Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions,employing a deep learning classification algorithm for speech signal analysis.In this study,speech samples are categorized for both training and testing purposes based on their geographical origin.Category 1 comprises speech samples from speakers outside of India,whereas Category 2 comprises live-recorded speech samples from Indian speakers.Testing speech samples are likewise classified into four distinct sets,taking into consideration both geographical origin and the language spoken by the speakers.Significantly,the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas.Indian speakers,utilizing 52 Hindi and 26 English phonemes in their speech,demonstrate a notably higher gender identification accuracy of 85.75%compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers.The gender identification accuracy of the proposed model reaches 83.20%when the system is trained using speech samples from speakers outside of India.In the analysis of speech signals,Mel Frequency Cepstral Coefficients(MFCCs)serve as relevant features for the speech data.The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory(BiLSTM)architecture within a Recurrent Neural Network(RNN)model.

Deep Structural Framework and Genetic Analysis of Gold Concentration Areas in the Northwestern Jiaodong Peninsula, China： A New Understanding based on High-Resolution Reflective Seismic Survey

The gold concentration areas in the northwestern Jiaodong Peninsula constituted an important gold metallogenetic region in Eastern China during the Mesozoic. The deep geological bodies＇ texture characteristic is important for exploring the resources thoroughly and understanding the metallogenic process. The detailed textures were revealed using high-resolution seismic profiles through the three major ore-controlling structures-Sanshandao fault zone, Jiaojia fault zone and Zhaoping fault zone. This study aims to establish a deep structural framework of this area. Based on their formation mechanism, the fault structures developed in the area can be divided into regional and local fault structures. The structural styles are characterised by superimposing their compressional, strike-slip and extensional multi-stage activities. The crust is cut by vertical structures corresponding to a left-lateral strike-slip fault system on the surface. Nearby these structures are the arc-shape structures formed by multi-stage magma intrusions into the upper crust. Bounded by the Tancheng–Lujiang and Muping–Jimo fault zones, the current Jiaodong block, developed a series of NE-trending strike-slip fault systems, was probably formed by the assemblage of several obliquely aligned blocks. The intensive magmatism and hydrothermal activity between the blocks induced large-scale mineralisation. It provides a new angle of view for understanding the cratonic destruction and large ore-concentration formed during the Mesozoic.

New perspective of Miocene marine hydrocarbon source rocks in deep-water area in Qiongdongnan Basin of northern South China Sea

Drilling wells reveal that the organic matter abundance of Miocene marine source rocks in shallow water area of the Qiongdongnan Basin is relatively low with poor hydrocarbon generation poten- tial. However, in some drilling wells of deep water area close to the central depression belt, Miocene marine source rocks with better organic matter abundance and hydrocarbon generation have been found, which have achieved better source rock standard based on the analysis of geochemical charac- teristics. Although there are no exploratory wells in deep water area of the research region, through the comparative analysis of geochemical data of several typical exploratory wells respectively from shallow water area in the basin, central depression belt margin in deep-water area of the basin and Site 1148 of deep sea drilling in the South China Sea Basin, it reveals that the tendency of the quality of source rocks becomes positive gradually from delta to bathyal environment, which then becomes negative as in deep oceanic environment. Owing to the lack of terrestrial organic matter input, the important controlling factors of Miocene marine source rocks in the Qiongdongnan Basin are ocean productivity and preservation conditions of organic matter. The element geochemistry data indicate that the tendency of the paleoproductivity and the preservation conditions of organic matter become positive as water depth increase from shallow area to bathyal area close to central depression belt. So it is speculated that there must exist high quality source rocks in the central depression area where the preservation conditions of organic matter are much better. Besides, in theory, in oxygen-poor zone of oceanic environment at the water depth 400–1 000 m, the preservation conditions of organic matter are well thus forming high-quality marine source rocks. The result- ing speculation, it is reasonable to consider that there are high hydrocarbon generation potential source rocks in bathyal environment of the Qiongdongnan Basin, especially at the water depth 400– 1 000 m.

Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

The ever-changing battlefield environment requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned combat aerial vehicles(UCAVs) aim to integrate such advanced technologies while increasing the tactical capabilities of combat aircraft. As a research object, common UCAV uses the neural network fitting strategy to obtain values of attack areas. However, this simple strategy cannot cope with complex environmental changes and autonomously optimize decision-making problems. To solve the problem, this paper proposes a new deep deterministic policy gradient(DDPG) strategy based on deep reinforcement learning for the attack area fitting of UCAVs in the future battlefield. Simulation results show that the autonomy and environmental adaptability of UCAVs in the future battlefield will be improved based on the new DDPG algorithm and the training process converges quickly. We can obtain the optimal values of attack areas in real time during the whole flight with the well-trained deep network.

