Artificial Intelligence in Geosciences最新文献

{"title":"A study on geological structure prediction based on random forest method","authors":"Zhen Chen ,&nbsp;Qingsong Wu ,&nbsp;Sipeng Han ,&nbsp;Jungui Zhang ,&nbsp;Peng Yang ,&nbsp;Xingwu Liu","doi":"10.1016/j.aiig.2023.01.004","DOIUrl":"10.1016/j.aiig.2023.01.004","url":null,"abstract":"<div><p>The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt, which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt. At present, there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt. One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear. Due to the diversity of volcanic rock geochemical characteristics and its related interpretations, there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt: island arc and continental rift. In recent years, it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt. This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia, Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt. Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt, and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt. The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1, which indicates that the island arc tectonic setting is more credible. In this paper, it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction, while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage. This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east. Therefore, the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 226-236"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544123000047/pdfft?md5=c26b8709a9ee5b82ab298ec4fcc8969f&pid=1-s2.0-S2666544123000047-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84905138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning","authors":"Yihuai Lou ,&nbsp;Lukun Wu ,&nbsp;Lin Liu ,&nbsp;Kai Yu ,&nbsp;Naihao Liu ,&nbsp;Zhiguo Wang ,&nbsp;Wei Wang","doi":"10.1016/j.aiig.2022.12.001","DOIUrl":"10.1016/j.aiig.2022.12.001","url":null,"abstract":"<div><p>Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning and deep learning, convolutional neural networks (CNNs) are applied for interpolating irregularly sampled seismic data. CNN based approaches can address the apparent defects of traditional interpolation methods, such as the low computational efficiency and the difficulty on parameters selection. However, current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data, which fail to consider the frequency features of seismic data, i.e., the multi-scale features. To overcome these drawbacks, we propose a wavelet-based convolutional block attention deep learning (W-CBADL) network for irregularly sampled seismic data reconstruction. We firstly introduce the discrete wavelet transform (DWT) and the inverse wavelet transform (IWT) to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data. Moreover, we propose to adopt the convolutional block attention module (CBAM) to precisely restore sampled seismic traces, which could apply the attention to both channel and spatial dimensions. Finally, we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness. The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 192-202"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654412200034X/pdfft?md5=a7f25b94bfb7bb32ee52a3b804ec30b9&pid=1-s2.0-S266654412200034X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81928190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence","authors":"David A. Wood","doi":"10.1016/j.aiig.2022.11.003","DOIUrl":"10.1016/j.aiig.2022.11.003","url":null,"abstract":"<div><p>Machine learning (ML) to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields. Meandering, braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels. Three cored wellbores drilled through such a reservoir in a large oil field, with just four recorded well logs available, are used to classify four lithofacies using ML models. To augment the well-log data, six derivative and volatility attributes were calculated from the recorded gamma ray and density logs, providing sixteen log features for the ML models to select from. A novel, multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation. Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation. When the trained ML models were applied to a third well for testing, lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features. However, an accuracy of ∼0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well. A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with ∼0.6 accuracy. Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 132-147"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000326/pdfft?md5=47841f260127b1f2246f19d39a782263&pid=1-s2.0-S2666544122000326-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88197973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study on small magnitude seismic phase identification using 1D deep residual neural network","authors":"Wei Li ,&nbsp;Megha Chakraborty ,&nbsp;Yu Sha ,&nbsp;Kai Zhou ,&nbsp;Johannes Faber ,&nbsp;Georg Rümpker ,&nbsp;Horst Stöcker ,&nbsp;Nishtha Srivastava","doi":"10.1016/j.aiig.2022.10.002","DOIUrl":"10.1016/j.aiig.2022.10.002","url":null,"abstract":"<div><p>Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the volume of recorded seismic data has been achieved. This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods. In this study, we develop 1D deep Residual Neural Network (ResNet), for tackling the problem of seismic signal detection and phase identification. This method is trained and tested on the dataset recorded by the Southern California Seismic Network. Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases. Compared to previously proposed deep learning methods, the introduced framework achieves around 4% improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center. The model generalizability is also tested further on the STanford EArthquake Dataset. In addition, the experimental result on the same subset of the STanford EArthquake Dataset, when masked by different noise levels, demonstrates the model’s robustness in identifying the seismic phases of small magnitude.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 115-122"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000284/pdfft?md5=05413cd07c32af1496b39542470c3a8b&pid=1-s2.0-S2666544122000284-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75916788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data","authors":"Taneesh Gupta ,&nbsp;Paul Zwartjes ,&nbsp;Udbhav Bamba ,&nbsp;Koustav Ghosal ,&nbsp;Deepak K. Gupta","doi":"10.1016/j.aiig.2023.01.001","DOIUrl":"https://doi.org/10.1016/j.aiig.2023.01.001","url":null,"abstract":"<div><p>Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase velocity-frequency panels in a data-driven manner using deep neural networks. This is a different approach from many recent works that attempt to estimate velocity from directly reflected body waves or guided waves. A secondary objective is to analyze the influence on the prediction accuracy of various commonly employed deep learning practices, such as transfer learning and data augmentations. Through numerical experiments on synthetic data as well as a real geophysical example, we demonstrate that transfer learning as well as data augmentations are helpful when using deep learning for velocity estimation. A third and final objective is to study lack of generalization of deep learning models for out-of-distribution (OOD) data in the context of our problem, and present a novel approach to tackle it. We propose a domain adaptation network for training deep learning models that uses a priori knowledge on the range of velocity values in order to constrain mapping of the output. The final comparison on field data, which was not part of the training data, show the deep neural network predictions compare favorably with a conventional velocity model estimation obtained with a dispersion curve inversion workflow.