From October to December 2024, the level of seismicity in the Mozhugongka area, Tibetan Plateau, was extremely high. Owing to the complex geological and tectonic environment in the area, the seismogenic structure is not clear. In this study, by collecting earthquake bulletins and three-component seismic event waveforms recorded by the permanent stations of the China Seismic Network, using the double-difference relocation, the focal mechanism solution inversion and the stress field inversion, we obtain the relocation results for 1701 earthquakes and focal mechanism solutions for 19 earthquakes of M ≥ 3.0 to reveal and analyze the characteristics of the seismicity and the corresponding dynamic mechanisms. Our results show that the earthquake swarm is distributed in a strip with a NNE‒SSW strike, with a length of approximately 20 km and a depth of 5–18 km, and it has the characteristics of a shallow normal fault and deep strike-slip fault, suggesting that the earthquakes in the swarm are controlled by a concealed fault. The regional stress field is dominated by normal fault properties of almost north–south compression and almost east–west tension. However, after the M4.5 earthquake, the stress field changed from exhibiting the characteristics of a normal fault controlled by a shallow structure to exhibiting those of a strike-slip fault that is dominated by the deeper faults, reflecting the layered stress release caused by the oblique subduction of the Indian Plate beneath the Tibetan Plateau. The excess fluid pressure within the earthquake swarm exhibits periodic variations, suggesting that deep fluids repeatedly trigger seismic activity, likely linked to locally elevated background heat flow. The Mozhugongka earthquake swarm reflects adjustments of the local seismogenic environment of the Lhasa block in response to the India-Eurasia plate collision. These results provide new insights into the seismogenic mechanism operating on the Tibetan Plateau.

Accurate prediction of total organic carbon (TOC) content is critical for evaluating the quality of hydrocarbon source rocks at drilling sites. Conventional well logging data, however, fall short in providing three-dimensional (3D) quantitative assessments of shale TOC, primarily due to their inability to fully capture physical properties that are strongly associated with organic carbon enrichment, such as resistivity and porosity. In view of this limitation, this study introduces a quantitative method for shale TOC prediction based on inverted parameter volumes that are sensitive to TOC. By integrating neural network modeling and waveform indication simulation, the proposed method uses logging parameters that exhibit strong correlations with TOC. This multi-parameter, nonlinear geophysical prediction technique achieves higher accuracy than conventional approaches and provides a means of establishing the relationship between TOC content and geophysical logging parameters for 3D shale TOC evaluation. Correlation analysis between measured TOC values in core samples and logging parameters identifies density, acoustic transit time, porosity, resistivity, potassium content, uranium content, and others as TOC-sensitive parameters. These parameters are then used to constrain the post-stack seismic waveform indication inversion model. Subsequently, a nonlinear mapping between these TOC-sensitive parameters and measured TOC values is established using a neural network resulting in a quantitative TOC prediction model. Application of the developed inversion model across the study area demonstrates strong agreement between predicted organic carbon levels and laboratory measurements, confirming that the proposed method provides an accurate and feasible geophysical approach for quantitative shale TOC prediction. (c) 2026 Sinopec Petroleum Exploration and Protection Research Institute. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Pore heterogeneity in marine shales critically controls gas storage and transport; yet, how pore surface fractal attributes and pore network geometry differentially regulate adsorbed and free gas among lithofacies remains unclear. This study investigates Longmaxi marine shales from the Weiyuan area, Sichuan Basin, using XRD, FE-SEM, low-pressure N2 adsorption, and high-pressure CH4 isothermal adsorption. According to mineral composition, samples are divided into four lithofacies: quartz–calcite (#QC), quartz–pyrite (#QP), quartz–dolomite (#QD), and quartz–illite (#QI). Results show that adsorbed and free gas are controlled by different pore structure descriptors. Adsorbed gas content decreases as #QC > #QP > #QI > #QD and is mainly governed by micropore development, specific surface area, and surface roughness/complexity. Free gas content follows #QP > #QC > #QI > #QD and is primarily controlled by pore network effectiveness: connectivity enlarges connected pore space, whereas the tortuosity factor increases flow resistance and restricts migration. The #QP lithofacies have the most favorable network conditions, with high connectivity and a low tortuosity factor, indicating the greatest free gas enrichment potential. These findings provide a lithofacies-specific framework for reservoir assessment and sweet spot identification in Longmaxi shale.

