Deep neural networks are built by a composition of hierarchical linear and non-linear functions (layers). The high-capacity networks trained using large datasets enable tasks beyond traditional methods, such as high-resolution velocity model building [17]. At the same time, seismic impedance inversion has also made many contributions using deep learning methods. In 2019, [PERSON] et al. used Convolutional Neural Networks (CNNs) to estimate acoustic impedance and elastic impedance from seismic data [18]. [PERSON] et al. used a Fully Convolutional Neural Network (FCN) to invert P-wave impedance [19]. [PERSON] et al. [20] introduced a Recurrent Neural Network (RNN) based on serial modeling to estimate petrophysical properties and [PERSON] et al. [21] introduced a Temporal Convolutional Network (TCN) to estimate various rock properties from seismic data. [PERSON] et al. [22] used geological and geophysical model-driven CNNs (GGCNNs) to estimate elastic properties from pre-stack seismic data. [PERSON] et al. proposed a Fully Convolutional Residual Network (FCRN) combined with transfer learning for seismic impedance inversion [23], and then extended their work to semi-supervised learning seismic impedance inversion based on a Generative Adversarial Network (GAN) [24; 25]. [PERSON] et al. [26] proposed a novel seismic impedance inversion method based on a Cycle-consistent Generative Adversarial Network (Cycle-GAN). To fully explore the multichannel characteristics of the seismic data [27], [PERSON] et al. proposed a deep learning method for multidimensional seismic impedance inversion [28]. These studies show that neural networks have great potential for seismic inversion. All of these methods are based on learning the mapping from seismic data to well logging data, and then using the learned mapping to estimate properties for off-well locations. This approach usually requires a large amount of labeled training data to improve generalization performance. However, due to the high drilling cost, the number of wells in most exploration operations is limited, leading to a trained model with poor generalization. MTL provides an effective way to mitigate this problem [29; 30]. Compared with single task learning, multi-task learning network is in the fashion of \"single input and multi output\", with an incomparable advantage over a single task network [31]. Simultaneously learning multiple related tasks can improve the model generalization ability, thus improving the performance of the main task on the same amount of labeled data [32]. Meanwhile, multi-task learning can help extract multi-scale texture information from datasets [33]. There are two main ways to implement MTL: hard parameter sharing and soft parameter sharing. Hard parameter sharing involves the hidden layer of a two-task sharing network, and uses different output layers to complete different tasks. The soft parameter sharing method means that each task has its own model and parameters, and the distance between model parameters is then regularized to increase the similarity of models [34]. When there is a high correlation between tasks, the hard parameter sharing method is more suitable, and the higher the correlation between tasks, the greater the proportion of sharing layers in hard parameter sharing [35]. [PERSON] et al. proposed an example of multi-task learning via representation sharing where multiple tasks (seismic impedance inversion and data reconstruction) are learnt simultaneously in the hope that the network can learn more generalizable feature representations leading to better performance in all tasks [1]. This is especially the case when the tasks are highly related to each other. In this paper, we propose a multi-task FCRN for simultaneous seismic impedance inversion and seismic data reconstruction. The performance of hard parameter sharing deep learning is highly dependent on the loss weight of each task, and the weighted linear sum of the loss for each individual task is usually used for training.