Chemical kinetics evaluation and its application of natural gas generation derived from the Yacheng Formation in the deep-water area of the Qiongdongnan Basin,China

The natural gas generation process is simulated by heating source rocks of the Yacheng Formation, including the onshore-offshore mudstone and coal with kerogens of Type II2-III in the Qiongdongnan Basin. The aim is to quantify the natural gas generation from the Yacheng Formation and to evaluate the geological prediction and kinetic parameters using an optimization procedure based on the basin modeling of the shallow-water area. For this, the hydrocarbons produced have been grouped into four classes（C1, C2, C3 and C4-6）. The results show that the onset temperature of methane generation is predicted to occur at 110℃ during the thermal history of sediments since 5.3 Ma by using data extrapolation. The hydrocarbon potential for ethane, propane and heavy gaseous hydrocarbons（C4-6） is found to be almost exhausted at geological temperature of 200℃ when the transformation ratio（TR） is over 0.8, but for which methane is determined to be about 0.5 in the shallow-water area. In contrast, the end temperature of the methane generation in the deep-water area was over 300℃ with a TR over 0.8. It plays an important role in the natural gas exploration of the deep-water basin and other basins in the broad ocean areas of China. Therefore, the natural gas exploration for the deep-water area in the Qiongdongnan Basin shall first aim at the structural traps in the Ledong, Lingshui and Beijiao sags, and in the forward direction of the structure around the sags, and then gradually develop toward the non-structural trap in the deep-water area basin of the broad ocean areas of China.

Basic Features of the Crustal Structure in the Lower Yangtze and Its Neighboring Area in the Chinese Mainland： Review of Deep Seismic Sounding Research

The Deep Seismic Sounding( DSS) projects carried out from the 1970 s in the lower Yangtze region and its neighboring area were reviewed in this paper,then the basic wave group features of those wide angle reflection / refraction record sections,and of the crustal structure are summarized. It shows that there were in total five clear wave groups on the record sections,which include the first arrival Pg,the reflection P1 from the bottom interface of the upper crust,the reflection P3 from the bottom interface of the middle crust,the strong reflection Pm from the Moho boundary,and the refraction Pn from uppermost mantle. In general,these phases are easily consistently traced and compared,despite some first arrivals being delayed or arriving earlier than normal due to the shallow sedimentary cover or bedrocks. In particular,in the Dabie Mountain region the seismic events of a few gathered shots always have weak reflection energy,are twisted,or exhibit disorganized waveforms, which could be attributed to the disruption variations of reflection depth,the broken Moho,and the discontinuity of the reflection boundary within crust. The regional crustal structures are composed of the upper,middle and lower crust,of which the middle and lower layers can be divided into two weak reflection ones. The crustal thickness of the North China and Yangtze platform are 30km- 36 km,and the Moho exhibits a flat geometry despite some local uplifts. The average pressure velocity in lower crust beneath this two tectonic area is 6. 7 ± 0. 3km / s. Nevertheless,beneath the Dabieshan area the crustal thickness is 32km- 41 km,the Moho bends down sharply andtakes an abrupt 4km- 7km dislocation in the vertical direction. The average pressure velocity in the lower crust beneath the Dabieshan area is 6. 8 ± 0. 2km / s.

Gravity inversion of deep-crust and mantle interfaces in the Three Gorges area

To better understand the heterogeneity of deep-crust and mantle interfaces in the region of the Three Gorges, China, we used the Parker-Oldenburg iterative inversion method to invert existing Bouguer gravity data from the Three Gorges area （ 1 ： 500000） , a new gravity map of the Three Gorges Dam （ 1 ： 200000） , and the results of deep seismic soundings. The inversion results show a Moho depth of 42 km be- tween Badong and Zigui and the depth of the B2 lower-crustal interface beneath the Jianghan Plain and sur- rounding areas at 21 -25 km. The morphology of crustal interfaces and the surface geology present an over- pass structure. The mid-crust beneath the Three Gorges Dam is approximately 9 km thick, which is the thin- nest in the Three Gorges area and may be related to the shallow low-density body near the Huangling anti- cline. The upper crust is seismogenic, and there is a close relationship between seismicity and the deep- crust and mantle interfaces. For example, the MS. 1 Zigui earthquake occurred where the gradients of the Moho and the B2 interface are the steepest, showing that deep structure has a very important effect on re- gional seismicity.

