Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Tunnel seismic tomography method for geological prediction and its application

Typical existing methods of tunnel geological prediction include negative apparent velocity, horizontal seismic profile, and the Tunnel Seismic Prediction （TSP） method as this technology is under development at home and abroad. Considering simpler observational methods and data processing, it is hard to accurately determine the seismic velocity of the wall rock in the front of the tunnel face. Therefore, applying these defective methods may result in inaccurate geological inferences which will not provide sufficient evidence for classifying the wall rock characteristics. This paper proposes the Tunnel Seismic Tomography （TST） method using a spatial observation arrangement and migration and travel time inversion image processing to solve the problem of analyzing the velocity structure of wall rock in the front of the tunnel face and realize accurate imaging of the geological framework of the tunnel wall rock. This method is very appropriate for geological prediction under complex geological conditions.

Quantitative analysis of geological ore-controlling factors and stereoscopic quantitative prediction of concealed ore bodies

To address the issues for assessing and prospecting the replaceable resource of crisis mines, a geological ore-controlling field model and a mineralization distribution field model were proposed from the viewpoint of field analysis. By dint of solving the field models through transferring the continuous models into the discrete ones, the relationship between the geological ore-controlling effect field and the mineralization distribution field was analyzed, and the quantitative and located parameters were extracted for describing the geological factors controlling mineralization enrichment. The method was applied to the 3-dimensional localization and quantitative prediction for concealed ore bodies in the depths and margins of the Daehang mine in Guangxi, China, and the 3-dimensional distribution models of mineralization indexes and ore-controlling factors such as magmatic rocks, strata, faults, lithology and folds were built. With the methods of statistical analysis and the non-linear programming, the quantitative index set of the geological ore-controlling factors was obtained. In addition, the stereoscopic located and quantitative prediction models were set up by exploring the relationship between the mineralization indexes and the geological ore-controlling factors. So far, some concealed ore bodies with the resource volume of a medium-sized mineral deposit are found in the deep parts of the Dachang Mine by means of the deep prospecting drills following the prediction results, from which the effectiveness of the predication models and results is proved.

Applications of Pre-Geological Prediction in Tunnel Construction

Pre-geological prediction (PGP) is defined as the prediction of engineering geologic condition and hy-drogeological condition certain distance ahead of the working face. The purpose of this paper is to introduce mainlygeologic survey before and in excavation, to clarify their emphasis on PGP. At the same time, the technique is appliedto an engineering case, the longest highway tunnel in Gansu province. Data of geological survey of outside tunnels,sound wave detection, and geologic sketch for both tunnel face and sidewalls within the tunnel are analyzed. Afteranalyzing these data, long-term pre-geological prediction forecasting basic geological conditions of fault 4 such aslithology, scope, location, etc., and short-term and more accurate pre-geological prediction are reported.

Application of Integrated Geological Prediction Technologies in Sub-river Tunnel of Liuyang

Because of the frequent serious geo-hazards met in constructing sub-river tunnels,the application of geological prediction is necessary to reduce the risk. Taking the Liuyang River Tunnel(10.1 km,with 362 m of the sub-river part) which is one of the key projects of the dedicated-passenger railway from Wuhan to Guangzhou as an example,the application of integrated geological prediction technologies is expounded in detail.The effects of TSP(tunnel seismic prediction) and infrared water detectors are analyzed as key points in order to summarize the advantages and disadvantages of these devices.The results of this research which can be adopted to improve the effects of the

Analysis and Prediction of Foundation Settlement of High-Rise Buildings under Complex Geological Conditions

Based on an example of a project in Tangshan, the high-rise buildings are built in karst area and mined out affected area which is treated by high pressure grouting, and foundation is adopted the form of pile raft foundation. By long-term measured settlement of high-rise buildings, It is found that foundation settlement is linear increase with the increase of load before the building is roof-sealed, and the settlement increases slowly after the building is roof-sealed, and the curve tends to converge, and the foundation consolidation is completed. The settlement of the foundation is about 80% - 84% of the total settlement before the building is roof-sealed.Three layer BP neural network model is used to predict the settlement in the karst area and mined affected area.Compared with the measured data, the relative difference of the prediction is 0.91% - 2.08% in the karst area, and is 0.95% - 2.11% in mined affected area. The prediction results of high precision can meet the engineering requirements.

Predicting geological hazards during tunnel construction

The complicated geological conditions and geological hazards are challenging problems during tunnel construction,which will cause great losses of life and property.Therefore,reliable prediction of geological defective features,such as faults,karst caves and groundwater,has important practical significances and theoretical values.In this paper,we presented the criteria for detecting typical geological anomalies using the tunnel seismic prediction（TSP） method.The ground penetrating radar（GPR） signal response to water-bearing structures was used for theoretical derivations.And the 3D tomography of the transient electromagnetic method（TEM） was used to develop an equivalent conductance method.Based on the improvement of a single prediction technique,we developed a technical system for reliable prediction of geological defective features by analyzing the advantages and disadvantages of all prediction methods.The procedure of the application of this system was introduced in detail.For prediction,the selection of prediction methods is an important and challenging work.The analytic hierarchy process（AHP） was developed for prediction optimization.We applied the newly developed prediction system to several important projects in China,including Hurongxi highway,Jinping II hydropower station,and Kiaochow Bay subsea tunnel.The case studies show that the geological defective features can be successfully detected with good precision and efficiency,and the prediction system is proved to be an effective means to minimize the risks of geological hazards during tunnel construction.

