Artificial Intelligence in Geosciences最新文献

Multiple-Point Simulation (MPS) is a geostatistical simulation technique commonly used to model complex geological patterns and subsurface heterogeneity. There have been a great variety of implementation methods developed within MPS, of which Patch-Based Simulation is a more recently developed class. While we have witnessed great progress in Patch-Based algorithms lately, they are still faced with two challenges: conditioning to point data and the occurrence of verbatim copy. Both of them are partly due to finite size of Training image, from which a limited size of pattern database is constructed. To address these questions, we propose a novel approach that we call Generative-Patched-Simulation (GPSim), which is based on Generative Adversarial Networks (GAN). With this method, we are able to generate sufficient (in theory an infinite) number of new patches based on the current pattern database. As demonstrated by the results on a simple 2D binary image, this approach shows its potential to address the two issues and thus improve Patch-based Simulation methods.

Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance of existing (i.e., pre-trained) deep-learning detector/picker models, there are still some cases where the direct applications of such models do not generalize well. In such cases, substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one. To address this challenge, we present Blockly Earthquake Transformer(BET), a deep-learning platform for efficient customization of deep-learning phase pickers. BET implements Earthquake Transformer as its baseline model, and offers transfer learning and fine-tuning extensions. BET provides an interactive dashboard to customize a model based on a particular dataset. Once the parameters are specified, BET executes the corresponding phase-picking task without direct user interaction with the base code. Within the transfer-learning module, BET extends the application of a deep-learning P and S phase picker to more specific phases (e.g., Pn, Pg, Sn and Sg phases). In the fine-tuning module, the model performance is enhanced by customizing the model architecture. This no-code platform is designed to quickly deploy reusable workflows, build customized models, visualize training processes, and produce publishable figures in a lightweight, interactive, and open-source Python toolbox.

In this investigation, the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model. The lower and upper bound finite element limit analysis (FELA) approaches are utilized to establish precise modeling and derive the numerical outcomes of a strip footing's bearing capacity. All analyses use effective automated adaptive meshes with three iteration stages to enhance the accuracy of the outcomes. The parametric analysis is performed to examine the influence of four dimensionless parameters which are taken into account in this study, namely the anisotropic strength ratio, the dimensionless eccentricity, the load inclination angle, and the adhesion factor to the bearing capacity factor. Furthermore, a new model has been proposed to predict the bearing capacity factor for the calculation of the undrained bearing capacity for footings resting on an anisotropic clay using an advanced data-driven method (MOGA-EPR). The new model takes into account the anisotropy, eccentricity, and inclination of the applied load and could be used with confidence in routine designs of shallow foundations in undrained conditions with the consideration of the anisotropic strengths of clays.

A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologic features provides important input in understanding the basin's evolution and petroleum prospects. Here, we introduce a new attribute called the Reef Cube (RC) meta-attribute that has been computed by fusing several other seismic attributes that are characteristics of the reef through a supervised machine-learning algorithm. The neural learning resulted in a minimum nRMS error of 0.28 and 0.30 and a misclassification percentage of 1.13% and 1.06% for the train and test data sets. The Reef Cube meta-attribute has efficiently captured the anatomy of carbonate reef buried at ∼450 m below the seafloor from high-resolution 3D seismic data in the NW shelf of Australia. The novel approach not only picks up the subsurface architecture of the carbonate reef accurately but also accelerates the process of interpretation with a much-reduced intervention of human analysts. This can be efficiently suited for delimiting any subsurface geologic feature from a large volume of surface seismic data.