Crustal Electrical Structure and Deep Metallogenic Potential in Northern Wuyi Area(South China),based on Magnetotelluric Data

The northern Wuyi area,which is located in the northern Wuyi metallogenic belt,has superior mineralization conditions.The Pingxiang-Guangfeng-Jiangshan-Shaoxing fault(PSF)extends across the whole region regardless of whether or how the PSF relates to the near-surface mineralization.We carried out an MT survey in the region and obtained a reliable 2D model of the crustal electrical structure to a depth of 30 km.In the resistivity model,we inferred that a continuous high conductivity belt that ranges from the shallow to deep crust is a part of the PSF.Then,we estimated the fluid content and pressure gradient to identify the deep sources of fluid as well as its pattern of motion pattern.Finally,we proposed a model for the deep metallogenic migration processes that combines geological data,fluid content data,pressure gradient data,and the subsurface resistivity model.The model analysis showed that the Jiangnan orogenic belt and the Cathaysia block formed the PSF during the process of com.The deep fluid migrated upward through the PSF to the shallow crust.Therefore,we believe that the PSF is an ore-forming fluid migration channel and that it laid the material basis for large-scale mineralization in the shallow crust.

Predicting the 25th solar cycle using deep learning methods based on sunspot area data

It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.

The Relationship between Tectonic Subsidence and BSR of Upper Neogene in the Deep-Water Area of the Northern Continental Slope, South China Sea

BSR （Bottom Simulating Reflector） occurs widely in the strata since the late Miocene in the deep-water area of the northern continental slope of South China Sea （SCS）. It is an important seismic reference mark which identifies the gas hydrate and its distribution influenced by the tectonic movements. Single-point basin modeling was conducted using 473 points in the study area. To discuss the relationships between the tectonic subsidence and BSR, the volume and rate of tectonic subsidence in each geological time have been simulated. The results show that there are three tectonic accelerate subsidence processes in the study area since the late Miocene, especially since 1.8Ma the tectonic subsidence accelerates more apparently. Since the Late Miocene to Pleistocene, the rate of tectonic subsidence in deep-water underwent a transformation from weak to strong. The ratio of tectonic subsidence to the total subsidence was relatively high （65-70%）. Through the superposition of the BSR developed areas and the contours of tectonic subsidence in this area, it was discovered that more than 80% of BSR tend to be distributed at the slope break or depression-uplift structural transfer zone and the average tectonic subsidence rate ranges from 70 m/Ma to 125 m/Ma.

Movement characteristics of Karst water in a deep mining area

In order to study the movement characteristics of groundwater in a deep mining area and solve the dispute of the distri- bution rule of hydro-chemical zoning which is contradicted by lixiviation water zoning in a horizontal direction, we directed our attention to the source of deep groundwater, its seepage and hydro-chemical characteristics in a typical mining area. We used a neotectonic water-control theory, chemical and isotope methods, as well as a method for analyzing dynamic groundwater conditions. The results indicate that 1) Karst water in the deep and medium parts of this mining area is recharged by vertical leakage through neotectonic fractures rather than seepage along strata from subcrop parts or surrounding flows; 2) from surface to deep leakage paths, the variation in the types of chemical groundwater agrees with the normal lixiviation water distribution rule and the age of mixed groundwater increases; 3) the water-rich zones along neotectonic fractures correspond with water-diluted zones in a hori-zontal direction; 4) the leakage coefficient and water capacity of aquifers increases during the flow process of Karst water along the antidip direction (from west to east) and 5) Karst water in shallow mining areas forms a strong runoff belt along strikes and quickly dilutes the water from deep and medium mining areas. Overall, chemical and dynamic water characteristics actually agree with in terms of the entire consideration for differences in vertical leakage and abnormalities in the zone of water chemical distribution, along a horizontal runoff direction.

Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model—A Case Study of Chennai Metropolitan Area, Tamil Nadu, India

Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the present scenario. Neural Network based Cellular Automata models have proved to predict the urban growth more close to reality. Recently, deep learning based techniques are being used for the prediction of urban growth. In this current study, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model and Deep belief based Cellular Automata (DB-CA) model using 2010 and 2013 urban maps. Since the study area experienced congested type of urban growth, “Existing Built-Up” of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model proved to be the better model, as it predicted 524.14 km2 of the study area as urban with higher accuracy (kappa co-efficient: 0.73) when compared to NN-CA model which predicted only 502.42 km2 as urban (kappa co-efficient: 0.71), while the observed urban cover of CMA in 2017 was 572.11 km2. This study also aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type of the urban growth, the study area was divided into five distance based zones with the State Secretariat as the center and entropy values were calculated for the zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporation boundary follow dispersed type of urban growth in 2017.