To investigate and analyze the impact of stress transfer and concentration driven by the 2008 Wenchuan Ms 8.0 earthquake on the Longmenshan-foreland basin, this study aims to predict the regions where future triggered earthquakes may occur and assess their seismic hazard levels.This paper utilizes finite element numerical simulations to assess the changes in regional stress fields after the Wenchuan earthquake, analyzes the entire process of stress transfer and redistribution under strong earthquake conditions, and fits the results with subsequent seismic events. The results are further validated by the stress fields revealed through the extensive aftershock source mechanism solutions in the study area, exploring the trends for future earthquake triggering. Results indicate: (1) During the earthquake, the stress concentration characteristics of the Beichuan-Yingxiu Fault is prominent, with the maximum principal stress ranging from (1.05-1.75) MPa. The cumulative strain energy in the segment north of Yingxiu along the Longmen Shan Fault is approximately 4-5 times that of the segment south of Yingxiu, which is speculated to be one of the important reasons for the frequent aftershocks in the northern segment of the Longmen Shan Fault. (2) After the earthquake, significant strain enhancement is observed in the southern segment of the Longmen Shan Fault and the Longquanshan Fault, with strain values ranging from (2.00-2.50) x 10- 2. This is inferred to be the cause of the triggering earthquakes that occurred sequentially in the southern segment of the Longmen Shan Fault and the Chengdu Basin after the Wenchuan earthquake. (3) Based on the simulation results and stress field data, we identify 4 seismic hazard zones in the study area. Notably, the fault segments in the Longmen Shan foothills, specifically the Dayi-Dujiangyan section and the southern segment of the Lushan-Yingxiu section (the Dayi seismic gap), exhibit high levels of seismic hazard.

Spatial patterns of rock uplift derived from geomorphic analysis provide important constraints on uplift models for the Longmen Shan (LMS), eastern Tibetan Plateau. Based on channel steepness indices defining three zones of distinct rock uplift intensity—high, moderate, and low, earlier work found the spatial distribution of uplift correlates poorly with surface main faults, and hence favored lower crustal flow as the dominant uplift model. However, such geomorphic indices are subject to multi-interpretability. High channel steepness indexes, for instance, may reflect either localized rapid uplift, knickpoint retreat, or contrasts in bedrock erodibility. To reduce this uncertainty, we refine the evolution models of hypsometric integral (HI) and relief in weak uplift regions, regional overall uplift regions and active orogenic belts, and propose a new method to identify weak uplift regions or regional overall uplift regions based on two geomorphic criteria: (1) a negative correlation between HI and relief, and (2) the presence of interfluvial platforms exhibiting high positive values of normalized HI minus normalized relief. Application of this method, combined with previous findings, allows us to delineate the regional overall uplift regions and reduce the multi-interpretability of similar geomorphic indices across the study area. Based on these findings, we reconstruct the spatial distribution of rock uplift intensity, unlike earlier steepness-based interpretations, it is closely associated with main faults. This finding supports upper crustal shortening as a more viable model for LMS uplift than lower crustal flow.