Modern geodynamics is based on the study of a large set of models, with the variation of many parameters, whose analysis in the future will require Machine Learning to be analyzed. We introduce here for the first time how a formulation of the Lattice Boltzmann Method capable of modeling plate tectonics, with the introduction of plastic non-linear rheology, is able to reproduce the breaking of the upper boundary layer of the convecting mantle in plates. Numerical simulation of the earth’s mantle and lithospheric plates is a challenging task for traditional methods of numerical solution to partial differential equations (PDE’s) due to the need to model sharp and large viscosity contrasts, temperature dependent viscosity and highly nonlinear rheologies. Nonlinear rheologies such as plastic or dislocation creep are important in giving mantle convection a past history. We present a thermal Lattice Boltzmann Method (LBM) as an alternative to PDE-based solutions for simulating time-dependent mantle dynamics, and demonstrate that the LBM is capable of modeling an extremely nonlinear plastic rheology. This nonlinear rheology leads to the emergence plate tectonic like behavior and history from a two layer viscosity model. These results demonstrate that the LBM offers a means to study the effect of highly nonlinear rheologies on earth and exoplanet dynamics and evolution.

Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude, i.e., large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category. However, the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance. Therefore, in this study, Deep Learning (DL) algorithms are introduced to assist with EEW. For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center (CENC), this paper proposes a DL model (EEWMagNet), which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention. Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude. Moreover, the comparison experiments demonstrate that the epicenter distance information is indispensable, and the normalization has a negative effect on the model to capture accurate amplitude information.

Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we propose a new denoising method based on convolutional blind denoising network (CBDNet). The method is partially modified for image denoising network CBDNet to make it suitable for one–dimensional data denoising. At present, most of the existing filtering methods are proposed for the Gaussian white noise denoising. In contrast, the proposed method also learns the wind noise, construction noise, traffic noise and mixed noise through the strategy of residual learning. The full convolution subnetwork is used to estimate the noise level, which significantly improves the signal-to-noise ratio and its performance of removing the correlated noise. The model is trained with different types of real noise and random noise. The denoising result is evaluated by corresponding indexes and compared with other denoising methods. The results show that the proposed method has better denoising performance than traditional methods, and it has a superior noise suppression level for oil well construction noise and mixed noise. The proposed method can suppress the interference of time–frequency overlapped end to end and still have noise suppression and event detection capability even if the signal is superimposed on other types of noise.

Seismic facies analysis plays important roles in geological research, especially in sedimentary environment identification. Traditional method is mainly based on seismic waveform or attributes of a single seismic gather to classify the seismic facies. Ignoring the correlation between adjacent seismic gathers leads to poor lateral continuities in generated facies map, which cannot fit the sedimentary characteristics well. In fact, according to sedimentology theory, the horizontal continuities of the stratum can be utilized as priori information to provide more information for waveform classification. Therefore, we develop an unsupervised method for pre-stack seismic facies analysis, which is constrained by spatial continuity. The proposed method establishes a probabilistic model to characterize the correlation between neighboring reflection elements. Subsequently, this correlation is used as a regularization term to modify the objective function of the clustering algorithm, allowing the mode assignment of reflective elements to be influenced by the labels of their neighbors. Test on synthetic data confirms that, compared with traditional seismic facies analysis methods, the facies maps generated by the proposed method have more continuous and homogeneous textures, and less uncertainty on the boundary. The test on actual seismic data further confirms that the proposed method can describe more details of the distribution of lithological bodies of interest. The proposed method is an effective tool for pre-stack seismic facies analysis.

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach, such as differential evolution (DE) and particle swarm optimization (PSO). Based on the well logs, the sparse probability distribution (PD) of the reflectivity distribution is spatial stationarity. Therefore, we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity, which helps to provide more accurate potential solutions for the seismic inversion. In the proposed scheme, as two key operations, the creating of probability density function library and probability transformation are inserted into standard SI algorithms. In particular, two targeted DE-PD and PSO-PD algorithms are implemented. Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO. In particular, the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion. Overall, the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of training data. Consequently, we are able to produce a high resolution (10 m grid size) gold concentration map of the TSF, which demonstrates the potential of our method to be used to guide extraction planning, online resource exploration, environmental monitoring and resource estimation.