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 209-224"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544123000011/pdfft?md5=46d6b925b7d294d096526d5cf8ce1950&pid=1-s2.0-S2666544123000011-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136978458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thank you reviewers!","authors":"","doi":"10.1016/j.aiig.2023.01.002","DOIUrl":"https://doi.org/10.1016/j.aiig.2023.01.002","url":null,"abstract":"","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Page 225"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544123000023/pdfft?md5=a664b2aa323028d8ef35534f8a60a26f&pid=1-s2.0-S2666544123000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91696382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning","authors":"Tariq Alkhalifah,&nbsp;Hanchen Wang,&nbsp;Oleg Ovcharenko","doi":"10.1016/j.aiig.2022.09.002","DOIUrl":"https://doi.org/10.1016/j.aiig.2022.09.002","url":null,"abstract":"<div><p>Among the biggest challenges we face in utilizing neural networks trained on waveform (i.e., seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement for accurate labels often forces us to train our networks using synthetic data, where labels are readily available. However, synthetic data often fail to capture the reality of the field/real experiment, and we end up with poor performance of the trained neural networks (NNs) at the inference stage. This is because synthetic data lack many of the realistic features embedded in real data, including an accurate waveform source signature, realistic noise, and accurate reflectivity. In other words, the real data set is far from being a sample from the distribution of the synthetic training set. Thus, we describe a novel approach to enhance our supervised neural network (NN) training on synthetic data with real data features (domain adaptation). Specifically, for tasks in which the absolute values of the vertical axis (time or depth) of the input section are not crucial to the prediction, like classification, or can be corrected after the prediction, like velocity model building using a well, we suggest a series of linear operations on the input to the network data so that the training and application data have similar distributions. This is accomplished by applying two operations on the input data to the NN, whether the input is from the synthetic or real data subset domain: (1) The crosscorrelation of the input data section (i.e., shot gather, seismic image, etc.) with a fixed-location reference trace from the input data section. (2) The convolution of the resulting data with the mean (or a random sample) of the autocorrelated sections from the other subset domain. In the training stage, the input data are from the synthetic subset domain and the auto-corrected (we crosscorrelate each trace with itself) sections are from the real subset domain, and the random selection of sections from the real data is implemented at every epoch of the training. In the inference/application stage, the input data are from the real subset domain and the mean of the autocorrelated sections are from the synthetic data subset domain. Example applications on passive seismic data for microseismic event source location determination and on active seismic data for predicting low frequencies are used to demonstrate the power of this approach in improving the applicability of our trained NNs to real data.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 101-114"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000260/pdfft?md5=3e63a5c64f3830cf6afacef439cdef2b&pid=1-s2.0-S2666544122000260-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91696854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns","authors":"Steven E. Zhang ,&nbsp;Julie E. Bourdeau ,&nbsp;Glen T. Nwaila ,&nbsp;Yousef Ghorbani","doi":"10.1016/j.aiig.2022.10.003","DOIUrl":"https://doi.org/10.1016/j.aiig.2022.10.003","url":null,"abstract":"<div><p>In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas. While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data, especially where modern data is considerably different than legacy data, it is an expensive exercise. The risk associated with modernizing such legacy data lies within its uncertainty in return (e.g., the possibility of new discoveries, in primarily greenfield settings). Without any advanced knowledge of yet unanalyzed elements, the importance of re-analyses remains ambiguous. To address this uncertainty, we apply machine learning to multivariate geochemical data from different regions in Canada (i.e., the Churchill Province and the Trans-Hudson Orogen) in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses. Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data (e.g., prospectivity mapping). Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 86-100"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000296/pdfft?md5=115f57a35bc434c4294614fe797ddff6&pid=1-s2.0-S2666544122000296-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91696855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil","authors":"Angelica N. Caseri ,&nbsp;Leonardo Bacelar Lima Santos ,&nbsp;Stephan Stephany","doi":"10.1016/j.aiig.2022.06.001","DOIUrl":"https://doi.org/10.1016/j.aiig.2022.06.001","url":null,"abstract":"<div><p>Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological numerical models. This work proposes the M5Images method for performing the very short-term prediction (nowcasting) of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network. The recurrent part of it is a Long Short-Term Memory (LSTM) neural network. Prediction tests were performed for the city and surroundings of Campinas, located in the Southeastern Brazil. The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events. The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 8-13"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000211/pdfft?md5=e23aece2442afd1d3bbbab2bff69ba36&pid=1-s2.0-S2666544122000211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91776947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature","authors":"Ziwei Chen ,&nbsp;Zhiguo Wang ,&nbsp;Yang Yang ,&nbsp;Jinghuai Gao","doi":"10.1016/j.aiig.2022.11.001","DOIUrl":"10.1016/j.aiig.2022.11.001","url":null,"abstract":"<div><p>Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"3 ","pages":"Pages 148-156"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544122000314/pdfft?md5=ee2f07a7b856a9a9839f99750242e44a&pid=1-s2.0-S2666544122000314-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85523982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}