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README.md

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+# Seismic-Data-Compression-using-Convolutional-Autoencoder
+Implementation of Autoencoder, DCT and DWT models for Seismic Data Compression.
+
+To address the exponential
+increase in seismic data, a variety of methods for seismic data compression have
+been created. In this work, we explore some of the different methods of seismic
+data compression. In this project, convolutional
+autoencoder models, discrete cosine transform (DCT), and discrete wavelet
+transform (DWT) models are implemented. Further, quantization techniques is used with the autoencoder model to create a
+model that gives much higher compression ratios as compared to the rest. All the
+models are compared on the Utah FORGE dataset and are quantitatively analyzed
+using the NMSE (Normalised Mean Square Error), NRMSE (Normalised Root Mean Square Error) and SNR (Signal to Noise Ratio) metrics.
+
+This project was done for Information Processing and Compression Course from Sep-Dec 2021.
+
+Project Members:
+
+Roshan Rangarajan
+
+Rohan Jijju