Seismic exploration is currently the most mature approach for investigating subsurface structures, yet the random noise greatly restricts its imaging accuracy. Previous methods face significant challenges: traditional computational methods are often computationally complex and their effectiveness is hard to guarantee; deep learning methods rely heavily on datasets, and the complexity of network training makes them difficult to apply in practical field scenarios. In this paper, we propose an unsupervised adaptive convolutional filtering (ACF) method based on a lightweight deep learning method. It is a lightweight adaptive denoising model with only 2,464 learnable parameters, representing a substantial reduction compared with mainstream deep learning networks. And ACF operates in a dataset-free manner, optimizing its parameters relying purely on internal data priors rather than external training data. We propose two types of priors: the local prior and the global variance prior for unsupervised learning, and put forward low-scale learning to further enhance its performance in noise processing. We validated our method on both 2D and 3D synthetic and field data, and the results demonstrate that ACF offers a favorable balance between noise attenuation and signal preservation, providing a competitive alternative to conventional and deep learning-based methods, especially in complex field scenarios.

The proven gold deposits in the Jiaodong Peninsula, North China Craton, exceed 5,500 tons; these deposits are termed “Jiaodong type” or “Craton destruction type” owing to their unique geological features. Metallogenic chronology has dated the formation of these deposits at 120 ± 2 Ma. The development of such significant gold deposits in a relatively short period can be characterized as “explosive gold mineralization” and its driving factors are still under investigation. To clarify the geochemical characteristics, genesis and thermal history of a newly discovered quartz monzodiorite in the Tianqishan area, Shandong Province and their relationship with gold mineralization, the geochemistry of major and trace elements, zircon U–Pb isotope chronology, zircon Hf isotopes, and apatite fission track (AFT) thermochronology are analyzed. Our results show that the Tianqishan quartz monzodiorite, emplaced at ca. 119 Ma, is a metaluminous, high-K calc-alkaline rock, formed by mixing of mantle and crustal melts in a tectonic setting of North China Craton thinning. The AFT thermal history modeling results reveal four cooling events since the formation of the Tianqishan quartz monzodiorite and affirm two extensional stages in the Jiaodong Peninsula during the Early Cretaceous. The rapid exhumation of the crust of the Jiaodong Peninsula terminated at approximately ca. 120 ± 2 Ma, which may be due to the decoupling between crustal detachment and lithospheric mantle detachment in the Jiaodong Peninsula. Notably, the explosive mineralization of gold deposits aligns with the end of rapid exhumation in the Early Cretaceous, suggesting that decoupling between crustal and lithospheric mantle detachment is a plausible explanation for this phenomenon. This study provides critical new insights into the geodynamic processes governing Early Cretaceous lithospheric thinning and offers key constraints on the mechanisms driving explosive gold mineralization in the Jiaodong Peninsula.

Active rift basins, such as the Red Sea, provide natural laboratories for studying sediment dynamics, with implications for global provenance and resource exploration. This study integrates geochemistry and detrital zircon U-Pb geochronology to investigate beach sediments along Sudan's Red Sea coast, a rift margin within the Neoproterozoic Arabian-Nubian Shield (ANS). Three kinds of sand deposits have been observed: white, black, and island sands. The Al2O3/TiO2 ratio is sensitive to the composition of the source rock, suggesting a predominantly intermediate source for the islands' sediments and a mafic source for Trinkitat beach, with low-tomoderate weathering (CIW' = 32-76.6), consistent with semiarid rift margins. Zircon ages reveal two populations from the ANS (595-945 Ma) and Cenozoic volcanic rocks (22-42 Ma). These ages are interpreted to originate primarily from the Pan-African orogeny and specific terranes within the ANS for the older population and from syn-rift volcanism in the Red Sea Hills Alkaline Province for the younger population. Coast sands are derived from the ANS, but the black sands are mixed with syn-rift volcanic input from the Red Sea Hills Alkaline Province. Heavy mineral enrichment (ilmenite, rutile, zircon) at Trinkitat, driven by longshore currents, suggests placer deposit potential, analogous to Australia's Murray Basin. These findings highlight tectonic controls on provenance, with aridity limiting chemical alteration, contributing to global debates on sediment dispersal in rift basins. By comparing Sudan's Red Sea coast to global rift systems, this study advances models of sediment transport and supports critical mineral exploration (e.g., Ti, Zr).

