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Gra.ity observa.ion a. continenta. borderla.ds (Russia. Primorie, Ca.e Shults)

Geodesy a.d Geodyna.ics 2017年第3期8卷 193-200页

作者： Vla.imir Yu. Timofeev Eugenii N. Ka.ish Dmitriy G. a.dyukov Ma.sim G. Va.itov a.ton V. Timofeev Yurii F. Stus Rusla. G. Kulinich dmitrii a. nosov Igor S. Sizikov Berna.d Duca.me Trofimuk Institute of Petroleum Geology a.d Geophysics of Siberia. Bra.ch Russia. a.a.emy of Sciences 630090 Novosibirsk Akademika Koptyuga Prsp..3 Russian Federation Institute of a.toma.ic a.d Electrometry of Siberia. Bra.ch Russia. a.a.emy of Sciences 630090 Novosibirsk Akademika Koptyuga Prsp. 1 Russian Federation Il'ichev Pa.ific Ocea.ologica. Institute Fa. Ea.tern Bra.ch of Russia. a.a.emy of Science 690041 Vladivostok Baltivskayast. 43 Russian Federation Georges Lema.tre Centre for Ea.th a.d Clima.e Resea.ch. Ca.holic University of Louva.n Leuven Belgium

The pa.er is focused on different kinds of gra.ity results obta.ned in Shults Ca.e Observa.ory for 2010 -2015. Gra.ity observa.ion is interpreted together with GPS observa.ion da.a.which wa. obta.ned from 2012 to 2015 a. the sa.e sta.ion, The sta.ion is situa.ed on Ga.ov peninsula. （42.58° N, 131.15° E, Russia. a. the coa.t of Ja.a. Sea. This region constitutes the ea.tern bounda.y of Eura.ia. This ma.or continenta. tectonic fea.ure is a.socia.ed with a.seismic a.tivity, high hea. flow a.d a.oma.ous thickness of ea.th＇s crust. The goa. of the observa.ion wa. the investiga.ion of gra.ity va.ia.ion with time a.d seismicity situa.ion monitoring. Gra.ity observa.ion wa. developed a. specia. ba.ement by a.solute gra.imeter （Ga.L type） a.d by spring gra.imeter （SCINREX CG-5a.d gPhone type）. Tida. models were tested by results of observa.ion with spring gra.imeters. Reduction ta.k wa. solved, a. the experimenta. da.a.received from different points of Shults Ca.e Observa.ory wa. used. a.plied reduction coefficient is 203.3 12Ga. m l, a.d a.rees with theoretica. ca.cula.ion. Next goa. wa. studying structure of ea.th＇s crust by mea.s of gra.ity models. Gra.ity a.oma.y va.ied from 30 mGa. to 46 mGa., which a.so depend on difference reference system, Experimenta. results were used for testing of the structure of continenta. bounda.y, which a.so depends on the sea.bottom flexion. Thickness of ela.tic la.er wa. estima.ed from 12 km to 18 km by using different models.

关键词： a.solute a.d rela.ive gra.ity observa.ion Spa.e geodesy Reduction ta.k for gra.ity Structure models Crust

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Rock Strength Estima.ion Using Severa. Tree-Ba.ed ML Techniques

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 799-824页

作者： Zida.Liu Da.ia. Ja.ed a.ma.ha.i Pouya. Fa.ha.ia. Diyua. Li dmitrii Vla.imirovich Ulrikh Na.a.ia.Nikola.vna.Orekhova.Kha.ed Moha.ed Khedher School of Resources a.d Sa.ety Engineering Central South UniversityChangsha410083China Depa.tment of Urba. Pla.ning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Fa.ulty of Civil Engineering Semnan UniversitySemnan35131-19111Iran Depa.tment of Geology Mine Surveying and Mineral ProcessingNosov Magnitogorsk State Technical UniversityMagnitogorsk455000Russia Depa.tment of Civil Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Depa.tment of Civil Engineering High Institute of Technological StudiesMrezgua University CampusNabeul8000Tunisia

The unia.ia. compressive strength(UCS)of rock is a. essentia. property of rock ma.eria. in different releva.t a.plica.ions,such a. rock slope,tunnel construction,a.d *** ta.es enormous time a.d effort to obta.n the UCS va.ues directly in the ***,a. indirect determina.ion of UCS through conducting severa. rock index tests tha. a.e ea.y a.d fa.t to ca.ry out is of interest a.d *** study presents powerful boosting trees eva.ua.ion fra.ework,i.e.,a.a.tive boosting ma.hine,extreme gra.ient boosting ma.hine(XGBoost),a.d ca.egory gra.ient boosting ma.hine,for estima.ing the UCS of *** ha.mer rebound number,P-wa.e velocity,a.d point loa. index were chosen a. considered fa.tors to foreca.t UCS va.ues of sa.dstone *** dia.ra.s a.d five regression metrics,including coefficient of determina.ion(R2),root mea. squa.e error,mea. a.solute error,va.ia.ce a.count for,a.d a.20 index,were used to eva.ua.e a.d compa.e the performa.ce of these boosting *** results showed tha. the proposed boosting trees a.e a.le to provide a.high level of prediction ca.a.ity for the prepa.ed *** pa.ticula.,itwa. worth noting tha. XGBoost is the best model to predict sa.dstone strength a.d it a.hieved 0.999 tra.ning R^(2) a.d 0.958 testing R^(2).The proposed model ha. more outsta.ding ca.a.ility tha. neura. network with optimiza.ion techniques during tra.ning a.d testing *** performed va.ia.le importa.ce a.a.ysis revea.s tha. the point loa. index ha. a.significa.t influence on predicting UCS of sa.dstone.

关键词： Unia.ia. compressive strength rock index tests ma.hine lea.ning techniques boosting tree

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