Evidence for crustal brines and deep fluid infiltration in an oceanic transform fault. [PDF]
Chesley C+14 more
europepmc +1 more source
Joint interpretation of magnetic, transient electromagnetic, and electric resistivity tomography data for landfill characterization and contamination detection. [PDF]
Ibraheem IM+3 more
europepmc +1 more source
Inversion in geology by interactive evolutionary computation
We present the first step in the development of a system that would allow geological models to evolve backwards in time. The method of interactive evolutionary computation provides for the inclusion of geological knowledge and expertise in a rigorous mathematical inversion scheme, by simply asking an expert user to visually evaluate different ...
openaire
Synthesis and quantum crystallographic evaluation of WYLID: YLID's red rival
The synthesis and quantum crystallographic analysis of the chemical bonding within WYLID, 2‐(dimethyl‐λ4‐sulfaneylidene)‐[1,2′‐biindenylidene]‐1′,3,3′(2H)‐trione, a condensation product of YLID which is the most widely used calibrant for laboratory diffractometers, is presented.
Florian Meurer+6 more
wiley +1 more source
Magnetotelluric evidence for the deep causes of different eruptive styles of Changbaishan Tianchi and Longgang volcanoes. [PDF]
Zhao L+8 more
europepmc +1 more source
Deep learning for predicting porosity in ultra-deep fractured vuggy reservoirs from the Shunbei oilfield in Tarim Basin, China. [PDF]
Deng Z+5 more
europepmc +1 more source
Magnetic structure determination of multiple phases in the multiferroic candidate GdCrO3
Using neutron powder diffraction, the successive magnetic structures in multiferroic candidate GdCrO3 have been determined, including a complex incommensurate phase below 2.3 K. The symmetry analysis uses a combination of little group formalism and superspace group approach highlighting the strength of each method and the results are reported using the
Pascal Manuel+7 more
wiley +1 more source
Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning. [PDF]
Liu N, Shen Y, Zhang S, Zhu X.
europepmc +1 more source