青藏高原复杂地层地质钻探低固相冲洗液试验研究

青藏高原由于地质条件的多样性,分布有松散破碎地层、高应力地层、断层破碎带等复杂地层,在地质钻探中经常遇到塌孔、漏浆、缩径、卡钻、埋钻等问题。同时配套使用的冲洗液形成的泥皮质量差、胶结能力较差、携岩粉能力较弱等不足。冲洗液的性能是实现复杂地层顺利钻进的关键。结合青藏高原复杂地层的地质特点,研发了低固相聚合物新型冲洗液,该冲洗液以钠质膨润土和微泡剂等外加剂为基础,具有流动性好、胶结性强、降失水量适中的特点,适宜在青藏高原复杂地层地质钻探中使用。     

广西巴马玉的矿物学特征及其成因探讨

继贵州罗甸、广西大化发现多个新型透闪石玉矿床之后,在同属黔桂地区的广西巴马亦发现了透闪石玉矿。通过偏光显微镜镜下观察、X射线衍射仪、电子探针及重砂分析等测试手段对广西巴马玉的宝玉石矿物学特征进行了研究,发现巴马玉主要由微晶透闪石组成,具毛毡状结构和束状结构,多见水草花结构,其主要矿物成分为微晶透闪石集合体,并含有滑石、褐铁矿、高岭石等次要矿物,Mg~(2+)/(Mg~(2+)+Fe~(2+))-Si投点显示数个巴马玉样品分别属于透闪石和阳起石,定名为闪石玉。巴马透闪石玉的密度为2.81～3.00 g/cm3,折射率为1.61,硬度为5.19～5.32,次要矿物滑石是导致其硬度低的原因。根据电子探针数据计算出巴马玉相关化学式并对比分析,发现巴马玉样品中C位Fe~(2+)的数量为0.08～0.67 a.p.f.u,随着样品颜色从淡紫到青灰到深青灰变化,C位Fe~(2+)的数量也相继增加,说明颜色的变青加深与巴马玉Fe元素的含量增加有关。X射线衍射分析结果显示水草花的矿物成分中有针铁矿的存在,说明巴马玉水草花为地表次生结构。通过野外观察,对其主要化学元素、稀有元素以及稀土元素等特征进行综合分析并与其它矿点的相关特征进行横向对比,基本确定了巴马玉矿床为辉绿岩侵入碳酸盐岩经过岩浆热液接触交代作用而形成的层控性接触交代型岩浆热液矿床,与之前发现的位于黔桂地区的贵州罗甸、广西大化闪石矿的成因一致,它们应属于同一矿带。     

内蒙古鄂尔多斯高原古冰缘遗迹科学考察研究进展

冰缘遗迹（特别是冷生楔形构造及融冻褶皱）是重建古气候及第四纪晚期多年冻土环境的重要证据。内蒙古鄂尔多斯高原是我国北方地区冰缘现象最为发育的地区之一。为准确了解鄂尔多斯高原冰缘遗迹类型及其分布特征、区域冻土演化历史等,中国科学院西北生态环境资源研究院和荷兰自由大学共同组成科研小组,于2018年5—6月组织了“鄂尔多斯高原冰缘遗迹科学考察”。考察区域涉及靖边—城川—乌审旗—鄂尔多斯东胜区一带约12 000 km²的范围。考察内容主要包括鄂尔多斯高原冰缘遗迹类型及特征、分布区域、各类型冰缘遗迹所指示的气候条件的初步推断等。结果表明：冻融褶皱和冷生楔体构造是鄂尔多斯高原主要存在的两大类冰缘遗迹。基于本次考察中关于冰缘遗迹的分布与特征等新发现,并综合前人研究成果,初步推断：在气温极低、多年冻土非常发育的时段,有利于形成各类冷生楔状构造,如冰楔假形和大型原生砂楔等;在气候转暖、多年冻土退化,但还没有全部融化完阶段,可能形成融冻褶皱;区域性大面积分布和成群出现的融冻褶皱一般反映较暖气候环境下,多年冻土层上部已退化到一定程度。基于光释光（OSL）年代测试结果,结合冰缘遗迹的特征及其所指示的古气候环境,初步重建了鄂尔多斯5万年以来的冻土环境变化序列。区内多年冻土在多年冻土最大期（LPM,25~19 ka BP）时最发育,以大面积连续多年冻土为主;之后,随气温转暖,总趋势呈退化状态,多年冻土分布逐渐变为片状→岛状→零星斑状,直至现今全部融完变为深季节冻土区。     

Backward automatic calibration for three-dimensional landslide models

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.     

Resistivity of reservoir sandstones and organic rich shales on the Barents Shelf:Implications for interpreting CSEM data