In recent years, significant breakthroughs have been achieved in deep carbonate hydrocarbon exploration in the Shunbei area of the Tarim Basin in China. However, the sequence development models and their key controlling factors remain poorly understood. Based on global eustatic changes, integrated with drilling, outcrop, and seismic data, this study divides the Middle-Lower Ordovician carbonate sequences in the Shunbei area, clarifies the sedimentary facies distribution patterns within the sequence stratigraphic framework, and establishes a sequence development model for these carbonates. The results indicate that: (1) The Middle-Lower Ordovician carbonates in the Shunbei area can be subdivided into 8 third-order sequences; (2) Sedimentary facies within the sequence framework exhibit an east-to-west distribution pattern of “carbonate platform-platform margin-slope-basin”; (3) Three sequence development models are identified in the Ordovician carbonates: transgressive systems tract (TST), highstand system tract(HST) and lowstand systems tract (LST); (4) The primary reservoir spaces in the Ordovician carbonates are medium- to large-scale karst caves, with lowstand and highstand karstification controlled by third-order sequence boundaries acting as the dominant factors in reservoir formation. This research provides novel insights for deep carbonate hydrocarbon exploration in the Tarim Basin and analogous basins worldwide.

With the increasing attention to shale oil and gas in the field of oil and gas exploration and development, accurate prediction of TOC content has become the key to evaluating shale gas sweet spots. This paper studies a method for predicting shale TOC content using a BP neural network optimized by an improved cuckoo search algorithm. First, for the Longmaxi Formation shale, through logging sensitivity analysis, seven logging parameters sensitive to TOC content were determined: DEN, AC, RT, U, K, GR, and CNL. Using these parameters, a CSBP model was established and compared with the traditional BP neural network, multiple linear fitting method, and extended ∆lgR method. The results show that the CSBP model has higher prediction accuracy and generalization ability, with the mean absolute error and mean absolute percentage error being 0.38 and 15.00% respectively, which are significantly better than other methods. Further, the CSBP model was applied to predict the TOC content of Well W16 in the study area and verified by comparing with the measured TOC values. The correlation between the predicted and measured values is 0.89, and the change trends are consistent, confirming the applicability of the CSBP model. Finally, combined with the seismic waveform-guided simulation inversion technology, the planar and spatial distribution of TOC in the study area was predicted. The correlations between the predicted and measured values of four wells in the study area are all greater than 0.89. This method has high accuracy in the three-dimensional TOC content prediction of shale reservoirs and provides technical support for the evaluation of shale gas sweet spots in the work area.

On 5 September 2022, a Mw 6.6 earthquake occurred in Luding, Sichuan Province, China. The epicenter of this earthquake was located in the vicinity of Mt. Gongga. The China Earthquake Administration employed the Global Navigation Satellite System (GNSS) to conduct concurrent deformation field monitoring of the main fault associated with the Luding earthquake. The research area surrounding Mt. Gongga exhibits intricate structural and dynamic processes. However, previous studies have lacked a comprehensive three-dimensional analysis of the uplift mechanism of Mt. Gongga. This study utilizes GNSS data to constrain simulations and employs the FLAC3D numerical model to simulate the primary fault movement during the earthquake and the subsequent changes in the uplift of Mt. Gongga. These investigations are supported by seismic analysis, mechanical analysis, and inversion studies, facilitating the formulation of its uplift mechanism. The results indicate the following: (1) The seismic source analysis of the earthquake reveals a steep dip angle of the primary fault plane, with a predominant inclination toward the northeast. (2) Numerical simulations demonstrate a consistent correlation between the horizontal displacement pattern and the arcuate structure of the Sichuan–Yunnan block, promoting the counterclockwise uplift of Mt. Gongga. The vertical displacement pattern indicates that this earthquake accelerated the overall uplift of Mt. Gongga. (3) Mt. Gongga undergoes a multiple coupling uplift mechanism characterized by “clockwise uplift + rotational flexure + asthenospheric upwelling”. Seismic analysis, mechanical analysis and the results of numerical inversion serve as a useful basis for understanding the uplift of Mt. Gongga and for understanding high mountain uplift in orogen-foreland systems in general.

