. The proposed algorithm is composed of two primary components: the data preprocessing module and the GNSS-R-based sea ice multi-task retrieval network (SIMTRN). SIMTRN contains a sea ice detection (SID) module and a sea ice concentration retrieval (SICR) module, and utilizes the inter-module data feature sharing mechanism to realize  SID and SICR tasks based on a single network, which solves the problem of structural redundancy. In addition, for the SICR task, this paper considers the relationship between different atmospheric and meteorological features and sea ice concentration, and improves the prediction accuracy by fusing with sea ice delay Doppler maps’ features. Experimental results demonstrate that SIMTRN achieves a SID accuracy of 0.973 3 and a SICR correlation coefficient of 0.969 9, outperforming four existing machine learning algorithms: The backpropagation-learning multilayer perceptron neural network, the convolutional neural network based on single-layer convolution, the support vector based on feature selection, and the convolutional neural network based on double-layer convolution. Additionally, this study confirms that the proposed algorithm possesses superior generalization capability to the existing methods.