Forming Condition and Geology Prediction Techniques of Deep Clastic Reservoirs

1 Introduction As new exploration domain for oil and gas,reservoirs with low porosity and low permeability have become a hotspot in recent years(Li Daopin,1997).With the improvement of technology,low porosity and low

Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction.However,regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics,especially at tunnel portals with complex geological conditions.This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis,with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals.The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages.To improve the accuracy of the models,a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation.Finally,a regional-scale model and an engineering-scale model are built,providing a clear insight into geological phenomena and supporting numerical calculation.In addition,the proposed framework is applied to a case study,the Long-tou mountain tunnel project in Guangzhou,China,where the dominant rock type is granite.The results show that the data integration and modeling methods effectively improve model structure refinement.The improved model’s calculation deviation is reduced by about 10%to 20%in the mechanical analysis.This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling.

RS and GIS-based Statistical Analysis of Secondary Geological Disasters after the 2008 Wenchuan Earthquake

Using RS and GIS means, this article analyzes the general geological characteristics and the structural belt distribution features in Wenchuan County, Sichuan province, P.R. China as well as the characteristics of the large-scale landslides, mud-rock flows, earthquake lakes, etc., after the earthquake on May 12, 2008. Based on the above work, comprehensive indoor and outdoor research is launched on disaster distribution characteristics and their relationship with earthquakes, terrains, strata, lithology, and structures. Weights of evidence method is utilized to quantitatively analyze and evaluate the spatial distribution of secondary geological disasters after the earthquake occurred. 3 remedying grades for secondary geological disasters are derived from the results of the weights of Evidence, followed by suggestions given to remedy earthquake secondary disasters.

Forecast of Geological Gas Hazards for “Three-Soft” Coal Seams in Gliding Structural Areas

Gas outbursts from "three-soft" coal seams (soft roof,soft floor and soft coal) constitute a very serious prob-lem in the Ludian gliding structure area in western Henan. By means of theories and methods of gas geology,structural geology,coal petrology and rock tests,we have discussed the effect of control of several physical properties of soft roof on gas preservation and proposed a new method of forecasting gas geological hazards under open structural conditions. The result shows that the areas with type Ⅲ or Ⅳ soft roofs are the most dangerous areas where gas outburst most likely can take place. Therefore,countermeasures should be taken in these areas to prevent gas outbursts.

Geological Model of Member 3 of Shahejie Formation Reservoir in Liuzan Oilfield, Eastern Hebei Province

A new geological model of Member 3 of Shahejie Formation reservoir in the Liuzan Oilfield, eastern Hebei Province was constructed by using modem reservoir modeling technology as sequence stratigraphy and conditional simulation combined with traditional geological analysis. The model consists of a stratigraphic framework model, a structural model, a sedimentary model and a reservoir model. The study shows that the reservoir is a relatively integrated nose structure, whose strata can be divided into 4 sets of parasequence, 12 parasequences. The submerged branched channel of fan delta front is the favorable microfacies, which controls the geometric shape and physical properties of reservoir sandstone. Oil is distributed in premium reservoir sandstones at structural high positions. According to the new geological model, not only the geological contradictions appearing during oil field development are resolved, but also the oil-bearing area is enlarged by 2.7km^2 and geological reserves increased by 156.9 million tons. The production capacity of the Liuzan Oilfield is increased by 0.27 million tons per year.

Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling process.In this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield machine.Firstly,we select 30 important parameters according to statistical analysis method and the working principle of the shield machine.Then,we delete the non-working sample data,and combine 10 consecutive data as the input of the model.Thereafter,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature extractor.The performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,China.The results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature extractor.Therefore,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.

Expert System for Statistical Prediction of Mineral Deposits on Middle and Large Scales

The expert system for statistical prediction of mineral deposits on middle and large scales takes the system of scientific exploration theories, criteria and methods proposed by Professor Zhao Pengda as the field expert knowledge. At present the developed system focuses on two aspects: synthetic exploration and quantitative exploration. Among the three basic theories for the prediction of deposits, it highlights the applications of seeking anomaly theory. This system is characteristic in the determination of geological background, the study of geological anomalies and the delineation of geological background, the study of geological anomalies and the delineation of mineralization anomalies. The system combines closely the knowledge base, method base and database .integrates the input and output information of multi - sources and mul-ti - variables , data , graphs and imagine processing system and inquiring system as a whole . So the system can meet in general all kinds of demands in statistical prediction of mineral deposits . Since the statistical prediction of mineral resources is a kind of systematic engineering pro ject , a further study should be carried out on the fields of theoretical exploration and ster eo - exploration on the basis of unceasingly perfecting the above-mentioned fields in order to establish a comprehensive intelligent system for scientific exploration , to provide new methods , new techniques and new ideas for fast prospecting appraisal of mineral resources .

