The cost of obtaining a complete porosity value using traditional coring methods is relatively high, and as the drilling depth increases, the difficulty of obtaining the porosity value also increases. Nowadays, the prediction of fine reservoir parameters for oil and gas exploration is becoming more and more important. Therefore, high-efficiency and low-cost prediction of porosity based on logging data is necessary. We have developed a machine-learning method based on the traditional long short-term memory (LSTM) model, called multilayer LSTM (MLSTM), to perform the porosity prediction task. We used three different wells in a block in southern China for the prediction task, including a training well and two test wells. One test well has the same logging data type as the training well, whereas the other test well differs from the training well in the logging depth and parameter types. Two different types of test data sets are used to detect the generalization ability of the network. A set of data was used to train the MLSTM network, and the hyperparameters of the network were adjusted through experimental accuracy feedback. We also tested the performance of the network using two sets of log data from different regions, including generalization and sensitivity of the network. During the training phase of the porosity prediction model, the developed MLSTM establishes a minimized objective function, uses the Adam optimization algorithm to update the weight of the network, and adjusts the network hyperparameters to select the best target according to the feedback of the network accuracy. Compared with conventional sequence neural networks, such as the gated recurrent unit and recurrent neural network, the logging data experiments show that MLSTM has better robustness and accuracy in depth sequence prediction. Especially, the porosity value at the depth inflection point can be better predicted when the trend of the depth sequence was predicted. This framework is expected to reduce the porosity prediction errors when data are insufficient and log depths are different.

Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.

Random noise attenuation is one of the most essential steps in seismic signal processing. We propose a novel approach to attenuate seismic random noise based on deep convolutional neural network (CNN) in an unsupervised learning manner. First, normalization and patch sampling are required to build training dataset and test dataset from raw noisy data. Instead of using synthetic noise-free data or denoised results via conventional methods as training labels, we adopt only the training set constructed from the raw noisy data as the input and design a robust deep CNN that just relies on the noisy input to learn the hidden features. The cross-entropy is chosen as the error criterion for establishing the cost function, which is minimized by the back-propagation algorithm to obtain the optimized parameters of the network. Then, we can reconstruct all patches of the test dataset via the optimized CNN. After patching processing and inverse normalization, the final denoised result can be obtained from reconstructed patches. Experimental tests on synthetic and real data demonstrate the effectiveness and superiority of the proposed method compared with state-of-the-art denoising methods.

Noise attenuation plays an important role in seismic data processing. We propose a novel denoising method for seismic data based on unsupervised sparse feature learning. Our goal is to obtain the identifiable feature of the noisy seismic data and then to represent the effective signals. By preprocessing the raw data and training the autoencoder neural network with sparse constraint, the sparse feature of the seismic data can be learned and stored in the neural network. We use the adaptive moment estimation as a backpropagation algorithm to minimize the cost function with a sparse penalty term and combine the dropout technique in the training process to improve the feature extraction and generalization capability of the neural network. Then, the test data set can be reconstructed by the most important sparse features. The final denoising result can be obtained by rearranging the output test data set. Compared with three commonly used state-of-the-art denoising methods, the proposed method performs well in applications to denoising for synthetic and real seismic data.

Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal‐to‐noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low‐amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth‐domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real‐world applications.

Background: The prevalence of cigarette smoking among patients with major psychiatric conditions is significantly higher than that in the general population. However, whether there is a causal association between cigarette smoking and major psychiatric disorders remains unclear. Therefore, we conducted a systematic review and meta-analysis of cohort studies to elucidate the association between cigarette smoking and the risk of major psychiatric disorders, including depression, schizophrenia, and bipolar disorder.Methods: We systematically searched PubMed, Embase, and the Cochrane Library for potentially eligible studies from their inception until March 2, 2024. All pooled analyses were performed using a random-effects model, and exploratory sensitivity and subgroup analyses were conducted.Results: Twenty-five cohort studies involving 2,917,030 individuals were included in the meta-analysis. The summary results indicated that both current smoking (relative risk [RR], 1.30; 95% confidence interval [CI], 1.18–1.43; P < 0.001) and former smoking (RR, 1.16; 95% CI, 1.09–1.23; P < 0.001) were associated with an elevated risk of major depression. Additionally, current smoking was significantly associated with an elevated risk of schizophrenia (RR, 1.84; 95% CI, 1.07–3.19; P = 0.028) and bipolar disorder (RR, 1.54; 95% CI, 1.22–1.95; P < 0.001).Conclusion: Current smoking is significantly associated with an elevated risk of major psychiatric disorders, including major depression, schizophrenia, and bipolar disorder. Former smokers also have an elevated risk of major depression. However, it should be noted that, despite these significant associations, due to the nature of the cohort studies included, this study cannot establish a causal relationship between cigarette smoking and major psychiatric disorders.Systematic review registration: https://inplasy.com/inplasy-2024-3-0093/, identifier INPLASY202430093.