The Bailongshan Pegmatite deposit, located in the West Kunlun Orogenic Belt, Northwest China, is a newly discovered, super-large Li-Rb (Be-Ta-Nb) rare-metal deposit. Since complex magmatic-hydrothermal processes are responsible for the mineralization of such rare-element pegmatites, it is desirable to study the evolution and sources of ore-forming fluids to analyze the genesis of ore deposits. In this study, the Ar-40/Ar-39 plateau ages of muscovite and biotite were determined to be 171.36 +/- 1.87 and 172.39 +/- 1.66 Ma, respectively, indicating that the duration of hydrothermal mineralization was approximately 170 Ma. Based on the zonal nature of the mineral assemblage, the Bailongshan area was divided into four zones and stages (I-IV), namely the albite-quartz-lithium tourmaline (AQT, stage I), albite-quartz-bearing mica (AQM, stage II), albite-quartz-spodumene (AQS, stage I), and spodumene-quartz (SQ, stage IV) zones. Among these, AQS and SQ were the main ore-bearing areas. In terms of the fluid inclusions found in quartz and spodumene, the different types include a gas-rich phase (V-type), a liquid-rich phase (L-type), a daughter mineral-bearing three-phase (S-type), and a carbon dioxide-bearing phase (C-type). In stage I, the homogeneous temperatures of the V- and S-type fluid inclusions varied from 365 to 415 degrees C, while their corresponding salinities were 8.5-12.9 and 44.8-47.2 wt% NaCl equiv., respectively. In stage II, the homogeneous temperature and salinity of the L-type inclusions were 315-365 degrees C and 9.9-13.3 wt% NaCl equiv., respectively, while in stages I and IV, the homogeneous temperatures of the L- and S-type fluid inclusions were between 235 and 335 degrees C, while their salinities were 7.2-12.3 and 32.1-37.0 wt% NaCl equiv., respectively. Furthermore, for the C-type inclusions, the homogeneous temperature and salinity were 235-320 degrees C and 4.9-10.6 wt% NaCl equiv., respectively. The laser Raman results showed that the fluid in the metallogenic stage was an H2O-NaCl-CO2-CH4 system. Based on the homogeneous temperature and salinity results, the fluid capture pressure from stage III to stage IV was calculated to be 280-150 MPa, and the depth of the capture was >6 km. Moreover, the H-O isotope results suggested that the early ore-forming fluids are mainly magmatic hydrothermal fluids, whereas the later (stage IV) mineralizing fluids may be mixed with a small amount of meteoric water. The subsequent immiscibility of the fluid may be one of the factors responsible for the discharge and precipitation of minerals.

We report isolated postcranial materials newly excavated from the Middle Jurassic Dongdaqiao Formation in Chaya County, Qamdo City, eastern Tibet. The specimens are assignable to Eusauropoda based on the following combination of characters: huge size of caudal vertebrae and humeral shaft, weakly developed amphicoelous caudal centrum, femoral distal ends with two condyles and a shallow intercondylar groove, and rod-like transverse process of the anterior caudal vertebra with its base not extending to the neural arch. Due to the fragmentary nature of the specimens, we refrain from assigning them to lower taxonomic levels or new species of sauropod until more complete materials are excavated from Qamdo. Nevertheless, the new materials from Qamdo demonstrate that some gigantic sauropods migrated to eastern Tibet during the Middle Jurassic and were more widely distributed than previously known.