Marine controlled source electromagnetic(CSEM)data have been utilized in the past decade during petroleum exploration of the Barents Shelf,particularly for de-risking the highly porous sandstone reservoirs of the Upper Triassic to Middle Jurassic Realgrunnen Subgroup.In this contribution we compare the resistivity response from CSEM data to resistivity from wireline logs in both water-and hydrocarbon-bearing wells.We show that there is a very good match between these types of data,particularly when reservoirs are shallow.CSEM data,however,only provide information on the subsurface resistivity.Careful,geology-driven interpretation of CSEM data is required to maximize the impact on exploration success.This is particularly important when quantifying the relative re-sistivity contribution of high-saturation hydrocarbon-bearing sandstone and that of the overlying cap rock.In the presented case the cap rock comprises predominantly organic rich Upper Jurassic-Early Cretaceous shales of the Hekkingen Formation(i.e.a regional source rock).The resistivity response of the reservoir and its cap rock become merged in CSEM data due to the transverse resistance equivalence principle.As a result of this,it is imperative to understand both the relative contributions from reservoir and cap rock,and the geological sig-nificance of any lateral resistivity variation in each of the units.In this contribution,we quantify the resistivity of organic rich mudstone,i.e.source rock,and reservoir sandstones,using 131 exploration boreholes from the Barents Shelf.The highest resistivity(＞10,000 Ωm)is evident in the hydrocarbon-bearing Realgrunnen Subgroup which is reported from 48 boreholes,43 of which are used for this study.Pay zone resistivity is primarily controlled by reservoir quality(i.e.porosity and shale fraction)and fluid phase(i.e.gas,oil and water saturation).In the investigated wells,the shale dominated Hekkingen Formation exhibits enhanced resistivity compared to the background(i.e.the underlying and overlying stratigraphy),though rarely exceeds 20Ωm.Marine mudstones typically show good correlation between measured organic richness and resistivity/sonic velocity log signatures.We conclude that the resistivity contribution to the CSEM response from hydrocarbon-bearing sandstones out-weighs that of the organic rich cap rocks.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

Timing of Proterozoic magmatism in the Sunsas belt,Bolivian Precambrian Shield,SW Amazonian Craton

We present new U-Pb zircon and monazite ages from the Sunsas belt granitic magmatism in Bolivia,SW Amazonian Craton.The geochronological results revealed four major magmatic events recorded along the Sunsas belt domains.The older igneous event formed a granitic basement coeval to the Rio Apa Terrane(1.95-1.85 Ga)in the southern domain.The second magmatic episode is represented by 1.68 Ga granites associated to the Paraguá Terrane(1.69-1.66 Ga)in the northern domain.The 1.37-1.34 Ga granites related to San Ignacio orogeny represent the third and more pervasive magmatic event,recorded throughout the Sunsas belt.Moreover,magmatic ages of～1.42 Ga revealed that the granitogenesis asso-ciated to the Santa Helena orogeny also affected the Sunsas belt,indicating that it was not restricted to the Jauru Terrane.Lastly,the 1.10-1.04 Ga youngest magmatism was developed during the Sunsas oro-geny and represents the final magmatic evolution related to Rodinia assembly.Likewise,the 1.95-1.85 and 1.68 Ga inherited zircon cores obtained in the～1.3 Ga and 1.0 Ga granite samples suggest strong par-tial melting of the Paleoproterozoic sources.The 1079±14 Ma and 1018±6 Ma monazite crystallization ages can be correlated to the collisional tectono-thermal event of the Sunsas orogeny,associated to reac-tions of medium-to high-grade metamorphism.Thus,the Sunsas belt was built by heterogeneous 1.95-1.85 Ga and 1.68 Ga crustal fragments that were reworked at 1.37-1.34 Ga and 1.10-1.04 Ga related to orogenic collages.Furthermore,the 1.01 Ga monazite age suggests that granites previously dated by zir-con can bear evidence of a younger thermal history.Therefore,the geochronological evolution of the Sunsas belt may have been more complex than previously thought.     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

Structural and tectonothermal evolution of the ultrahigh-temperature Bakhuis Granulite Belt,Guiana Shield,Surinam:Palaeoproterozoic to recent

We constrain the multistage tectonic evolution of the Palaeoproterozoic UHT metamorphic(P=0.9–1.0 GPa,T>1000℃,t=2088–2031 Ma)Bakhuis Granulite Belt(BGB)in Surinam on the Guiana Shield,using large-to small-scale structures,Al-in-hornblende thermobarometry and published fluid inclusion and zircon geochronological data.The BGB forms a narrow,NE–SW striking belt between two formerly connected,~E–W oriented granite-greenstone belts,formed between converging Amazonian and West African continental masses prior to collision and Transamazonian orogeny.Inherited detrital zircon in BGB metasediments conforms agewise to Birimian zircon of West Africa and suggests derivation from the subsequently subducted African passive margin.Ultrahigh-temperature metamorphism may have followed slab break-off and asthenospheric heat advection.Peak metamorphic structures result from layer-parallel shearing and folding,reflecting initial transtensional exhumation of the subducted African margin after slab break-off.A second HT event involves intrusion,at ca.0.49 GPa,of charnockites and metagabbros at 1993–1984 Ma and a layered anorthosite at 1980 Ma,after the BGB had already cooled to<400℃.The event is related to northward subduction under the greenstone belts,along a new active margin to their south.A pronounced syntaxial bend in the new margin points northward towards the BGB and is likely the result of indentation by an anticlinorial flexural bulge of the subducting plate.Tearing of the subducting oceanic plate along this bulge explains why the charnockites are restricted to the BGB.The BGB subsequently experienced doming under an extensional detachment exposed in its southwestern border zone.Exhumation was focused in the BGB as a result of the flexural bulge in the subducting plate and localised heating of the overriding plate by charnockite magmatism.The present,straight NE–SW long-side boundaries of the BGB are superimposed mylonite zones,overprinted by pseudotachylites,previously dated at ca.1200 Ma and 950 Ma,respectively.The 1200 Ma mylonites reflect transpressional popping-up of the BGB,caused by EW-directed intraplate principal compressive stresses from Grenvillian collision preserved under the eastern Andes.Further exhumation of the BGB involved the 950 Ma pseudotachylite decorated faulting,and Phanerozoic faulting along reactivated Meso-and Neoproterozoic lineaments.