Deep-derived enclaves (belonging to middle-lower crust metamorphic rocks) in the Liuhe-Xiangduo area, eastern Tibet: Evidence from petrogeochemistry

Petrological and geochemical studies of deep-derived enclaves from the Liuhe-Xiangduo area, eastern Tibet, showed that the enclaves involve five types of rocks, i.e., garnet diopsidite, garnet amphibolite, garnet hornblendite, amphibolite and hornblendite, whose main mineral assemblages are Grt+Di+Hbl, Grt+Pl+Hbl+Di, Grt+Hbl+Pl, Pl+Hbl, and Hbl+Bt, respectively. The enclaves exhibit typical crystalloblastic texture, and growth zones are well developed in garnet (Grt) in the enclaves. In view of major element geochemistry, the deep|derived enclaves are characterized by high MgO and FeO+*, ranging from {12.00%} to {12.30%} and {8.15%} to {10.94%}, respectively. The protolith restoration of metamorphic rocks revealed that the enclaves belong to ortho-metamorphic rocks. The REE abundances vary over a wide range, and ∑REE ranges from {53.39} to {129.04} μg/g. The REE patterns slightly incline toward the HREE side with weak LREE enrichment. The contents of Rb, Sr, and Ba range from {8.34} to 101μg/g, 165 to 1485 μg/g, and 105 to 721 μg/g, respectively. The primitive mantle-normalized spider diagrams of trace elements show obvious negative Nb, Ta, Zr and Hf anomalies. Sr-Nd isotopic compositions of the enclaves indicated that the potential source of deep-derived enclaves is similar to the depleted|mantle, and their {({}+{87}Sr/+{86}Sr)-i} ratios vary from {0.706314} to {0.707198}, {({}+{147}Nd/+{144}Nd)-i} ratios from {0.512947} to {0.513046}, and {ε-{Nd}(T)} values from {+7.0} to {+9.0}, respectively. The potential source of the enclaves is obviously different from the EM2-type mantle from which high-K igneous rocks stemmed (the host rocks), i.e., there is no direct genetic relationship between the enclaves and the host rocks. Deep-derived enclaves in the host rocks belong to mafic xenoliths, and those in the Liuhe-Xiangduo area, eastern Tibet, are some middle-lower crust ortho-metamorphic rocks which were accidentally captured at 20-50 km level by rapidly entrained high-temperature high-K magma, whose source is considered to be located at 50-km depth or so.

Petroleum geology controlled by extensive detachment thinning of continental margin crust: A case study of Baiyun sag in the deep-water area of northern South China Sea

The relationships between crustal stretching and thinning,basin structure and petroleum geology in Baiyun deep-water area were analyzed using large area 3D seismic,gravity,magnetic,ocean bottom seismic(OBS),deep-water exploration wells and integrated ocean drilling program(IODP).During the early syn-rifting period,deep-water area was a half-graben controlled by high angle faults influenced by the brittle extension of upper crust.In the mid syn-rifting period,this area was a broad-deep fault depression controlled by detachment faults undergone brittle-ductile deformation and differentiated extension in the crust.In the late syn-rifting period,this area experienced fault-sag transition due to saucer-shaped rheology change dominated by crustal ductile deformation.A broad-deep fault depression controlled by the large detachment faults penetrating through the crust is an important feature of deep-water basin.The study suggests that the broad-deep Baiyun sag provides great accommodation space for the development of massive deltaic-lacustrine deposition system and hydrocarbon source rocks.The differentiated lithospheric thinning also resulted in the different thermal subsidence during post-rifting period,and then controlled the development of continental shelf break and deep-water reservoir sedimentary environment.The high heat flow background caused by the strong thinning of lithosphere and the rise of mantle source resulted in particularities in the reservoir diagenesis,hydrocarbon generation process and accumulation of deep-water area in northern South China Sea.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

Delineation of Prospecting Prospect Area Based on Maximum Entropy Model

Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was established by using the exploration data of the deposit and related regions in this area, so as to determine the prospecting prospect area in the study area. In this paper, the Jacknife analysis module of maximum entropy model is used to quantitatively rank the importance of 39 geochemical element variables, and finally obtain the prospecting prospect map of the study area. The research results show that the Dapingzhang mining area has the potential to find hidden ore in the deep and surrounding areas, and the northern and southern ends and western sides of the rock ore control structural belt in the eastern region of the mining area have good prospecting prospects. The research results provide an important basis for the deployment of follow-up exploration work in the study area, and the maximum entropy model has a good application effect in mineral resources exploration.

