Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.

Computers and Geosciences publishes high-impact, original research at the intersection of Computer Science and Geosciences. We welcome studies that apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.

《Computers and Geosciences》期刊致力于发表计算机科学与地球科学交叉领域的高影响力原创研究。我们欢迎应用现代计算机科学范式，无论是计算方法还是信息学基础，来解决地球科学中的问题的研究成果。

Computers and Geosciences

Zujian Yang

Xiao Tian

Xiangteng Wang

Yue Wang

Xiong Zhang

Computers & Geosciences

Discrimination of earthquakes, explosions, and collapses based on the deep learning: Applications to DiTing 2.0 dataset

基于深度学习的地震、爆炸和塌陷识别：应用于DiTing 2.0数据集

Neural networks excel in various machine learning applications; however, they lack the physical interpretability and constraints crucial for numerous scientific and engineering problems. This limitation hinders their ability to accurately capture and predict complex physical systems' behavior, potentially yielding inaccurate or unreliable results. Physics-Informed Neural Networks (PINNs) are a class of machine learning models that integrate the power of neural networks with the physical laws governing natural phenomena. PINNs provide an effective tool for solving intricate physical problems, ranging from fluid dynamics to materials science, by incorporating physical constraints into the neural network architecture. PINNs can substantially enhance the accuracy and efficiency of model predictions, even in data-limited situations. This work offers insight into recent developments in the PINN field, including their mathematical formulation and training algorithms, and emphasizes their application in solving transient unconfined groundwater flow. In this context, the phreatic surface acts as a spatiotemporally varying boundary condition, and properly accounting for its position is vital for precise predictions of unconfined groundwater flow and related environmental and engineering applications. The study's objective is to develop a reliable model for estimating the phreatic surface and the spatiotemporal distribution of piezometric heads in a vertical cross-section of an unconfined aquifer. Two cases are examined: the first involves a homogeneous and isotropic aquifer, while the second comprises a mildly heterogeneous and anisotropic one. The challenges and opportunities arising from this emerging research area are also explored, and essential directions for future research are underscored.

Daniele Secci

Vanessa A. Godoy

J. Jaime Gómez-Hernández

Physics-Informed Neural Networks for solving transient unconfined groundwater flow

用于求解非稳定无压地下水流动的物理信息神经网络

Pre-stack AVO inversion of seismic data is a modeling tool for estimating subsurface elastic properties. Our focus is on the model-based inversion method where then unknown variables are estimated by minimizing the misfit to the observed data. Standard approaches for non-linear AVO inversion are based on the gradient descent optimization algorithms that require the calculation of the gradient equations of the objective function. To improve the accuracy and efficiency of these methods, we developed a technique that uses an implementation of the adjoint-state-based gradient computation. The inversion algorithm relies on three basic modeling components consisting of a convolution-based forward model using a linearized approximation of the Zoeppritz equation, the definition of the objective function, and the adjoint-computed gradient. To achieve an accurate solution, we choose a second-order optimization algorithm known as the Limited memory-BFGS (L-BFGS) that implicitly approximates the inverse Hessian matrix. This approach is more efficient than traditional optimization methods. The main novelty of the proposed approach is the derivation of the adjoint-state equations for the gradient of the objective function. The application of the proposed method is demonstrated using 1D and 2D synthetic datasets based on data from the Edvard Grieg oil field. The seismic data for these applications is generated by using both convolutional modeling and finite difference methods. The results of the proposed method are accurate and the computational approach is efficient. The results show that the algorithm reliably retrieves the elastic variables, P- and S-wave velocities and density for both convolutional and finite difference models.

Nisar Ahmed

Wiktor Waldemar Weibull

Dario Grana

Constrained non-linear AVO inversion based on the adjoint-state optimization

基于伴随状态优化的约束非线性AVO反演

Jarom D. Hogue

Rosemary Anne Renaut

Saeed Vatankhah

A tutorial and open source software for the efficient evaluation of gravity and magnetic kernels

重力和磁场核函数高效评估的教程及开源软件

A. Lurka

Combining Deep Neural Network And Spatio-Temporal Clustering To Automatically Assess Rockburst And Seismic Hazard – Case Study From Marcel Coal Mine in Upper Silesian Basin, Poland.

结合深度神经网络与时空聚类自动评估岩爆与地震灾害——来自波兰上西里西亚盆地Marcel煤矿的案例研究

Hongzhu Cai

Siyuan He

Ziang He

Shuang Liu

Lichao Liu

Xiangyun Hu

Effective Gravity Inversion of Basement Relief with Unfixed Density Contrast Using Deep Learning

利用深度学习进行密度对比不固定的基底起伏有效重力反演

L. Castrogiovanni

P. Sternai

N. Piana Agostinetti

C. Pasquero

A reversible-jump Markov chain Monte Carlo algorithm to estimate paleo surface CO2 fluxes linking temperature to atmospheric CO2 concentration time series

一种可逆跳跃马尔可夫链蒙特卡罗算法，用于估计古地表二氧化碳通量，将温度与大气二氧化碳浓度时间序列联系起来

Jinyong Zhang

Yi Hong

Lizhong Wang

Xiaochun Li

Hongwu Lei

Fangfang Li

Bo Gao

Jia-nan Zheng

LSTM-based proxy model combined with wellbore-reservoir coupling simulations for predicting multi-dimensional state parameters in depleted gas reservoirs

基于LSTM的代理模型结合井筒-储层耦合模拟用于预测衰竭气藏多维状态参数

Abbreviation	Comput. Geosci.
Journal Impact	4.17
Quartiles(Global)	GEOSCIENCES, MULTIDISCIPLINARY(Q1)

ISSN	0098-3004, 1873-7803
h-index	151

Publication Information	Publisher: Elsevier Ltd，Publishing cycle: Monthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1975，Proportion of original research papers: 97.44%，Self Citation Rate:4.80%， Gold OA Rate: 25.49%
Average review cycle	网友分享经验：平均3.0个月来源Elsevier官网：平均12.6周
Average recruitment ratio	网友分享经验：容易

Computers and Geosciences

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