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area, which is also of great significance for understanding coastal ecological environment changes and even global changes. Based on field measurements, combined with Linear Regression (LR) model and Inverse Distance Weighing (IDW) method, this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay. The shoreline faces a high erosion chance under the action of natural factors, while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities. The implication of change rule for ecological protection and recovery is also discussed. Measures should be taken to protect the coastal ecological environment. The models used in this paper show a high correlation coefficient between observed and modeling data, which means that this method can be used to predict the changing trend of shoreline and tidal flat. The research results of present study can provide scientific supports for future coastal protection and management.

Seismic data noise processing is an important part of seismic exploration data processing, and the effect of noise elimination is directly related to the follow-up processing of data. In response to this problem, many authors have proposed methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, such methods are often not ideal when faced with strong noise. Therefore, we propose to use diffusion model theory for noise removal. The Bayesian equation is used to reverse the noise addition process, and the noise reduction work is divided into multiple steps to effectively deal with high-noise situations. Furthermore, we propose to evaluate the noise level of blind Gaussian seismic data using principal component analysis to determine the number of steps for noise reduction processing of seismic data. We train the model on synthetic data and validate it on field data through transfer learning. Experiments show that our proposed method can identify most of the noise with less signal leakage. This has positive significance for high-precision seismic exploration and future seismic data signal processing research.

Iron ore is a kind of indispensable mineral resources for industrial development, and most iron ores occurred within the Precambrian Banded Iron Formation (BIF). The Paleoproterozoic was an important period for the formation of Superior type BIFs, however, this type BIFs are rare in the North China Craton. There are iron-rich supracrustal sequences with economically valuable massive and layered iron ores developed in the 2.2 similar to 2.1Ga Liaohe Group, which is extending along the Jiao-Liao-Ji belt. Studies on the genesis of the magnetite from the Liaohe iron-rich supracrustal sequences will provide new clues for the prospecting of Proterozoic iron deposits in the North China Craton. In this paper, studies on petrography and trace element geochemistry of the magnetite from the massive and layered iron ores, the infected and vein mineralized fine-grained gneiss and the weakly mineralized magnetite fine-grained gneiss exposed in the Zhoujia area were carried out to reveal their genesis, from which three types of magnetite are recognized in the regional supracrustal sequences. The first type magnetite came from the massive iron ore and infected mineralized fine-grained gneiss, which exhibited relatively higher Mg and Mn and lower Ti, V and Co contents. The second type magnetite is derived from the layered iron ore and weakly mineralized magnetite fine-grained gneiss. Contrast to the first type, they show lower Mg and Mn and higher Ti, V and Co contents. The third type magnetite is only from the vein mineralized fine-grained gneiss with Cr-Ni-V-Ti-Mn systematics unrelated to the other magnetite types. The similarities of trace element contents and their varying trends indicated that the formation of massive and layered iron ore is attributed to the continuous growth and enrichment of the magnetite from the infected mineralized and magnetite fine-grained gneiss, respectively. On the Cr-Ni, Ti-Ni/Cr and V-Ti diagrams, most of the studied magnetites are characterized by hydrothermal fluid originated geochemical affinities. Negative correlations between Ti and Ni of the magnetites from the weakly mineralized fine-grained gneiss suggested that they should be under gone a cooling process during their growth. Our study revealed that the magnetite from the Liaohe supracrustal sequences showed great difference from those of the Hamersley BIFs in composition, although the later were also subjected to metamorphic-hydrothermal processes. It can be inferred that the magnetite of the Liaohe iron-rich sequences should be generated from different temperature, oxygen fugacity and protolith from those of the Paleoproterozoic Superior type BIF.