Research progress in the efficacy of deep brain stimulation with different targets in Parkinson's disease

Parkinson's disease(PD)is a chronic progressive neurodegenerative disease.Deep brain stimulation(DBS)is an effective treatment for patients with advanced PD.There are many DBS targets for PD,including subthalamic nucleus(STN),globus pallidus(GPi),meso-ventral thalamic nucleus(VIM),pontine peduncle nucleus(PPN),posterior subthalamic region(PSA)and zonation of undetermined zone(ZI).This paper summarizes the efficacy of each target in the treatment of PD with DBS,not only makes a systematic analysis and comparison of motor symptoms,but also makes a detailed description of the efficacy of non-motor symptoms,so as to provide a personalized treatment basis for PD patients to select appropriate target targets in DBS.

Deep geothermal resources in China:Potential,distribution,exploitation,and utilization

Deep geothermal resources mainly refer to the thermal energy stored in subsurface rocks and fluids therein at a depth of 3-10 km,which is a kind of renewable and sustainable clean energy unaffected by weather and seasonal changes.Large scale exploitation of the deep geothermal resources is of great significance to ensuring national energy security and achieving the“Carbon Peak and Carbon Neutrality”.Based on the latest terrestrial heat flow data,this paper estimated the potential of deep geothermal resources in the terrestrial areas of China,and the results show that the total amount of geothermal resources within 3e10 km under the Earth's surface in the terrestrial areas of China is 24.6×10^(15)GJ.In line with climate zones categorized,the geothermal resource proportion is 43.81%for severe cold regions,29.19%for cold regions,6.92%for mild regions,13.82%for hot summer and cold winter regions,and 6.26%for hot summer and warm winter regions.Statistics according to the burial depth range reveal that the resources within depth ranges of 3-5 km,5-7 km and 7-10 km under the Earth's surface are 4.3119×10^(15)GJ,6.37674×10^(15)GJ and 13.89594×10^(15)GJ respectively,showing an increasing trend of geothermal potential with increasing burial depth.The deep geothermal resources are mainly of medium-to-high temperature reserves,and the energy supply strategy can be optimized by combining the climate conditions and population distribution,as well as considering power generation.In regions of cold or severe cold climate,the geothermal resources may be applied to geothermal power generation and district heating in combination;in regions of hot summer and cold winter or mild climates,the resources can be used for geothermal power generation combined with cooling and heating;in regions of hot summer and warm winter climates,the resources may be applied to geothermal power generation combined with cooling and industrial and agricultural utilization.Exploitation of deep geothermal resources also can be combined with carbon dioxide sequestration,multi-mineral resources extraction and energy storage to realize comprehensive exploitation and utilization of various energy resources.It is suggested that theoretical technology research should be combined with pilot tests and field demonstrations,and large-scale economic exploitation of deep geothermal resources should be arranged in a coordinated manner,following the principles of overall planning and step-by-step implementation.

极端环境隧道建造面临的主要问题及发展趋势

聚焦艰险山区、深水海域与城市敏感区3类极端环境,全面梳理和总结了在此环境下隧道建造面临的主要问题、相关技术突破和未来发展趋势。针对艰险山区中极高地应力、高地温、高海拔以及活动断裂带等极端条件,总结分析围岩和隧道的变形机制、破坏机制及防护措施,并指出未来可通过大数据、机器学习等手段建立更精准的预测模型,以实现隧道建造过程的实时监控和风险评估;针对深水海域中高水压、高烈度地震、强侵蚀环境等极端条件,分析多因素耦合下沉管法及盾构法隧道管片及接头的劣损和破坏机制,总结提升其力学和耐久性能的主要技术措施,并提出未来仍需针对海底隧道管片接头的防水、抗震及抗侵蚀性能开展更深入的研究,以形成完整的理论和技术体系;针对城市敏感区隧道穿越既有隧道、敏感建构筑物、地下障碍物等挑战,总结分析盾构法和顶管法施工环境响应规律、地层变形评估方法和控制技术措施,提出未来可采用机器学习等技术辅助隧道建设,以提升预测和控制的准确性,减小建设过程对城市环境的扰动。目前,相关研究成果为极端环境下的隧道工程建设提供了重要支撑,但部分技术仍需进一步在实践中完善并形成规范,以指导后续的工程建设;而随着新一代信息技术的发展,未来隧道建造必将朝着数字化、智能化、自动化的方向发展,从而保障极端环境下隧道建设的安全、绿色、高效。