Prediction of the geological indicators in TBM tunnel based on optimized proportion of surrounding rock grades

With tunnel boring machine being used in underground engineering,accurate geological indicators have been the important basis for tunnel boring machine(TBM)construction.Back propagation neural network(BPNN)has been used to predict the geological indicators of tunnels in previous studies.Nevertheless,these studies ignored the imbalance proportion of surrounding rock grades,leading to the indiscriminate use of data,thus affecting the predictive effect of BPNN.In order to prove the importance of the proportion of surround-ing rock grade in geological prediction,we mainly attempt to utilize particle swarm optimization(PSO)to optimize the proportion of sample data,and integrate with BPNN to establish a PSO-BPNN theoretical model to predict geological indicators.At the same time,combined with the actual engineering data,5 tunneling indicators were selected as input and 4 geological indicators were selected as out-put by a variety of dimensionality reduction methods.The geological indicators are density,uniaxial compressive strength,internal fric-tion angle(u)and Poisson’s ratio(e).On this basis,the PSO-BPNN prediction model was established in detail.By comparing the prediction of traditional BPNN,PSO-BPNN and other optimization-integrated models,the result shows that optimized proportion of surrounding rock grades reduces the prediction error and improves the interpretability of the prediction model.Meanwhile,we com-bined the theory of surrounding rock partition to illustrate the rationality of surrounding rock proportion in PSO result,that is,the proportion of complex surrounding rock should be increased appropriately to improve the prediction result.Ultimately,based on the optimization-integrated models with engineering data and the surrounding rock classification theory,the importance of proportion of surrounding rock grades for tunnel geological prediction is confirmed.

Prediction of Favorable Gas Accumulation Zones in the Western Part of Southern Margin in Junggar Basin

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Interlayer Prediction Method for Braid River Reservoirs in Offshore Oil Field

The interlayer structure of braid river reservoirs is complex and the interwell prediction is difficult in offshore oil field. Taking CFD11-1 oilfield of NgⅢ sand as an example, based on layer contrasting, the interlayer is divided into muddy interlayer, clay boulder interlayer, and physical interlayer according to lithology. Under the guidance of sedimentary model, we use the geology statistical inversion method to predict the clay boulder interlayer, consistent with the dynamic characteristics of oil production which is used for the prior quality control. The results of this study can objectively reveal the characteristics of interlayer space distribution. Compared with the traditional multi-well comparison and stochastic simulation model, this method is applied to the offshore oil field which is character with wide well space, sparse well network, which has very high application value in predicting the interlayer and deploying of inter-well encryption in the similar oilfield.

The Validity of the Thermohydrogravidynamic Theory Concerning the Predicted Dates of the Maximal Temporal Intensifications of the Global Seismotectonic Processes of the Earth during the Range 2020 - 2023 AD

We present (on the 13th International Conference on Geology and Geophysics) the convincing evidence that the strongest earthquakes (according to the U.S. Geological Survey) of the Earth (during the range 2020 - 2023 AD) occurred near the predicted (calculated in advance based on the global prediction thermohydrogravidynamic principles determining the maximal temporal intensifications of the global seismotectonic, volcanic, climatic and magnetic processes of the Earth) dates 2020.016666667 AD (Simonenko, 2020), 2021.1 AD (Simonenko, 2019, 2020), 2022.18333333 AD (Simonenko, 2021), 2023.26666666 AD (Simonenko, 2022) and 2020.55 AD, 2021.65 AD (Simonenko, 2019, 2021), 2022.716666666 AD (Simonenko, 2022), respectively, corresponding to the local maximal and to the local minimal, respectively, combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth. We present the short-term thermohydrogravidynamic technology (based on the generalized differential formulation of the first law of thermodynamics and the first global prediction thermohydrogravidynamic principle) for evaluation of the maximal magnitude of the strongest (during the March, 2023 AD) earthquake of the Earth occurred on March 16, 2023 AD (according to the U.S. Geological Survey). .

基于地质三维建模的矿床蚀变带深部资源预测方法

随着矿产资源需求的持续增加,加之浅表矿产资源的逐渐匮乏,如何有效开发深部矿产资源成为国家重点研究课题之一。以往二维矿床信息无法确定精准的深部资源储量,阻碍矿产资源的开发进度,本文提出基于地质三维建模的矿床蚀变带深部资源预测方法研究。应用地质三维建模软件(GOCAD)与计算机技术,构建矿床蚀变带深部地质三维模型,以此为基础,确定最佳单元尺度,划分矿床蚀变带深部地质统计单元,提取矿床蚀变带深部地质变量,计算地质变量权重数值,探究深部地质统计单元之间的联系度,基于三维证据权法预测矿床蚀变带深部资源。实验数据显示:应用该方法获得的矿床蚀变带深部资源预测误差最小值为1.01%,充分证实了该方法的深部资源预测精度更高。