The Liaodong Peninsula (LP) is an important metallogenic region of boron in China. The Jiao-Liao-Ji Palcoproterozoic orogenic belt/active belt (JIJ) adjacent to the LP hosts boron deposit formed in a continental active margin setting. The boran deposits in the LP are considered to be resulted from sedimentary-metamorphic reworking mineralization. The boron materials in the LP originated from altered seafloor, subducted sediments, serpentinized mantle wedge in the boron-enriched subducted ovcarde system. The boron materials were released from the subducted units at sub-are depth and were transported upward into the upper mantle and island are volcanic system. The pristine borate beds in the volcanic basin were enriched by the submarine hot spring, and were isolated by subsequent volcanic-sedimentary covers. The mineralization of borate was associated with the regional metamorphism and deformation. The hydrous borates underwent metamorphie dehydration and re-crystallization and then precipitated in deformation- developed regions. The boron isotopes of boron deposits in the LP strongly inherit from the heavy B isotopic source reservoir, and thus provide insights into the metallogenic environment of the boron deposits. The copper-bearing pyrite deposits posterior to the boron deposit are located adjacent to the volcanic feeders, whereas the boron deposits formed in the volcanic basin. Accordingly the boron prospeeling is suggested to taking into account of the paleogeographic study by combining the spatiotemporal relations betsteen pyrite deposits and boron deposits. The Hupiyu-Hongshilazi-Qinghe actielinoria, result of multiple and superimposed deformation in the Liaohe Lectonic cycling, broadly controlled the spatial patterns of the boron deposits. In conclusion, we established the metallogenic and prospecting models of boron deposits in the LP, and analyzed prospecting criteria of boron deposits in the L.P. It is empllsized that the stratum arcas partially covered by the Gaixian Formation have the potential for prospecting the concealed boron deposits in future.

Jiaodong is the largest gold province in China and has great potential for further prospecting. It has been confirmed that the enrichment of Au is always accompanied by pyritization in Jiaodong gold deposits, so the genesis of pyritization in gold deposits and the mechanism of Au enrichment associated with pyritization has always been an issue of discussion to the researchers, the relationship between pyritization and Au enrichment is still unclear. In order to solve this problem, this paper studied the occurrence state and transformation characteristics of S in the ore-bearing geological bodies and discussed the mechanism of Au enrichment. 84 samples were collected from the drill hole ZK3006 of the Haiyu gold deposit, the average sampling interval was 15m, and 7 specific elements/analytical indexes were measured (Total sulfur, Reduced sulfur, Na2O,CaO, Sr, Ba, and Au), Burning-iodine quantity method was used to measure the content of Total sulfur, Combustion neutralization method was used to measure the content of Reduced sulfur Inductively coupled plasma-atomic emission spectrometry was used to measure the content of Na2O and CaO, Wavelength dispersive X-Ray fluorescence spectrometry was used to measure the content of Sr and Ba, and Foamed plastic enrichment-inductively coupled plasma-mass spectrometry was used to measure the content of Au.

Seismic exploration is one of the main methods used in geophysical exploration. However, due to the limitations related to high exploration costs and the environmental conditions, the obtained seismic traces may not be uniformly sampled along the spatial direction, which significantly impacts the processing and interpretation of seismic data. The conventional interpolation methods are not sufficiently accurate and robust, while some of them are inefficient. Furthermore, several authors have proposed deep learning (DL) methods based on convolutional neural networks (CNNs), which improve the efficiency of interpolation but also result in poor interpolation effects and poor robustness. To achieve enhanced robustness and ensure the efficiency of the interpolation method, we used transformer as the backbone network. Through patch segmentation and skip connections, the operational efficiency and effect of the model are guaranteed. Furthermore, we implemented self-supervised training strategy to mitigate the impact caused by limited dataset generalization. Experiments conducted on irregularly sampled spatial field ocean and land seismic data demonstrate that our proposed method has better robustness and accuracy than competing approaches. In addition, the efficiency of our method is much higher than that of competing conventional approaches. Thus, our method provides improvements that have important implications for geophysical exploration and the application of transformers in the field of seismic data interpretation.