Study on Ecological Change Remote Sensing Monitoring Method Based on Elman Dynamic Recurrent Neural Network

In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.

Geology and mineralization of the Duobaoshan supergiant porphyry Cu-Au-Mo-Ag deposit(2.36 Mt)in Heilongjiang Province,China:A review

The reserves of the Duobaoshan porphyry Cu-Au-Mo-Ag deposit(also referred to as the Duobaoshan porphyry Cu deposit)ranks first among the copper deposits in China and 33rd among the porphyry copper deposits in the world.It has proven resources of copper(Cu),molybdenum(Mo),gold(Au),and silver(Ag)of 2.28×10^(6)t,80×10^(3)t,73 t,and 1046 t,respectively.The major characteristics of the Duobaoshan porphyry Cu deposit are as follows.It is located in a zone sandwiched by the Siberian,North China,and paleo-Pacific plates in an island arc tectonic setting and was formed by the Paleozoic mineralization and the Mesozoic mineralization induced by superposition and transformation.The metallogenic porphyries are the Middle Hercynian granodiorite porphyries.The alterations of surrounding rocks are distributed in a ring form.With silicified porphyries at the center,the alteration zones of K-feldspar,biotite,sericite,and propylite occur from inside to outside.This deposit is composed of 215 ore bodies(including 14 major ore bodies)in four mineralized zones.Ore body No.X in the No.3 mineralized zone has the largest resource reserves,accounting for more than 78%of the total reserves of the deposit.Major ore components include Cu,Mo,Au,Ag,Se,and Ga,which have an average content of 0.46%,0.015%,0.16 g/t,1.22 g/t,0.0003%,and 0.001%-0.003%,respectively.The ore minerals of this deposit primarily include pyrite,chalcopyrite,bornite,and molybdenite,followed by magnetite,hematite,rutile,gelenite,and sphalerite.The ore-forming fluids of this deposit were magmatic water in the early metallogenic stage and then the mixture of meteoric water and magmatic water at the late metallogenic stage.The ore-forming fluids experienced three stages.The ore-forming fluids of stageⅠhad a hydrochemical type of H_(2)O-CO_(2)-Na Cl,an ore-forming temperature of 375-650℃,and ore-forming pressure of 110-160 MPa.The ore-forming fluids of stageⅡhad a hydrochemical type of H_(2)O-CO_(2)-Na Cl,an ore-forming temperature of 310-350℃,and ore-forming pressure of 58-80 MPa.The ore-forming fluids of stageⅢhad a hydrochemical type of Na Cl-H_(2)O,an ore-forming temperature of 210-290℃,and ore-forming pressure of 5-12 MPa.The CuAu-Mo-Ag mineralization mainly occurred at stagesⅠandⅡ,with the ore-forming materials having a mixed crust-mantle source.The Duobaoshan porphyry Cu deposit was formed in the initial subduction environment of the Paleo-Asian Ocean Plate during the Early Ordovician.Then,due to the closure of the Mongol-Okhotsk Ocean and the subduction and compression of the Paleo-Pacific Ocean,a composite orogenic metallogenic model of the deposit was formed.In other words,it is a porphyry-epithermal copper-gold polymetallic mineralization system of composite orogeny consisting of Paleozoic island arcs and Mesozoic orogeny and extension.

Geology and mineralization of the Daheishan supergiant porphyry molybdenum deposit(1.65 Bt),Jilin,China:A review

The Daheishan supergiant porphyry molybdenum deposit(also referred to as the Daheishan deposit)is the second largest molybdenum deposit in Asia and ranks fifth among the top seven molybdenum deposits globally with total molybdenum reserves of 1.65 billion tons,an average molybdenum ore grade of 0.081%,and molybdenum resources of 1.09 million tons.The main ore body is housed in the granodiorite porphyry plutons and their surrounding inequigranular granodiorite plutons,with high-grade ores largely located in the ore-bearing granodiorite porphyries in the middle-upper part of the porphyry plutons.Specifically,it appears as an ore pipe with a large upper part and a small lower part,measuring about 1700 m in length and width,extending for about 500 m vertically,and covering an area of 2.3 km^(2).Mineralogically,the main ore body consists of molybdenite,chalcopyrite,and sphalerite horizontally from its center outward and exhibits molybdenite,azurite,and pyrite vertically from top to bottom.The primary ore minerals include pyrite and molybdenite,and the secondary ore minerals include sphalerite,chalcopyrite,tetrahedrite,and scheelite,with average grades of molybdenum,copper,sulfur,gallium,and rhenium being 0.081%,0.033%,1.67%,0.001%,and 0.0012%,respectively.The ore-forming fluids of the Daheishan deposit originated as the CO_(2)-H_(2)O-NaCl multiphase magmatic fluid system,rich in CO_(2)and bearing minor amounts of CH4,N2,and H2S,and later mixed with meteoric precipitation.In various mineralization stages,the ore-forming fluids had homogenization temperatures of>420℃‒400℃,360℃‒350℃,340℃‒230℃,220℃‒210℃,and 180℃‒160℃and salinities of>41.05%‒9.8%NaCleqv,38.16%‒4.48%NaCleqv,35.78%‒4.49%NaCleqv,7.43%NaCleqv,and 7.8%‒9.5%NaCleqv,respectively.The mineralization of the Daheishan deposit occurred at 186‒167 Ma.The granites closely related to the mineralization include granodiorites(granodiorite porphyries)and monzogranites(monzogranite porphyries),which were mineralized after magmatic evolution(189‒167 Ma).Moreover,these mineralization-related granites exhibit low initial strontium content and high initial neodymium content,indicating that these granites underwent crust-mantle mixing.The Daheishan deposit formed during the Early-Middle Jurassic,during which basaltic magma underplating induced the lower-crust melting,leading to the formation of magma chambers.After the fractional crystallization of magmas,ore-bearing fluids formed.As the temperature and pressure decreased,the ore-bearing fluids boiled drops while ascending,leading to massive unloading of metal elements.Consequently,brecciated and veinlet-disseminated ore bodies formed.

Remote Sensing Landslide Monitoring Based on Machine Learning Method

The susceptibility evaluation of landslides has become one of the key environmental issues that people are concerned about. This study took the land-slides in Xishuangbanna, Yunnan Province as the study object, and selected 10 evaluation factors such as digital elevation model (DEM), slope aspect, precipitation, land use, water system, roads, population density, lithology, faults, and NDVI. Different machine learning methods were compared and studied, and the ROC (receiver operating characteristics) curve verification revealed that the accuracy of the random forest evaluation model was high. In the prediction and evaluation of the susceptibility of landslides, five risk levels were divided. After the superimposed analysis, 87.26% of the disaster points fell in the first and second susceptibility areas. The spot analysis found that the distribution of hot spots is consistent with the distribution of disaster spots. In a word, the results of this study can provide better technical support for the evaluation and early warning of landslides in Southwest China.

Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

Research on Surface Information Extraction Based on Deep Learning and Transfer Learning

The land cover types in South China are varied, and the terrain is undulating, and the area of different land types is small, and the remote sensing monitoring work was difficult. In order to solve these problems, an automatic classification method based on transfer learning and convolutional neural network model was established in this paper, with a total classification accuracy of 98.1611%. This paper proposes a land use classification remote sensing method based on deep learning, which improved the automation level and monitoring accuracy of complex land surface remote sensing monitoring in South China, and it provided technical support for the land consolidation work in China.