The knowledge of hidden resources present inside the earth layers is vital for the exploration of petroleum and hydrocarbons. However, the recorded seismic data is noisy and incomplete with missing traces that leads to misinterpretation of the earth layers. In this manuscript, we consider seismic data with Gaussian, non-Gaussian noise distribution, regular and irregular missing traces. We propose a method for simultaneous noise attenuation and reconstruction of the incomplete seismic data with attention based wavelet convolutional neural network (AWUN). The wavelet transform is used as pooling layer and inverse wavelet transform is used for upsampling layers to avoid information loss. The attention module is used to obtain weights for various feature channels with higher weights assigned to the more significant information. In addition, we propose to use hybrid loss function (logcosh + huberloss) to denoise and accurately reconstruct the seismic data. Moreover, the effect of various hyper-parameters in the training process of convolutional neural networks is studied. Further, we tested the performance of proposed method on synthetically generated data and field data examples. The quantitative results demonstrated that our proposed deep learning method has shown improved signal-to-noise ratio (SNR) and mean squared error (MSE) when compared to the existing state-of-the-art methods.

Agriculture holds a predominant position in the growth of any country's prosperity. However, there exists a major threat in the crop yield due to unpredictable and uncontrolled climatic changes, traditional farming methods, and poor irrigation services. In the past, crop yield was predicted based on farmer's experience. At present, the challenge is to increase the crop yield to meet the needs of the growing population. Currently, Machine Learning (ML) techniques are being used in multiple fields to accomplish practical and productive solutions. There are various algorithms in ML based on classification, clustering, and neural networks which can be used to predict the crop yield. In this work, we propose a method based on K-Nearest Neighbors (KNN) algorithm which detects the soil quality and predicts the suitable crop for cultivation. We consider temperature and soil quality as inputs to our algorithm. In addition, our method suggests the fertilizer based on the crop predicted. The test results show that our method accurately predicts the crop selection and yield which helps the farmers to great extent.

In seismic data processing, denoising is one of the important steps to get the earth subsurface layers’ information accurately. The dictionary learning (DL) method is one of the prominent methods to denoise the seismic data. In the DL method, there are various parameters involved for denoising such as patch size, dictionary size, number of training patches, choice of threshold, sparsity level, computational cost, and number of iterations for DL. In this work, we study each parameter and its effects on seismic denoising in terms of signal-to-noise ratio and mean square error between the true and denoised seismic data. We examined the performance of the DL method on synthetic and field seismic data for various choices of parameters.

In seismic data processing, attenuation of erratic noise is a challenging task due to the unknown noise distribution. Erratic noise consists of high amplitude peaks and conventional sparse transforms based on least-square (LS) approach that is not appropriate for erratic noise attenuation. An alternative approach, where the data with erratic noise are transformed into pseudodata and then denoised based on fast discrete curvelet transform with structure-oriented space-varying median filtering, is performed and achieves better attenuation. However, the fast discrete curvelet transform with a fixed basis lacks the adaptivity for various data patterns of seismic data. Hence, in this article, we propose a double sparsity dictionary learning (DSDL) method which performs denoising and also preserves the original features of seismic data. The proposed method combines the strength of the analytical transform and adaptive transform to attenuate both random noise and erratic noise in data. The performance of proposed DSDL method is studied on synthetic datasets and field datasets. The numerical results demonstrate that the proposed method gives a better signal-to-noise ratio (SNR), a lower mean-squared error, and energy values for the denoised data in comparison to the existing methods.

Seismic inversion is an approach to obtain the physical properties of the earth layers from the seismic data which aids in reservoir characterization. In seismic inversion, spatially variable physical parameters such as impedance ( $Z$ ), wave velocities $(V_{p}$ , $V_{s})$ and density can be determined from the seismic data. Among these, impedance is an important parameter used for lithology interpretation. However, the inversion problem is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation, and noise. This requires complex wave equation analysis, prior assumptions, human expert effort, and time to analyze the seismic data. To address these issues, deep learning methods were deployed to solve the seismic inversion problem. In this paper, we develop a deep learning framework with an attention module for seismic impedance inversion. The relevant features from the seismic data are emphasized with the integration of the attention module into the network. First, we train the attention-based deep convolutional neural network (ADCNN) by supervised learning with pre-defined acoustic impedance (AI) labels. Next, we train the ADCNN in an unsupervised way with the physics of the forward problem. In the proposed method, the predicted AI is used to calculate the seismic data (calculated seismic) and error is minimized between the input seismic data and calculated seismic data. Unsupervised learning has an advantage when the labeled data is inadequate. The proposed network is trained with Marmousi 2 dataset, and the predicted experimental results show that the proposed method outperforms in comparison to the existing state-of-the-art method.

In seismic data processing, denoising is one of the essential steps to identifying the earth’s subsurface layer information. The noise present in the seismic data is categorized into two types: random and erratic noise. The random noise is distributed uniformly over the seismic data. The erratic noise attenuation is always challenging due to the unknown distribution of high-amplitude peaks over seismic data. The existing double sparsity dictionary learning (DSDL) method performs with analytical and adaptive transforms; both the transforms include iterative algorithms with K-singular-value decomposition (SVD); it is computationally costly, and the dictionary is initialized with trained data. To address these limitations, we propose a novel method of dictionary learning with double regularization (DLDR) to denoise both random and erratic noise from seismic data. In double regularization, we used with $\ell _{1}$ -norm and nuclear norm. The denoised data is applied to the alternating direction method of multipliers (ADMMs) to improve denoising while preserving the signal features from seismic data while reducing the computational cost. We evaluated the performance of the proposed method using signal-to-noise ratio (SNR), mean squared error (MSE), and local similarity map. The numerical results demonstrated that the proposed method resulted in higher SNR, lower MSE, and less signal leakage from seismic data. The method gives precise interpretation from the denoised seismic data.